Database Systems Journal

ISSN 2069 - 3230

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

Database Systems Journal, Vol. V, Issue 1/2014
Issue Topic: Data Analytics

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1. New Classes of Applications in the Cloud. Evaluating Advantages and Disadvantages of Cloud Computing for Telemetry Applications (p. 3-14)
Anca APOSTU, University of Economic Studies, Bucharest, Romania
Florina PUICAN, University of Economic Studies, Bucharest, Romania
Geanina ULARU, University of Economic Studies, Bucharest, Romania
George SUCIU, University Politehnica of Bucharest, Romania
Gyorgy TODORAN, University Politehnica of Bucharest, Romania
Nowadays companies are moving some parts of their businesses to the cloud. Industry predictions are that this trend will continue to grow and develop even further in the coming few years. While Cloud computing is undoubtedly beneficial for mid-size to large companies, it is not without its downsides, especially for smaller businesses. This paper's aim is to deliver an analysis based on advantages and disadvantages of Cloud computing technology, in order to help organizations fully understand and adopt this new computing technology. Finally, to prove the advantages that Cloud technology can have for this domain, we are presenting a cloud application for telemetry, with a focus on monitoring hydro-energy. We consider that the way to attain the benefits of Cloud technology is to understand its strengths and weaknesses and adapt to them accordingly.
Keywords: Advantages, Architecture, Cloud Computing, Grid Computing, Telemetry.
2. Measuring Data Quality in Analytical Projects (p. 15-25)
Anca Ioana ANDREESCU, University of Economic Studies, Bucharest, Romania
Anda BELCIU, University of Economic Studies, Bucharest, Romania
Alexandra FLOREA, University of Economic Studies, Bucharest, Romania
Vlad DIACONITA, University of Economic Studies, Bucharest, Romania
Measuring and assuring data quality in analytical projects are considered very important issues and overseeing their benefits may cause serious consequences for the efficiency of organizations. Data profiling and data cleaning are two essential activities in a data quality process, along with data integration, enrichment and monitoring. Data warehouses require and provide extensive support for data cleaning. These loads and renew continuously huge amounts of data from a variety of sources, so the probability that some of the sources contain "dirty data" is great. Also, analytics tools offer, to some extent, facilities for assessing and assuring data quality as a built in support or by using their proprietary programming languages. This paper emphasizes the scope and relevance of a data quality measurement in analytical projects by the means of two intensively used tools such as Oracle Warehouse Builder and SAS 9.3.
Keywords: Data Quality, Data Profiling, Analytical Tools, Data Warehouses.
3. Model-Based Testing: The New Revolution in Software Testing (p. 26-31)
Hitesh KUMAR SHARMA, University of Petroleum and Energy Studies, India
Sanjeev KUMAR SINGH, Galgotia University Noida, India
Prashant AHLAWAT, Global Institute of Technology and Management Gurgaon, India
The efforts spent on testing are enormous due to the continuing quest for better software quality, and the ever growing complexity of software systems. The situation is aggravated by the fact that the complexity of testing tends to grow faster than the complexity of the systems being tested, in the worst case even exponentially. Whereas development and construction methods for software allow the building of ever larger and more complex systems, there is a real danger that testing methods cannot keep pace with construction, hence these new systems cannot be sufficiently fast and thoroughly be tested. This may seriously hamper the development of future generations of software systems.
One of the new technologies to meet the challenges imposed on software testing is model-based testing. Models can be utilized in many ways throughout the product life-cycle, including: improved quality of specifications, code generation, reliability analysis, and test generation.
This paper will focus on the testing benefits from MBT methods and review some of the historical challenges that prevented model based testing and we also try to present the solutions that can overcome these challenges.
Keywords: MBT, Test Cases, SUT, Test Suite.
4. Big Data: present and future (p. 32-41)
Mircea Raducu TRIFU, University of Economic Studies, Bucharest, Romania
Mihaela Laura IVAN, University of Economic Studies, Bucharest, Romania
The paper explains the importance of the Big Data concept, a concept that even now, after years of development, is for the most companies just a cool keyword. The paper also describes the level of the actual big data development and the things it can do, and also the things that can be done in the near future.
The paper focuses on explaining to nontechnical and non-database related technical specialists what basically is big data, presents the three most important V's, as well as the new ones, the most important solutions used by companies like Google or Amazon, as well as some interesting perceptions based on this subject.
Keywords: Big Data, Domains, Risks, Resources, Information.
5. Forecasting Final Energy Consumption using the Centered Moving Average Method and Time Series Analysis (p. 42-50)
Janina POPEANGA, University of Economic Studies, Bucharest, Romania
Ion LUNGU, University of Economic Studies, Bucharest, Romania
The forecasting of energy consumption has become one of the major fields of research in recent years. Accurate energy demand forecasting is essential in energy system operations and planning.
In this paper, we will describe a method to determine the information that is useful for a good forecasting. Further, we adopt the time series modeling approach to model final energy consumption in Romania using previous data of 2010 to 2013. This method is implemented using stored procedures, developed in Oracle PL/SQL programming language.
Finally, the developed model is compared for goodness of fit to the historical data and forecasting accuracy, and results are encouraging, showing that the forecast model is in control and is working correctly.
Keywords: Forecasting, Energy, Centered Moving Average Method, Time Series, Accuracy.