Computing the Non-Dominated Flexible Skyline in Vertically Distributed Datasets with No Random Access
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| Gepubliceerd in: | arXiv.org (Dec 20, 2024), p. n/a |
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| Gepubliceerd in: |
Cornell University Library, arXiv.org
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| Online toegang: | Citation/Abstract Full text outside of ProQuest |
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| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3148683291 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2331-8422 | ||
| 035 | |a 3148683291 | ||
| 045 | 0 | |b d20241220 | |
| 100 | 1 | |a Martinenghi, Davide | |
| 245 | 1 | |a Computing the Non-Dominated Flexible Skyline in Vertically Distributed Datasets with No Random Access | |
| 260 | |b Cornell University Library, arXiv.org |c Dec 20, 2024 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a In today's data-driven world, algorithms operating with vertically distributed datasets are crucial due to the increasing prevalence of large-scale, decentralized data storage. These algorithms enhance data privacy by processing data locally, reducing the need for data transfer and minimizing exposure to breaches. They also improve scalability, as they can handle vast amounts of data spread across multiple locations without requiring centralized access. Top-k queries have been studied extensively under this lens, and are particularly suitable in applications involving healthcare, finance, and IoT, where data is often sensitive and distributed across various sources. Classical top-k algorithms are based on the availability of two kinds of access to sources: sorted access, i.e., a sequential scan in the internal sort order, one tuple at a time, of the dataset; random access, which provides all the information available at a data source for a tuple whose id is known. However, in scenarios where data retrieval costs are high or data is streamed in real-time or, simply, data are from external sources that only offer sorted access, random access may become impractical or impossible, due to latency issues or data access constraints. Fortunately, a long tradition of algorithms designed for the "no random access" (NRA) scenario exists for classical top-k queries. Yet, these do not cover the recent advances in ranking queries, proposing hybridizations of top-k queries (which are preference-aware and control the output size) and skyline queries (which are preference-agnostic and have uncontrolled output size). The non-dominated flexible skyline (ND) is one such proposal. We introduce an algorithm for computing ND in the NRA scenario, prove its correctness and optimality within its class, and provide an experimental evaluation covering a wide range of cases, with both synthetic and real datasets. | |
| 610 | 4 | |a National Rifle Association--NRA | |
| 653 | |a Data transfer (computers) | ||
| 653 | |a Datasets | ||
| 653 | |a Computation | ||
| 653 | |a Data processing | ||
| 653 | |a Random access | ||
| 653 | |a Information retrieval | ||
| 653 | |a Algorithms | ||
| 653 | |a Availability | ||
| 653 | |a Data storage | ||
| 653 | |a Real time | ||
| 653 | |a Data retrieval | ||
| 773 | 0 | |t arXiv.org |g (Dec 20, 2024), p. n/a | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3148683291/abstract/embedded/ZKJTFFSVAI7CB62C?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://arxiv.org/abs/2412.15468 |