WAIMS© World Academy of Informatics and Management Sciences, Vol 2 Issue 3, April‐May 2013 ISSN (online):2278‐1315 www.waims.co.in World Academy of Informatics and Management Sciences www.waims.co.in [451] A PRIORITY QUEUE APPROACH TO EVALUATE AGGREGATE QUERIES EFFICIENTLY M.Laxmaiah 1 , Dr.A.Govardhan 2 , Dr. C.Sunil Kumar 3 1 JNTUH University, Tirumala Engineering College, Hyderabad, 2 JNTUH University, Director of Evaluation, Hyderabad, 3 JNTUH University, Vaageswari College of Engineering, Karimnagar, Abstract In particular, an Aggregate query by name Iceberg (IB) query is a special kind of an aggregation query that calculates an aggregate value based on user preferred threshold (TH). The bitmap index (BI) is a common data structure (DS) for fast retrieval of matching rows in a relational database table. These resultant rows are useful to compute aggregate functions. In this work; the propose priority queue (PQ) approach to evaluate iceberg (IB) queries efficiently using compacted BI are proposed. The approach organizes the vectors in PQ by allowing for high density of 1’s count to achieve finest reducing effect. In‐depth experimentation demonstrates our proposed model is much more efficient than existing strategy. Keywords: ‐ Database, IB Query, Bitmap index (BI), priority queue (PQ), Data warehouse (DW), XOR I. INTRODUCTION The size of the data warehouse (DW) is increasing extremely as the need of client requirements every day. Most aggregated value indicates input information of business such as revenue, sales, income etc. Business Analysts (BA) are often responsible to evaluate and use these aggregated values to compete with present competitive present world. Mostly data mining (DM) queries are IB queries. In particular, IB query is a unique class of aggregation query that compute an aggregate value above user specified threshold (T) [1, 2]. IB queries were first considered in DM field. The syntax of an IB query on a relation R (A1, A2… An) is stated below: SELECT Ai, Aj, …, Am, AGG(*), FROM R, GROUP BY Ai, Aj…, Am, .HAVING AGG (*) > = TH. Where Ai, Aj….Am represents a subset of attributes in R and referred as aggregate attributes. AGG represents an aggregation function. The greater than or equal to (>=) is a symbol used as a evaluation predicate. In this work, an IB query with aggregation function COUNT having the anti‐ monotone property is focused. IB queries have an interesting anti‐monotone property for many of the aggregation functions and predicates. For e.g. if the count of a group is below TH. IB queries are today being processing no of techniques that do not scale well to large data sets. Hence, it is necessary to develop well‐organized techniques to process them easily. One simple technique to reply is an IB query by organizing an array of counters in the memory. These counters are used to count the data values of each unique target attribute value for every single pass of data. However, this is hard because relational database table is several times larger than main memory. In another method, the records of the database table were sorted on the hard disk and then passed the sorted records in to main memory to form an aggregation. Further it selects aggregation values which are greater than a specified TH. If the available memory is less than the table size then the data is to be passed over in more number of times from the hard disk. Therefore query evaluations (QE) consume long execution time and extremely large hard disk requirements. To quickly evaluate the IB query, all the bitmap vectors of attributes in the selection are indexed. A bitmap for an attribute in a database table can be viewed as a matrix having ‘R’ rows consisting corresponding number of rows and ‘C’ columns indicating the number of distinct values of an attribute to quickly evaluate the IB query, all the bitmap vectors of attributes in the selection are indexed. A bitmap for an attribute in a database table can be viewed as a matrix having ‘R’ rows consisting corresponding number of rows and ‘C’ columns indicating the number of distinct values of an attribute. If there is a bitmap vector in the k th position of the attribute then the element in the matrix is 1 else 0. Then the original bitmap vectors were aligned with available free space in the memory using word aligned hybrid compression technique (WAH). A couple of bitmap vectors with similar 1 bit positions were obtained to make a bitwise‐ AND operation. The resulting bitmap vector overcomes greater number of 1 bit than the TH specified in the IB query. Then that couple together with its count of 1 bit were added to the IB result set. The couple was next examined for the subsequent 1 bit positions in each of them after bitwise‐XOR with resulting vector. Now, if the number of 1s were more than TH in this result then this bitmap vector was preserved for further processing. The IB queries are efficiently computed using compacted BI by deferring bitwise‐XOR operations. In this job, the delayed strategy exclude disqualified bitwise‐XOR