An Improved Framework for Bitmap Indexes and their Use in Data Warehouse Optimization

Compression technique is basically used to compress the size of table or reduce the storage area. Oracle already gives this feature for the table compression as well as for the index compression. when index is created on particular column of a table then it contain some space, which require some storage or disk space by this technique we can save our disk space because in industry the company have to purchase the disk space according to the size of the their data and pay according to their disk space. To utilize this disk space for useful records data rather than wasting it. In this paper used the data pump utility for the compression of Bitmap index and table. Data pump utility performed for the logical backups in database.in this paper implemented data pump for compression, to release the space and change the index pointing location. It will not release the space even after deletion of records. This is of special interest for the case to compress the bitmap index and table space along with the’S (Data Manipulation Language).

Design and Analysis of Optimization and Tuning in Data Warehouses Using Bitmap Indexes

Introduction: An Index for Bitmaps is a special category that uses bitmaps or bit arrays in a database. Apache stores a bitmap for every index key in a bitmap file. Each main index stores multi-line pointers. Bitmap database management requires several time, but bitmap indexes are only appropriate for tables or tables that have occasionally updates. Method: Each bit of the map corresponds to a possible row id. If the bit is 1, it means that the row id contains this key value. An internal Oracle function converts the bit position to the corresponding row id, so that bitmap indexes offer the same functionality as B-tree indexes, despite the different internal representation. If the number of different values of the index is small, then the bitmap index will become very efficient in terms of the use of physical space. Result: Oracle involves the following compression features which are possible during the various operations in the database. This means we can compress the data on the following modes. There are several types of backup is possible in the database: • Whole Backup or partial backup • Full Backup or incremental backup • Cold or consistent backup • Hot or inconsistent backup Discussion: We study the current compression technologies, and add the compression of the bitmap index via the data pump. The bitmap index is more effective, for a minimum unique value, according to conventional wisdom. But it doesn't need either a bitmap index built on a high degree of cardinality or a low degree of cardinality through the data pump. In this paper, after deletion of documents, we propose data pump utility for releasing disk space in database. Bitmap index points the old location even after the table deletes information, this function does not release disk space. Conclusion: In this paper, we present the experiment evaluation of Bitmap Index Compression and release occupied disk space of database objects like table and indexes after deletion of records. Industrial database frequently allows the bulk data insertion and deletion. In database deletion of millions records from the table doesn't release occupied disk space immediately. Next steps in our research will be to release the disk space along with the deletion of records.

Optimizing Range Query Processing for Dual Bitmap Index

Bitmap-based indexes are known to be the most effective indexing method for retrieving and answering selective queries in a read-only environment. Various types of encoding bitmap indexes significantly improve query time efficiency by utilizing fast Boolean operations directly on the index before retrieving the raw data. In particular, the dual bitmap index improves the performance of equality queries in terms of the space vs. time trade-off. However, the performance of range queries is unsatisfactory. In this paper, an optimizing algorithm is proposed to improve the range query processing for the dual bitmap index. The results of the experiment conducted show that the proposed algorithm, called Dual-simRQ, reduces the number of bitmap vectors scanned and the Boolean operations performed, which impacts the overall performance for range query processing.