Phang Keat Keong

Automatic Web Services Deployment

As web service has become the emerging paradigm, the area of web service research has received a lot of attention in recent years. Most of the web services are deployed by site administrators. As the number of request of web services increase tremendously, there is a need to replicate the services to multiple resources. Although manual deployment allow the services to be deployed safely, it is impossible to scale. To solve this problem, we propose in this paper a new architecture for automatic web services deployment. Through the experiment, we proved that automatic deployment strategy able to handle the increasing number of users’ request in an effective manner.

Automated query transformation for searching semantically rich structured collections

The availability of semantically rich structured resources makes the search of such resources on the web even more interesting, where the formation of a meaningful structured query is now possible for better retrieval. Incorporation of concepts like role, category, topic, class, attributes, etc. to indicate the search target and constraints in a structured query enable better definition of information needs. However, the varieties of usages in application contexts create heterogeneity of structures definition and presentation, thus making it hard of using this information when formulating structured query for a search process. Hence, we are motivated to automate transformation of unstructured queries (which are more common for web user), to structured queries (which support better specification of information needs). This paper first explores two important issues arisen in the process of query transformation, i.e. multiple structures (with respect to the needs of handling multiple structured queries type) and multiple semantics (with respect to the needs of handling different collections of structured resources). Along with these issues, we define research problems, followed by an approach of mediating multiple structures and multiple semantics to support the query transformation from unstructured to structured form.

Minexml: bridging unstructured query with structured resources via mediated query

EXTENDED ABSTRACT The retrieval of structured resources using unstructured queries is challenging as we need to deal with the matching between entities of two different types. Consider an unstructured query, “publications of K.H. Gan in WI”, in a structured retrieval system. To match this query to structured resources, the system needs to transform it into a format that is comparable to the structure of the resources. As such, we develop a solution that automatically transform unstructured query to a mediated query which is enhanced with structural information. The mediated query is then matched against structured resources to obtain relevant results.

A Semantic Learning Approach for Mapping Unstructured Query to Web Resources

The search that involves structured Web resources like XML data, services is still lagging of its own method and relying on contemporary search systems. This paper presents a method that learns semantics from structured information of these resources. Instead of committing the semantic meaning of resources to strict and formal vocabularies like ontology or data dictionary, we are interested to interpret the meaning based on the natural context of the resources. The semantics are used in search process, i.e. query reasoning and resource selection, to provide better answer in terms of context relevancy and clearer result description

Beyond Map-Reduce: LATNODE – A New Programming Paradigm for Big Data Systems

The Compute Aggregate model used to model Map Reduce does not allow for dynamic node reordering once a job has started, assumes homogenous nodes and a balanced tree layout. We introduce heterogeneous nodes into the tree structure, thereby causing unbalanced trees. Finally, we present a new programming abstraction to allow for dynamic tree balancing.

Transforming unstructured query to mediated query for structured retrieval

Recent years, the availability of public accessible structured resources like XML on the web has led to active developments of structural retrieval systems. With these systems, users will be able to query for information from structured resources on the web efficiently. When querying, it is obvious that usage of structural information in query increases the precision of retrieval system. However, general web users are more familiar with unstructured query such as natural language or keywords, which contains no structural information. This motivates us to find a retrieval method that supports querying which is simpler and familiar to user, i.e. unstructured query, but at the same time, does not overlook the usage of structural information in query. Hence, we propose a solution that automatically adds structural information to the unstructured query, and represents it as a Mediated Query. The mediated query is an intermediate query in structured form to bridge the gap of structural differences between unstructured query and structured resources. As the selection of correct structural information that reflects the query context is crucial for better retrieval performance, we develop a method to obtain this information by learning the semantics of a set of terms extracted from structured resources. The semantics of a term is defined by its concept and context. We represent the term and its semantics using the Semantic Prediction Model. The model will be used in reasoning the context of query and the process of creating mediated query. The mediated query is then matched against structured resources to obtain relevant results.