scholarly journals A Big Data Analytics Approach for Dynamic Feedback Warning for Complex Systems

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Wenrui Li ◽  
Menggang Li ◽  
Yiduo Mei ◽  
Ting Li ◽  
Fang Wang
Keyword(s):  
Big Data ◽  
Complex Systems ◽  
Early Warning ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Theoretical Models ◽  
Linearization Method ◽  
Dynamic Feedback ◽  
International Financial Crisis ◽  
The One

With the development of science and technology, the application of big data is becoming more and more widespread, and it has gradually expanded to various fields such as economy and commerce. Since the 2008 international financial crisis, the mainstream economics has shown deficiencies to a certain extent. On the one hand, the expressions pursued by mainstream economic theories are too strict, restricting its processing capabilities. On the other hand, the linearization method ignores the diversity, complexity, and variability of changes in the economic system, which may ignore the emergence of some serious crises. Due to the increasing distance between theoretical models and practice, theoretical models cannot guide the practice and sometimes even mislead the latter. In this paper, we propose a method of dynamic feedback early warning based on big data, which uses the LPPL model to fit parameters. Finally, we used this method to analyze the case of the A-share disaster. The research results show that the method makes the early warning coefficients of dynamic and complex systems more scientific and accurate.

Coding Inequality

2020 ◽  
pp. 100-117
Author(s):  
Sarah Brayne
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Analytics ◽  
Social Inequalities ◽  
Big Data Analytics ◽  
The Other ◽  
Surveillance Network ◽  
Data Intensive ◽  
The One ◽  
Reproduction Of Inequality

This chapter looks at the promise and peril of police use of big data analytics for inequality. On the one hand, big data analytics may be a means by which to ameliorate persistent inequalities in policing. Data can be used to “police the police” and replace unparticularized suspicion of racial minorities and human exaggeration of patterns with less biased predictions of risk. On the other hand, data-intensive police surveillance practices are implicated in the reproduction of inequality in at least four ways: by deepening the surveillance of individuals already under suspicion, codifying a secondary surveillance network of individuals with no direct police contact, widening the criminal justice dragnet unequally, and leading people to avoid institutions that collect data and are fundamental to social integration. Crucially, as currently implemented, “data-driven” decision-making techwashes, both obscuring and amplifying social inequalities under a patina of objectivity.

scholarly journals Use of Big-Data Analytics with the Interactive Advertisement for Product/Service Representation towards its Customers

2019 ◽  
Vol 4 (2) ◽  
Author(s):  
HarshmitKaur Saluja ◽  
Vinod Kumar Yadav ◽  
K.M. Mohapatra
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Service Performance ◽  
Data Sets ◽  
Complex Data ◽  
Product Service ◽  
Literature Reviews ◽  
Complex Data Sets ◽  
The One

On the one hand, big-data analytics has brought revolution in the predictive modeler by enabling the complex data sets getting structured. On the other hand, the interactive advertisement has changed the complete scenario of the advertising sector by making advertisements content structured in such a way that it is customer-centric. The paper helps to widen the view to explore the growing urge of customization technique in advertising sector with interactive enablers. The paper further examines that how interactive advertisement and big-data has helped to represent product/service from the view of a customer and also improved the product/service performance. In order of study, exhaustive literature reviews resulting in three hypothesis are developed to take on the above-mentioned concerns.

scholarly journals Big data analytics in returns management – Are complex techniques necessary to forecast consumer returns properly?

2018 ◽  
Author(s):  
Björn Asdecker ◽  
David Karl
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
State Of The Art ◽  
Big Data Analytics ◽  
Real Data ◽  
Vast Number ◽  
Consumer Returns ◽  
The One ◽  
Returns Management ◽  
Complex Methods

The more people shop online, the more consumer returns e-tailers face. In order to plan the returns management process capacity adequately, it is necessary to forecast the expected amount of returned parcels. Big data analytics provides a vast number of methods to perform such tasks. However, it should be noted that particularly small- and medium-sized e-tailers lack the capabilities and resources to employ such complex techniques. Against this background, this paper analyses the performance of several data analysis methods that differ in application complexitiy using real data from an apparel e-tailer. On the one hand, we find that –as expected– complex methods outperform simple ones. On the other hand, and from a practitioner’s perspective probably even more interesting, we also conclude that a binary logistical regression as the simplest analyzed method may already provide satisfactory results. The findings indicate that the use of big data analytics is of great value to effectively and efficiently manage consumer returns – even if not the most sophisticated state-of-the-art method is used.

scholarly journals Big Data Analytics and Discrete Choice Model for Enterprise Credit Risk Early Warning Algorithm

2022 ◽  
Vol 2022 ◽  
pp. 1-13
Author(s):  
Jiangbo Yu
Keyword(s):  
Big Data ◽  
Credit Risk ◽  
Discrete Choice ◽  
Early Warning ◽  
Data Analytics ◽  
Choice Model ◽  
Big Data Analytics ◽  
Discrete Choice Model ◽  
Big Data Analysis ◽  
Enterprise Credit

A business credit risk early warning algorithm based on big data analysis and discrete selection model is presented to address the issues of poor sample fitting performance, long warning time, and low warning accuracy that plague the traditional enterprise credit risk early warning algorithm. A-share listed enterprises in China were chosen as the credit data source for screening the samples based on big data analysis. After screening, financial failure firms were coupled, and paired samples were created. The credit risk variables, which included financial and corporate governance characteristics, were chosen based on the created samples. The enterprise financial risk submodel and the nonfinancial risk submodel were built based on the enterprise credit risk variables, and the financial and nonfinancial index scores of enterprise customers were evaluated separately to develop a discrete choice model of enterprise credit risk. The algorithm’s sample fitting performance was employed to achieve early warning of corporate credit risk. The algorithm based on big data analytics and discrete choice model is compared to the traditional method in order to verify its validity. The findings of the experiment reveal that the algorithm’s sample fitting performance is superior to the traditional one, making it more suitable for enterprise credit risk early warning. The proposed model depicts 85% accuracy.

scholarly journals Handling Data Skew in MapReduce Cluster by Using Partition Tuning

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Yufei Gao ◽  
Yanjie Zhou ◽  
Bing Zhou ◽  
Lei Shi ◽  
Jiacai Zhang
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Programming Model ◽  
Big Data Analytics ◽  
Rule Mining ◽  
Data Skew ◽  
Healthcare Data ◽  
Tuning Method ◽  
Mapreduce Model ◽  
The One

The healthcare industry has generated large amounts of data, and analyzing these has emerged as an important problem in recent years. The MapReduce programming model has been successfully used for big data analytics. However, data skew invariably occurs in big data analytics and seriously affects efficiency. To overcome the data skew problem in MapReduce, we have in the past proposed a data processing algorithm called Partition Tuning-based Skew Handling (PTSH). In comparison with the one-stage partitioning strategy used in the traditional MapReduce model, PTSH uses a two-stage strategy and the partition tuning method to disperse key-value pairs in virtual partitions and recombines each partition in case of data skew. The robustness and efficiency of the proposed algorithm were tested on a wide variety of simulated datasets and real healthcare datasets. The results showed that PTSH algorithm can handle data skew in MapReduce efficiently and improve the performance of MapReduce jobs in comparison with the native Hadoop, Closer, and locality-aware and fairness-aware key partitioning (LEEN). We also found that the time needed for rule extraction can be reduced significantly by adopting the PTSH algorithm, since it is more suitable for association rule mining (ARM) on healthcare data.

Big Data Analytics: The Next Big Thing

2019 ◽  
Vol 54 (5) ◽  
pp. 20
Author(s):  
Dheeraj Kumar Pradhan
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics

Strategy in Discovery Mode - Wie Big Data & Analytics strategisches Denken verdrängen kann

2020 ◽  
Vol 49 (5) ◽  
pp. 11-17
Author(s):  
Thomas Wrona ◽  
Pauline Reinecke
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics

Big Data & Analytics (BDA) ist zu einer kaum hinterfragten Institution für Effizienz und Wettbewerbsvorteil von Unternehmen geworden. Zu viele prominente Beispiele, wie der Erfolg von Google oder Amazon, scheinen die Bedeutung zu bestätigen, die Daten und Algorithmen zur Erlangung von langfristigen Wettbewerbsvorteilen zukommt. Sowohl die Praxis als auch die Wissenschaft scheinen geradezu euphorisch auf den „Datenzug“ aufzuspringen. Wenn Risiken thematisiert werden, dann handelt es sich meist um ethische Fragen. Dabei wird häufig übersehen, dass die diskutierten Vorteile sich primär aus einer operativen Effizienzperspektive ergeben. Strategische Wirkungen werden allenfalls in Bezug auf Geschäftsmodellinnovationen diskutiert, deren tatsächlicher Innovationsgrad noch zu beurteilen ist. Im Folgenden soll gezeigt werden, dass durch BDA zwar Wettbewerbsvorteile erzeugt werden können, dass aber hiermit auch große strategische Risiken verbunden sind, die derzeit kaum beachtet werden.

A Review on Big Data Analytics Tools in Context with Scalability

2019 ◽  
Vol 7 (2) ◽  
pp. 273-277
Author(s):  
Ajay Kumar Bharti ◽  
Neha Verma ◽  
Deepak Kumar Verma
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics
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