No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Research on digital audit of electric waste materials based on big data platform
... Das et al. (2020) proposed a cloudbased framework for storing electronic goods sales data records from e-commerce and offline sales. In addition, Kazancoglu et al. (2020) applied machine learning algorithms for e-waste classification, and Xi et al. (2021) examined electrical materials classification. ...
Smart Waste Management (SWM) discusses the waste management process for different types of waste while introducing an intelligent approach to controlling the amount of waste. This paper introduces SWM4.0, which applies Industry4.0 (I4.0) technologies in various related events. First, the paper presents a systematic literature review on the role of I4.0 technologies in SWM activities regarding waste types, waste management processes, and 5R strategies. Then, existing solutions supporting SWM4.0 are extracted to develop a framework for exploring the use of I4.0 technologies. This framework includes sharing the four main pillars that contribute to the success of SWM4.0, namely smart people, smart cities, smart enterprises, and smart factories. Furthermore, this review suggests the possibility of unifying and extending existing solutions and identifying the necessary links and interfaces for researchers. For managerial implications, the framework identifies future strategies to fulfill specific SWM tasks and to foster new technological solutions for future research.
Hadoop MapReduce reactively detects and recovers faults after they occur based on the static heartbeat detection and the re-execution from scratch techniques. However, these techniques lead to excessive response time penalties and inefficient resource consumption during detection and recovery. Existing fault-tolerance solutions intend to mitigate the limitations without considering critical conditions such as fail-slow faults, the impact of faults at various infrastructure levels and the relationship between the detection and recovery stages. This paper analyses the response time under two main conditions: fail-stop and fail-slow, when they manifest with node, service, and the task at runtime. In addition, we focus on the relationship between the time for detecting and recovering faults. The experimental analysis is conducted on a real Hadoop cluster comprising MapReduce, YARN and HDFS frameworks. Our analysis shows that the recovery of a single fault leads to an average of 67.6% response time penalty. Even though the detection and recovery times are well-turned, data locality and resource availability must also be considered to obtain the optimum tolerance time and the lowest penalties.
The wave of big data has promoted the transformation of auditing technology, which has brought about tremendous changes to auditing models and auditing methods. The traditional audit data analysis methods cannot analyze semi-structured and unstructured data, nor can they meet the requirements of the development of audit informatization in the context of big data. New ideas and methods for audit data analysis are urgently needed. In this context, the article proposes an audit data analysis framework based on text mining, and describes the detailed audit data analysis process of collecting and storing, mining and analysing, summarizing and publishing. By using text mining technology to mine the unstructured original audit data collected, different text mining models are established according to the clear audit requirements, the audit data is analysed, and then audit doubts are found, and ultimately formable audit evidence and audit trail. The construction of the framework aims to provide new ideas for big data audits to reduce the risk of big data audits and improve audit quality.
Electricity data could generate a large number of records from smart meter day by day. The traditional architecture might not properly handle the increasingly dynamic data that need flexibility. For effective storing and analytics, efficient architecture is needed to provide much greater data volumes and varieties. In this paper, we proposed the architecture of data storage and analytic in the big data lake of electricity usage using Spark. Apache Sqoop was used to migrate historical data to Apache Hive for processing from an existing system. Apache Kafka was used as the input source for Spark to stream data to Apache HBase to ensure the integrity of the streaming data. In order to integrate the data, we use the Hive and HBase principle of Data Lake as search engines for Hive and HBase. Apache Impala and Apache Phoenix are used separately. This work also analyzes electricity usage and power failure with Apache Spark. All of the visualizations of this project are presented in Apache Superset. Moreover, the usage prediction comparison is presented using HoltWinters algorithm.
Modern audit engagements often involve examination of clients that are using big data and analytics to remain competitive and relevant in today's business environment. Client systems now are integrated with the cloud, the Internet of Things, and external data sources such as social media. Furthermore, many engagement clients are now integrating this big data with new and complex business analytical approaches to generate intelligence for decision making. This scenario provides almost limitless opportunities and also the urgency for the external auditor to utilize advanced analytics. This paper first positions the need for the external audit profession to move towards big data and audit analytics. It then reviews the regulations regarding audit evidence and analytical procedures, in contrast to the emerging environment of big data and advanced analytics. In a big data environment, the audit profession has the potential to undertake more advanced predictive and prescriptive oriented analytics. The next section proposes and discusses six key research questions and ideas followed with particular emphasis on the research needs of quantification of measurement and reporting. This paper provides a synthesis and review of the concerns facing the audit community with the growing use of big data and complex analytics by their clients. It contributes to the literature by expanding upon these emerging concerns and providing opportunities for future research.
This open source computing framework unifies streaming, batch, and interactive big data workloads to unlock new applications.
A novel structure of transmission line transformer (TLT) with mutually coupled windings is described in this paper. All transmission lines except the first stage of the transformer are wound on a common ferrite core for the TLT with this structure. A referral method was introduced to analyze the TLT with this structure, and an analytic expression of the step response was derived. It is shown that a TLT with this structure has a significantly slower droop rate than a TLT with other winding structures and the number of ferrite cores needed is largely reduced. A four-stage TLT with this structure was developed, whose input and output impedance were 4.2 Ω and 67.7 Ω, respectively. A frequency response test of the TLT was carried out. The test results showed that pulse response time of the TLT is several nanoseconds. The TLT described in this paper has the potential to be used as a rectangle pulse transformer with very fast response time.
Waste from the construction sector poses huge challenges for sustainable waste management. This is not only due to the vast amount of waste produced in construction and demolition activities, but also due to pollutants potentially contained in these products. Subject to these conditions, waste management must ensure recovery of as many resources as possible, while making sure to keep material loops clean. This demanding task requires more knowledge about the existing building stock and an adaptation of current demolition processes. Innovative technologies, such as Building Information Modelling, or modern frameworks, such as Geographic Information Systems, offer a high potential to synoptically provide stock material information for future demolition activities for individual objects to be deconstructed as well as for whole cities as a basis for managing the anthropogenic stock and potential urban mining. Suitable methods of data collection allow for acquiring the desired input for the generation of building stock models enriched with demolition-related information. With the latter, selective deconstruction strategies as well as appropriate waste stream routing agendas can be planned and executed, thereby securing safety at work during the demolition process itself and a waste stream routing according to the waste hierarchy. This review article gives an overview of currently deployed building material assessment tools (data capture and visualisation), both a prerequisite for improved information on materials and geometry (and thereby mass/volume). In addition, this article describes workflows employable for the purpose of urban mining in end-of-life buildings, of which one holistic approach will be described in depth.
This paper examines the association between the corruption level of a country and audit market concentration. Using a sample from 78 countries over the 2003–2012 period, we document a positive association between corruption and Big 4 audit market concentration, suggesting that in more corrupt countries, the audit market at the industry level is dominated by one or two Big 4 audit firms rather than shared equally. To provide further understanding about how corruption is associated with Big 4 audit market concentration, we conduct a path analysis and find that corruption has an indirect effect on audit market concentration through collusion. These results are consistent with the notion that corrupt governments do not effectively control collusion and thus decrease market competition, leading to a decrease in the perceived severity of information leakage and a lower concern about sharing a common auditor. Finally, we conduct a battery of sensitivity tests. Our results are robust to change analysis, to controlling for other factors that are likely to influence auditor choices and the market structure, to an alternative measure of concentration, and to alternative samples. Overall, we provide evidence to suggest that country-level corruption plays a role in Big 4 audit market concentration.
This paper discusses the challenges of performing a forensic investigation against a multi-node Hadoop cluster and proposes a methodology for examiners to use in such situations. The procedure's aim of minimising disruption to the data centre during the acquisition process is achieved through the use of RAM forensics. This affords initial cluster reconnaissance which in turn facilitates targeted data acquisition on the identified DataNodes. To evaluate the methodology's feasibility, a small Hadoop Distributed File System (HDFS) was configured and forensic artefacts simulated upon it by deleting data originally stored in the cluster. RAM acquisition and analysis was then performed on the NameNode in order to test the validity of the suggested methodology. The results are cautiously positive in establishing that RAM analysis of the NameNode can be used to pinpoint the data blocks affected by the attack, allowing a targeted approach to the acquisition of data from the DataNodes, provided that the physical locations can be determined. A full forensic analysis of the DataNodes was beyond the scope of this project.
Exploration and Practice of Waste Materials Disposal by ERP System in Power Grid Enterprises[J]
- wei
Innovative practice of internal control audit of power grid project materials[J]
- cui
Innovative practice of internal control audit of power grid project materials[J]
- R Cui
- T T Chen
- Y H Shi
Exploration and Practice of Waste Materials Disposal by ERP System in Power Grid Enterprises[J]
- F D Wei
- L De-Shan
- L I Gang