2 research outputs found
A Novel Model for Distributed Big Data Service Composition using Stratified Functional Graph Matching
A significant number of current industrial applications rely on web services.
A cornerstone task in these applications is discovering a suitable service that
meets the threshold of some user needs. Then, those services can be composed to
perform specific functionalities. We argue that the prevailing approach to
compose services based on the "all or nothing" paradigm is limiting and leads
to exceedingly high rejection of potentially suitable services. Furthermore,
contemporary models do not allow "mix and match" composition from atomic
services of different composite services when binary matching is not possible
or desired. In this paper, we propose a new model for service composition based
on "stratified graph summarization" and "service stitching". We discuss the
limitations of existing approaches with a motivating example, present our
approach to overcome these limitations, and outline a possible architecture for
service composition from atomic services. Our thesis is that, with the advent
of Big Data, our approach will reduce latency in service discovery, and will
improve efficiency and accuracy of matchmaking and composition of services.Comment: 15 page
Trade-offs Computing Minimum Hub Cover toward Optimized Graph Query Processing
As techniques for graph query processing mature, the need for optimization is
increasingly becoming an imperative. Indices are one of the key ingredients
toward efficient query processing strategies via cost-based optimization. Due
to the apparent absence of a common representation model, it is difficult to
make a focused effort toward developing access structures, metrics to evaluate
query costs, and choose alternatives. In this context, recent interests in
covering-based graph matching appears to be a promising direction of research.
In this paper, our goal is to formally introduce a new graph representation
model, called Minimum Hub Cover, and demonstrate that this representation
offers interesting strategic advantages, facilitates construction of candidate
graphs from graph fragments, and helps leverage indices in novel ways for query
optimization. However, similar to other covering problems, minimum hub cover is
NP-hard, and thus is a natural candidate for optimization. We claim that
computing the minimum hub cover leads to substantial cost reduction for graph
query processing. We present a computational characterization of minimum hub
cover based on integer programming to substantiate our claim and investigate
its computational cost on various graph types.Comment: 12 pages, 6 figures and 2 algorithm