Commercial and Open Source Business Intelligence Platforms for Big Data Warehousing

2019 ◽  
pp. 158-181 ◽  
Author(s):  
Jorge Bernardino ◽  
Joaquim Lapa ◽  
Ana Almeida
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Decision Support ◽  
Comparative Analysis ◽  
Open Source ◽  
Data Warehouse ◽  
Business Intelligence ◽  
Selection Process ◽  
Decision Making Processes ◽  
Big Data Warehouse

A big data warehouse enables the analysis of large amounts of information that typically comes from the organization's transactional systems (OLTP). However, today's data warehouse systems do not have the capacity to handle the massive amount of data that is currently produced. Business intelligence (BI) is a collection of decision support technologies that enable executives, managers, and analysts to make better and faster decisions. Organizations must make good use of business intelligence platforms to quickly acquire desirable information from the huge volume of data to reduce the time and increase the efficiency of decision-making processes. In this chapter, the authors present a comparative analysis of commercial and open source BI tools capabilities, in order to aid organizations in the selection process of the most suitable BI platform. They also evaluated and compared six major open source BI platforms: Actuate, Jaspersoft, Jedox/Palo, Pentaho, SpagoBI, and Vanilla; and six major commercial BI platforms: IBM Cognos, Microsoft BI, MicroStrategy, Oracle BI, SAP BI, and SAS BI & Analytics.

Conquery: an Open Source Application to analyze High Content Healthcare Data (Preprint)

2021 ◽  
Author(s):  
Fabian Kovacs ◽  
Max Thonagel ◽  
Marion Ludwig ◽  
Alexander Albrecht ◽  
Manuel Hegner ◽  
...  
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Analysis ◽  
Open Source ◽  
Open Source Software ◽  
Medical Records ◽  
Healthcare Sector ◽  
Study Cohort ◽  
Decision Making Processes ◽  
Analytical Approaches

BACKGROUND Big data in healthcare must be exploited to achieve a substantial increase in efficiency and competitiveness. Especially the analysis of patient-related data possesses huge potential to improve decision-making processes. However, most analytical approaches used today are highly time- and resource-consuming. OBJECTIVE The presented software solution Conquery is an open-source software tool providing advanced, but intuitive data analysis without the need for specialized statistical training. Conquery aims to simplify big data analysis for novice database users in the medical sector. METHODS Conquery is a document-oriented distributed timeseries database and analysis platform. Its main application is the analysis of per-person medical records by non-technical medical professionals. Complex analyses are realized in the Conquery frontend by dragging tree nodes into the query editor. Queries are evaluated by a bespoke distributed query-engine for medical records in a column-oriented fashion. We present a custom compression scheme to facilitate low response times that uses online calculated as well as precomputed metadata and data statistics. RESULTS Conquery allows for easy navigation through the hierarchy and enables complex study cohort construction whilst reducing the demand on time and resources. The UI of Conquery and a query output is exemplified by the construction of a relevant clinical cohort. CONCLUSIONS Conquery is an efficient and intuitive open-source software for performant and secure data analysis and aims at supporting decision-making processes in the healthcare sector.

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.

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

Web Services ◽  
2019 ◽  
pp. 431-458 ◽  
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.

scholarly journals Decision support system for promotion of Faculty of Business and Economics Mendel University

2012 ◽  
Vol 60 (2) ◽  
pp. 443-448
Author(s):  
Pavel Turčínek ◽  
Arnošt Motyčka
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Secondary School ◽  
Knowledge Discovery ◽  
Business Intelligence ◽  
Knowledge Discovery In Databases ◽  
Optimum Number ◽  
Product Knowledge ◽  
Study Programs ◽  
Decision Making Processes

Decreasing number of secondary school graduates means that, for college, it becomes more difficult to fulfill guide number of newly admitted students. In order to maintain an optimum number of registered students, the Faculty of Business and Economics decided to support activities which increase the interest of its accredited programs.Potential students should be treated as customers to whom we want to offer a product – knowledge, skills and competencies. Promoting study programs PEF MENDELU is handled by PR department in collaboration with several students.Availability of resources for promotion is limited. It is crucial to deciding how to deal with these sources. By creating a system for monitoring and decision support, we provide all interested collaborators tool to improve decision-making processes.The system itself will be built on the tools of Business Intelligence (BI) that can observe consumer trends, identify customer segments and other important information. The BI emphasizes the use of OLAP technology for data processing. In the collected data about students is hidden a large amount of information that can be obtained using techniques such as knowledge discovery in databases.This article aims to describe the methodology for solving problems and show the application, which result in support of decision-making processes in the propagation PEF MENDELU, which should also lead to the efficiency of spending on this activity.

Internet-Based Spatial Decision Support Using Open Source Tools

2011 ◽  
pp. 237-262 ◽  
Author(s):  
G. Brent Hall ◽  
Michael G. Leahy
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Open Source ◽  
Gis Technology ◽  
Spatial Concepts ◽  
Geographically Dispersed ◽  
Decision Making Processes ◽  
User Groups ◽  
Planning Decision ◽  
Use Of The Internet

In the last half decade, there has been growing interest in the concept of collaborative geographic information systems (GIS) in support of decision making, especially in the context of various domains of planning. This interest has spawned an already substantial literature in what is now becoming popularly known as public participation GIS (PPGIS) or community GIS. A central and general objective of PPGIS is to encourage the use of GIS technology by broadly based and geographically dispersed nonexpert users. In the context of planning decision support, this involves creating software with map-based functionality that is responsive to the needs of user groups that have limited experience with computers and only a rudimentary knowledge of even simple spatial analysis concepts. This functionality should be designed to enable these individuals to communicate and interact with higher level users and agencies on an equal footing so that all participants can be both better informed of each others perspectives and more involved in decision-making processes that involve land and resource use planning and management. This chapter considers the general issue of PPGIS in the context of use of the Internet and the World Wide Web as a means of achieving broad participation and collaboration in decision making among dispersed participants with a diversity of backgrounds and competencies in using spatial concepts and analyses. The chapter also considers the role that open source software tools can play in crafting accessible and highly customizable solutions using an example for assessing the quality of primary-level education in Peru.

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.

Decision Support Systems

2020 ◽  
Author(s):  
Sean B. Eom
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Decision Support ◽  
Decision Support Systems ◽  
Business Intelligence ◽  
Data Analytics ◽  
Support Systems ◽  
Academic Discipline ◽  
Big Data Analytics ◽  
Academic Field

A decision support system is an interactive human–computer decision-making system that supports decision makers rather than replaces them, utilizing data and models. It solves unstructured and semi-structured problems with a focus on effectiveness rather than efficiency in decision processes. In the early 1970s, scholars in this field began to recognize the important roles that decision support systems (DSS) play in supporting managers in their semi-structured or unstructured decision-making activities. Over the past five decades, DSS has made progress toward becoming a solid academic field. Nevertheless, since the mid-1990s, the inability of DSS to fully satisfy a wide range of information needs of practitioners provided an impetus for a new breed of DSS, business intelligence systems (BIS). The academic discipline of DSS has undergone numerous changes in technological environments including the adoption of data warehouses. Until the late 1990s, most textbooks referred to “decision support systems.” Nowadays, many of them have replaced “decision support systems” with “business intelligence.” While DSS/BIS began in academia and were quickly adopted in business, in recent years these tools have moved into government and the academic field of public administration. In addition, modern political campaigns, especially at the national level, are based on data analytics and the use of big data analytics. The first section of this article reviews the development of DSS as an academic discipline. The second section discusses BIS and their components (the data warehousing environment and the analytical environment). The final section introduces two emerging topics in DSS/BIS: big data analytics and cloud computing analytics. Before the era of big data, most data collected by business organizations could easily be managed by traditional relational database management systems with a serial processing system. Social networks, e-business networks, Internet of Things (IoT), and many other wireless sensor networks are generating huge volumes of data every day. The challenge of big data has demanded a new business intelligence infrastructure with new tools (Hadoop cluster, the data warehousing environment, and the business analytical environment).

scholarly journals RELATIONSHIP BETWEEN BIG DATA AND DECISION SUPPORT SYSTEMS

2020 ◽  
Vol 11 (1) ◽  
pp. 82-93
Author(s):  
Gustavo Grander ◽  
Luciano Ferreira da Silva ◽  
Alan Tadeu Moraes Moraes ◽  
Paulo Sergio Gonçalves de Oliveira
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Big Data ◽  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Analytical Techniques ◽  
Strong Relationship ◽  
Decision Making Processes ◽  
Scopus Database

This article aimed to identify relationships between Big Data and Decision Support Systems. For this, we conducted a search in the Scopus database and as a result, we identified a report according to the increased frequency of publications, frequency of publications in journals and, using the VOSviewer software, we performed an analysis of words co-citation. We identified 5 groups of keywords that suggest different areas of study (e.g., logistics, health and social media), as well as a more recent focus on studies aimed at sustainable development, machine learning, analytical techniques and decision-making processes decision. An important contribution that should also be highlighted was the strong relationship between the keywords Big Data, artificial intelligence and decision making, suggesting studies involving the three terms in a large number of works. 

Internet-Based Spatial Decision Support Using Open Source Tools

2008 ◽  
pp. 1001-1020
Author(s):  
G. Brent Hall ◽  
Michael G. Leahy
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Open Source ◽  
Gis Technology ◽  
Spatial Concepts ◽  
Geographically Dispersed ◽  
Decision Making Processes ◽  
User Groups ◽  
Planning Decision ◽  
Use Of The Internet

In the last half decade, there has been growing interest in the concept of collaborative geographic information systems (GIS) in support of decision making, especially in the context of various domains of planning. This interest has spawned an already substantial literature in what is now becoming popularly known as public participation GIS (PPGIS) or community GIS. A central and general objective of PPGIS is to encourage the use of GIS technology by broadly based and geographically dispersed nonexpert users. In the context of planning decision support, this involves creating software with map-based functionality that is responsive to the needs of user groups that have limited experience with computers and only a rudimentary knowledge of even simple spatial analysis concepts. This functionality should be designed to enable these individuals to communicate and interact with higher level users and agencies on an equal footing so that all participants can be both better informed of each others perspectives and more involved in decision-making processes that involve land and resource use planning and management. This chapter considers the general issue of PPGIS in the context of use of the Internet and the World Wide Web as a means of achieving broad participation and collaboration in decision making among dispersed participants with a diversity of backgrounds and competencies in using spatial concepts and analyses. The chapter also considers the role that open source software tools can play in crafting accessible and highly customizable solutions using an example for assessing the quality of primary-level education in Peru.

scholarly journals Tobit Regressive Based Gaussian Independence Bayes Map Reduce Classifier on Data Warehouse for Predictive Analytics

2019 ◽  
Vol 8 (12) ◽  
pp. 4269-4280
Keyword(s):  
Decision Making ◽  
Feature Selection ◽  
Big Data ◽  
Data Warehouse ◽  
System Integration ◽  
Predictive Analytics ◽  
Selection Process ◽  
Map Reduce ◽  
Time Consumption ◽  
Heterogeneous Resources

Data warehouse comprises of data collected from different probable heterogeneous resources at different time intervals with the objective of responding to user analytic queries. Big data is a field that helps in analysing and extracting information from large datasets. The unfolding Big Data incorporation inflicts multiple confronts, compromising the feasible business research practice. Heterogeneous resources, high dimensionality and massive volumes that confront Big Data prototype may prevent the effectual data and system integration processes. In this work, we plan to develop a Tobit Regressive based Gaussian Independence Bayes Map Reduce Classifier (TRGIBMRC) method for categorizing the collected and stored data which helps the users in making decision with minimum time consumption. The TR-GIBMRC method consists of two processes. They are, Tobit Regressive Feature Selection and Gaussian Independence Bayes Map Reduce Classification. Tobit Regressive Feature Selection process is used to select relevant features from collected and stored data. Tobit statistical model, used to describe the relationship between non-negative dependent variable and an independent variable for selecting relevant features. Next, Gaussian Independence Bayes Map Reduce Classifier is used to classify the selected relevant features for decision making with lesser time consumption. Gaussian Independence Bayes Map Reduce Classifier, a probabilistic classifier segments the data by class by measuring the mean and variance of data in each class. The data point gets allocated to the class with minimal variance. This in turn helps to perform efficient data classification for accurate decision making. Experimental evaluation is carried out on the factors such as feature selection rate, classification accuracy, classification time and error rate with respect to number of features and number of data points. Keyword

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