© 2013, IJARCSMS All Rights Reserved 58 | P a g e ISSN: 2321-7782 (Online) Volume 1, Issue 4, September 2013 International Journal of Advance Research in Computer Science and Management Studies Research Paper Available online at: www.ijarcsms.com Efficient Indexing Technique Using Bitmap Indices for Data Warehouses Nikhil Dasharath Karande Assistant Professor Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering Kolhapur – India Abstract: The bitmap index technology is efficient for query processing in data warehousing applications. This paper focuses on efficient bitmap compression algorithm and examines the space and time complexity of the compressed bitmap index on large data sets from real applications. According to the conventional wisdom, bitmap indices are only efficient for low- cardinality attributes. However, the results show that the compressed bitmap indices are also efficient for high-cardinality attributes. Timing results demonstrate that the bitmap indices significantly outperform the projection index, which is often considered to be the most efficient access method for multi-dimensional queries. The bitmap index technology currently supported by commonly used commercial database systems and finally, discusses open issues for future research and development. Keywords: Bitmap Index, Data Warehousing, Compression Algorithm, Projection Index, Multidimensional Queries. I. INTRODUCTION The most common task in data warehousing applications is to be queried large data sets to locate some selected records. To answer these queries efficiently is often difficult due to the complex nature of both the data and the queries. This paper focuses the most straightforward way of evaluating a query is to sequentially scan all data records to determine whether each record satisfies the specified conditions. A typical query condition is as follows “Count the number of cars sold by producer P in the time interval T”. This search procedure could usually be accelerated by indices, such as variations of B-Trees or kd-Trees (Comer, 1979; Gaede & Guenther, 1998). Generally, as the number of attributes in a data set increases, the number of possible indexes combinations increases as well. To answer multi-dimensional queries efficiently, one faces a difficult choice. One possibility is to construct a separate index for each combination of attributes, which requires an impractical amount of space. Another possibility is to choose one of the multi-dimensional indices, which is only efficient for some of the queries. This paper focuses an indexing technology that holds a great promise in breaking the curse of dimensionality for data warehousing applications, namely the bitmap index. A very noticeable character of a bitmap index is that its primary solution to a query is a bitmap. One way to break the curse of dimensionality is to build a bitmap index for each attribute of the data set. To resolve a query involving conditions on multiple attributes, we first resolve the conditions on each attribute using the corresponding bitmap index, and obtain a solution for each condition as a bitmap. We then obtain the answer to the overall query by combining these bitmaps. Because the operations on bitmaps are well supported by computer hardware, the bitmaps can be combined easily and efficiently. Overall, result except the total query response time to scale linearly in the number of attributes involved in the query, rather than exponentially in the number of dimensions (attributes) of the data set, thus breaking the curse of dimensionality.