International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 04 Issue: 07 | July -2017
p-ISSN: 2395-0072
www.irjet.net
EFFICIENT INFORMATION RETRIEVAL USING MULTIDIMENSIONAL OLAP CUBE Neha1, Kanwal Garg2 1Research
Scholar, M.Tech. (CSE), Department of Computer Science & Applications, Kurukshetra University Kurukshetra, India. 2Assistant Professor, Department of Computer Science & Applications, Kurukshetra University Kurukshetra, India. ---------------------------------------------------------------------***--------------------------------------------------------------------ABSTRACT : Data Warehouse is a repository to store huge amount of data, which can be further used for future decision-making process. But the most complicated question raise here is about the accuracy & efficiency of data. Many techniques & methods were proposed by many researchers, so that the knowledgeable & accurate data can be fetched from data warehouse. OLAP is one of the best data analytical techniques developed till now which gives multidimensional view of data to end-user which improve the quality of decision-making process. The objective of this paper is to discuss about the retrieval of efficient information by using multidimensional OLAP cube and after that perform a comparative analysis between SQL queries for relational databases and MDX queries for OLAP cube on the basis of query execution time.
2. RELATED WORK To carry on the present research work the researcher has reviewed various research papers from 1999 to 2012 onwards. The outcome of the research is discussed in the upcoming paragraphs. C.Sapia et.al (1999)[2] observed the requirements for a proper multidimensional model that is suitable for OLAP applications. In this paper, they choose six models according to these identified requirements and evaluate them using example of ‘vehicle repair’. All the models have its specific strength and when they compare all the models then none of the model satisfied all the requirements. But the combination of all these approaches gives a resulting model and which satisfies all requirements [2]. Aparajita Suman (2004) exploring the features of data warehousing, OLAP and their application in library system. The problem of inconsistent and lengthy response time with less flexible systems can be identified and resolved by OLAP [3]. Bora Beran et.al (2008)[3] applied OLAP technology to environmental data catalogs using SQL server 2008 analysis services. And to visualize the query results they used excel and virtual earth [4]. Sellappan Palaniappan et.al (2008)[1] presents a prototype model for clinical decisions support system which combines the power of both OLAP and data mining. In this, they provide integrated architecture of OLAP & Data mining. System can predict future state and can generate useful information for good decision-making. They build OLAP cube for each disease and also diagnosed the disease by using mining functionality. For this they used clinical data of two years [5]. Constanta Zoie Radulescu et.al (2009)[2] presents OLAP cube called CUBETECH. Cube accepts queries on various dimension & hierarchies. In this they used an example of agricultural production. To perform analysis of some commercial features include crops, cropping system, fertilizers consumption & types of farmers of agricultural production OLAP operations are used. This example proves the flexibility of OLAP tool and that is suitable for the complicated analyses of multidimensional data [6]. Joseph M. Firestone (1998), conclude that not every E-R i.e. entity relationship model can represented as set of star schemas but every E-R data warehousing model which are properly constructed can be represented. The data warehousing E-R models which specifying atomic data
Keyword: Data Warehouse, OLAP, OLTP, SSMS, BIDS, MDX, SQL 1. INTRODUCTION TO OLAP OLAP stands for on-line analytical processing is an analytical processing tool. It mainly used for the analyzing business data together from daily transactions like health care data and sales data. OLAP is a powerful tool to support decisionmaking. An OLAP system permits the user to easily extract and view the data from different point of view. It also allows users to perform quick and effective analysis on huge amount of data. OLAP systems stored the data in the multidimensional form. OLAP is able to provide the summary data efficiently and enable users to access this summary data faster and easier [1]. OLAP cube (Data cube or Multidimensional cube) is a method of storing data in the multidimensional form; which allows the faster analysis of data. An OLAP cube has capability to operate and analyze the data from the different or multiple angles. The cube comprises numeric facts called measures, which are characterized as dimensions. After combining the facts and dimension get a multidimensional view of data and which is known as OLAP cube. A multidimensional cube or OLAP cube combines the data from the various sources and store these data into a form that consistent for business users. When arrange data into cube it overcomes the limitation of relational database.
© 2017, IRJET
|
Impact Factor value: 5.181
|
ISO 9001:2008 Certified Journal
| Page 2885