1. SEDEX: Scalable Entity Preserving Data Exchange
IEEE Transactions on Knowledge and Data Engineering, 28(7),
Yoones A. Sekhavat1; Jeffrey Parsons2
1Tabriz Islamic Art University, 2Memorial University of Newfoundland
Introduction
Overview of Approach
We study the problem of information integration through data exchange.
The task is to generate an instance of a target schema from an instance of a source schema, such that the generated instance adheres to the target schema.
Prevailing approaches are based on schema mappings – high level expressions that describe relationships between database schemas.
Schema mapping-based approaches suffer from two problems:
Entity Fragmentation – information about one entity is spread across several tuples in the target
Generalization ambiguity – incorrect mappings resulting from different methods to represent entity type generalization in source and target
We propose and evaluate an approach that addresses these problems by combining schema and data level information
Preserve source entities in the target, regardless of classification of entities
Identify entities in the source and find best relations in target to host source entities
Our approach finds tree similarity between source tuple trees and target relation trees by computing distance functions between trees
Pay-as-you-go workflow that reuses data generation scripts using pq-grams
Prior Work vs. Our Approach
Evaluation
Clio introduced the idea of data exchange based on schema mappings [1].
Idea is to generate a core (optimal) solution from among universal solutions [2] by post-processing [3] or pre-processing [4].
Existing solutions do not handle some situations properly, due to gaps between schema mapping and data mapping.
We propose a scalable method (SEDEX) to bridge the gap between schema-level and data level approaches in data exchange that:
Avoids entity fragmentation
Resolves ambiguous data exchange scenarios resulting from different implementations of generalization relations
We performed a comprehensive set of experiments to evaluate the quality and scalability of data exchange compared to ++Spicy [4] and EDEX (an earlier version of SEDEX [5]).
Used iBench to generate schemas, schema constraints, and correspondences between schemas [6].
Size of target instance was used as measure of quality.
SEDEX Architecture
Conclusions
To address the problems of entity fragmentation and ambiguity in traditional schema-mapping data exchange scenarios, we propose SEDEX, a scalable data exchange method that focuses on preserving source entities in the target.
The method works by creating tree representations of source entities and target relations.
Using tree similarity metrics, we find the best target relation to match a source entity.
We show that SEDEX generates good solutions, resolves ambiguities that state-of-the-art methods do not handle, and scales to large data exchange scenarios.
Further research is needed to examine other types of ambiguous data exchange scenarios.
Problem Statement
Given a source instance I (that may contain null values representing nonexistent properties), a set of target egds (i.e., primary key constraints denoted G), and a set of direct correspondences ∑ between schema level properties of source and target, generate an instance J of the target schema, such that all source properties having a correspondence in the target are reflected in the target without entity fragmentation and ambiguity.
References
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P. C. Arocena, B. Glavic, R. Ciucanu, and R. J. Miller, “The iBench integration metadata generator,” Proc. Very Large Data Bases Endowment, vol. 9, no. 3, pp. 108–119, 2015.