Bringing Together Physical Design and Fast Querying of Large Data Warehouses

Kechar, Mohamed; Nait-Bahloul, Safia

doi:10.1145/3372938.3372947

Cited by 3 publications

(3 citation statements)

References 9 publications

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“…They proposed to split only the fact relation without using the primary HF of the dimension relations. Later, in [15], the authors introduced an improved version of their approach presented in [14].…”

Section: State Of the Art Of Horizontal Fragmentation Methodsmentioning

confidence: 99%

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Decision-Tree-Based Horizontal Fragmentation Method for Data Warehouses

et al. 2022

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Data warehousing gives frameworks and means for enterprise administrators to methodically prepare, comprehend, and utilize the data to improve strategic decision-making skills. One of the principal challenges to data warehouse designers is fragmentation. Currently, several fragmentation approaches for data warehouses have been developed since this technique can decrease the OLAP (online analytical processing) query response time and it provides considerable benefits in table loading and maintenance tasks. In this paper, a horizontal fragmentation method, called FTree, that uses decision trees to fragment data warehouses is presented to take advantage of the effectiveness that this technique provides in classification. FTree determines the OLAP queries with major relevance, evaluates the predicates found in the workload, and according to this, builds the decision tree to select the horizontal fragmentation scheme. To verify that the design is correct, the SSB (star schema benchmark) was used in the first instance; later, a tourist data warehouse was built, and the fragmentation method was tested on it. The results of the experiments proved the efficacy of the method.

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Section: State Of the Art Of Horizontal Fragmentation Methodsmentioning

confidence: 99%

“…[14] Cost APB-1 benchmark. [15] Cost SSB (star schema benchmark). [16] Data mining (association) TPC-DS benchmark.…”

Section: Workmentioning

confidence: 99%

Decision-Tree-Based Horizontal Fragmentation Method for Data Warehouses

et al. 2022

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“…On the other hand, Kechar and Nait-Bahloul (2019) presented a horizontal data partitioning approach tailored to a large DW, interrogated through a high number of queries, the idea was to fragment horizontally only the large fact table based on partitioning predicates, elected from the set of selection predicates used by analytic queries. While, in , the authors assured that horizontal partitioning techniques have been used for many purposes in big data processing, such as load balancing, skipping unnecessary data loads, and guiding the physical design of a DW.…”

Section: Related Workmentioning

confidence: 99%

Comparative Analysis of Decision Tree Algorithms for Data Warehouse Fragmentation

Mazahua

Rodríguez-Mazahua

López-Chau

et al. 2021

Studies in Computational Intelligence

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Comparative Analysis of Decision Tree Algorithms for Data Warehouse Fragmentation*

Mazahua

Rodríguez-Mazahua

López-Chau³

et al. 2020

REVISTA PERSPECT. EMPRESARIAL

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One of the main problems faced by Data Warehouse designers is fragmentation.Several studies have proposed data mining-based horizontal fragmentation methods.However, not exists a horizontal fragmentation technique that uses a decision tree. This paper presents the analysis of different decision tree algorithms to select the best one to implement the fragmentation method. Such analysis was performed under version 3.9.4 of Weka, considering four evaluation metrics (Precision, ROC Area, Recall and F-measure) for different selected data sets using the Star Schema Benchmark. The results showed that the two best algorithms were J48 and Random Forest in most cases; nevertheless, J48 was selected because it is more efficient in building the model.

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Bringing Together Physical Design and Fast Querying of Large Data Warehouses

Cited by 3 publications

References 9 publications

Decision-Tree-Based Horizontal Fragmentation Method for Data Warehouses

Decision-Tree-Based Horizontal Fragmentation Method for Data Warehouses

Comparative Analysis of Decision Tree Algorithms for Data Warehouse Fragmentation

Comparative Analysis of Decision Tree Algorithms for Data Warehouse Fragmentation*

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