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MAD Skills: New Analysis Practices for Big Data.
Abstract
As massive data acquisition and storage becomes increas- ingly aordable, a wide variety of enterprises are employing statisticians to engage in sophisticated data analysis. In this paper we highlight the emerging practice of Magnetic, Ag- ile, Deep (MAD) data analysis as a radical departure from traditional Enterprise Data Warehouses and Business Intel- ligence. We present our design philosophy, techniques and experience providing MAD analytics for one of the world's largest advertising networks at Fox Interactive Media, us- ing the Greenplum parallel database system. We describe database design methodologies that support the agile work- ing style of analysts in these settings. We present data- parallel algorithms for sophisticated statistical techniques, with a focus on density methods. Finally, we reect on database system features that enable agile design and exi- ble algorithm development using both SQL and MapReduce interfaces over a variety of storage mechanisms.
... Another research approach to using kernel canonical correlation analysis (KCCA) replaces Euclidean dot products with kernel functions [31][32][33]. Kernel functions are at the heart of many recent developments in machine learning, as they provide expressive, computationally tractable notions of similarity [31][32][33]. The KCCA model approach is based on the kernel function to compute a "distance metric" between every pair of query vectors and performance vectors. ...
... Another research approach to using kernel canonical correlation analysis (KCCA) replaces Euclidean dot products with kernel functions [31][32][33]. Kernel functions are at the heart of many recent developments in machine learning, as they provide expressive, computationally tractable notions of similarity [31][32][33]. The KCCA model approach is based on the kernel function to compute a "distance metric" between every pair of query vectors and performance vectors. ...
... Finally, the map takes place from the performance projection back to the metrics when we want to predict. Finding a reverse mapping from the feature space back to the input space is known as a hard problem, because of the complexity of the mapping algorithm and because of the dimensionality of the feature space that can be much higher or lower than the input space [33,34]. ...
A query optimizer attempts to predict a performance metric based on the amount of time elapsed. Theoretically, this would necessitate the creation of a significant overhead on the core engine to provide the necessary query optimizing statistics. Machine learning is increasingly being used to improve query performance by incorporating regression models. To predict the response time for a query, most query performance approaches rely on DBMS optimizing statistics and the cost estimation of each operator in the query execution plan, which also focuses on resource utilization (CPU, I/O). Modeling query features is thus a critical step in developing a robust query performance prediction model. In this paper, we propose a new framework based on query feature modeling and ensemble learning to predict query performance and use this framework as a query performance predictor simulator to optimize the query features that influence query performance. In query feature modeling, we propose five dimensions used to model query features. The query features dimensions are syntax, hardware, software, data architecture, and historical performance logs. These features will be based on developing training datasets for the performance prediction model that employs the ensemble learning model. As a result, ensemble learning leverages the query performance prediction problem to deal with missing values. Handling overfitting via regularization. The section on experimental work will go over how to use the proposed framework in experimental work. The training dataset in this paper is made up of performance data logs from various real-world environments. The outcomes were compared to show the difference between the actual and expected performance of the proposed prediction model. Empirical work shows the effectiveness of the proposed approach compared to related work.
... Computations involving matrix and vector primitives are expressible in SQL with the aid of UDFs. For example, MAD [16,28] is a in-database analytics library for matrix and vector operators. Luo et. ...
... For the other, we can use the vector/matrix operations to support complicated linear algebra computation in a concise SQL query. As most RDBMSs have provided the array/vector datatype internally, apart from the build-in array functions, many extended libraries of database [16,50] also provide additional statistical function and vector/matrix operations for multivariable statistical analysis and basic linear algebra calculus. These high-level abstractions avoid letting end-users specify the arithmetic operations on each dimension of the data point so that serve as a set of building blocks of machine learning algorithms. ...
... (15)), the means 'mean' (Eq. (16)) and the covariances 'cov' (Eq. (17)- (18)) are re-estimated based on their update formulas in Eq. (4)-(6), respectively. ...
Integrating machine learning techniques into RDBMSs is an important task since there are many real applications that require modeling (e.g., business intelligence, strategic analysis) as well as querying data in RDBMSs. In this paper, we provide an SQL solution that has the potential to support different machine learning modelings. As an example, we study how to support unsupervised probabilistic modeling, that has a wide range of applications in clustering, density estimation and data summarization, and focus on Expectation-Maximization (EM) algorithms, which is a general technique for finding maximum likelihood estimators. To train a model by EM, it needs to update the model parameters by an E-step and an M-step in a while-loop iteratively until it converges to a level controled by some threshold or repeats a certain number of iterations. To support EM in RDBMSs, we show our answers to the matrix/vectors representations in RDBMSs, the relational algebra operations to support the linear algebra operations required by EM, parameters update by relational algebra, and the support of a while-loop. It is important to note that the SQL'99 recursion cannot be used to handle such a while-loop since the M-step is non-monotonic. In addition, assume that a model has been trained by an EM algorithm, we further design an automatic in-database model maintenance mechanism to maintain the model when the underlying training data changes.We have conducted experimental studies and will report our findings in this paper.
... An increasing number of major database vendors include in their products data mining and machine learning analytic tools. PostgreSQL, MySQL, MADLib (over PostgreSQL) [21] and commercial tools like Oracle Data Miner, IBM Intelligent Miner and Microsoft SQL Server Data Mining provide SQL-like interfaces for analysts to specify regression tasks. Academic efforts include MauveDB [23], which integrates regression models into a DMS, while similarly FunctionDB [50] allows analysts to directly pose regression queries against a DMS. ...
... The objective function in (21) corresponds to optimal partitioning of the query space into K partitions (OP1), each with a prototype. The objective function in (22) corresponds to a conditional EPE conditioned on the k-th query prototype w k , which is the closest to the query q (OP2). ...
... We adopt SGD to minimize both (21) and (22). J and H are minimized by updating α = {y k , w k , b k } in the negative direction of their sum of gradients. ...
Regression analytics has been the standard approach to modeling the relationship between input and output variables, while recent trends aim to incorporate advanced regression analytics capabilities within data management systems (DMS). Linear regression queries are fundamental to exploratory analytics and predictive modeling. However, computing their exact answers leaves a lot to be desired in terms of efficiency and scalability. We contribute with a novel predictive analytics model and an associated statistical learning methodology, which are efficient, scalable and accurate in discovering piecewise linear dependencies among variables by observing only regression queries and their answers issued to a DMS. We focus on in-DMS piecewise linear regression and specifically in predicting the answers to mean-value aggregate queries, identifying and delivering the piecewise linear dependencies between variables to regression queries and predicting the data dependent variables within specific data subspaces defined by analysts and data scientists. Our goal is to discover a piecewise linear data function approximation over the underlying data only through query–answer pairs that is competitive with the best piecewise linear approximation to the ground truth. Our methodology is analyzed, evaluated and compared with exact solution and near-perfect approximations of the underlying relationships among variables achieving orders of magnitude improvement in analytics processing.
... What are the distinct concepts in BDA related to power systems? What are the challenges in generation, communications, management and analysis of BD? What are the new core theories that furnish BDA in • Volume: Many IT-related organizations define BD in terabytes-sometimes petabytes (Cohen et al., 2009;Huang et al., 2014). For instance, the data warehouse of Fox Audience Network (a large advertisement network) holds over 200 terabytes of production data (Cohen et al., 2009). ...
... What are the challenges in generation, communications, management and analysis of BD? What are the new core theories that furnish BDA in • Volume: Many IT-related organizations define BD in terabytes-sometimes petabytes (Cohen et al., 2009;Huang et al., 2014). For instance, the data warehouse of Fox Audience Network (a large advertisement network) holds over 200 terabytes of production data (Cohen et al., 2009). The scope of BD also affects its quantification. ...
... Accordingly, we now not only have the need for BDA but also the tools to do so, e.g., in form of predictive analytics (domain and off-domain data forecasting), data mining and machine learning (classification, regression, clustering), artificial intelligence (cognitive simulation, expert systems, perception, pattern recognition), statistical analysis, natural language processing, and advanced data visualization, cf. (Cohen et al., 2009;Slavakis et al., 2014;Bertsekas and Tsitsiklis, 1989;Chen et al., 2014;Zaki and Ho, 2000). Note that, the majority of these new tools and techniques have discovery/exploratory natures. ...
Electric power systems are taking drastic advances in deployment of information and communication technologies; numerous new measurement devices are installed in forms of advanced metering infrastructure , distributed energy resources (DER) monitoring systems, high frequency synchronized wide-area awareness systems that with great speed are generating immense volume of energy data. However, it is still questioned that whether the today's power system data, the structures and the tools being developed are indeed aligned with the pillars of the big data science. Further, several requirements and especial features of power systems and energy big data call for customized methods and platforms. This paper provides an assessment of the distinguished aspects in big data analytics developments in the domain of power systems. We perform several taxonomy of the existing and the missing elements in the structures and methods associated with big data analytics in power systems. We also provide a holistic outline, classifications, and concise discussions on the technical approaches, research opportunities, and application areas for energy big data analytics.
... User-defined function (μ) It performs a time-consuming procedural computation (e.g., machine learning and data analytics [8]) over the operand relation, elaborating the values of a set A of attributes (all plaintext or encrypted) in its schema. We assume a general udf operator with a set (A) of attributes as input and an attribute (a) as output. ...
... ∀a ∈ A, ∀n ∈ N : i p a ,n +ie a ,n ≤ 1 ( 8 ) If an attribute is represented both in plaintext and encrypted in a node, both i p a ,n and ie a ,n are equal to 1, hence their sum is 2, violating the constraint. -Base relations have all their attributes in plaintext and have no implicit attributes. ...
We present a novel approach for the specification and enforcement of authorizations that enables controlled data sharing for collaborative queries in the cloud. Data authorities can establish authorizations regulating access to their data distinguishing three visibility levels (no visibility, encrypted visibility, and plaintext visibility). Authorizations are enforced accounting for the information content carried in the computation to ensure no information is improperly leaked and adjusting visibility of data on-the-fly. Assignment of operations to subjects takes into consideration the cost of operation execution as well as of the encryption/decryption operations needed to make the assignment authorized. Our approach enables users and data authorities to fully enjoy the benefits and economic savings of the competitive open cloud market, while maintaining control over data.
... However, recent efforts have shown that data management systems have much more to offer. For example, materialization and reuse opportunities [15,16,31,90,103], costbased optimization of linear algebraic operators [24,29,48], array-based representations [58,93], avoiding denormalization [63,64,86], lazy evaluation [106], declarative interfaces [79,92,98], and query planning [62,80,91] are all readily available (or at least familiar) database functionalities that can deliver significant speedups for various ML workloads. ...
... Hemingway [80], MLBase [60], and TuPAQ [91] automatically choose an optimal plan for a given ML workload. SciDB [58,93], MADLib [29,54], and RIOT [106] exploit in-database computing. Kumar et al. [62] uses a model selection management system to unify feature engineering [15], algorithm selection, and parameter tuning. ...
The rising volume of datasets has made training machine learning (ML) models a major computational cost in the enterprise. Given the iterative nature of model and parameter tuning, many analysts use a small sample of their entire data during their initial stage of analysis to make quick decisions (e.g., what features or hyperparameters to use) and use the entire dataset only in later stages (i.e., when they have converged to a specific model). This sampling, however, is performed in an ad-hoc fashion. Most practitioners cannot precisely capture the effect of sampling on the quality of their model, and eventually on their decision-making process during the tuning phase. Moreover, without systematic support for sampling operators, many optimizations and reuse opportunities are lost. In this paper, we introduce BlinkML, a system for fast, quality-guaranteed ML training. BlinkML allows users to make error-computation tradeoffs: instead of training a model on their full data (i.e., full model), BlinkML can quickly train an approximate model with quality guarantees using a sample. The quality guarantees ensure that, with high probability, the approximate model makes the same predictions as the full model. BlinkML currently supports any ML model that relies on maximum likelihood estimation (MLE), which includes Generalized Linear Models (e.g., linear regression, logistic regression, max entropy classifier, Poisson regression) as well as PPCA (Probabilistic Principal Component Analysis). Our experiments show that BlinkML can speed up the training of large-scale ML tasks by 6.26x-629x while guaranteeing the same predictions, with 95% probability, as the full model.
... However, the big data environment,such as Magnetic Agile Deep (MAD) analysis skills, which differ from the points of a traditional (EDW) environment. First of all, traditional EDW approaches deject the incorporation of new data sources till they are cleansed and integrated.Now, big data environments need to be magnetic because of the ubiquity of the data, subsequently attracting all the data sources, irrespective of the data quality [5]. Moreover, because of the growing numbers of data sources, as well as the complexity of the analyses of the data, big data storage ought to allow analysts to easily yield and adapt data promptly. ...
In the information era, huge amounts of data have become available to decision-makers. Big data can be referred to as datasets that are not only big but also high in variety and velocity, which makes them tough to handle using traditional tools and techniques. Due to the fast growth of such data, solutions need to be provided to handle and extract value and knowledge from these data. Moreover, decision-makers need to be able to advance the valuable insights from such varied and rapidly changing data, ranging from daily transactions to customer interactions and social network data. Such value can be delivered using big data analytics, which is the application of progressive analytics techniques on big data. This paper's objective is to analyze some of the different analytics methods and tools which can be applied to big data, like the yarn and rack-aware model as well as the opportunities provided by the application of big data analytics in several decision domains.
... In the data upload process this repository stores the submitted clinical and image related information before the metadata extraction and their persistence in the Metadata Catalogue. In this way, the data ingestion follows an Extract-Load-Transform (ELT) [12] design where this repository is responsible for maintaining the data in their original submitted format. The Clinical Repository is similar to a "data lake" that contains all of the uploaded information (except the imaging data) in the format that was uploaded. ...
Prostate cancer (PCa) is one of the most prevalent cancers in the male population. Current clinical practices lead to overdiagnosis and overtreatment necessitating more effective tools for improving diagnosis, thus the quality of life of patients. Recent advances in infrastructure, computing power and artificial intelligence enable the collection of tremendous amounts of clinical and imaging data that could assist towards this end. ProCAncer-I project aims to develop an AI platform integrating imaging data and models and hosting the largest collection of PCa (mp)MRI, anonymized image data worldwide. In this paper, we present an overview of the overall architecture focusing on the data ingestion part of the platform. We describe the workflow followed for uploading the data and the main repositories for storing imaging data, clinical data and their corresponding metadata.
... Data visualization is the integrated use of computer graphics, image processing, humancomputer interaction, and other technologies to convert data into recognizable graphics, symbols, images, videos, or animations and present information valuable to users. Through this visualization, users can analyze data and acquire knowledge, improving their comprehension (33). Data visualization is not limited to data presentation but involves presenting data in ways that are more conducive to human understanding and acceptance through data mining and the creation of charts and other graphic representations. ...
Background:
Over the past decade, there has been a significant increase in research on the use of mobile health (mHealth) apps as disease management tools. However, very few apps are currently available for prostate cancer (PCa) patient management, and the available apps do not combine the needs of physicians with the requirements of patients. This study aimed to describe the development of a mHealth application for PCa survivors called RyPros, which includes dynamic visualization, intelligent reminders, and instant messaging to support decision-making regarding treatment and follow-up and test the initial accessibility and acceptability application.
Methods:
The application was developed through a three-step procedure: logical structure design, application programming, and testing. Dynamic visualization, intelligent reminders, and instant messaging were the core functions of RyPros. Twenty-eight participants who had PCa were enrolled in four weeks of follow-up using the RyPros App. We initially evaluated participants' acceptance of RyPros based on their use of the app (login data, questionnaire completion) and a satisfaction survey.
Results:
We successfully designed and tested the application. A total of 32 participants were enrolled, of whom 28 completed the 4-week follow-up, yielding a participation rate of 87.5%. Each participant logged on an average of 2.82 times and achieved an average of 0.89 questionnaires per week over the four weeks. Most participants (64%) liked the app, and most participants (71%) were satisfied, giving the RyPros app a rating of 4 or 5. More than half of the participants (61%) intended to use the RyPros app regularly, and the majority of participants agreed that the three core functionalities of RyPros were helpful (20/28, 71% for instant messaging; 16/28, 57% for visualization; and 18/28, 64% for reminders and assessments).
Conclusions:
The mHealth application we developed for PCa survivor management provided dynamic visualization, reminders, assessments, and instant messaging to support decision-making based on multidisciplinary collaboration. PCa survivors showed high acceptance of the RyPros app.
... One method of accomplishing this goal is to promote the employees' creativity through competitions to solve BDArelated problems. Other methods include freeing them from having to follow extremely rigid procedures, and incentivizing their involvement in collaborative projects using the information management system (Cohen et al., 2009). We also advise top managers to drive and guide this transformation by empowering people who have strong problemsolving skills with regard to big data processes so they exploit its potentialities. ...
In order to optimise the corporate HR information system, IoT first-off technology is used, together with the system demand phase, with edge control in mind. initially, the hardware and software system are set up, and only then the communication models between the edge layer of the system and other parties are created edge layer, and the business type-driven connection selection method are considered. To ensure communication reliability across various levels of the system, northbound multilink switching algorithms are created and implemented, accordingly. After implementing the functions described above, the edge control system may be implemented. Ensure IoT applications satisfy intelligence, expandability, and security needs. due diligence and due diligence are required The campaign was aimed primarily at the enterprise, with the goal of defining the functional and performance requirements of the product. Ensuring the fundamental logical structure in the system design phase; in the system architecture design phase, the other features of the system architecture Realization of design is accomplished. a set of system module functions exists planned down to the last detail. Pay and benefits management are only two aspects of the whole management function. Employee personnel change is one of the e system's modules. Payroll, benefits, and personnel administration are referred to as department management. Human resource management is a process system modules go through; as far as system implementation is concerned, system coding occurs. In addition, development tool and software development methodology support the creation of pages. The system has finally been realised the system design goals are set up to mimic the business's real needs, which are then put to the test.
... Sometimes it is required to integrate numerous data sources, so Cohen et al. (2009) provided a parallel database model for analyzing and integrating the several data sources and this database design supports SQL and MapReduce for combining data sources. Birst provided SaaS as a solution which offered analytics functionalities and business analytics infrastructure which gave customers a model to move gradually from on premise analytics to cloud analytics infrastructure. ...
In today's world where data is accumulating at an ever-increasing rate, processing of this big data was a necessity rather than a need. This required some tools for processing as well as analysis of the data that could be achieved to obtain some meaningful result or outcome out of it. There are many tools available in market which could be used for processing of big data. But the main focus on this chapter is on Apache Hadoop which could be regarded as an open source software based framework which could be efficiently deployed for processing, storing, analyzing, and to produce meaningful insights from large sets of data. It is always said that if exponential increase of data is processing challenge then Hadoop could be considered as one of the effective solution for processing, managing, analyzing, and storing this big data. Hadoop versions and components are also illustrated in the later section of the paper. This chapter majorly focuses on the technique, methodology, components, and methodologies adopted by Apache Hadoop software framework for big data processing.
... Data scientists have the job to extract knowledge and give insights into all the data described above. Therefore data scientists need strong skills in statistics, machine learning, programming and algorithms [17,18]. ...
Data Warehouses are an established approach for analyzing data. But with the advent of big data the approach hits its limits due to lack of agility, flexibility and system complexity. To overcome these limits, the idea of data lakes has been proposed. The data lake is not a replacement for data warehouses. Moreover, both solutions have their application areas. So it is necessary to integrate both approaches into a common architecture. This paper describes and compares both approaches, shows different ways of integrating data lakes into data warehouse architectures.
... Since the beginning of the new century, data has been growing at an unprecedented rate, with the popularity and development of the Internet, as well as the rise of forum/BBS, Weibo, WeChat, Twitter and other online communities from the media. The McKinsey Global Research Institute forecast that global data usage would be expected to reach 35ZB in 2020 [1]. The massive data cloud storage and cloud computing solved the technical problems in data centralized storage and centralized computing, which made it possible for big data to enter the practical application level as a technology, method and concept. ...
... RDBMS could be retrieved from http:// mysql-com.en.softonic.com/. Google's success in text processing and their embrace of statistical machine learning was decoded as an endorsement that facilitated Hadoop's wide-spread adoption (Cohen, Dolan, Dunlap, Hellerstein, & Welton, 2009 On the other hand, additional technologies and software are available for use with big data sets and samples. They represent reasonable alternatives to Hadoop, especially when data sets display unique characteristics that can be best addressed with specialized software. ...
The chapter reviews traditional sampling techniques and suggests adaptations relevant to big data studies of text downloaded from online media such as email messages, online gaming, blogs, micro-blogs (e.g., Twitter), and social networking websites (e.g., Facebook). The authors review methods of probability, purposeful, and adaptive sampling of online data. They illustrate the use of these sampling techniques via published studies that report analysis of online text.
... In this experiment we have gathered empirical proof to a common intuition [13,20,21,24,25] that for every data partitioning scheme there is a possible worst-case and well as best-case workload. These can be summarized by the following table, listing QT1-QT6 as representative access patterns ( Table 4). ...
Multidimensional numeric arrays are often serialized to binary formats for efficient storage and processing. These representations can be stored as binary objects in existing relational database management systems. To minimize data transfer overhead when arrays are large and only parts of arrays are accessed, it is favorable to split these arrays into separately stored chunks. We process queries expressed in an extended graph query language SPARQL, treating arrays as node values and having syntax for specifying array projection, element and range selection operations as part of a query. When a query selects parts of one or more arrays, only the relevant chunks of each array should be retrieved from the relational database. The retrieval is made by automatically generated SQL queries. We evaluate different strategies for partitioning the array content, and for generating the SQL queries that retrieve it on demand. For this purpose, we present a mini-benchmark, featuring a number of typical array access patterns. We draw some actionable conclusions from the performance numbers.
... disadvantages of the different approaches. We conclude this discussion on analytical systems by highlighting a new direction in data analysis referred to as "deep analytics" [9,16]. This new class of data analysis applications are driven by the application of complex statistical analysis and machine learning techniques on huge amounts of data to garner intelligence from the data. ...
Scalable database management systems (DBMS)-both for update intensive application workloads as well as decision support systems for descriptive and deep analytics-are a critical part of the cloud infrastructure and play an important role in ensuring the smooth transition of applications from the traditional enterprise infrastructures to next generation cloud infrastructures. Though scalable data management has been a vision for more than three decades and much research has focussed on large scale data management in traditional enterprise setting, cloud computing brings its own set of novel challenges that must be addressed to ensure the success of data management solutions in the cloud environment. This paper presents an organized picture of the challenges faced by application developers and DBMS designers in developing and deploying internet scale applications. Our background study encompasses both classes of systems: (i) for supporting update heavy applications, and (it) for ad-hoc analytics and decision support. We then focus on providing an in-depth analysis of systems for supporting update intensive web-applications and provide a survey of the state-of-the-art in this domain. We crystallize the design choices made by some successful systems large scale database management systems, analyze the application Demands and access patterns, and enumerate the desiderata for a cloud-bound DBMS. Keyword:-Database management systems, Cloud infrastructure and decision support.
... Therefore, additional big data analytics tools have been developed to make data mining in large-database systems more effective with traditional techniques. These methods include parallel processing algorithms, such as ordinary least squares, conjugate gradient, Mann-Whitney U testing [117], etc. In addition, big data researchers have developed synchrophasor data processing frameworks that can more effectively handle billions of data points [118]. ...
Oscillatory stability has received immense attention in recent years due to the significant increase of power-electronic converter (PEC)-interfaced renewable energy sources. Synchrophasor technology offers superior capability to measure and monitor power systems in real time, and power system operators require better understanding of how it can be used to effectively analyze and control oscillations. This paper reviews state-of-the-art oscillatory stability monitoring, analysis, and control techniques reported in the published literature based on synchrophasor technology. An updated classification is presented for power system oscillations with a special emphasis on oscillations induced from PEC-interfaced renewable energy generation. Oscillatory stability analysis techniques based on synchrophasor technology are well established in power system engineering, but further research is required to effectively utilize synchrophasor based oscillatory stability monitoring, analysis and control techniques to characterize and mitigate PEC-induced oscillations. In particular, emerging big-data analytics techniques could be used on synchrophasor data streams to develop oscillatory stability monitoring, analysis and damping techniques.
... To overcome the drawbacks of traditional ETL and to speed up the data preparation process, the processes of ELT was devised [23][24][25]. The nature of traditional ETL is to perform transform immediately after the extract operation and then start the load operation. ...
The conventional extracting–transforming–loading (ETL) system is typically operated on a single machine not capable of handling huge volumes of geospatial big data. To deal with the considerable amount of big data in the ETL process, we propose D_ELT (delayed extracting–loading –transforming) by utilizing MapReduce-based parallelization. Among various kinds of big data, we concentrate on geospatial big data generated via sensors using Internet of Things (IoT) technology. In the IoT environment, update latency for sensor big data is typically short and old data are not worth further analysis, so the speed of data preparation is even more significant. We conducted several experiments measuring the overall performance of D_ELT and compared it with both traditional ETL and extracting–loading– transforming (ELT) systems, using different sizes of data and complexity levels for analysis. The experimental results show that D_ELT outperforms the other two approaches, ETL and ELT. In addition, the larger the amount of data or the higher the complexity of the analysis, the greater the parallelization effect of transform in D_ELT, leading to better performance over the traditional ETL and ELT approaches.
... One method of accomplishing this goal is to promote the employees' creativity through competitions to solve BDArelated problems. Other methods include freeing them from having to follow extremely rigid procedures, and incentivizing their involvement in collaborative projects using the information management system (Cohen et al., 2009). We also advise top managers to drive and guide this transformation by empowering people who have strong problemsolving skills with regard to big data processes so they exploit its potentialities. ...
Big data analytics (BDA) have the power to revolutionize traditional ways of doing business. Nevertheless, the impact of BDA capabilities on a firm’s performance is still not fully understood. These capabilities relate to the flexibility of the BDA infrastructure and the skills of the management and the firm’s personnel. Most scholars explored the phenomenon either from a theoretical standpoint or neglected intermediate factors, such as organizational traits. This article builds on the dynamic capabilities view to propose and empirically test a model exploring whether organizational ambidexterity and agility mediate the relationship between BDA capabilities and organizational performance. Using data from surveys of 259 managers of large European organizations, we tested a proposed model using bootsrapped moderated mediation analysis. We found that organizational BDA capabilities impact a firm’s ambidexterity and agility, which, in turn, affect its performance. These results establish ambidexterity and agility as positive mediators in the relationship between organizational BDA capabilities and a firm’s performance. Furthermore, the organizational resistance to the implementation of information management systems and the fit between the organization and these systems also moderated this relationship. Practical implications for managers are also discussed.
... If we talk about the effectiveness of the data processing in data centers, then in this area there are many studies as well. Authors J. Cohen, B. Dolan [4] proposed their vision of a flexible and in-depth analysis of data using the parallelism of the database system of Greenplum. For problems of high complexity of processing data using MapReduce process, authors [5] proposed improvement plan of all three phases of MapReduce process, thus improving the accuracy of planning, however, reduce the efficiency of the system. ...
... In the recent time the concept of Big Data and its implications has been serving the computational world with different perspectives in many fields [6]. One can view the concept of Big Data Analytics as having the following characteristics [7]: ...
This article addresses the usage and scope of Big Data Analytics in video surveillance and its potential application areas. The current age of technology provides the users, ample opportunity to generate data at every instant of time. Thus in general, a tremendous amount of data is generated every instant throughout the world. Among them, amount of video data generated is having a major share. Education, healthcare, tours and travels, food and culture, geographical exploration, agriculture, safety and security, entertainment etc., are the key areas where a tremendous amount of video data is generated every day. A major share among it are taken by the daily used surveillance data captured from the security purpose camera and are recorded everyday. Storage, retrieval, processing, and analysis of such gigantic data require some specific platform. Big Data Analytics is such a platform, which eases this analysis task. The aim of this article is to investigate the current trends in video surveillance and its applications using Big Data Analytics. It also aims to focus on the research opportunities for visual surveillance in Big Data frameworks. We have reported here the state-of-the-art surveillance schemes for four different imaging modalities: conventional video scene, remotely sensed video, medical diagnostics, and underwater surveillance. Several works were reported in this research field over recent years and are categorized based on the challenges solved by the researchers. A list of tools used for video surveillance using Big Data framework is presented. Finally, research gaps in this domain are discussed.
... It follows from the above that when deciding on the use of a particular system, it is important to understand the features of each of them. [8] ...
The article provides detailed information about the new technologies based on cluster computing Hadoop and Apache Spark. The experimental task of processing logistic regression with the help of these technologies is considered. The findings on the comparison of the performance of cluster computing of Hadoop and Apache Spark are revealed and substantiated.
... Large-scale ML leverages large data collections to find interesting patterns or build robust predictive models. 7 Applications range from traditional regression, classification, and clustering to user recommendations and deep learning for unstructured data. The labeled data required to train these ML models is now abundant, thanks to feedback loops in data products and weak supervision techniques. ...
Large-scale Machine Learning (ML) algorithms are often iterative, using repeated read-only data access and I/O-bound matrix-vector multiplications. Hence, it is crucial for performance to fit the data into single-node or distributed main memory to enable fast matrix-vector operations. General-purpose compression struggles to achieve both good compression ratios and fast decompression for block-wise uncompressed operations. Therefore, we introduce Compressed Linear Algebra (CLA) for lossless matrix compression. CLA encodes matrices with lightweight, value-based compression techniques and executes linear algebra operations directly on the compressed representations. We contribute effective column compression schemes, cache-conscious operations, and an efficient sampling-based compression algorithm. Our experiments show good compression ratios and operations performance close to the uncompressed case, which enables fitting larger datasets into available memory. We thereby obtain significant end-to-end performance improvements.
... Classification; A technique in which to identify the categories of new datasets and assign into predefined classes for exampleclassification of mushroom as edible or poisonous [4]. It is used for data mining. ...
Big data analytics is a trending practice that many companies are adopting. The analytics process includes the deployment and use of big data analytics tools, that improves operational efficiency, drive new revenue and gain competitive advantages over business rivals. The descriptive analytics focuses on describing something that has already happened, as well as suggesting its root causes. Descriptive analytics, which remains the lion's share of the analysis performed, typically hinges on basic querying, reporting and visualization of historical data. Alternatively, more complex predictive and prescriptive modeling can help companies anticipate business opportunities and make decisions that affect profits in areas such as targeting marketing campaigns, reducing customer churn and avoiding equipment failures. With predictive analytics, historical data sets are mined for patterns indicative of future situations and behaviors, while prescriptive analytics subsumes the results of predictive analytics to suggest actions that will best take advantage of the predicted scenarios.
... Although some node tables required over one million probability values, the time to learn the 29-parameter network is under 13 min in R using a dataset with around 24 000 cases in a 4-yr (2014-2017) dataset, which would be the approximate size of a dataset from a clinic treating 1200 patients per year for 4 yr. This work was performed with the goal of keeping the dataset size and processing to a level in which contemporary "big data" methods 29 were not required, while also representing as closely as possible the conditions of practical clinical datasets. ...
Purpose:
The current process for radiotherapy treatment plan quality assurance relies on human inspection of treatment plans, which is time-consuming, error prone and oft reliant on inconsistently applied professional judgments. A previous proof-of-principle paper describes the use of a Bayesian network (BN) to aid in this process. This work studied how such a BN could be expanded and trained to better represent clinical practice.
Methods:
We obtained 51 540 unique radiotherapy cases including diagnostic, prescription, plan/beam, and therapy setup factors from a de-identified Elekta oncology information system from the years 2010-2017 from a single institution. Using a knowledge base derived from clinical experience, factors were coordinated into a 29-node, 40-edge BN representing dependencies among the variables. Conditional probabilities were machine learned using expectation maximization module using all data except a subset of 500 patient cases withheld for testing. Different classes of errors that were obtained from incident learning systems were introduced to the testing set of cases which were withheld from the dataset used for building the BN. Different sizes of datasets were used to train the network. In addition, the BN was trained using data from different length epochs as well as different eras. Its performance under these different conditions was evaluated by means of Areas Under the receiver operating characteristic Curve (AUC).
Results:
Our performance analysis found AUCs of 0.82, 0.85, 0.89, and 0.88 in networks trained with 2-yr, 3-yr 4-yr and 5-yr windows. With a 4-yr sliding window, we found AUC reduction of 3% per year when moving the window back in time in 1-yr steps. Compared to the 4-yr window moved back by 4 yrs (2010-2013 vs 2014-2017), the largest component of overall reduction in AUC over time was from the loss of detection performance in plan/beam error types.
Conclusions:
The expanded BN method demonstrates the ability to detect classes of errors commonly encountered in radiotherapy planning. The results suggest that a 4-yr training dataset optimizes the performance of the network in this institutional dataset, and that yearly updates are sufficient to capture the evolution of clinical practice and maintain fidelity.
... One method of accomplishing this goal is to promote the employees' creativity through competitions to solve BDArelated problems. Other methods include freeing them from having to follow extremely rigid procedures, and incentivizing their involvement in collaborative projects using the information management system (Cohen et al., 2009). We also advise top managers to drive and guide this transformation by empowering people who have strong problemsolving skills with regard to big data processes so they exploit its potentialities. ...
In recent times, scholars have stressed how big data hold the power to revolutionize traditional ways of doing business. As a proof, McAfee and Brynjolfsson (2012) seminal paper has defined big data as the next great managerial revolution. Building on these premises, pertinent literature focused on observing how organizational big data analytics (BDA) capabilities - which are a set of organizational capabilities deriving from BDA infrastructure flexibility, BDA management capabilities and BDA personnel capabilities - may affect performance. This phenomenon was particularly observed in large organizations. Yet, the most of scholars explored the phenomenon either from a theoretical standpoint or did not consider potential factors influencing this relationship. In this perspective, the present study builds on dynamic capabilities to propose and empirically test a conceptual model exploring whether organizational ambidexterity and agility mediate the relationship between BDA capabilities and organizational performance. Additionally, the moderating role of organizational resistance to information system (IS) implementation and IS-organizational fit will be explored. A total of 259 surveys were collected from managers of European large organizations. The proposed model was tested using structural equation modelling (SEM). Findings emphasize how organizational BDA capabilities influence ambidexterity, agility and, in turn, performance. Several theoretical implications for scholars and practical suggestions for managers seeking to develop organizational BDA capabilities are provided.
... Real-time data warehousing systems (e.g., [11,17,43,45,48,49,[55][56][57]61,63,69,[76][77][78]81,82]) represent a relevant class of data warehouses (e.g., [13]) where the main requirement consists in executing classical data warehousing operations (e.g., loading, aggregation, indexing, OLAP query answering, and so forth) under real-time constraints (e.g., [7,81]). This makes classical data warehousing architectures not suitable to this goal, and puts the basis for a novel research area which has tight relationship with emerging Big Data (e.g., [28,30]) and Cloud architectures (e.g., [1,14,33]), which also very often expose interesting convergences (e.g., [19]). ...
This paper proposes and experimentally assesses a rewrite/merge approach for supporting real-time data warehousing via lightweight data integration. Real-time data warehouses are becoming more and more relevant actually, due to emerging research challenges such as Big Data and Cloud Computing. Our contribution fulfills limitations of actual data warehousing architectures, which are no suitable to perform classical operations (e.g., loading, aggregation, indexing, OLAP query answering, and so forth) under real-time constraints. The proposed approach is based on intelligent manipulation of SQL statements of input queries, which are decomposed in suitable sub-queries (the rewrite phase) that are finally submitted as (final) input queries to an ad hoc component responsible for the cooperative query answering via a parallel query processing inspired method (the merge phase). This method induces in a novel data warehousing framework where the static phase is separated by the dynamic phase, in order to achieve the real-time processing features. We complete our analytical contributions by means of an extensive experimental campaign where we stress the performance of our proposed real-time data warehousing framework against a popular data warehouse benchmark, and in comparison with traditional architectures, which finally confirms the benefits deriving from our proposal.
... For example, Jacob [39] discussed the challenges posed by the big data and highlighted possible solutions to overcome the challenges. Cohen et al. [16] discussed that the cost of data acquisition and storage has reduced considerably, and sophisticated data analysis has become a norm. They introduced Magnetic, Agile, Deep (MAD) data analysis practice. ...
Bibliometrics is a quantitative tool for the analysis of literature published in a scientific field. Using Scopus as the data source, we perform a thorough analysis of scholarly works published in the field of big data from 2008 to 2017. The objective of the work is to find the most cited articles in the given time frame, the citation trends, the authorship trends as well as the trends of research work in the related area. The analysis shows that over 50% of publications do not receive any citations, and the average number of citations per publication is 3.17. It is also observed that single authorship of research publications has declined over the time. The analysis reveals the pioneering role played by the USA in advancing the research in big data, which has lately been taken over by China, and the large-scale usage of big data analytics in various domains of science.
This paper provides an in-depth survey on the integration of machine learning and array databases. First,machine learning support in modern database management systems is introduced. From straightforward implementations of linear algebra operations in SQL to machine learning capabilities of specialized database managers designed to process specific types of data, a number of different approaches are overviewed. Then, the paper covers the database features already implemented in current machine learning systems. Features such as rewriting, compression, and caching allow users to implement more efficient machine learning applications. The underlying linear algebra computations in some of the most used machine learning algorithms are studied in order to determine which linear algebra operations should be efficiently implemented by array databases. An exhaustive overview of array data and relevant array database managers is also provided. Those database features that have been proven of special importance for efficient execution of machine learning algorithms are analyzed in detail for each relevant array database management system. Finally, current state of array databases capabilities for machine learning implementation is shown through two example implementations in Rasdaman and SciDB.
SQL database systems support user-defined functions (UDFs), but they hardly encourage programming with these functions. Quite the contrary: the systems’ focus on plan-based query evaluation penalizes every function call at runtime, rendering programming with UDFs—especially if these are recursive—largely impractical. We propose to take UDFs for what they are (namely functions) and subject UDFs to a pipeline of function compilation techniques well-established by the FP community (CPS conversion, defunctionalization, and translation into trampolined style, in particular). The result is a non-invasive SQL-level compiler for recursive UDFs that naturally supports memoization and emits iterative CTEs which contemporary SQL engines evaluate efficiently. Functions may not be first class in SQL, but functional programming close to the data can still be efficient.
To an increasing degree, data is a driving force for digitization, and hence also a key asset for numerous companies. In many businesses, various sources of data exist, which are isolated from one another in different domains, across a heterogeneous application landscape. Well-known centralized solution technologies, such as data warehouses and data lakes, exist to integrate data into one system, but they do not always scale well. Therefore, robust and decentralized ways to manage data can provide the companies with better value give companies a competitive edge over a single central repository. In this paper, we address why and when a monolithic data storage should be decentralized for improved scalability, and how to perform the decentralization. The paper is based on industrial experiences and the findings show empirically the potential of a distributed system as well as pinpoint the core pieces that are needed for its central management.
Maritime transports play a critical role in international trade and commerce. Massive vessels sailing around the world continuously generate vessel trajectory data that contain rich spatial- temporal patterns of vessel navigations. Analyzing and understanding these patterns are valuable for maritime traffic surveillance and management. As essential techniques in complex data analy- sis and understanding, visualization and visual analysis have been widely used in vessel trajectory data analysis. This paper presents a literature review on the visualization and visual analysis of vessel trajectory data. First, we introduce commonly used vessel trajectory data sets and sum- marize main operations in vessel trajectory data preprocessing. Then, we provide a taxonomy of visualization and visual analysis of vessel trajectory data based on existing approaches and introduce representative works in details. Finally, we expound on the prospects of the remaining challenges and directions for future research.
Big data sizes are constantly increasing, currently ranging from a few dozen tera-bytes (TB) to many petabytes (PB) of data in a single data set. Consequently, some of the difficulties related to big data include capture, storage, search, sharing, analytics, and visualizing. Today, enterprises are exploring large volumes of highly detailed data so as to discover facts they didn't know before. Analytics based on large data samples reveals and leverages business change. However, the larger the set of data, the more difficult it becomes to manage. Naturally, business benefit can commonly be derived from analyzing larger and more complex data sets that require real time or near-real time capabilities; however, this leads to a need for new data architectures, analytical methods, and tools.
Making co-existent and convergent the need for efficiency of relational query processing over Clouds and the security of data themselves is figuring-out how one of the most challenging research problems in the Big Data era. Indeed, in actual analytics-oriented engines, such as Google Analytics and Amazon S3, where key–value storage-representation and efficient-management models are employed as to cope with the simultaneous processing of billions of transactions, querying encrypted data is becoming one of the most annoying problem, which has also attracted a great deal of attention from the research community. While this issue has been applied to a large variety of data formats, e.g. relational, RDF and multidimensional data, very few initiatives have pointed-out skyline query processing over encrypted data, which is, indeed, relevant for database analytics. In order to fulfill this methodological and technological gap, in this paper we introduce an innovative algorithm for effectively and efficiently supporting skyline query processing over encrypted data in Cloud-enabled databases, named as Attribute-Order-Preserving-Free-SFS (AOPF-SFS), a suitable extension of the well-known Sort-Filter-Skyline (SFS) algorithm. The proposed algorithm enables the processing of skyline queries over encrypted data, even without preserving the order on each attribute as order-preserving encryption would do. We also present eSkyline, a prototype system that embeds AOPF-SFS equipped with a suitable query interface comprising an encryption scheme that facilitates the evaluation of domination relationships, hence allows for state-of-the-art skyline processing algorithms to be used. In order to prove the effectiveness and the reliability of our system, we also provide the details of the underlying encryption scheme, plus a suitable GUI that allows a user to interact with a server, and showcases the efficiency of computing skyline queries and decrypting the results.
Process event data is usually stored either in a sequential process event log or in a relational database. While the sequential, single-dimensional nature of event logs aids querying for (sub)sequences of events based on temporal relations such as “directly/eventually-follows,” it does not support querying multi-dimensional event data of multiple related entities. Relational databases allow storing multi-dimensional event data, but existing query languages do not support querying for sequences or paths of events in terms of temporal relations. In this paper, we propose a general data model for multi-dimensional event data based on labeled property graphs that allows storing structural and temporal relations in a single, integrated graph-based data structure in a systematic way. We provide semantics for all concepts of our data model, and generic queries for modeling event data over multiple entities that interact synchronously and asynchronously . The queries allow for efficiently converting large real-life event data sets into our data model, and we provide 5 converted data sets for further research. We show that typical and advanced queries for retrieving and aggregating such multi-dimensional event data can be formulated and executed efficiently in the existing query language Cypher, giving rise to several new research questions. Specifically, aggregation queries on our data model enable process mining over multiple inter-related entities using off-the-shelf technology.
Complex expressions are the basis of data analytics. To process complex expressions on big data efficiently, we developed a novel optimization strategy for parallel computation platforms such as Hadoop and Spark. We attempted to minimize the rounds of data repartition to achieve high performance. Aiming at this goal, we modeled the expression as a graph and developed a simplification algorithm for this graph. Based on the graph, we converted the round minimization problem into a graph decomposition problem and developed a linear algorithm for it. We also designed appropriated implementation for the optimization strategy. Extensive experimental results demonstrate that the proposed approach could optimize the computation of complex expressions effectively with small cost.
Regression Models (RMs) and Machine Learning models (ML) in general, aim to offer high prediction accuracy, even for unforeseen queries/datasets. This depends on their fundamental ability to generalize. However, overfitting a model, with respect to the current DB state, may be best suited to offer excellent accuracy. This overfit-generalize divide bears many practical implications faced by a data analyst. The paper will reveal, shed light, and quantify this divide using a large number of real-world datasets and a large number of RMs. It will show that different RMs occupy different positions in this divide, which results in different RMs being better suited to answer queries on different parts of the same dataset (as queries typically target specific data subspaces defined using selection operators on attributes). It will study in detail 8 real-life data sets and from the TPC-DS benchmark and experiment with various dimensionalities therein. It will employ new appropriate metrics that will reveal the performance differences of RMs and will substantiate the problem across a wide variety of popular RMs, ranging from simple linear models to advanced, state-of-the-art, ensembles (which enjoy excellent generalization performance). It will put forth and study a new, query-centric, model that addresses this problem, improving per-query accuracy, while also offering excellent overall accuracy. Finally, it will study the effects of scale on the problem and its solutions.
Identifying health issues before to become serious is one of the best ways to stay healthy. The better chance to cure disease or successful treatment, when we diagnose in early condition. As we know, Wellbeing is the level of valuable and metabolic strength of a living being. Sound health is obligatory to do the daily work properly. With the rapid growth of the Internet of Things. Artificial Intelligence and the emergence of wearable devices & algorithms, it is possible to monitor and screening important aspects of our daily life, to encourage a healthier lifestyle. This paper proposes a dynamic healthcare system that will allow the patient- user to proactive health monitoring, tracking and reviewing their time to time physical & mental activities. Such as patient’s emerging cancer, pulse-rate, heartbeat rate, pressure level rate, body-temperature, neuron functionality, etc. using IoT devices and AI system algorithms. If the framework identifies any sudden changes in persistent observed information. Then the
framework system will automatically warn the doctors and patient user. As the patient data standings over the IoT network so furthermore appears details of the patient’s real-time report live over the internet using login panel.
Space technology and geotechnology, such as geographic information system (GIS), plays a vital role in the day‐to‐day activity of the society. In the initial days, the method of data collection was very rudimentary and primitive. The quality of the data collected was a subject of verification and the accuracy of the data was also questionable. With the advent of newer technology, the problems, however, have been overcome. Using a modern sophisticated system, the way space science was depicted has changed drastically. Implementing cutting edge spaceborne sensors, it has been possible now to capture real‐time data from space. A spacecraft can maneuver itself when it encounters a dust devil, and accordingly, can suspend the desired task for a certain period of time. Similarly, the geodata collection mechanism, cartography, and other amenities related to GIS has been changing rapidly with the inclusion of newer technologies. Inclusion of big data systems for data storage, access, manipulation, and prediction is changing both space technology and GIS swiftly. Nevertheless, the scope in this kind of study is boundless.
Bugün devasa veri yığınları arasından anlamlı ilişkilerin, örüntülerin ve eğilimlerin ortaya çıkarılmasına büyük ihtiyaç duyulmaktadır. Kurumsal içerik ve uygulama verileri, sosyal medyadan gelen veriler, algılayıcı verileri ve üçüncü taraflardan gelen veri akışları da dâhil olmak üzere veri çeşitliliği ve hacmindeki patlama, işletmelerin ve müşterilerinin birbirleriyle etkileşim yöntemini önemli şeklide değiştirmektedir. Bu baskı özellikle birbirine entegre olma becerisini geliştirmeye çalışan tedarik zincirlerindeki inovasyon yönetiminde bilginin güvenirliği, doğru bilginin, doğru yöntemler ile ortaya konulmasında çok daha fazla hissedilmektedir. Bu durum, işletmeleri hem yapılandırılmış hem yapılandırılmamış verileri yönetmede “büyük veri” yi kullanmaya yönlendirmektedir. Büyük veri, çok daha yüksek frekanstaki bilgiyi şeffaf ve kullanışlı hale getirerek önemli potansiyeli ortaya çıkarabilmektedir. Böylece, içsel ve dışsal bilginin kullanımına dengeli bir bakış getirmekte, geleceği daha iyi tahmin yeterliliklerini destekleyen ve “büyük resmi” görmeyi sağlayan iş analitiği uygulamalarını iyileştirebilmekte ve müşterilere ulaşma konusunda daha derinlemesine bilgiyi sağlayabilmektedir. Tedarik zinciri ortakları arasında iyileştirilen iletişim ve bilgi bağlantıları; işletmelere ait hem iç hem dış tüm kaynakları bir araya getirerek müşterilerine, ortaklarına, paydaşlarına ve tedarikçilerine inovasyonu yönetmede değerler sunan bir ana enformasyon kaynağı oluşturabilir. Bu çalışmada tedarik zinciri yeterliliklerini artırmada inovasyon ve büyük verinin etkileşimini ele almak üzere literatür taraması yapılarak, bu etkileşimin itici güçleri, karşılaşılan sorunlar ve engeller, kullanılan teknolojiler ve yöntemlerle geleceğe ilişkin öngörülerin incelenmesi amaçlanmaktadır.
Currently information community has been bombarded by Big Data in Cloud computing to classify and coordinate into basic management process. On the other hand, the development of mobile computing is to extent “individual clouds”, or open cloud resources in order to understanding the properties over the personal computing gadget like, advanced cells, tablets, workstations, shrewd TVs, and even joined frameworks inside automobiles. Big data, as it looks is enormous data used to designate the huge volume of data in unstructured and semi-structured format. Also, that is cloud computing comes in for the utilization the cloud as receptors for the majority of that information regardless of claim cloud or private cloud without particular amount in Petabytes and Exabyte of information. So it is increasingly using cloud deployments and therefore analytics needs to be surveyed with the aim at increasing value to address big data. In addition the entire consumer in Cloud computing, clients, servers, applications and other elements related to data centers are made available to IT and end users via the Internet. Organization needs to pay only as much for the computing infrastructure as they use. The way of billing type in cloud computing is similar to the electricity payment that we do on the basis of usage. It is a function of the allocation of resources on demand. The best booking ahead of time of assets is hard to be accomplished because of vulnerability of customer’s future interest and supplier’s asset costs. This paper renders the techniques behind maximizing Big Data in cloud computing. The issues, insights, analysis and management of Big Data, and advantages and learning outcome of Big Data in cloud computing, resource Provisioning Cost also have been studied.
The hospital information system should realize automation of the management, storage and transmission of each issue process inside the hospital, and provide the foundation and platform for the hospital to carry out the high efficiency of medical information work. Because the system has not realized the orderly integration and utilization of information resources, its integrity, convenience and timeliness can’t be improved. In addition, problems such as single service form and insufficient information service quality have affected the utilization of information resources. Therefore, it is an important task for the hospital intelligence department to explore the medical information work mode on the base of the internal network. Efficient integration of internal and external resources of the hospital can provide multi-level and deep-level network intelligence services, and expand the depth and breadth of the hospital intelligence work.
High dimensional data analysis within relational database management systems (RDBMS) is challenging because of inadequate support from SQL. Currently, subspace clustering of high dimensional data is implemented either outside DBMS using wrapper code or inside DBMS using SQL User Defined Functions/Aggregates(UDFs/UDAs). However, both these approaches have potential disadvantages from performance, resource usage, and security perspective for voluminous and frequently updated data. Hence, we propose an efficient querying system, named SubspaceDB, that implements subspace clustering directly within an RDBMS. SubspaceDB provides a novel set of query operators, each with an optimization objective, to facilitate interactive analysis for subspace clustering. The query operators focus on retrieving optimal answers to four key query types: (a) Medoid queries, (b) Neighbourhood queries, (c) Partial similarity queries, and (d) Prominence queries, that aid the formation of subspace clusters. Experimental studies on real and synthetic databases of size 15M tuples and 104 attributes show that our proposed approach SubspaceDB can be over 10 times faster as compared to a conventional wrapper-based or SQL UDF approach. The proposed approach is also efficient in retrieving at least 50% data with performance improvement of at least 25%.
Prior studies on big data analytics have emphasized the importance of specific big data skills and capabilities for organizational success; however, they have largely neglected to investigate the use of cross‐functional teams’ skills and links to the role played by relevant data‐driven actions and business performance. Drawing on the resource‐based view (RBV) of the firm and on unique data collected from 240 big data experts working in global agrifood networks, we examine the links between the use of big data‐savvy (BDS) teams’ skills, big data‐driven (BDD) actions and business performance. BDS teams depend on multi‐disciplinary skills (e.g. computing, mathematics, statistics, machine learning and business domain knowledge) that help them turn their traditional business operations into modern data‐driven insights (e.g. knowing real‐time price changes and customer preferences), leading to BDD actions that enhance business performance. Our results, raised from structural equation modelling, indicate that BDS teams’ skills that produce valuable insights are the key determinants for BDD actions, which ultimately contribute to business performance. We further demonstrate that those organizations that emphasize BDD actions perform better compared to those that do not focus on such applications and relevant insights.
Nowadays, most of information saved in companies are unstructured models. Retrieval and extraction of the information is essential works and importance in semantic web areas. Many of these requirements will be depend on the unstructured data analysis. More than 80% of all potentially useful business information is unstructured data, in kind of sensor readings, console logs and so on. The large number and complexity of unstructured data opens up many new possibilities for the analyst. Text mining and natural language processing are two techniques with their methods for knowledge discovery from textual context in documents. This is an approach to organize a complex unstructured data and to retrieve necessary information. The paper is to find an efficient way of storing unstructured data and appropriate approach of fetching data. Unstructured data targeted in this work to organize, is the public tweets of Twitter. Building an Big Data application that gets stream of public tweets from twitter which is latter stored in the HBase using Hadoop cluster and followed by data analysis for data retrieved from HBase by REST calls is the pragmatic approach of this project.
Relational Database Systems are the predominant repositories to store mission-critical information collected from industrial sensor devices, business transactions and sourcing activities, among others. As such, they provide an exceptional gateway for data science. However, conventional knowledge discovery processes require data to be transported to external mining tools, which is a very challenging exercise in practice. To get over this dilemma, equipping databases with predictive capabilities is a promising direction. Using Rough Set Theory is particularly interesting for this subject, because it has the ability to discover hidden patterns while founded on well-defined set operations. Unfortunately, existing implementations consider data to be static, which is a prohibitive assumption in situations where data evolve over time and concepts tend to drift. Therefore, we propose an in-database rule learner for nonstationary environments in this chapter. The assessment under different scenarios with other state-of-the-art rule inducers demonstrate the algorithm is comparable with existing methods, but superior when applied to critical applications that anticipate further confidence from the decision-making process.
We are at the beginning of the multicore era. Computers will have increasingly many cores (processors), but there is still no good programming framework for these architectures, and thus no simple and unified way for machine learning to take advantage of the potential speed up. In this paper, we develop a broadly ap- plicable parallel programming method, one that is easily applied to many different learning algorithms. Our work is in distinct contrast to the tradition in machine learning of designing (often ingenious) ways to speed up a single algorithm at a time. Specifically, we show that algorithms that fit the Statistical Query model (15) can be written in a certain "summation form," which allows them to be easily par- allelized on multicore computers. We adapt Google's map-reduce (7) paradigm to demonstrate this parallel speed up technique on a variety of learning algorithms including locally weighted linear regression (LWLR), k-means, logistic regres- sion (LR), naive Bayes (NB), SVM, ICA, PCA, gaussian discriminant analysis (GDA), EM, and backpropagation (NN). Our experimental results show basically linear speedup with an increasing number of processors.
Garlic is a middleware system that provides an in- tegrated view of a variety of legacy data sources, without changing how or where data is stored. In this paper, we describe our architecture for wrap- pers, key components of Garlic that encapsulate data sources and mediate between them and the middleware. Garlic wrappers model legacy data as objects, participate in query planning, and provide standard interfaces for method invocation and query execution. To date, we have built wrappers for 10 data sources. Our experience shows that Garlic wrappers can be written quickly and that our architecture is flexible enough to accommo- date data sources with a variety of data models and a broad range of traditional and non-tradition- al query processing capabilities.
The next-generation astronomy digital archives will cover most of the sky at fine resolution in many wavelengths, from X-rays, through ultraviolet, optical, and infrared. The archives will be stored at diverse geographical locations. One of the first of these projects, the Sloan Digital Sky Survey (SDSS) is creating a 5-wavelength catalog over 10,000 square degrees of the sky (see http://www.sdss.org/). The 200 million objects in the multi-terabyte database will have mostly numerical attributes in a 100+ dimensional space. Points in this space have highly correlated distributions.
The archive will enable astronomers to explore the data interactively. Data access will be aided by multidimensional spatial and attribute indices. The data will be partitioned in many ways. Small tag objects consisting of the most popular attributes will accelerate frequent searches. Splitting the data among multiple servers will allow parallel, scalable I/O and parallel data analysis. Hashing techniques will allow efficient clustering, and pair-wise comparison algorithms that should parallelize nicely. Randomly sampled subsets will allow de-bugging otherwise large queries at the desktop. Central servers will operate a data pump to support sweep searches touching most of the data. The anticipated queries will require special operators related to angular distances and complex similarity tests of object properties, like shapes, colors, velocity vectors, or temporal behaviors. These issues pose interesting data management challenges.
For the past year, we have been assembling requirements from a collection of scientific data base users from astronomy, particle physics, fusion, remote sensing, oceanography, and biology. The intent has been to specify a common set of requirements for a new science data base system, which we call SciDB. In addition, we have discovered that very complex business analytics share most of the same requirements as "big science". We have also constructed a partnership of companies to fund the development of SciDB, including eBay, the Large Synoptic Survey Telescope (LSST), Microsoft, the Stanford Linear Accelerator Center (SLAC) and Vertica. Lastly, we have identified two "lighthouse customers" (LSST and eBay) who will run the initial system, once it is constructed. In this paper, we report on the requirements we have identified and briefly sketch some of the SciDB design. I INTRODUCTION
R is a numerical computing environment that is widely popular for statistical data analysis. Like many such environments, R performs poorly for large datasets whose sizes exceed that of physical mem- ory. We present our vision of RIOT (R with I/O Transparency), a system that makes R programs I/O-efficient in a way transpar- ent to the users. We describe our experience with RIOT-DB, an initial prototype that uses a relational database system as a back- end. Despite the overhead and inadequacy of generic database sys- tems in handling array data and numerical computation, RIOT-DB significantly outperforms R in many large-data scenarios, thanks to a suite of high-level, inter-operation optimizations that integrate seamlessly into R. While many techniques in RIOT are inspired by databases (and, for RIOT-DB, realized by a database system), RIOT users are insulated from anything database related. Compared with previous approaches that require users to learn new languages and rewrite their programs to interface with a database, RIOT will, we believe, be easier to adopt by the majority of the R users.
Over the past 40 years, database management systems (DBMSs) have evolved to provide a sophisticated variety of data manage- ment capabilities. At the same time, tools for managing queries over the data have remained relatively primitive. One reason for this is that queries are typically issued through applications. They are thus debugged once and re-used repeatedly. This mode of inter- action, however, is changing. As scientists (and others) store and share increasingly large volumes of data in data centers, they need the ability to analyze the data by issuing exploratory queries. In this paper, we argue that, in these new settings, data management sys- tems must provide powerful query management capabilities, from query browsing to automatic query recommendations. We first dis- cuss the requirements for a collaborative query management sys- tem. We outline an early system architecture and discuss the many research challenges associated with building such an engine.
The Optimized Sparse Kernel Interface (OSKI) is a collection of low-level primitives that provide automatically tuned computational kernels on sparse matrices, for use by solver libraries and applications. These kernels include sparse matrix-vector multiply and sparse triangular solve, among others. The primary aim of this interface is to hide the complex decisionmaking process needed to tune the performance of a kernel implementation for a particular user's sparse matrix and machine, while also exposing the steps and potentially non-trivial costs of tuning at run-time. This paper provides an overview of OSKI, which is based on our research on automatically tuned sparse kernels for modern cache-based superscalar machines.
Most data management scenarios today rarely have a situation in which all the data that needs to be managed can fit nicely into a conventional relational DBMS, or into any other single data model or system. Instead, we see a set of loosely connected data sources, typically with the following recurring challenges:
– Users want be able to search the entire collection without having knowledge of individual sources, their schemas or interfaces. In some cases, they merely want to know where the information exists as a starting point to further exploration.
– An organization may want to enforce certain rules, integrity constraints, or conventions (e.g., on naming entities) across the entire collection, or track flow and lineage between systems. Furthermore, the organization needs to create a coherent external view of the data.
– The administrators may want to impose a single “support system” in terms of recovery, availability, and redundancy, as well as uniform security and access controls.
– Users and administrators need to manage the evolution of the data, both in terms of content and schemas, in particular as new data sources get added (e.g., as a result of mergers or new partnerships).
This paper outlines the content and performance of ScaLAPACK, a collection of mathematical software for linear algebra computations on distributed memory computers. The importance of developing standards for computational and message passing interfaces is discussed. We present the different components and building blocks of ScaLAPACK. This paper outlines the difficulties inherent in producing correct codes for networks of heterogeneous processors. We define a theoretical model of parallel computers dedicated to linear algebra applications: the Distributed Linear Algebra Machine (DLAM). This model provides a convenient framework for developing parallel algorithms and investigating their scalability, performance and programmability. Extensive performance results on various platforms are presented and analyzed with the help of the DLAM. Finally, this paper briefly describes future directions for the ScaLAPACK library and concludes by suggesting alternative approaches to mathematical libraries, explaining how ScaLAPACK could be integrated into efficient and user-friendly distributed systems.
Volcano is a new dataflow query processing system we have developed for database systems research and education. The uniform interface between operators makes Volcano extensible by new operators. All operators are designed and coded as if they were meant for a single-process system only. When attempting to parallelize Volcano, we had to choose between two models of parallelization, called here the bracket and operator models. We describe the reasons for not choosing the bracket model, introduce the novel operator model, and provide details of Volcano's exchange operator that parallelizes all other operators. It allows intra-operator parallelism on partitioned datasets and both vertical and horizontal inter-operator parallelism. The exchange operator encapsulates all parallelism issues and therefore makes implementation of parallel database algorithms significantly easier and more robust. Included in this encapsulation is the translation between demand-driven dataflow within processes and data-driven dataflow between processes. Since the interface between Volcano operators is similar to the one used in “real,” commercial systems, the techniques described here can be used to parallelize other query processing engines.
MapReduce is a programming model and an associated implementation for processing and generating large datasets that is amenable to a broad variety of real-world tasks. Users specify the computation in terms of a map and a reduce function, and the underlying runtime system automatically parallelizes the computation across large-scale clusters of machines, handles machine failures, and schedules inter-machine communication to make efficient use of the network and disks. Programmers find the system easy to use: more than ten thousand distinct MapReduce programs have been implemented internally at Google over the past four years, and an average of one hundred thousand MapReduce jobs are executed on Google's clusters every day, processing a total of more than twenty petabytes of data per day.
This paper describes a parallel database load prototype for Digital's Rdb database product. The prototype takes a dataflow approach to database parallelism. It includes an explorer that discovers and records the cluster configuration in a database, a client CUI interface that gathers the load job description from the user and from the Rdb catalogs, and an optimizer that picks the best parallel execution plan and records it in a web data structure. The web describes the data operators, the dataflow rivers among them, the binding of operators to processes, processes to processors, and files to discs and tapes. This paper describes the optimizer's cost-based hierarchical optimization strategy in some detail. The prototype executes the web's plan by spawning a web manager process at each node of the cluster. The managers create the local executor processes, and orchestrate startup, phasing, checkpoint, and shutdown. The execution processes perform one or more operators. Data flows among the operators are via memory-to-memory streams within a node, and via web-manager multiplexed tcp/ip streams among nodes. The design of the transaction and checkpoint/restart mechanisms are also described. Preliminary measurements indicate that this design will give excellent scaleups.
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This paper explores a mechanism to support user-defined data types for columns in a relational data base system. Previous work suggested how to support new operators and new data types. The contribution of this work is to suggest ways to allow query optimization on commands which include new data types and operators and ways to allow access methods to be used for new data types. 1. INTRODUCTION The collection of built-in data types in a data base system (e.g. integer, floating point number, character string) and built-in operators (e.g. +, -, *, /) were motivated by the needs of business data processing applications. However, in many engineering applications this collection of types is not appropriate. For example, in a geographic application a user typically wants points, lines, line groups and polygons as basic data types and operators which include intersection, distance and containment. In scientific application, one requires complex numbers and time series with appropriate operat...
Downloaded from http
- W Holland
W. Holland, February 2009. Downloaded from
http://www.urbandictionary.com/define.php?term=mad.
Hal Varian answers your questions
- S Dubner
S. Dubner. Hal Varian answers your questions,
February 2008.
Web Analytics: An Hour a Day
- A Kaushik
A. Kaushik. Web Analytics: An Hour a Day. Sybex,
2007.