Database Systems Journal

ISSN 2069 - 3230

Published with the support of:
The Bucharest University of Economic Studies

Database Systems Journal, Vol. IV, Issue 4/2013
Issue Topic: Business Intelligence

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1. E-COCOMO: The Extended COst Constructive MOdel for Cleanroom Software Engineering (p. 3-11)
Hitesh KUMAR SHARMA, University of Petroleum and Energy Studies, India
Mistakes create rework. Rework takes time and increases costs. The traditional software engineering methodology defines the ratio of Design:Code:Test as 40:20:40. As we can easily see that 40% time and efforts are used in testing phase in traditional approach, that means we have to perform rework again if we found some bugs in testing phase. This rework is being performed after Design and code phase. This rework will increase the cost exponentially. The cleanroom software engineering methodology controls the exponential growth in cost by removing this rework. It says that "do the work correct in first attempt and move to next phase after getting the proof of correctness". This new approach minimized the rework and reduces the cost in the exponential ratio. Due to the removal of testing phase, the COCOMO (COst COnstructive MOdel) used for the traditional engineering is not directly applicable in cleanroom software engineering. The traditional cost drivers used for traditional COCOMO needs to be revised. We have proposed the Extended version of COCOMO (i.e. E-COCOMO) in which we have incorporated some new cost drivers. This paper explains the proposed E-COCOMO and the detailed description of proposed new cost driver.
Keywords: Cleanroom Software Engineering, COCOMO, Effort Estimation, Cost Drivers, SDLC.
2. Business Intelligence Systems (p. 12-20)
Bogdan NEDELCU, University of Economic Studies, Bucharest, Romania
The aim of this article is to show the importance of business intelligence and its growing influence. It also shows when the concept of business intelligence was used for the first time and how it evolved over time. The paper discusses the utility of a business intelligence system in any organization and its contribution to daily activities. Furthermore, we highlight the role and the objectives of business intelligence systems inside an organization and the needs to grow the incomes and reduce the costs, to manage the complexity of the business environment and to cut IT costs so that the organization survives in the current competitive climate. The article contains information about architectural principles of a business intelligence system and how such a system can be achieved.
Keywords: Business Intelligence, Data warehouse, OLAP.
3. Data Mining Solutions for the Business Environment (p. 21-29)
Ruxandra-Stefania PETRE, University of Economic Studies, Bucharest, Romania
Over the past years, data mining became a matter of considerable importance due to the large amounts of data available in the applications belonging to various domains. Data mining, a dynamic and fast-expanding field, that applies advanced data analysis techniques, from statistics, machine learning, database systems or artificial intelligence, in order to discover relevant patterns, trends and relations contained within the data, information impossible to observe using other techniques. The paper focuses on presenting the applications of data mining in the business environment. It contains a general overview of data mining, providing a definition of the concept, enumerating six primary data mining techniques and mentioning the main fields for which data mining can be applied. The paper also presents the main business areas which can benefit from the use of data mining tools, along with their use cases: retail, banking and insurance. Also the main commercially available data mining tools and their key features are presented within the paper. Besides the analysis of data mining and the business areas that can successfully apply it, the paper presents the main features of a data mining solution that can be applied for the business environment and the architecture, with its main components, for the solution, that would help improve customer experiences and decision-making.
Keywords: Data mining, Business, Architecture, Data warehouse.
4. Big Data and Specific Analysis Methods for Insurance Fraud Detection (p. 30-39)
Ramona BOLOGA, University of Economic Studies, Bucharest, Romania
Razvan BOLOGA, University of Economic Studies, Bucharest, Romania
Alexandra FLOREA, University of Economic Studies, Bucharest, Romania
Analytics is the future of big data because only transforming data into information gives them value and can turn data in business in competitive advantage. Large data volumes, their variety and the increasing speed their growth, stretch the boundaries of traditional data warehouses and ETL tools. This paper investigates the benefits of Big Data technology and main methods of analysis that can be applied to the particular case of fraud detection in public health insurance system in Romania.
Keywords: Big Data, Social Networks, Data Mining, Fraud Detection.
5. IBM & BDSA Collaborative Program (p. 40)
IBM & BDSA Collaborative Program is an initiative that aims to create a resourceful environment for students by providing them with IBM software, experienced trainers and classroom courses. The program is developed by IBM GDC Romania in collaboration with University Relation team and the master program Databases - Support for Business managed by the Economic Informatics Department within Bucharest University of Economic Studies. These courses, held directly by Business Analytics & Optimization (BAO) practitioners, IBM GDC Romania, provide solid information in areas like: Advanced Analytics and Optimization, Business Intelligence and Performance Management, and Enterprise Information Management.