Dual Bitmap Index: Space-Time Efficient Bitmap Index for Equality and Membership Queries

1. Dual Bitmap Index: Space-Time Efficient Bitmap Index for Equality and Membership Queries Niwan Wattanakitrungroj and Sirirut Vanichayobon Artificial Intelligence Research Laboratory Department of Computer Science, Prince of Songkla University

2. Introduction Variations of Bitmap Index - Simple Bitmap Index - Interval Bitmap Index - Scatter Bitmap Index - Encoded BitmapIndex - Dual Bitmap Index Performance Study Conclusion Outline

3. Introduction - A data warehouse is a large repository of information accessed through OLAP application. • A majority of requests for information from a data warehouse • involve dynamic ad hoc queries. • The ability to answer these queries quickly is a critical issue in • the data warehouse environment.

4. To speed up query processing : Summary tables Indexes Parallel machines Introduction

5. Introduction: Characteristic :  simple to represent  uses less space  more CPU-efficient  low-cost Boolean operations Bitmap Index

6. Introduction: Bitmap Index Employee Table Select Count(*) From Employee Where Gender=“F”; RID 1 2 3 4 5 … Answer : 2 Equality Query Select Name From Employee Where Gender=“M” and Education=“MS” RID 1 2 3 4 5 … Answer : John Membership Query Select Name From Employee Where Education in {MS,PhD} Answer : John, Marry

7. Introduction Variations of Bitmap Index - Simple Bitmap Index - Interval Bitmap Index - Scatter Bitmap Index - Encoded BitmapIndex - Dual Bitmap Index Performance Study Conclusion Outline

8. Variations of Bitmap Index 15 bitmap vectors C = 15 Query : Bitmap vectors : Simple Bitmap Index Related Work Let C be a number of distinct values of the indexed attribute(Cardinallity).

9. Variations of Bitmap Index 8 bitmap vectors C = 15 Bitmap vectors : Interval Bitmap Index Related Work Query

10. Variations of Bitmap Index 8 bitmap vectors, C = 15 Bitmap vectors : Scatter Bitmap Index Related Work m = 5 Query

11. Variations of Bitmap Index 4 bitmap vectors C = 15 Bitmap vectors : Query : Mapping all Bitmap Vector Encoded Bitmap Index Related Work

12. Variations of Bitmap Index Introduction Variations of Bitmap Index - Simple Bitmap Index - Interval Bitmap Index - Scatter Bitmap Index - Encoded BitmapIndex - Dual Bitmap Index Performance Study Conclusion     Outline

13. Variations of Bitmap Index Encoding Scheme of five bitmap indices Need C bitmap vectors Need bitmap vectors Need bitmap vectors Need bitmap vectors Need bitmap vectors Dual Bitmap Index

14. Variations of Bitmap Index Dual Bitmap Index

15. Variations of Bitmap Index C =15 A = {0,1,2,…,14} n = 6 (The total number of bitmap vectors created ) 2. Calculate n : hiC = 15 (the highest value of C that can be represent by n bitmap vector) 3. Calculate : 4. For each value v on record at position i in A if i = r ands otherwise where and v is the value of an indexed attribute for any record. Creation of Dual Bitmap Index • Assign an increasing sequence of numbers to each of the distinct values of A (i.e., 0,1,…,C-1).

16. Variations of Bitmap Index : Propose Bitmap Index “A = 2” 2. where and v is the value of an indexed attribute for any record. Equality and Membership Queries 1. Find the sequence number of the searching value.

17. Introduction Variations of Bitmap Index - Simple Bitmap Index - Interval Bitmap Index - Scatter Bitmap Index - Encoded BitmapIndex - Dual Bitmap Index Performance Study Conclusion Outline

18. Performance study

19. Simple Scatter Dual Interval Encoded Scatter Dual Encoded Performance study (Space)

20. Performance study

21. Space-Time Trade-off for five Bitmap Indices C=50, N=1,000,000 (The data sets from TPC-H Benchmark) Simple Interval Scatter Dual Encoded Performance study

22. Introduction Variations of Bitmap Index - Simple Bitmap Index - Interval Bitmap Index - Scatter Bitmap Index - Encoded BitmapIndex - Dual Bitmap Index Performance Study Conclusion Outline

23. Conclusion Simple Bitmap Index requires the most space. Encoded Bitmap Index’ s processing time is the worst. Dual bitmap index uses less space while maintaining query processing time for equality and membership queries. Dual Bitmap Index achieves this by representing each attribute value using only two bitmap vectors, and only the low-cost Boolean AND operation is used to answer equality query. Dual Bitmap Index has better space-time performance than the other bitmap indexing techniques.

24. Thank You Question & answer