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Characterizing and selecting fresh data sources

Published: 18 June 2014 Publication History

Abstract

Data integration is a challenging task due to the large numbers of autonomous data sources. This necessitates the development of techniques to reason about the benefits and costs of acquiring and integrating data. Recently the problem of source selection (i.e., identifying the subset of sources that maximizes the profit from integration) was introduced as a preprocessing step before the actual integration. The problem was studied for static sources and used the accuracy of data fusion to quantify the integration profit.
In this paper, we study the problem of source selection considering dynamic data sources whose content changes over time. We define a set of time-dependent metrics, including coverage, freshness and accuracy, to characterize the quality of integrated data. We show how statistical models for the evolution of sources can be used to estimate these metrics. While source selection is NP-complete, we show that for a large class of practical cases, near-optimal solutions can be found, propose an algorithmic framework with theoretical guarantees for our problem and show its effectiveness with an extensive experimental evaluation on both real-world and synthetic data.

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cover image ACM Conferences
SIGMOD '14: Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data
June 2014
1645 pages
ISBN:9781450323765
DOI:10.1145/2588555
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 18 June 2014

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Author Tags

  1. data integration
  2. dynamic data sources
  3. source selection

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SIGMOD '14 Paper Acceptance Rate 107 of 421 submissions, 25%;
Overall Acceptance Rate 785 of 4,003 submissions, 20%

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  • (2023)On the Replicability of Data Collection Using Online News DatabasesPS: Political Science & Politics10.1017/S1049096522001317(1-8)Online publication date: 11-Jan-2023
  • (2021)Data source selection for approximate queryJournal of Combinatorial Optimization10.1007/s10878-021-00760-y44:4(2443-2459)Online publication date: 24-May-2021
  • (2021)Data Source Selection Support in the Big Data Integration Process – Towards a TaxonomyInnovation Through Information Systems10.1007/978-3-030-86800-0_1(5-21)Online publication date: 29-Oct-2021
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