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. VIII, Issue 2/2017
Issue Topic: IoT Technologies


Open PDF Journal


CONTENTS


1. Data model for Demand Side Management (p. 3-11)
Simona-Vasilica OPREA, The Bucharest University of Economic Studies, Romania
Osman Bulent TOR, EPRA Engineering Procurement Research Analysis, Turkey
Demand Side Management (DSM) is a portfolio of measures to improve the energy system mainly at the consumption level. In this paper we propose a data model for DSM stating from the optimization methods approach in SMARTRADE project from different perspectives of several entities that include: Transmission System Operator (TSO)/Distribution System Operators (DSOs) perspectives in case of security/reliability concerns: minimum amount of load (or generation) shedding; aggregators perspective in case of demand or generation shedding request: Which demand (or generators) should be shed?; consumers perspective: load shifting (time-of-use (ToU) tariffs) and optimum contract strategies with the aggregators (also known as balancing responsible parties- BRP) for load shedding.
Keywords: data model, demand side management, optimization process, mixed integer linear programming, electricity consumption, load/generation shedding.
2. A Modeling methodology for NoSQL Key-Value databases (p. 12-18)
Gerardo ROSSEL, Facultad de Ciencias Exactas y Naturales, Departamento de Computacion, Buenos Aires, Argentina
Andrea MANNA, Facultad de Ciencias Exactas y Naturales, Departamento de Computacion, Buenos Aires, Argentina
In recent years, there has been an increasing interest in the field of non-relational databases. However, far too little attention has been paid to design methodology. Key-value data stores are an important component of a class of non-relational technologies that are grouped under the name of NoSQL databases. The aim of this paper is to propose a design methodology for this type of database that allows overcoming the limitations of the traditional techniques. The proposed methodology leads to a clean design that also allows for better data management and consistency.
Keywords: NoSQL, Key-Value Store, Conceptual modeling, NoSQL, Database design, Big Data.
3. Databases in Cloud - Solutions for Developing Renewable Energy Informatics Systems (p. 19-28)
Adela BARA, The Bucharest University of Economic Studies, Romania
Iuliana BOTHA, The Bucharest University of Economic Studies, Romania
Anda VELICANU, The Bucharest University of Economic Studies, Romania
The paper presents the data model of a decision support prototype developed for generation monitoring, forecasting and advanced analysis in the renewable energy filed. The solutions considered for developing this system include databases in cloud, XML integration, spatial data representation and multidimensional modeling. This material shows the advantages of Cloud databases and spatial data representation and their implementation in Oracle Database 12 c. Also, it contains a data integration part and a multidimensional analysis. The presentation of output data is made using dashboards.
Keywords: Renewable Energy, Data Model, Databases in Cloud, Spatial Data, Data Warehouse.
4. Analysis of value added services on GDP Growth Rate using Data Mining Techniques (p. 29-43)
Douglas KUNDA, Department of Computer Science, School of Science Engineering and Technology, Mulungushi University, Zambia
Sipiwe CHIHANA, Department of Computer Science, School of Science Engineering and Technology, Mulungushi University, Zambia
The growth of Information Technology has spawned large amount of databases and huge data in numerous areas. The research in databases and information technology has given rise to an approach to store and manipulate this data for further decision making. In this paper certain data mining techniques were adopted to analyze the data that shows relevance with desired attributes. Regression technique was adopted to help us find out the influence of Agriculture, Service and Manufacturing on the performance of gross domestic product (GDP). Trend and time series technique was applied to the data to help us find out what trend of GDP with respect to service, agriculture and manufacturing sector for the past decade has been. Finally Correlation was also used to help us analyze the relationship among the variables (service, agriculture and manufacturing sector). From the three techniques analyzed, service value added variable was the most prominent variable which showed the strong influence on GDP growth rate.
Keywords: GDP, Regression, Time-series/trends analysis, Correlation, Data mining, Predictions.
5. Assessment of the Effects of Electricity consumption on the Economy using Granger Causality: Zambian Case (p. 44-56)
Douglas KUNDA, Department of Computer Science, School of Science Engineering and Technology, Mulungushi University, Zambia
Mumbi CHISIMBA, Department of Computer Science, School of Science Engineering and Technology, Mulungushi University, Zambia
Electricity consumption in developing countries such as Zambia continues to grow as the economy grows. As a result, it is important to study how the rate of electricity consumption affects the economy of a country. For this study, the economic variables that were used are the Gross Domestic Product and the Consumer Price index. The results from this study are that there is a unidirectional relationship between electricity consumption and the consumer price index where the rate of electricity consumption Granger causes the consumer price index. The study also showed that there is no causal relationship between electricity and GDP and that there was no causal relationship between electricity consumption and the Consumer Price Index.
Keywords: Electricity consumption; GDP; CPI; Granger Causality; Zambia.