Efficient Iceberg query evaluation using compressed bitmap index by deferring bitwise-XOR operations

Rao, Chinthalapally V.; Shankar, Vuppu

doi:10.1109/iadcc.2013.6514418

Cited by 5 publications

(6 citation statements)

References 15 publications

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“…5 to 14 for COUNT, SUM, MIN and MAX aggregate functions. We have compare the performance of TP and LAM approach with the BIA and DPA suggested in previous work He et al (2011;Guru Rao and Shankar, 2012;. We observe that as we go on increasing size of data set and threshold value then also query performance is goes on increasing which is as shown in Fig.…”

Section: Experimental Analysismentioning

confidence: 78%

“…The problem with this algorithm is that all vectors may not have 1 bit at same position and if it is not at same position then all the AND as well as XOR operations are fruitless and time consuming.In this way both the above approaches suffer from fruitless AND as well as XOR operations. Guru Rao and Shankar (2012) try to handle empty XOR operation problem but did not able to solve fruitless bit wise AND operation problem. Both the research He et al (2011;Guru Rao and Shankar, 2012) faces the problem of futile queue pushing and empty bitwise operation.…”

Section: Overview Of Iceberg Driven Query (Idq) and Its Processing Mementioning

confidence: 99%

“…Guru Rao and Shankar (2012) try to handle empty XOR operation problem but did not able to solve fruitless bit wise AND operation problem. Both the research He et al (2011;Guru Rao and Shankar, 2012) faces the problem of futile queue pushing and empty bitwise operation.…”

Section: Overview Of Iceberg Driven Query (Idq) and Its Processing Mementioning

confidence: 99%

“…Number of researches He et al (2011;Guru Rao and Shankar, 2012;Shankar and Guru Rao, 2014; work to improve performance of IDQ. But all of them faces the problem of empty bitwise AND operation, XOR operation, OR operation and futile queue pushing.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Tracking Pointer and Look Ahead Matching Strategy to Evaluate Iceberg Driven Query

Prakash¹,

Prathap²

2017

Journal of Computer Science

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Iceberg driven query is important and common in many applications of data mining and data warehousing. Main property of iceberg driven query is it extracts small set of data from huge database. It works on aggregation function followed by GROUP BY and HAVING clause. Due to involvement of aggregation function execution of iceberg driven query becomes tedious and complex work. Main objective of this research is to improve the performance of iceberg driven query by reducing the time, number of iteration and I/O access required to execute it. Currently available iceberg driven query processing technique faces the problems of empty bitwise AND, OR and XOR operation. Because of these problems they require more time and I/O access to execute query. To overcome above problems this research proposes tracking pointer and look ahead matching strategy to evaluate iceberg driven query. Tracking pointer will initiate the evaluation process as per the priority of vector. Look ahead matching strategy help to identify probable vector instead of generating one by one till the end of vector list. This strategy decides the probability of bitmap vector to be executed. Thus in advance it identifies and avoids unnecessary operations to be performed on bitmap vector. Our experimental result shows that time and number of iteration required to evaluate iceberg driven query using proposed approach is reduced [40 to 50] % even though data size increases. Thus we prove the effectiveness and efficiency of proposed approach to process iceberg driven query.

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Section: Experimental Analysismentioning

confidence: 78%

Section: Overview Of Iceberg Driven Query (Idq) and Its Processing Mementioning

confidence: 99%

Section: Overview Of Iceberg Driven Query (Idq) and Its Processing Mementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Tracking Pointer and Look Ahead Matching Strategy to Evaluate Iceberg Driven Query

Prakash¹,

Prathap²

2017

Journal of Computer Science

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show abstract

“…The problem with this algorithm is that all vectors may not have 1 bit at same position and if it is not at same position then all the AND as well as XOR operations are fruitless and time consuming.In this way both the above approaches [4] suffer from fruitless AND as well as XOR operations. Research [5] try to handle empty XOR operation problem but did not able to solve fruitless bit wise AND operation problem. Both the research [4] and [5] faces the problem of futile queue pushing.…”

Section: Review Of Bi Aggregate Function and Ibq Processing Methodsmentioning

confidence: 99%

Evaluating Aggregate Functions of Iceberg Query Using Priority Based Bitmap Indexing Strategy

Kale

Prathap

2017

IJECE

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Aggregate function and iceberg queries are important and common in many applications of data warehouse because users are generally interested in looking for variance or unusual patterns. Normally, the nature of the queries to be executed on data warehouse are the queries with aggregate function followed by having clause, these type of queries are known as iceberg query. Especially to have efficient techniques for processing aggregate function of iceberg query is very important because their processing cost is much higher than that of the other basic relational operations such as SELECT and PROJECT. Presently available iceberg query processing techniques faces the problem of empty bitwise AND,OR XOR operation and requires more I/O access and time.To overcome these problems proposed research provides efficient algorithm to execute iceberg queries using priority based bitmap indexing strategy. Priority based approach consider bitmap vector to be executed as per the priority.Intermediate results are evaluated to find probability of result.Fruitless operations are identified and skipped in advance which help to reduce I/O access and time.Time and iteration required to process query is reduced [45-50] % compare to previous strategy. Experimental result proves the superiority of priorty based approach compare to previous bitmap processing approach.

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Association Rule Modeling using UML and Apriori Algorithm

Angulakshmi

Deepa

Sudha

et al. 2020

2020 International Conference on Emerging Trends in Information Technology and Engineering (Ic-Etite)

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Efficient Iceberg query evaluation using compressed bitmap index by deferring bitwise-XOR operations

Cited by 5 publications

References 15 publications

Tracking Pointer and Look Ahead Matching Strategy to Evaluate Iceberg Driven Query

Tracking Pointer and Look Ahead Matching Strategy to Evaluate Iceberg Driven Query

Evaluating Aggregate Functions of Iceberg Query Using Priority Based Bitmap Indexing Strategy

Association Rule Modeling using UML and Apriori Algorithm

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