A best-effort integration framework for imperfect information spaces. (2018)

• Record Type: Journal Article
• Authors: Jaradat, Ashraf; Halimeh, Ahmed Abu; Deraman, Aziz; Safieddine, Fadi
• Is Part Of: International journal of intelligent information and database systems. Volume 11:Number 4(2018)
• Page Start: 296
• Page End: 314
• Publication Date: 2018
• Subjects: data integration -- information integration -- uncertainty management -- best-effort integration framework -- probabilistic instance integration -- data quality; Database management -- Computer programs -- Periodicals; Information retrieval -- Computer programs -- Periodicals; Information storage and retrieval systems -- Computer programs -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligent agents (Computer software) -- Periodicals; 006.33
• Languages: English
• ISSNs: 1751-5858

Abstract

Entity resolution (ER) with imperfection management has been accepted as a major aspect while integrating heterogeneous information sources that exhibit entities with varied identifiers, abbreviated names, and multi-valued attributes. Many of novel integration applications such as personal information management and web-scale information management require the ability to represent and manipulate imperfect data. This requirement signifies the issues of starting with imperfect data to the production of probabilistic database. However, classical data integration (CDI) framework fails to cope with such requirement of explicit imperfect information management. This paper introduces an alternative integration framework based on the best-effort perspective to support instance integration automation. The new framework explicitly incorporates probabilistic management to the ER tasks. The probabilistic management includes a new probabilistic global entity, a new pair-wise-source-to-target ER process, and probabilistic decision model logic as alternatives. Together, the paper presents how these processes operate to support the current heterogeneous sources integration challenges.