A Generic Functional Architecture for Operational BI System

2018 ◽  
Vol 9 (1) ◽  
pp. 64-77 ◽  
Author(s):  
A.D.N. Sarma
Keyword(s):  
Software Design ◽  
Business Intelligence ◽  
Business Processes ◽  
Functional Modules ◽  
Functional Architecture ◽  
Important Trend ◽  
Tightly Coupled ◽  
User Access ◽  
Analytical Processing ◽  
Key Features

In recent years, Operational Business Intelligence has emerged as an important trend in the Business Intelligence (BI) market. Majority of BI application architectures are bespoke in nature which have several architectural limitations like tightly coupled, static, historic, subjective, no performance measurement of business processes, limited user access, limited analytical processing, querying and reporting features. In this article, a generic functional architecture for Operational BI systems based on software architecture principles is presented. All functional modules of the system are derived from the key features of the system and by using top down approach of software design principles. The similar functional modules are grouped into sub-systems and a set of these sub-systems constitutes overall functional architecture. The proposed architecture overcomes the limitations of traditional BI architectures.

scholarly journals Methods of structural analysis of business processes

2018 ◽  
Vol 210 ◽  
pp. 04016
Author(s):  
Jarosław Koszela
Keyword(s):  
Structural Analysis ◽  
Business Intelligence ◽  
Business Processes ◽  
Process Mining ◽  
Central Element ◽  
Structural Similarity ◽  
Similarity Analysis ◽  
Large Sets ◽  
Analysis Methods ◽  
Analytical Processing

The article outlines selected methods for analyzing business processes: their definitions and instances. The methods for analytical processing of processes constitute a component of the Business Intelligence environment - process warehouses, including methods for analytical processing and exploration of the collected process definitions and instances - i.e. process mining. One of the main elements of the analysis of processes is to determine the similarity between them. In systems for analyzing large sets of elements, the method of determining similarity should be efficiency because is the basis for others analysis methods, e.g. clustering, classification, etc. A method for analyzing structural similarity of business processes, based on the similarity of sequences of genetic tags of such processes, was presented using the similarity analysis methods based on the editing distance and the developed methods of structural similarity: GNM, DNM, GCM, DCM. The presented similarity methods were used to clustering processes and to determine the central element of the cluster. The developed methods form the basis for the development of similarity methods extended to aspects of semantic similarity of business processes and methods of analysis and exploration of processes.

Evolutionary Intelligent Data Warehousing Approach to Knowledge Discovery Systems: Dynamic Cubing

2019 ◽  
Vol 13 ◽  
Author(s):  
Harkiran Kaur ◽  
Kawaljeet Singh ◽  
Tejinder Kaur
Keyword(s):  
Knowledge Discovery ◽  
Data Warehouse ◽  
Business Intelligence ◽  
End Users ◽  
Processing Stage ◽  
Information Update ◽  
On Line ◽  
Analytical Processing ◽  
Warehouse Operations ◽  
Made In

Background: Numerous E – Migrants databases assist the migrants to locate their peers in various countries; hence contributing largely in communication of migrants, staying overseas. Presently, these traditional E – Migrants databases face the issues of non – scalability, difficult search mechanisms and burdensome information update routines. Furthermore, analysis of migrants’ profiles in these databases has remained unhandled till date and hence do not generate any knowledge. Objective: To design and develop an efficient and multidimensional knowledge discovery framework for E - Migrants databases. Method: In the proposed technique, results of complex calculations related to most probable On-Line Analytical Processing operations required by end users, are stored in the form of Decision Trees, at the pre- processing stage of data analysis. While browsing the Cube, these pre-computed results are called; thus offering Dynamic Cubing feature to end users at runtime. This data-tuning step reduces the query processing time and increases efficiency of required data warehouse operations. Results: Experiments conducted with Data Warehouse of around 1000 migrants’ profiles confirm the knowledge discovery power of this proposal. Using the proposed methodology, authors have designed a framework efficient enough to incorporate the amendments made in the E – Migrants Data Warehouse systems on regular intervals, which was totally missing in the traditional E – Migrants databases. Conclusion: The proposed methodology facilitate migrants to generate dynamic knowledge and visualize it in the form of dynamic cubes. Applying Business Intelligence mechanisms, blending it with tuned OLAP operations, the authors have managed to transform traditional datasets into intelligent migrants Data Warehouse.

Agent Mediated Code Comprehension: A Cognitive Challenge

2019 ◽  
Vol 11 (2) ◽  
Author(s):  
Ram Gopal Gupta ◽  
Bireshwar Dass Mazumdar ◽  
Kuldeep Yadav
Keyword(s):  
Information Systems ◽  
Software Design ◽  
Business Processes ◽  
Software Agents ◽  
Cognitive Model ◽  
Cognitive Models ◽  
Software Market ◽  
Computing Paradigm ◽  
Cognitive Challenge ◽  
Software Code

The rapidly changing needs and opportunities of today’s global software market require unprecedented levels of code comprehension to integrate diverse information systems to share knowledge and collaborate among organizations. The combination of code comprehension with software agents not only provides a promising computing paradigm for efficient agent mediated code comprehension service for selection and integration of inter-organizational business processes but this combination also raises certain cognitive issues that need to be addressed. We will review some of the key cognitive models and theories of code comprehension that have emerged in software code comprehension. This paper will propose a cognitive model which will bring forth cognitive challenges, if handled properly by the organization would help in leveraging software design and dependencies.

Schema Evolution in Multiversion Data Warehouses

2021 ◽  
Vol 17 (4) ◽  
pp. 1-28
Author(s):  
Waqas Ahmed ◽  
Esteban Zimányi ◽  
Alejandro A. Vaisman ◽  
Robert Wrembel
Keyword(s):  
Efficient Solution ◽  
Business Processes ◽  
Online Analytical Processing ◽  
User Requirements ◽  
Schema Evolution ◽  
Data Warehouses ◽  
Relational Schema ◽  
External Sources ◽  
Analytical Processing

Data warehouses (DWs) evolve in both their content and schema due to changes of user requirements, business processes, or external sources to name a few. Although multiple approaches using temporal and/or multiversion DWs have been proposed to handle these changes, an efficient solution for this problem is still lacking. The authors' approach is to separate concerns and use temporal DWs to deal with content changes, and multiversion DWs to deal with schema changes. To address the former, previously, they have proposed a temporal multidimensional (MD) model. In this paper, they propose a multiversion MD model for schema evolution to tackle the latter problem. The two models complement each other and allow managing both content and schema evolution. In this paper, the semantics of schema modification operators (SMOs) to derive various schema versions are given. It is also shown how online analytical processing (OLAP) operations like roll-up work on the model. Finally, the mapping from the multiversion MD model to a relational schema is given along with OLAP operations in standard SQL.

scholarly journals Business Intelligence and its role to enhance Corporate Performance Management

2013 ◽  
Vol 3 (3) ◽  
pp. 08-15
Author(s):  
Mostafa Medhat Nazier ◽  
Dr. Ayman Khedr ◽  
Assoc. Prof. Mohamed Haggag
Keyword(s):  
Performance Management ◽  
Business Intelligence ◽  
Future Trends ◽  
Large Organization ◽  
Meaningful Information ◽  
Effective Decision ◽  
Analytical Processing ◽  
Market Trends ◽  
Disparate Data ◽  
Effective Decision Making

As every small or large organization requires information to promote their business by forecasting the future trends, information is now the primary tool to understand the market trends and understand their own position in the market comparison to its competitors. Business intelligence is the use of an organizations disparate data to provide meaningful information and analyses to employees, customers, suppliers, and partners for more efficient and effective decision-making. BI applications include the activities of decision support systems, query and reporting, online analytical processing (OLAP), data warehouse (DW), statistical analysis, forecasting, and data mining.

How Can Success of IT Implementations Be Measured?

2014 ◽  
pp. 18-32
Author(s):  
Elad Harison
Keyword(s):  
Business Intelligence ◽  
Business Processes ◽  
Academic Research ◽  
Analytical Framework ◽  
Personal Factors ◽  
Case Study Analysis ◽  
Business Applications ◽  
Decision Supporting ◽  
Business Entities

The number of applied Business Intelligence (BI) systems is rapidly increasing worldwide, serving a broad range of sectors and business applications. BI systems serve a broad range of sectors and business applications by performing functions that consist of managing clients, resources, and employees through the collection and analysis of data that assist in describing these business entities and the various attributes of these objectives. Even though BI solutions have been implemented worldwide and the experience gained in implementation projects has largely enriched the academic research in this field, IT literature still lacks a uniform methodology for assessing the effects that BI systems have on business processes and organizations. Additionally, should any part of the BI implementation project fail to satisfy user needs or achieve the benefits expected from them, it is important to identify the failure's extent and sources in order to avoid financial and operational losses in similar projects. This chapter presents an analytical framework to help measure the success of implementations of various types of Business Intelligence systems, including Online Analytical Processing, Knowledge Management, and Decision Supporting tools. The framework and methodology presented here serve as a basis for evaluating the possible effects of technical, organizational, and personal factors on the success, partial success, or failure of BI system implementations. The framework is demonstrated via a case study analysis of a BI system implementation in an energy firm.

Business Intelligence and Data Mining

2013 ◽  
pp. 57-70
Author(s):  
Zsolt T. Kardkovács
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Strategic Planning ◽  
Decision Support Systems ◽  
Business Intelligence ◽  
Business Processes ◽  
Support Systems ◽  
Decision Makers ◽  
Key Factors ◽  
Short Introduction

Whenever decision makers find out that they want to know more about how the business works and progresses, or why customers do what they do, then data miners are summoned, and business intelligence is to be built or altered. Data mining aims at retrieving valid, interesting, explicable connection between key factors for either operative reporting or supporting strategic planning. While data mining discovers static connections between factors, business intelligence visualizes relevant data for decision makers in order to make them identify fast changes and analyze precisely business states. In this chapter, the authors give a short introduction for data oriented decision support systems with data mining and business intelligence in it. While these techniques are widely used in business processes, there are much more bad practices than good ones. We try to make an attempt to demystify and clear the myths about these technologies, and determine who should and how (not) to use them.

Enterprise Integrated Business Process Management and Business Intelligence Framework for Business Process Sustainability

2018 ◽  
pp. 228-238
Author(s):  
Gaurav Kabra ◽  
Vinit Ghosh ◽  
A. Ramesh
Keyword(s):  
Business Process ◽  
Business Intelligence ◽  
Operations Management ◽  
Business Process Management ◽  
Process Management ◽  
Management Practices ◽  
Business Processes ◽  
Architecture Framework ◽  
Modern Business ◽  
Information And Communication

In the modern business scenario, organizations are vesting high efforts in managing process sustainability as part of their operations management practices. The global environmental concerns for the welfare of the society have facilitated this change. Research studies have reported Information and Communication Technology (ICT) as one of the prerequisites in developing and maintaining efficient business processes. The process sustainability related initiatives and various processes related regulatory compliances have created the need for sophisticated IT tools like BPM (Business Process Management) and BI (Business Intelligence) in organizations. Thus with the advancement of ICT, a strong desire to enhance the business process performance through BPM and BI applications is felt across organizations. However, there is scant research available on leveraging the advantages of these applications in sustainability development. Therefore, this paper aims to present a conceptual architecture framework using an integrated BPM and BI solution to develop an orientation among practitioners and academicians towards the inclusion of ICT in attaining a sustainable, energy efficient business operations or processes. The framework is based on the literature pertaining to the role of BPM and BI in process sustainability as well as from the inputs of practitioners involved in the field of BPM and BI.

Business Intelligence in Corporate Governance and Business Processes Management

2013 ◽  
pp. 249-269
Author(s):  
Alexander Yakovlev
Keyword(s):  
Corporate Governance ◽  
Business Intelligence ◽  
Cost Reduction ◽  
Business Processes ◽  
Transnational Corporations ◽  
Accurate Information ◽  
Main Field ◽  
Maximum Cost ◽  
Sarbanes Oxley ◽  
The U.S

Today is the time of transnational corporations and large companies. They bring to their shareholders and owners the major profits, and they are the main sponsors of scientific and technological progress. However, the extensive way of its development is not possible for environmental, marketing, resource, and many other reasons. So, the main field of competition between companies becomes a fight for the client, the individualization of approach to him, and the maximum cost reduction. At the same time, a series of scandals that erupted in the early 2000s with such major corporations as Enron Corporation, WorldCom, Tyco International, Adelphia, and Peregrine Systems has shown that the system of corporate governance, on which depends the welfare of hundreds of thousands of people, requires serious improvements in terms of transparency and openness. In this regard, the U.S. adopted the Sarbanes-Oxley Act of 2002, under which management companies legally obliged to prove that his decisions are based on reliable, relevant, credible and accurate information (Devenport & Harris, 2010).

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