Said Mirza Pahlevi

RDFCube: a P2P-based three-dimensional index for structural joins on distributed triple stores

Today, RDF data/triples are scattered everywhere and their total size is rapidly increasing. Centralized RDF triple stores have limitations on both their failure tolerance and scalability. Therefore, RDF query processing in a P2P environment is an important issue. So far, several conventional P2P-based RDF triple stores have been proposed. They, however, are designed merely for triple retrieval rather than for triple join query processing. Consequently, they suffer from an unnecessary data transfer problem. This paper presents an RDF query processing technique based on a three-dimensional hash index. The triples are mapped into the index; then bit information that represents the presence or absence of triples in the index is introduced. We implemented our approach on the top of an emulated P2P environment. Evaluation results show that our approach can achieve good performance and scalability.

Taxonomy-based adaptive Web search method

Current crawler-based search engines usually return a long list of search results containing a lot of noise documents. By indexing collected documents on a topic path in taxonomy, taxonomy-based search engines can improve the search result quality. However the searches are limited to the locally compiled databases. We propose an adaptive Web search method to improve the search result quality enabling the users to search many databases existing in the Web space. The method has a characteristic that combines the taxonomy-based search engines and a machine learning technique. More specifically, we construct a rule-based classifier using pre-classified documents provided by a taxonomy-based search engine based on a selected context category on its taxonomy, and then use it to modify the user query. The resulting modified query will be sent to the crawler-based search engines and the returned results will be presented to the user. We evaluate the effectiveness of our method by showing that the returned results from the modified query almost contain documents that will be categorized into the selected context category.

Service-based data integration using OGSA-DQP and OGSA-WebDB

OGSA-DQP is a service-based distributed query processor that is able to execute queries over data services and combine data integration with data analysis by invoking Web services. OGSA-DQP currently supports only one type of data source, relational databases wrapped using OGSA-DAI (a middleware tool that exposes XML or relational database management systems as Grid services). OGSA-WebDB is another middleware tool based on OGSA-DAI that exposes Web databases via the OGSA-DAI interface. The prevalence of XML encoded data and Web-accessible resources means that it is desirable to extend the current functionality of OGSA-DQP to provide support for such resources. This paper presents an extension to OGSA-DQP that allow queries over relational, XML and Web databases wrapped by OGSA-DAI and OGSA-WebDB. An application is presented that illustrates how these features complement each other to provide data integration and analysis in service-based Grids. Experimental results are presented that investigate the benefit of the approach within the application.

OGSA-WebDB: an OGSA-based system for bringing Web databases into the grid

An OGSA-based system, OGSA-WebDB, is proposed, which enables local database applications to query Web databases using a standard database query language (SQL). OGSA-WebDB consists of two main components: proxy databases and a mediator. Proxy databases represent the desired Web databases while the mediator acts as an interface between the proxy databases and the Web databases. The mediator accepts an SQL query from local applications and transforms the query into one or more Boolean conditions that are then sent to the target Web databases. The mediator processes the SQL query in parallel, taking into account the characteristics of the Web databases. Experimental results revealed that the query processing time is so small that it can be ignored for timing considerations. The system has been fully implemented on top of Globus Toolkit and OGSA-DAI software components.

Semantic grid resource monitoring and discovery with rule processing based on the time-series statistical data

This paper presents a new extension to a semantic grid resource monitoring and discovery system called S-MDS[1]. The extension requires S-MDS to store a sequence of resource property values into an RDF database so that aggregate and statistical calculation can be performed over the values. By using a rule-based approach combined with the inference capability of the RDF database, S-MDS provides a novel important function, namely, resource anomaly detection in OGSA-based grids. For example, it is possible to lively monitor and detect CPU load anomaly that exceeds 3 times of the standard deviation from the average of past two weeks loads. Finally, the extension gives great flexibility to users to define their own monitoring rules by using a general purpose rule language.

Towards Automatic Service Discovery and Monitoring in WS-Resource Framework

We present a general framework intended to support automatic service discovery and monitoring in the WS- resource framework (WSRF). The framework describes the metadata of WS-resources using ontology and provides an efficient mechanism to aggregate and maintain the ontology instances. The mechanism utilizes the constructs and message exchanges defined by the WSRF specifications so that it is applicable in any WSRF implementation.

Conveying taxonomy context for topic‐focused Web search

Introducing context to a user query is effective to improve the search effectiveness. In this article we propose a method employing the taxonomy-based search services such as Web directories to facilitate searches in any Web search interfaces that support Boolean queries. The proposed method enables one to convey current search context on taxonomy of a taxonomy-based search service to the searches conducted with the Web search interfaces. The basic idea is to learn the search context in the form of a Boolean condition that is commonly accepted by many Web search interfaces, and to use the condition to modify the user query before forwarding it to the Web search interfaces. To guarantee that the modified query can always be processed by the Web search interfaces and to make the method adaptive to different user requirements on search result effectiveness, we have developed new fast classification learning algorithms.

TAX-PQ: dynamic taxonomy probing and query modification for topic-focused Web search

We propose a novel Web search scheme TAX-PQ. TAX-PQ enables taxonomy-based topic-focused Web search on ordinary Boolean Web search interfaces. TAX-PQ utilizes a taxonomy and the data set maintained in an existing taxonomy-based search facility for this purpose. The search is initiated by designating an initial query and a context category in the taxonomy. The data set in the taxonomy-based search facility is probed with a technique combining the initial query with sampling, and a decision tree is constructed from the sampled query result. A query modifier is then derived from the decision tree to focus the initial query on the selected context category. To adapt TAX-PQ to different user requirements on search result effectiveness and properties of target Web search interfaces, we have developed a new decision tree construction algorithm. Experiments involving real Web sites show that TAX-PQ can significantly improve the Web search process and result. The results comply with user requirements under constraints of the target Web search interfaces.

SPARQL-based set-matching for semantic grid resource selection

Grid is an emerging technology that enables the sharing of a wide variety of resources. However, effective and accurate grid resource matching is difficult because of the dynamic characteristics and heterogeneity of grid resources. Grid resource matching mechanisms that utilize semantic Web technologies have been proposed to deal with this issue, although none support set matching of grid resources based on their semantic description. This paper proposes a novel set-matching algorithm that uses standard RDF query language SPARQL [1] to semantically match a set of grid resources and SPARQL query features to efficiently perform set matching. We evaluated the efficiency and effectiveness of the algorithm by performing a set of experiments and present the results.

Ontology-Based grid index service for advanced resource discovery and monitoring

This paper proposes a framework for advanced Grid resource discovery and monitoring. In the framework, a services state data are mapped into ontologies so that service owners may enrich them with semantic and other useful data, while keeping the state data unchanged. The Index Service, based on the OGSA framework, aggregates and maintains the ontology data, and allows users to query the data by using ontology-based query languages. The Index Service also provides a Continual Query mechanism that enables users to submit ontology queries as subscription messages and to be notified when the query results change. This paper also proposes an automatic ontology update mechanism, to keep the ontology data up-to-date.