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MongoDB vs. Oracle: A comparative study using classification algorithms
... Analytical algorithms in the relevant database provide the business success. Such as the database is essential for everyday business life and it considers dynamic markets that are nothing but set of records that have predefined values (Anju and Swathi, 2016). Liu et al. (2015) identified governance, management, architecture, usage, quality, security and privacy at newer risk as the seven key steps to big data success. ...
... To benefit the unstructured data, NoSQL has high scalability, fast queries, an alternative basically available, soft state, eventual (BASE consistency) properties for better performance than relational databases with the atomicity, consistency, isolation, and durability (ACID) operation (Clarke, 2016;Tyagi, 2003). The NoSQL-MongoDB provides granular insights for an easy mapping in their existing programming languages, requires no translation and far efficient when compared to other's DB (Anju and Swathi, 2016). It also, has some shared features with high speed inserting data per time than other database type of Cassandra and MySQL (Ekbia et al., 2015). ...
The small and medium businesses are working hard to make sense on the information data that has been collected from network sources and to translate it into tangible results. In fact, the major data growth trends and shifts in information. Big data have coined to generate and extremely more complex to associate in business databases. Most researcher work focuses on the relational database that requires lots of data processing. That's the reason, artificial intelligence (AI) can achieve input and ability to extend NoSQL document database depending on data type. This research recognises documented MongoDB as real-time access to data stored on various storage platform for all sizes of business. This paper proposed NoSQL-MongoDB model with data shared process embedded with AI and machine learning at the system-level by virtue datasets from the big data analytics. This methodology contributes a narrow view of database management turns on big data challenges for SMBs.
... There are few research works carried on data recovery and restoration procedure. Many papers are given importance in data processing performances [3][4][5][6][7][8][9][10][11][12][13][14] between different types of database systems but ignored the discussion of comparative analysis in data backup and recovery process. To the best of our knowledge, our observation says that there is no implementation of recovery procedure using the Point in Time Recovery. ...
Management of data backup and restore in case of emergency is a crucial process in every organization. This paper discusses an effective database recovery technique called Point In Time Recovery (PITR) in postgreSQL database management system. Despite emerging big data technology, relational database management system (RDBMS) is still performing the key role for storing and processing of data in most of the organizations. Almost all kinds of financial organizations like banks and mobile financial service (MFS) organizations use RDBMS as their database tool for storing their users information and all kinds of transactional information related to that organization. Nowadays those type of organizations focus on customer acquisition strategy and thus data is growing rapidly. In spite of proper system management system crash is not very uncommon while processing large volumes of data. It results loss of data and a huge financial loss for the organization. To tackle such tragedy for the business a proper data recovery system is required for every organization. Generally organizations use backup using pg dump command and restore using pg restore but this traditional recovery system cannot restore the data which is created or altered after the backup taken. Also this process is time inefficient because this process reconstruct the database to the state of the last dump file. Thus our research paper implements a potent process of data recovery technique in postgreSQL that can recover all data which is created or altered after the backup taken. Again this process is time efficient because it works restoring using Write Ahead Log (WAL) file from the base backup.
... Then they demonstrated the impact of compressed files on CPU utilization and memory usage. Anju R and Swathi B.P [12] conducted a comparison based research between MongoDB and Oracle. They used classification algorithm for their work. ...
A database is a collection of information that is organized so that it can easily be accessed, managed, and updated. There are many databases commonly, relational and non relational databases. Relational databases usually work with structured data and non relational databases are work with semi structured data. In this paper, the performance evaluation of MySQL and MongoDB is performed where MySQL is an example of relational database and MongoDB is an example of non relational databases. A relational database (the concept) is a data structure that allows you to link information from different 'tables', or different types of data buckets. A non-relational database just stores data without explicit and structured mechanisms to link data from different buckets to one another.
Data mining techniques are the result of a long process of research and product development. Data mining is the practice of automatically searching large stores of data to discover patterns and trends that go beyond simple analysis. Data mining uses sophisticated mathematical algorithms to segment the data and evaluate the probability of future events of real world problems. Each Data Mining model is produced by a specific algorithm. Some Data Mining problems can best be solved by using more than one algorithm. Support Vector Machines, a powerful algorithm based on statistical learning theory. Oracle Data mining implements Support Vector Machines for classification, regression, and anomaly detection. It also provides the scalability and usability that are needed in a production quality data mining system. This paper introduces and analyses SVM supervised algorithm, which will help to fresh researchers to understand the tuning, diagnostics & data preparation process and advantages of SVM in Oracle Data Mining. SVM can model complex, real-world problems such as text and image classification, hand-writing recognition, and bioinformatics and biosequence analysis.
This article introduces the concepts of Big Data and NoSQL and describes a semester long web-based project that uses both a relational database (Oracle 11g) and a NoSQL (MongoDB) database for an undergraduate database course. The relational database stores the record information of an online sales system and the NoSQL database stores three manuals. The semester long assignment and its implementation are available as a download.
With the development of internet Web2.0 technology, the traditional relational database is widely used in information management system. However, it is not effective, when we need to query a wide range of massive data, especially with multi-table join queries. Now, a kind of new technology emerged----NoSQL, which is non-relational database management system with format loose data storage, not support the join operation, the effective query capability etc advantages. This paper attempts to use NoSQL database to replace the relational database, applied to traditional information management systems, compare the two database technologies, give the key code of NoSQL implementation, and finally list the performance comparison of two schemes.
In this paper, we use decision trees to establish the decision models for insurance purchases. Five major types of insurances are involved in this study including life, annuity, health, accident, and investment-oriented insurances. Four decision tree methods were used to build the decision models including Chi-square Automatic Interaction Detector (CHAID), Exhaustive Chi-square Automatic Interaction Detector (ECHAID), Classification and Regression Tree (CRT), and Quick-Unbiased-Efficient Statistical Tree (QUEST). Six features were selected as the inputs of the decision trees including age, sex, annual income, educational level, occupation, and risk preference. Three hundred insurants from an insurance company in Taiwan were used as examples for establishing the decision models. Two experiments were conducted to evaluate the performance of the decision trees. The first one used the purchase records of primary insurances as examples. The second one used the purchase records of primary insurances and additional insurances. Each experiment contained four rounds according to different partitions of training sets and test sets. Discussion and concluding remarks are finally provided at the end of this paper.
SQL Support over MongoDB using Metadata
- Sanobar Khan
- Prof Vanita Mane
Sanobar Khan and Prof.Vanita Mane, "SQL Support over MongoDB
using Metadata," International Journal of Scientific and Research
Publications, vol. 3, October 2013.
Using Classification and Regression Trees (CART) in SAS Enterprise MinerTM For Applications in Public Health
- Leonard Gordon
Leonard Gordon, "Using Classification and Regression Trees (CART) in
SAS Enterprise MinerTM For Applications in Public Health," SAS
Global Forum 2013, Lexington, pages 089-2013, 2013.