Cost-driven vertical class partitioning for methods in object oriented databases

Fung, Chi-Wai; Karlapalem, Kamalakar; Li, Qing

doi:10.1007/s00778-002-0084-7

Cited by 15 publications

(10 citation statements)

References 22 publications

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“…In this section, we evaluate the effectiveness of CHAC Cost Model and compare the effectiveness and performance of the CHAC algorithm, with HCHA [16], CDA [16] and BEA [8] experimentally on the workload.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…Chi-Wai Fung et al proposed two cost-driven vertical class partitioning algorithms for deriving optimal and near-optimal schemes in object oriented databases [16].…”

Section: Related Workmentioning

confidence: 99%

“…Thus, as an effective way to reduce the query execution time, attribute clustering has been intensively studied in previous literatures [6]- [16].…”

Section: Related Workmentioning

confidence: 99%

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CHAC: An Effective Attribute Clustering Algorithm for Large-Scale Data Processing

Yang

Wang

et al. 2012

2012 IEEE Seventh International Conference on Networking, Architecture, and Storage

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Nowadays Hadoop has become a leading architecture for large-scale data processing. One of the efficient ways to accelerate data processing is column-oriented storage technique which has been integrated into Hadoop family recently. However, how to design an appropriate attribute clustering algorithm to achieve optimal data processing performance for columnoriented hadoop system is still a big problem. In this paper, we propose a novel algorithm called CHAC to solve this problem. Both cases of overlapping attribute cluster and non-overlapping attribute cluster are considered in CHAC. In addition, an adjustable parameter is also taken into account to prohibit excessive attribute redundancy via limiting space overhead. The experimental results on TPC-H Benchmark demonstrate the efficiency and effectiveness of the proposed algorithm.

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Section: Performance Evaluationmentioning

confidence: 99%

“…Chi-Wai Fung et al proposed two cost-driven vertical class partitioning algorithms for deriving optimal and near-optimal schemes in object oriented databases [16].…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

CHAC: An Effective Attribute Clustering Algorithm for Large-Scale Data Processing

Yang

Wang

et al. 2012

2012 IEEE Seventh International Conference on Networking, Architecture, and Storage

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“…It consists in fragmenting the dimension table D i into a set of fragments {D 1 , D 2 , ..., D L } such that: (1) the sum of the query processing cost when executed on top of the partitioned star schema is minimized and (2) L ≤ P , where P is a threshold, fixed by the DBA, representing the maximal number of dimension fragments of D i . We choose the number of vertical fragments as a constraint, because if we have a large number of fragments, some queries need to perform several join operations between these fragments [14].…”

Section: A Formalization Of Non Redundant Selection Problemmentioning

confidence: 99%

Selection of Redundant and non Redundant Optimization Structures in VLDBs

Bonifati

Cuzzocrea

2007

18th International Conference on Database and Expert Systems Applications (DEXA 2007)

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bstract-Very large database applications are usually modelled using huge schemas with several large tables with many columns. Queries defined on those schemas are complex since they contain join and aggregation operations. In large databases, join operations are the most used and the most expensive, especially when the size of tables is very large. To optimize these complex queries, several optimization structures have been proposed. Selecting any optimization structure is NP-hard problems since their search spaces are very large. In this paper, we classify these structures into two main categories: (1) non redundant structures (horizontal and vertical partitioning, parallel processing) and (2) redundant structures (materialized views, indexing schemes). Our study focuses on two non redundant structures: horizontal and vertical partitioning and one redundant structure: bitmap join indexes, where formalizations of their selection problems and algorithms are presented. We propose an approach combining horizontal partitioning and bitmap join indexes to speed up queries and to reduce storage and maintenance costs of indexes. Finally, our proposed algorithms are validated using experimental studies.

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“…We are currently working on an analytical cost model for method execution for both partitioned and unpartitioned scenarios, and algorithms to generate optimal vertical fragmentation for a given set of queries [6,8]. And the term ( )…”

Section: Discussionmentioning

confidence: 99%

An evaluation of vertical class partitioning for query processing in object-oriented databases

Fung¹,

Karlapalem²,

Li³

2002

IEEE Trans. Knowl. Data Eng.

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Abstract--Vertical partitioning is a design technique for reducing the number of disk accesses to execute a given set of queries by minimizing the number of irrelevant instance variables accessed. This is accomplished by grouping the frequently accessed instance variables as vertical class fragments. The complexity of object-oriented database models due to subclass hierarchy and class composition hierarchy complicates the definition and representation of vertical partitioning of the classes, which makes the problem of vertical partitioning in OODBs very challenging. In this paper, we develop a comprehensive analytical cost model for processing of queries on vertically partitioned OODB classes. A set of analytical evaluation results is presented to show the effect of vertical partitioning, and to study the trade-off between projection ratio vs. selectivity factor vis-a-vis sequential vs. index access. Furthermore, an empirical experimental prototype supporting vertical class partitioning has been implemented on a commercial OODB tool kit to validate our analytical cost model.

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Cost-driven vertical class partitioning for methods in object oriented databases

Cited by 15 publications

References 22 publications

CHAC: An Effective Attribute Clustering Algorithm for Large-Scale Data Processing

CHAC: An Effective Attribute Clustering Algorithm for Large-Scale Data Processing

Selection of Redundant and non Redundant Optimization Structures in VLDBs

An evaluation of vertical class partitioning for query processing in object-oriented databases

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