6 research outputs found
Functional Dependencies Unleashed for Scalable Data Exchange
We address the problem of efficiently evaluating target functional
dependencies (fds) in the Data Exchange (DE) process. Target fds naturally
occur in many DE scenarios, including the ones in Life Sciences in which
multiple source relations need to be structured under a constrained target
schema. However, despite their wide use, target fds' evaluation is still a
bottleneck in the state-of-the-art DE engines. Systems relying on an all-SQL
approach typically do not support target fds unless additional information is
provided. Alternatively, DE engines that do include these dependencies
typically pay the price of a significant drop in performance and scalability.
In this paper, we present a novel chase-based algorithm that can efficiently
handle arbitrary fds on the target. Our approach essentially relies on
exploiting the interactions between source-to-target (s-t) tuple-generating
dependencies (tgds) and target fds. This allows us to tame the size of the
intermediate chase results, by playing on a careful ordering of chase steps
interleaving fds and (chosen) tgds. As a direct consequence, we importantly
diminish the fd application scope, often a central cause of the dramatic
overhead induced by target fds. Moreover, reasoning on dependency interaction
further leads us to interesting parallelization opportunities, yielding
additional scalability gains. We provide a proof-of-concept implementation of
our chase-based algorithm and an experimental study aiming at gauging its
scalability with respect to a number of parameters, among which the size of
source instances and the number of dependencies of each tested scenario.
Finally, we empirically compare with the latest DE engines, and show that our
algorithm outperforms them
A framework for information integration using ontological foundations
With the increasing amount of data, ability to integrate information has always been a competitive advantage in information management. Semantic heterogeneity reconciliation is an important challenge of many information interoperability applications such as data exchange and data integration. In spite of a large amount of research in this area, the lack of theoretical foundations behind semantic heterogeneity reconciliation techniques has resulted in many ad-hoc approaches. In this thesis, I address this issue by providing ontological foundations for semantic heterogeneity reconciliation in information integration. In particular, I investigate fundamental semantic relations between properties from an ontological point of view and show how one of the basic and natural relations between properties – inferring implicit properties from existing properties – can be used to enhance information integration. These ontological foundations have been exploited in four aspects of information integration. First, I propose novel algorithms for semantic enrichment of schema mappings. Second, using correspondences between similar properties at different levels of abstraction, I propose a configurable data integration system, in which query rewriting techniques allows the tradeoff between accuracy and completeness in query answering. Third, to keep the semantics in data exchange, I propose an entity preserving data exchange approach that reflects source entities in the target independent of classification of entities. Finally, to improve the efficiency of the data exchange approach proposed in this thesis, I propose an extended model of the column-store model called sliced column store. Working prototypes of the techniques proposed in this thesis are implemented to show the feasibility of realizing these techniques. Experiments that have been performed using various datasets show the techniques proposed in this thesis outperform many existing techniques in terms of ability to handle semantic heterogeneities and performance of information exchange