Database Systems Journal

ISSN 2069 - 3230

The journal is published under the sponsorship of
The Bucharest University of Economic Studies
and it is produced by the university's own publishing division,
The Bucharest University of Economic Studies Publishing House

Database Systems Journal, Vol. VI, Issue 1/2015
Issue Topic: Data Mining

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1. Development of National Health Data Warehouse for Data Mining (p. 3-13)
Shahidul Islam Khan, Bangladesh University of Engineering & Technology, Dhaka, Bangladesh
Abu Sayed Md. Latiful Hoque, Bangladesh University of Engineering & Technology, Dhaka, Bangladesh
Health informatics is currently one of the top focuses of computer science researchers. Availability of timely and accurate data is essential for medical decision making. Health care organizations face a common problem with the large amount of data they have in numerous systems. Researchers, health care providers and patients will not be able to utilize the knowledge stored in different repositories unless amalgamate the information from disparate sources is done. This problem can be solved by Data warehousing. Data warehousing techniques share a common set of tasks, include requirements analysis, data design, architectural design, implementation and deployment. Developing health data warehouse is complex and time consuming but is also essential to deliver quality health services. This paper depicts prospects and complexities of health data warehousing and mining and illustrate a data-warehousing model suitable for integrating data from different health care sources to discover effective knowledge.
Keywords: Data Mining, Data Warehouse, Health Informatics, Clinical Database, Data Preprocessing.
2. Data Mining Smart Energy Time Series (p. 14-22)
Janina POPEANGA, University of Economic Studies, Bucharest, Romania
With the advent of smart metering technology the amount of energy data will increase significantly and utilities industry will have to face another big challenge - to find relationships within time-series data and even more - to analyze such huge numbers of time series to find useful patterns and trends with fast or even real-time response.
This study makes a small review of the literature in the field, trying to demonstrate how essential is the application of data mining techniques in the time series to make the best use of this large quantity of data, despite all the difficulties.
Also, the most important Time Series Data Mining techniques are presented, highlighting their applicability in the energy domain.
Keywords: Time Series Data Mining, Clustering, Classification, Motif Discovery, Data Reduction.
3. Big Data Analytics Platforms analyze from startups to traditional database players (p. 23-32)
Ionut TARANU, University of Economic Studies, Bucharest, Romania
Big data analytics enables organizations to analyze a mix of structured, semi-structured and unstructured data in search of valuable business information and insights. The analytical findings can lead to more effective marketing, new revenue opportunities, better customer service, improved operational efficiency, competitive advantages over rival organizations and other business benefits. With so many emerging trends around big data and analytics, IT organizations need to create conditions that will allow analysts and data scientists to experiment. "You need a way to evaluate, prototype and eventually integrate some of these technologies into the business," says Chris Curran[1]. In this paper we are going to review 10 Top Big Data Analytics Platforms and compare the key-features.
Keywords: Big Data, In-memory, Hadoop, Data Analysis.
4. Using Cloud Business Intelligence in competency assessment of IT professionals (p. 33-43)
Elena Alexandra TOADER, University of Economic Studies, Bucharest, Romania
During the last years, the organizations and individuals have adopted Cloud Business Intelligence applications in order to provide access to BI related data such a dashboards, KPIs, analytics. Enterprises have increasingly implementing cloud computing models, improving their availability and reducing costs. The Cloud computing models and the Bi Cloud architecture were outlined, highlighting the advantages and disadvantages of adopting this solutions. The paper outlines the applicability of using the Oracle Business Intelligence Publisher reports in analyzing the results obtained from the competency assessment process of the IT professionals that are working in Romanian Software Organizations.
Keywords: Cloud Computing, Business Intelligence, Cloud Business Intelligence, Competency Assessment Model, BI Publisher.
5. Cloud Computing and its Challenges and Benefits in the Bank System (p. 44-58)
Bogdan NEDELCU, University of Economic Studies, Bucharest, Romania
Madalina-Elena STEFANET, University of Economic Studies, Bucharest, Romania
Ioan-Florentin TAMASESCU, University of Economic Studies, Bucharest, Romania
Smaranda-Elena TINTOIU, University of Economic Studies, Bucharest, Romania
Alin VEZEANU, University of Economic Studies, Bucharest, Romania
The purpose of this article is to highlight the current situation of Cloud Computing systems. There is a tendency for enterprises and banks to seek such databases, so the article tries to answer the question: "Is Cloud Computing safe". Answering this question requires an analysis of the security system (strengths and weaknesses), accompanied by arguments for and against this trend and suggestions for improvement that can increase the customers confidence in the future.
Keywords: Cloud Computing, Bank System, Security.
6. In-memory databases and innovations in Business Intelligence (p. 59-67)
Ruxandra BABEANU, University of Economic Studies, Bucharest, Romania
Marian CIOBANU, University of Economic Studies, Bucharest, Romania
The large amount of data that companies are dealing with, day by day, is a big challenge for the traditional BI systems and databases. A significant part of this data is usually wasted because the companies do not own the appropriate capacity to process it. In the actual competitive environment, this lost data could point up valuable information if it was analyzed and put in the right context. In these circumstances, in-memory databases seem to be the solution. This innovative technology combined with specialized BI solutions offers high performance and satisfaction to users and comes up with new data modeling and processing options.
Keywords: Business Intelligence, in-memory Database, SAP HANA, Data Modeling, Text Processing.
7. Applying BI Techniques To Improve Decision Making And Provide Knowledge Based Management (p. 68-77)
Alexandra Maria Ioana FLOREA, University of Economic Studies, Bucharest, Romania
The paper focuses on BI techniques and especially data mining algorithms that can support and improve the decision making process, with applications within the financial sector. We consider the data mining techniques to be more efficient and thus we applied several techniques, supervised and unsupervised learning algorithms The case study in which these algorithms have been implemented regards the activity of a banking institution, with focus on the management of lending activities.
Keywords: Business Intelligence, Data Mining, Naïve Bayse, Support Vector Machine.
8. Approaches for parallel data loading and data querying (p. 78-85)
Vlad DIACONITA, University of Economic Studies, Bucharest, Romania
This paper aims to bring contributions in data loading and data querying using products from the Apache Hadoop ecosystem. Currently, we talk about Big Data at up to zettabytes scale (1021 bytes). Research in this area is usually interdisciplinary combining elements from statistics, system integration, parallel processing and cloud computing.
Keywords: Hadoop, loading data, Sqoop, Tez.