THE APPLICATION OF THE MS EXCEL PROGRAM AND THE INFORMALIZED BUSINESS INTELLIGENCE ANALYTICS PLATFORMS IN THE MANAGEMENT OF THE ENTERPRISES

2017 ◽  
Vol 5 (1) ◽  
pp. 0-0
Author(s):  
Dariusz Prokopowicz ◽  
Jan Grzegorek
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Business Intelligence ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Business Analytics ◽  
Business Decision ◽  
Decision Making Processes ◽  
Analytical Formulas

Rapid progress is being made in the field of IT applications in the analysis of the economic and financial situation of enterprises and in the processes supporting management of organizations. In terms of the fastest growing areas of information and communication technology, which are the prerequisites for the progress of online electronic banking, it is necessary to disseminate the standards of financial operations have been carried out. The cloud as well as the use of large data sets in the so-called. Big Data platforms. The current Big Data technology solutions are not just large databases, data warehouses allow for multifaceted analysis of huge volumes of quantitative data for periodic managerial reporting. Business decision-making processes should be based on the analysis of reliable and up-to-date market and business data. The information necessary for the decision-making processes has been collected, stored, ordered and pre-summed up in the form of Business Intelligence analytics reports in corporations. Business Intelligence analyzes give managers the ability to analyze the large data sets in real time, which significantly contributes to improving business management efficiency. At present, business analytics use either the advanced analytical formulas of Ms Excel or computerized platforms that include ready-made Business Intelligence reporting formulas.

Business analysis as an effective data processing tool

2020 ◽  
pp. 64-70
Author(s):  
Ponomarenko ◽  
Teleus
Keyword(s):  
Data Processing ◽  
Business Intelligence ◽  
Large Data ◽  
Comprehensive Analysis ◽  
Large Data Sets ◽  
Data Sets ◽  
Business Analytics ◽  
Software Products ◽  
Data Processing Tool ◽  
Analysis System

The subject of the research is the approach to the possibility of using business intelligence for integrated data processing and analysis in order to optimize the company’s activities. The purpose of writing this article is to study the concept of the BI-systems peculiarities use as one of the advanced approaches to the pro- cessing and analysis of large data sets that are continuously accumulated from various sources. Methodology. The research methodology is system-structural and comparative analyzes (to study the application of BI-systems in the process of working with large data sets); monograph (the study of various software solutions in the market of business intelligence); economic analysis (when assessing the pos- sibility of using business intelligence systems to strengthen the competitive position of companies). The scientific novelty consists the features of using the business analytics model in modern conditions to optimize the activities of companies through the use of complex information, which in many cases is unstructured, are identified. The main directions of working with big data are disclosed, starting from the stage of collection and storage in specialized repositories, and ending with a comprehensive analysis of information. The main advantages of using dashboards in the process of demonstrating research results are given. A comprehensive analysis of software products in the business intelligence market has been carried out. Conclusions. The use of business intelligence allows companies to optimize their activities by making effective management decisions. The availability of a large number of BI tools al- lows company to adapt the analysis system in accordance with available data and existing needs of the company. Software solutions make it possible to build dash- boards with the settings of the selected system of indicators.

Multidimensional Business Benchmarking Analysis on Data Warehouses

2017 ◽  
Vol 13 (1) ◽  
pp. 51-75 ◽  
Author(s):  
Akiko Campbell ◽  
Xiangbo Mao ◽  
Jian Pei ◽  
Abdullah Al-Barakati
Keyword(s):  
Business Intelligence ◽  
State Of The Art ◽  
Technical Problem ◽  
Large Data ◽  
Data Cube ◽  
Large Data Sets ◽  
Weather Data ◽  
Data Sets ◽  
Business Analytics ◽  
Data Set

Benchmarking analysis has been used extensively in industry for business analytics. Surprisingly, how to conduct benchmarking analysis efficiently over large data sets remains a technical problem untouched. In this paper, the authors formulate benchmark queries in the context of data warehousing and business intelligence, and develop a series of algorithms to answer benchmark queries efficiently. Their methods employ several interesting ideas and the state-of-the-art data cube computation techniques to reduce the number of aggregate cells that need to be computed and indexed. An empirical study using the TPC-H data sets and the Weather data set demonstrates the efficiency and scalability of their methods.

THE RELATIONSHIP OF SOCIOTECHNICAL AND TECHNOLOGICAL ASPECTS OF THE INTRODUCTION AND APPLICATION OF BUSINESS INTELLIGENCE SYSTEMS

2021 ◽  
Vol 5 (12) ◽  
pp. 30-35
Author(s):  
Edward N. Ozhiganov ◽  
◽  
Alexander A. Chursin ◽  
Alexey D. Linkov ◽  
◽  
...  
Keyword(s):  
Big Data ◽  
Business Intelligence ◽  
New Technologies ◽  
Business Analytics ◽  
Scientific Publications ◽  
Business Decisions ◽  
Business Decision ◽  
Business Intelligence Systems ◽  
Demand Fluctuations ◽  
New Research

This article describes a relation between sociotechnical and technological factors involved in launching and implementing Business Intelligence systems. Advanced BI systems include business analytics, data mining, data visualization, data tools and infrastructure, and advanced IT solutions to support business decisions based on big data. Various industries and businesses handle large amounts of data to adapt to changing markets and demand fluctuations, push new technologies, and repair ineffective strategies, etc. With an upsurge in data sizes, more and more new research papers are published today to describe BI implemen-tation, use and results. However, today most studies and scientific publications focus on Business Intelligence technological challenges, while sociotechnical aspects – that is processes involved in business decision mak-ing based on big data – are studied in much rarer cases.

A Detailed Study on Classification Algorithms in Big Data

2020 ◽  
pp. 30-46
Author(s):  
Saranya N. ◽  
Saravana Selvam
Keyword(s):  
Big Data ◽  
Random Forest ◽  
Linear Regression ◽  
Comprehensive Evaluation ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Classification Methods ◽  
Computing Science ◽  
Data Collections

After an era of managing data collection difficulties, these days the issue has turned into the problem of how to process these vast amounts of information. Scientists, as well as researchers, think that today, probably the most essential topic in computing science is Big Data. Big Data is used to clarify the huge volume of data that could exist in any structure. This makes it difficult for standard controlling approaches for mining the best possible data through such large data sets. Classification in Big Data is a procedure of summing up data sets dependent on various examples. There are distinctive classification frameworks which help us to classify data collections. A few methods that discussed in the chapter are Multi-Layer Perception Linear Regression, C4.5, CART, J48, SVM, ID3, Random Forest, and KNN. The target of this chapter is to provide a comprehensive evaluation of classification methods that are in effect commonly utilized.

Uncertainty-Based Clustering Algorithms for Large Data Sets

2018 ◽  
pp. 1-33 ◽  
Author(s):  
B. K. Tripathy ◽  
Hari Seetha ◽  
M. N. Murty
Keyword(s):  
Big Data ◽  
Data Clustering ◽  
Clustering Algorithms ◽  
Large Data ◽  
Large Data Sets ◽  
Mining Machine ◽  
Data Sets ◽  
Fuzzy C Means ◽  
Intuitionistic Fuzzy ◽  
New Algorithms

Data clustering plays a very important role in Data mining, machine learning and Image processing areas. As modern day databases have inherent uncertainties, many uncertainty-based data clustering algorithms have been developed in this direction. These algorithms are fuzzy c-means, rough c-means, intuitionistic fuzzy c-means and the means like rough fuzzy c-means, rough intuitionistic fuzzy c-means which base on hybrid models. Also, we find many variants of these algorithms which improve them in different directions like their Kernelised versions, possibilistic versions, and possibilistic Kernelised versions. However, all the above algorithms are not effective on big data for various reasons. So, researchers have been trying for the past few years to improve these algorithms in order they can be applied to cluster big data. The algorithms are relatively few in comparison to those for datasets of reasonable size. It is our aim in this chapter to present the uncertainty based clustering algorithms developed so far and proposes a few new algorithms which can be developed further.

Electronic Records Management - An Old Solution to a New Problem

Big Data ◽  
2016 ◽  
pp. 2249-2274
Author(s):  
Chinh Nguyen ◽  
Rosemary Stockdale ◽  
Helana Scheepers ◽  
Jason Sargent
Keyword(s):  
Big Data ◽  
Rapid Development ◽  
Large Data ◽  
Large Data Sets ◽  
Electronic Records ◽  
Future Research ◽  
Records Management ◽  
Data Sets ◽  
Interactive Nature ◽  
Electronic Records Management

The rapid development of technology and interactive nature of Government 2.0 (Gov 2.0) is generating large data sets for Government, resulting in a struggle to control, manage, and extract the right information. Therefore, research into these large data sets (termed Big Data) has become necessary. Governments are now spending significant finances on storing and processing vast amounts of information because of the huge proliferation and complexity of Big Data and a lack of effective records management. On the other hand, there is a method called Electronic Records Management (ERM), for controlling and governing the important data of an organisation. This paper investigates the challenges identified from reviewing the literature for Gov 2.0, Big Data, and ERM in order to develop a better understanding of the application of ERM to Big Data to extract useable information in the context of Gov 2.0. The paper suggests that a key building block in providing useable information to stakeholders could potentially be ERM with its well established governance policies. A framework is constructed to illustrate how ERM can play a role in the context of Gov 2.0. Future research is necessary to address the specific constraints and expectations placed on governments in terms of data retention and use.

The Role of Big Data and Business Analytics in Decision Making

2021 ◽  
pp. 226-257
Author(s):  
Pedro Caldeira Neves ◽  
Jorge Rodrigues Bernardino
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Open Source ◽  
Specific Area ◽  
Business Analytics ◽  
Business Decision ◽  
Business Sustainability ◽  
Open Source Systems ◽  
Fundamental Shift

The amount of data in our world has been exploding, and big data represents a fundamental shift in business decision-making. Analyzing such so-called big data is today a keystone of competition and the success of organizations depends on fast and well-founded decisions taken by relevant people in their specific area of responsibility. Business analytics (BA) represents a merger between data strategy and a collection of decision support technologies and mechanisms for enterprises aimed at enabling knowledge workers such as executives, managers, and analysts to make better and faster decisions. The authors review the concept of BA as an open innovation strategy and address the importance of BA in revolutionizing knowledge towards economics and business sustainability. Using big data with open source business analytics systems generates the greatest opportunities to increase competitiveness and differentiation in organizations. In this chapter, the authors describe and analyze business intelligence and analytics (BI&A) and four popular open source systems – BIRT, Jaspersoft, Pentaho, and SpagoBI.

Decision-Making with Big Data Using Open Source Business Intelligence Systems

2016 ◽  
pp. 120-147 ◽  
Author(s):  
Jorge Bernardino ◽  
Pedro Caldeira Neves
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Open Source ◽  
Business Intelligence ◽  
Business Performance ◽  
Specific Area ◽  
Enterprise Management ◽  
Business Decision ◽  
Business Intelligence Systems ◽  
Fundamental Shift

The importance of supporting decision making for improving business performance is a crucial, yet challenging task in enterprise management. The amount of data in our world has been exploding and Big Data represents a fundamental shift in business decision-making. Analyzing such so-called Big Data is becoming a keystone of competition and the success of organizations depends on fast and well-founded decisions taken by relevant people in their specific area of responsibility. Business Intelligence (BI) is a collection of decision support technologies for enterprises aimed at enabling knowledge workers such as executives, managers, and analysts to make better and faster decisions. We review the concept of BI as an open innovation strategy and address the importance of BI in revolutionizing knowledge towards economics and business sustainability. Using Big Data with Open Source Business Intelligence Systems will generate the biggest opportunities to increase competitiveness and differentiation in organizations. In this chapter, we describe and analyze four popular open source BI systems - Jaspersoft, Jedox, Pentaho and Actuate/BIRT.

Applying the K-Means Algorithm in Big Raw Data Sets with Hadoop and MapReduce

2016 ◽  
pp. 1220-1243
Author(s):  
Ilias K. Savvas ◽  
Georgia N. Sofianidou ◽  
M-Tahar Kechadi
Keyword(s):  
Big Data ◽  
Clustering Algorithm ◽  
File System ◽  
Large Data ◽  
Large Data Sets ◽  
Distributed File System ◽  
Data Sets ◽  
Raw Data ◽  
Hadoop Distributed File System ◽  
Access To Data

Big data refers to data sets whose size is beyond the capabilities of most current hardware and software technologies. The Apache Hadoop software library is a framework for distributed processing of large data sets, while HDFS is a distributed file system that provides high-throughput access to data-driven applications, and MapReduce is software framework for distributed computing of large data sets. Huge collections of raw data require fast and accurate mining processes in order to extract useful knowledge. One of the most popular techniques of data mining is the K-means clustering algorithm. In this study, the authors develop a distributed version of the K-means algorithm using the MapReduce framework on the Hadoop Distributed File System. The theoretical and experimental results of the technique prove its efficiency; thus, HDFS and MapReduce can apply to big data with very promising results.

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