Increasing the performance of iceberg query through summary tables
One of the key challenging problems in data mining is data retrieval from large data repositories, as the sizes of data are growing very fast, to deal with this situation, there is a need for efficient data mining techniques. For efficient mining tasks number of queries have been emerged. Iceberg qu...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | en |
| Published: |
The Science and Information (SAI) Organization Limited
2024
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| Subjects: | |
| Online Access: | https://eprints.ums.edu.my/id/eprint/43553/1/FULL%20TEXT.pdf https://eprints.ums.edu.my/id/eprint/43553/ https://dx.doi.org/10.14569/IJACSA.2024.0150975 |
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| Summary: | One of the key challenging problems in data mining is data retrieval from large data repositories, as the sizes of data are growing very fast, to deal with this situation, there is a need for efficient data mining techniques. For efficient mining tasks number of queries have been emerged. Iceberg query is one of them, in which the output is much smaller like the tip of the iceberg as compared to the large input dataset, these queries take very long processing time and require a huge amount of main memory. However the processing devices have limited memories, so the efficient processing of iceberg queries is a challenging problem for most of the researchers. In this paper we present a novel technique, namely a summary table, to address this problem. Specifically, we adopt the summary table technique to acquire the required results at summary levels. The experimental results demonstrate that the summary table technique is highly effective for large datasets. Compared to bitmap indexing and cubed techniques, the summary table offers faster retrieval capabilities. Furthermore, the proposed technique achieved state-of-the-art performance. |
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