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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.00105 |
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| _version_ | 1866911293895606272 |
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| author | Bekkoucha, Djawad Diop, Lamine Ouali, Abdelkader Crémilleux, Bruno Boizumault, Patrice |
| author_facet | Bekkoucha, Djawad Diop, Lamine Ouali, Abdelkader Crémilleux, Bruno Boizumault, Patrice |
| contents | Pattern sampling has emerged as a promising approach for information discovery in large databases, allowing analysts to focus on a manageable subset of patterns. In this approach, patterns are randomly drawn based on an interestingness measure, such as frequency or hyper-volume. This paper presents the first sampling approach designed to handle interval patterns in numerical databases. This approach, named Fips, samples interval patterns proportionally to their frequency. It uses a multi-step sampling procedure and addresses a key challenge in numerical data: accurately determining the number of interval patterns that cover each object. We extend this work with HFips, which samples interval patterns proportionally to both their frequency and hyper-volume. These methods efficiently tackle the well-known long-tail phenomenon in pattern sampling. We formally prove that Fips and HFips sample interval patterns in proportion to their frequency and the product of hyper-volume and frequency, respectively. Through experiments on several databases, we demonstrate the quality of the obtained patterns and their robustness against the long-tail phenomenon. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_00105 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Efficiently Sampling Interval Patterns from Numerical Databases Bekkoucha, Djawad Diop, Lamine Ouali, Abdelkader Crémilleux, Bruno Boizumault, Patrice Databases Artificial Intelligence Pattern sampling has emerged as a promising approach for information discovery in large databases, allowing analysts to focus on a manageable subset of patterns. In this approach, patterns are randomly drawn based on an interestingness measure, such as frequency or hyper-volume. This paper presents the first sampling approach designed to handle interval patterns in numerical databases. This approach, named Fips, samples interval patterns proportionally to their frequency. It uses a multi-step sampling procedure and addresses a key challenge in numerical data: accurately determining the number of interval patterns that cover each object. We extend this work with HFips, which samples interval patterns proportionally to both their frequency and hyper-volume. These methods efficiently tackle the well-known long-tail phenomenon in pattern sampling. We formally prove that Fips and HFips sample interval patterns in proportion to their frequency and the product of hyper-volume and frequency, respectively. Through experiments on several databases, we demonstrate the quality of the obtained patterns and their robustness against the long-tail phenomenon. |
| title | Efficiently Sampling Interval Patterns from Numerical Databases |
| topic | Databases Artificial Intelligence |
| url | https://arxiv.org/abs/2512.00105 |