The increasing availability of web services within an organization and on the Web demands for efficient search and composition mechanisms to find services satisfying user requirements. Often consumers may be unaware of exact service names that is fixed by service providers. Rather consumers being well aware of their requirements would like to search a service based on their commitments (inputs) and expectations (outputs). Based on this concept we have explored the feasibility of I O based web service search and composition in our previous work. The classical definition of service composition, i.e., one-to-one and onto mapping between input and output sets of composing services, is extended to give rise to three types of service match: Exact, Super and Partial match. Based on matches of all three types, different kinds of compositions are defined: Exact, Super and Collaborative Composition. Process of composition, being a match between inputs and outputs of services, is hastened by making use of information on service dependency that is made available in repository as an one time preprocessed information obtained from services populating the registry. Adopting three schemes for matching for a desired service outputs, the possibility of having different kinds of compositions is demonstrated in form of a Composition Search Tree. As an extension to our previous work, in this paper, we propose the utility of Composition Search Tree for finding compositions of interest like leanest and the shortest depth compositions.
The current techniques based on relational database are constrained by usage of multiple joins as well as malady of exact match of input and output parameters.In @cite_6 we propose an approach to overcome these difficulties. The criteria for matching is relaxed for partial matching allowing several services to collaborate and provide a desired service. In the current work we further extend the utility of Composition Search Tree for finding optimal service compositions.
@cite_6
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Relational Database for its huge success in data management has been used here as a scalable repository for storing webservices in UDDI framework. The operator ‘join’ has been used to derive service composition by matching inputs of a service to outputs of another. Adopting three modes of composability for a desired service, the possibility of having different kinds of compositions is demonstrated in form of a Composition Search Tree. In this paper, we propose a general framework for graph matching which is suitable for different problems of pattern recognition. The pattern representation we assume is at the same time highly structured, like for classic syntactic and structural approaches, and of subsymbolic nature with real-valued features, like for connectionist and statistic approaches. We show that random walk based models, inspired by Google's PageRank, give rise to a spectral theory that nicely enhances the graph topological features at node level. As a straightforward consequence, we derive a polynomial algorithm for the classic graph isomorphism problem, under the restriction of dealing with Markovian spectrally distinguishable graphs (MSD), a class of graphs that does not seem to be easily reducible to others proposed in the literature. The experimental results that we found on different test-beds of the TC-15 graph database show that the defined MSD class "almost always" covers the database, and that the proposed algorithm is significantly more efficient than top scoring VF algorithm on the same data. Most interestingly, the proposed approach is very well-suited for dealing with partial and approximate graph matching problems, derived for instance from image retrieval tasks. We consider the objects of the COIL-100 visual collection and provide a graph-based representation, whose node's labels contain appropriate visual features. We show that the adoption of classic bipartite graph matching algorithms offers a straightforward generalization of the algorithm given for graph isomorphism and, finally, we report very promising experimental results on the COIL-100 visual collection. In many applications, such as data integration and big data analytics, one has to integrate data from multiple sources without detailed and accurate schema information. The state of the art focuses on matching attributes among sources based on the information derived from the data in those sources. However, a best join result according to a method's own pre-determined criteria may not fit a user's best interest. In this paper, we tackle the challenge from a novel angle and investigate how to join schemaless tables to meet a user preference the best. We identify a set of essential preferences that are useful in various scenarios, such as minimizing the number of tuples in outer join results and maximizing the entropy of the joining key's distribution. We also develop a systematic method to compute the best join predicate optimizing an objective function representing a user preference. We conduct extensive experiments on 4 large datasets and compare with 4 baselines from the state of the art of schema matching and attribute clustering. The experimental results clearly show that our algorithm outperforms the baselines significantly in accuracy in all the cases, and consumes comparable running time. The growing popularity of graph databases has generated interesting data management problems, such as subgraph search, shortest-path query, reachability verification, and pattern match. Among these, a pattern match query is more flexible compared to a subgraph search and more informative compared to a shortest-path or reachability query. In this paper, we address pattern match problems over a large data graph G. Specifically, given a pattern graph (i.e., query Q), we want to find all matches (in G) that have the similar connections as those in Q. In order to reduce the search space significantly, we first transform the vertices into points in a vector space via graph embedding techniques, coverting a pattern match query into a distance-based multi-way join problem over the converted vector space. We also propose several pruning strategies and a join order selection method to process join processing efficiently. Extensive experiments on both real and synthetic datasets show that our method outperforms existing ones by orders of magnitude.