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Big Data has become a major technology hotbed between academics and practitioners in the digital competitive area. In developing a successful Big Data project, it is necessary to investigate the variables and category of Critical Success Factors (CSF) that impact the successful execution of Big Data implementation. Critical Success Factors (CSF) can be used as a benchmark for Big Data Projects implementation. This paper aims to examine the factors that decide a Big Data implementation can be a success. Based on Systematic Literature Review (SLR) in related papers in the past five years (2015-2020), this paper identifies seven categories of CSF for implementation Big Data Projects namely Technology, People, Top Management, Organization, Data Management, Process, and Governance. [ABSTRACT FROM AUTHOR]

The increase in data from modern sources, the heterogeneous nature of data, ambiguous and unstructured data, and the so-called Big Data with all of its five v's characteristics, indicate a growing need to use approaches that provide assistance in modeling and processing these data, provide additional automated data processing, and so on. The majority of all these studies involve one or more big data sets that may be used in various applications. Most of these selected papers use one or more distributed frameworks (such as MapReduce, Spark, or HDFS Hadoop distributed file systems). Furthermore, more than one strategy of Web mining is dealt with (such as Naive Bayes, Logistic Regression, and Random Forest, as examples) more than recommender system types (collaborative filtering, or/and content-based filtering). Various data sets were used (such as Movie Lens data set, LDOSCoMoDa data set, real bank loans dataset, using Facebook and Twitter for collecting data, data was taken across different companies, thousands of movies were used as exemplary data sets, or data sets used a sample data from different schools of Central Board of Secondary Education (CBSE) across India). Testing accuracy on the dataset obtained is required in the accuracy used (precision and recall). The best precision was 0.9886 and the best recall was 0.9835 in these studies. We have displayed some studies that have high precision and low recall and some other studies that have low precision and high recall. This paper introduces a literature survey about recommender systems that deal with big data through web mining techniques. [ABSTRACT FROM AUTHOR]

Food supply chains are characterized by innovation, not only in products but also in processes. This paper aims to identify big data applications in the food and drink sector and present its findings as a state-of-the-art literature review. Academic databases were searched using 'food' or 'drink' and 'big data' keywords. Scholarly publications from 2015 onward are identified and presented in broad categories of demand prediction and retail operations optimization. The review recognized big data applications as a great opportunity for food supply chains. The applications aimed 1) to understand the customer base and inform marketing communications strategy, 2) to predict demand and organize retail operations to meet this demand, and 3) to optimize prices, assortment, and inventories based on demand patterns. Applications in this review focused more on descriptive and predictive analytics than prescriptive analytics, possibly due to the emergent nature of these applications. Descriptive analytics applications focused on capturing data, summarizing the status quo, and developing customer segments which can then be managed using varying marketing strategies. Predictive analytics applications focused on demand prediction with novel approaches proposed by the machine learning community. Prescriptive analytics applications aimed at promotion optimization and pricing for profit maximization. Cognitive analytics applications extracted customer reviews from online stores to inform which products should be marketed in what way. The review offers managerial insights on circumstances where big data analytics could prove beneficial. Managerial implications suggest that data integrators enable big data applications by ensuring the data collected are accurate, timely, and complete to inform descriptive, predictive, and prescriptive analytical models. [ABSTRACT FROM AUTHOR]

Currently, due to the large and continuous increase in the volume of data, there are systems and applications developed to deal with the preparation of large data in storage, management and processing, and the traditional methods are not suitable for dealing with it due to the large and complex size Which leads to consumption of time and lack of efficiency and performance of the techniques used In this research paper, we proposed different algorithms and techniques to improve the performance and quality of big data including the process of data collection and processing in different ways to deal with it. In addition to the techniques used in the processing process, such as flaw detection technology and under sample technology, data clean and other technologies. A hybrid model was used, represented by feature selection based on the Genetic algorithm's best solution. With the Decision Tree algorithm as well as with the ANN algorithm. Accuracy calculation, recall, support, precision, and f1-score. The proposed system contains a data set of European card holders that contains 122 features. Finally, the results obtained by applying the proposed algorithms were compared using evaluation scales to determine the best model among the proposed models. After conducting the comparison process, it became clear that the GA model with DT beat the other model, as it achieved higher results, with an accuracy of 0.98, while the other model achieved an accuracy of 0.97. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to analyze Big Data processing technologies as well as to develop practical guidance to installing and working with the Apache Hadoop big data analysis ecosystem for its perspective application in universities. Specialists in the field of big data are in great demand, and with knowledge of Hadoop they are irreplaceable, since this system is fundamental and simpler analogues are based on it. This article can be used as the practical guidance to familiarize students with Apache Hadoop technology, as well as to provide theoretical material on "Big Data" technology. In the course of the work, the key definitions were given, the basic principles of working with "Big data" were formulated, and detailed instructions for installing and operating "Apache Hadoop" were developed. [ABSTRACT FROM AUTHOR]