scholarly journals REMS.PA: A Complex Framework for Supporting OLAP-based Big Data Analytics over Data-intensive Business Processes

2019 ◽  
Author(s):  
Alfredo Cuzzocrea ◽  
Salvatore Cavalieri ◽  
Orazio Tomarchio ◽  
Giuseppe Di Modica ◽  
Concetta Cantone ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Business Processes ◽  
Big Data Analytics ◽  
Data Intensive

Architecture of modern platforms for big data analytics

2021 ◽  
pp. 67-74
Author(s):  
Liudmyla Zubyk ◽  
Yaroslav Zubyk
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Business Processes ◽  
Service Providers ◽  
Big Data Analytics ◽  
Business Value ◽  
Third Party ◽  
Unstructured Data ◽  
Business Decision ◽  
World Industry

Big data is one of modern tools that have impacted the world industry a lot of. It also plays an important role in determining the ways in which businesses and organizations formulate their strategies and policies. However, very limited academic researches has been conducted into forecasting based on big data due to the difficulties in capturing, collecting, handling, and modeling of unstructured data, which is normally characterized by it’s confidential. We define big data in the context of ecosystem for future forecasting in business decision-making. It can be difficult for a single organization to possess all of the necessary capabilities to derive strategic business value from their findings. That’s why different organizations will build, and operate their own analytics ecosystems or tap into existing ones. An analytics ecosystem comprising a symbiosis of data, applications, platforms, talent, partnerships, and third-party service providers lets organizations be more agile and adapt to changing demands. Organizations participating in analytics ecosystems can examine, learn from, and influence not only their own business processes, but those of their partners. Architectures of popular platforms for forecasting based on big data are presented in this issue.

Analytics-as-a-Service (AaaS)

Web Services ◽  
2019 ◽  
pp. 1262-1281
Author(s):  
Chitresh Verma ◽  
Rajiv Pandey
Keyword(s):  
Big Data ◽  
Case Studies ◽  
Data Analytics ◽  
Data Science ◽  
Business Processes ◽  
Service Oriented Architecture ◽  
Big Data Analytics ◽  
Major Branch ◽  
Implementation Level ◽  
Industrial Level

Big Data Analytics is a major branch of data science where the huge amount raw data is processed to get insight for relevant business processes. Integration of big data, its analytics along with Service Oriented Architecture (SOA) is need of the hour, such integration shall render reusability and scalability to various business processes. This chapter explains the concept of Big Data and Big Data Analytics at its implementation level. The Chapter further describes Hadoop and its technologies which are one of the popular frameworks for Big Data Analytics and envisage integrating SOA with relevant case studies. The chapter demonstrates the SOA integration with Big Data through, two case studies of two different scenarios are incorporated that integrates real world implementation with theory and enables better understanding of the industrial level processes and practices.

Analytics-as-a-Service (AaaS)

2017 ◽  
pp. 26-45
Author(s):  
Chitresh Verma ◽  
Rajiv Pandey
Keyword(s):  
Big Data ◽  
Case Studies ◽  
Data Analytics ◽  
Data Science ◽  
Business Processes ◽  
Service Oriented Architecture ◽  
Big Data Analytics ◽  
Major Branch ◽  
Implementation Level ◽  
Industrial Level

Big Data Analytics is a major branch of data science where the huge amount raw data is processed to get insight for relevant business processes. Integration of big data, its analytics along with Service Oriented Architecture (SOA) is need of the hour, such integration shall render reusability and scalability to various business processes. This chapter explains the concept of Big Data and Big Data Analytics at its implementation level. The Chapter further describes Hadoop and its technologies which are one of the popular frameworks for Big Data Analytics and envisage integrating SOA with relevant case studies. The chapter demonstrates the SOA integration with Big Data through, two case studies of two different scenarios are incorporated that integrates real world implementation with theory and enables better understanding of the industrial level processes and practices.

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.

Business Analytics and Big Data

2015 ◽  
pp. 1-11 ◽  
Author(s):  
Dennis T. Kennedy ◽  
Dennis M. Crossen ◽  
Kathryn A. Szabat
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Quantitative Methods ◽  
Business Processes ◽  
Predictive Analytics ◽  
Big Data Analytics ◽  
Database Systems ◽  
Three Dimensions ◽  
Business Analytics ◽  
The People

Big Data Analytics has changed the way organizations make decisions, manage business processes, and create new products and services. Business analytics is the use of data, information technology, statistical analysis, and quantitative methods and models to support organizational decision making and problem solving. The main categories of business analytics are descriptive analytics, predictive analytics, and prescriptive analytics. Big Data is data that exceeds the processing capacity of conventional database systems and is typically defined by three dimensions known as the Three V's: Volume, Variety, and Velocity. Big Data brings big challenges. Big Data not only has influenced the analytics that are utilized but also has affected technologies and the people who use them. At the same time Big Data brings challenges, it presents opportunities. Those who embrace Big Data and effective Big Data Analytics as a business imperative can gain competitive advantage.

Big Data and Big Data Analytics

2021 ◽  
pp. 1-19
Author(s):  
Sam Goundar ◽  
Akashdeep Bhardwaj ◽  
Shavindar Singh ◽  
Mandeep Singh ◽  
Gururaj H. L.
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Business Processes ◽  
New Technologies ◽  
Big Data Analytics ◽  
Small Island ◽  
Island State ◽  
Diverse Data ◽  
Analytical Tools ◽  
Small Island State

Big data is emerging, and the latest developments in technology have spawned enormous amounts of data. The traditional databases lack the capabilities to handle this diverse data and thus has led to the employment of new technologies, methods, and tools. This research discusses big data, the available big data analytical tools, the need to use big data analytics with its benefits and challenges. Through a research drawing on survey questionnaires, observation of the business processes, interviews and secondary research methods, the organizations, and companies in a small island state are identified to survey which of them use analytical tools to handle big data and the benefits it proposes to these businesses. Organizations and companies that do not use these tools were also surveyed and reasons were outlined as to why these organizations hesitate to utilize such tools.

scholarly journals A Survey on Big Data Analytics Using HADOOP

2019 ◽  
Vol 8 (S3) ◽  
pp. 35-40
Author(s):  
S. Mamatha ◽  
T. Sudha
Keyword(s):  
Big Data ◽  
Social Networking Sites ◽  
Data Analytics ◽  
Business Processes ◽  
Big Data Analytics ◽  
Large Data ◽  
Structured Data ◽  
Map Reduce ◽  
Data Set ◽  
Digital World

In this digital world, as organizations are evolving rapidly with data centric asset the explosion of data and size of the databases have been growing exponentially. Data is generated from different sources like business processes, transactions, social networking sites, web servers, etc. and remains in structured as well as unstructured form. The term ― Big data is used for large data sets whose size is beyond the ability of commonly used software tools to capture, manage, and process the data within a tolerable elapsed time. Big data varies in size ranging from a few dozen terabytes to many petabytes of data in a single data set. Difficulties include capture, storage, search, sharing, analytics and visualizing. Big data is available in structured, unstructured and semi-structured data format. Relational database fails to store this multi-structured data. Apache Hadoop is efficient, robust, reliable and scalable framework to store, process, transforms and extracts big data. Hadoop framework is open source and fee software which is available at Apache Software Foundation. In this paper we will present Hadoop, HDFS, Map Reduce and c-means big data algorithm to minimize efforts of big data analysis using Map Reduce code. The objective of this paper is to summarize the state-of-the-art efforts in clinical big data analytics and highlight what might be needed to enhance the outcomes of clinical big data analytics tools and related fields.

scholarly journals “PENSI BINTEY”: PENGARUH IMPLEMENTASI BIG DATA ANALYTICS TERHADAP TERJADINYA AUDIT DELAY

2021 ◽  
Vol 16 (2) ◽  
pp. 109
Author(s):  
Nicholas Alexander Tunggal ◽  
Elliza Elliza
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Business Processes ◽  
Big Data Analytics ◽  
Publicly Traded ◽  
Public Accounting ◽  
Accounting Firms ◽  
Publicly Traded Companies ◽  
Selection For ◽  
Operational Activities

ABSTRACT Audit delay states the complexity of transactions that occur within a business entity. Many companies have tried to find ways to avoid audit delay conditions in their business processes, one of which is by implementing big data analytics in the company's operational activities. The purpose of this study was to determine the effect of implementing big data analytics on audit delay and several other factors such as company size, company age, company profit and loss, auditor opinion, and reputation of public accounting firms. This study will use empirical data based on publicly traded companies with the 2017-2019 period. The selection for the 2017-2019 period is based on the hypothesis that many companies are starting to apply big data analytics in carrying out their business processes. Big data analytics is projected based on disclosures made by companies. Based on the results of logistic regression analysis, big data analysis has no significant effect. This suggests that the accountant/auditor should consider implementing big data analytics because of its complexity.Keywords: audit delay, big data analytics ABSTRAK Audit delay mengindikasikan adanya kompleksitas transaksi yang terjadi dalam suatu entitas bisnis. Banyak perusahaan yang telah mencoba mencari cara agar terhindar dari kondisi audit delay dalam proses bisnisnya, salah satunya dengan mengimplementasikan big data analytics dalam kegiatan operasional perusahaan. Tujuan penelitian ini adalah untuk mengetahui pengaruh implementasi big dataanalytics terhadap audit delay serta beberapa faktor lainnya seperti ukuran perusahaan, umur perusahaan, laba rugi perusahaan, opini auditor, dan juga reputasi kantor akuntan publik. Penelitian ini akan menggunakan data empiris berdasarkan perusahaan go public dengan periode 2017-2019. Pemilihan periode 2017-2019 didasarkan pada mulai banyaknya perusahaan yang menerapkan big data analytics dalam menjalankan proses bisnisnya. Big data analytics diproyeksikan berdasarkan pengungkapan yang dilakukan perusahaan. Berdasarkan hasil analisis regresi logistik, big data analytics tidak berpengaruh signifikan. Hal ini menunjukkan bahwa akuntan/auditor harus mempertimbangkan pengimplementasian big data analytics karena terkait dengan kompleksitasnya.Kata kunci: audit delay, big data analytics

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