Chaitali Gupta

Improving Performance of Web Services Query Matchmaking with Automated Knowledge Acquisition

There is a critical need to design and develop tools that abstract away the fundamental complexity of XML-based Web services specifications and toolkits, and provide an elegant, intuitive, simple, and powerful query-based invocation system to end users. Web services based tools and standards have been designed to facilitate seamless integration and development for application developers. As a result, current implementations require the end user to have intimate knowledge of Web services and related toolkits, and users often play an informed role in the overall Web services execution process. We employ a self-learning mechanism and a set of algorithms and optimizations to match user queries with corresponding operations in Web services. Our system uses Semantic Web concepts and Ontologies in the process of automating Web services matchmaking. We present performance analysis of our system and quantify the exact gains in precision and recall due to the knowledge acquisition algorithms.

A Query-based System for Automatic Invocation of Web Services

There is a critical need to design and develop tools that abstract away the fundamental complexity of XML based Web services specifications and toolkits, and provide an elegant, intuitive, simple, and powerful query based invocation system to end users. Web services based tools and standards have been designed to facilitate seamless integration and development for application developers. As a result, current implementations require the end user to have intimate knowledge of Web services and related toolkits, and users often play an informed role in the overall Web services execution process We employ a set of algorithms and optimizations to match user queries with corresponding operations in Web services, invoke the operations with the correct set of parameters, and present the results to the end user. Our system uses the Semantic Web and Ontologies in the process of automating Web services invocation and execution.

Scalable and Distributed Processing of Scientific XML Data

A seamless and intuitive search capability for the vast amount of datasets generated by scientific experiments is critical to ensure effective use of such data by domain specific scientists. Currently, searches on enormous XML datasets is done manually via custom scripts or by using hard-to-customize queries developed by experts in complex and disparate XML query languages. Such approaches however do not provide acceptable performance for large-scale data since they are not based on a scalable distributed solution. Furthermore, it has been shown that databases are not optimized for queries on XML data generated by scientific experiments, as term kinship, range based queries, and constraints such as conjunction and negation need to be taken into account. There exists a critical need for an easy-to-use and scalable framework, specialized for scientific data, that provides natural-language-like syntax along with accurate results. As most existing search tools are designed for exact string matching, which is not adequate for scientific needs, we believe that such a framework will enhance the productivity and quality of scientific research by the data reduction capabilities it can provide. This paper presents how the MapReduce model should be used in XML metadata indexing for scientific datasets, specifically TeraGrid Information Services and the NeXus datasets generated by the Spallation Neutron Source (SNS) scientists. We present an indexing structure that scales well for large-scale MapReduce processing. We present performance results using two MapReduce implementations, Apache Hadoop and LEMO-MR, to emphasize the flexibility and adaptability of our framework in different MapReduce environments.

Semantic framework for free-form search of Grid resources and services

The model of free-form queries has been an enormous success for HTML-based search engines on the web. If the same free-form search is made available for grid services, it will serve as a powerful tool for scientists to retrieve information on resources, monitoring data, replica location sets, and meta-data on scientific data sets, in an intuitive manner. Current implementations of XML-based grid service descriptions require end users to have intimate knowledge of service descriptions, related toolkits, and query languages. We have developed a system that abstracts away these fundamental complexities and provides a simple free-form query based scientific discovery system for grid users. In this paper, we present the design and initial implementation results of our ontological framework that employs matching algorithms, automated extension of ontologies, and semantic Web and ontological concepts to match free-form queries with corresponding grid resource information stored in RDF/OWL format.

Optimizing XML processing for grid applications using an emulation framework

Chip multi-processors (CMPs), commonly referred to as multi-core processors, are being widely adopted for deployment as part of the grid infrastructure. This change in computer architecture requires corresponding design modifications in programming paradigms, including grid middleware tools, to harness the opportunities presented by multi-core processors. Simple and naive implementations of grid middleware on multi-core systems can severely impact performance. This is because programming for CMPs requires special consideration for issues such as limitations of shared bus bandwidth, cache size and coherency, and communication between threads. The goal of developing an optimized multi-threaded grid middleware for emerging multi-core processors will be realized only if researchers and developers have access to an in-depth analysis of the impact of several low level microarchitectural parameters on performance. None of the current grid simulators and emulators provide feedback at the microarchitectural level, which is essential for such an analysis. In earlier work we presented our initial results on the design and implementation of such an emulation framework, Multi- core Grid (McGrid). In this paper we extend that work and present a performance study on the effect of cache coherency, scheduling of processing threads to take advantage of data available in the cache of each core, and read and write access patterns for shared data structures. We present the performance results, analysis, and recommendations based on experiments conducted using the McGrid framework for processing XML-based grid data and documents.

Web Services Operation and Parameter Matchmaking Based on Free-Form User Queries

Service-oriented architectures (SOA), based on Web services as the underlying architecture, have the potential to facilitate dynamic evolution of business processes. However, to fully realize the benefit of the SOA vision, it is critical to shield application developers from the complexity of Web services and related XML-based formats. In the currently available toolkits, users often play an informed role in the mapping of XML based specifications, operation names, message structures, and parameter types. The focus of our work is to design and develop an elegant, intuitive, simple, and powerful free-form query based system that allows interaction with Web services, without requiring the end-user to understand the operation names, parameters types, and other XML-based information in a WSDL document. Our system uses semantic Web concepts, ontologies, and WordNet in the process of automating Web services matchmaking. In this paper we focus on presenting techniques for matching free-form queries with appropriate Web service operations and extracting parameter values from user queries. We quantify the accuracy of our methodologies in terms of precision and recall.

Framework for Efficient Indexing and Searching of Scientific Metadata

A seamless and intuitive data reduction capability for the vast amount of scientific metadata generated by experiments is critical to ensure effective use of the data by domain specific scientists. The portal environments and scientific gateways currently used by scientists provide search capability that is limited to the pre-defined pull-down menus and conditions set in the portal interface. Currently, data reduction can only be effectively achieved by scientists who have developed expertise in dealing with complex and disparate query languages. A common theme in our discussions with scientists is that data reduction capability, similar to web search in terms of ease-of-use, scalability, and freshness/accuracy of results, is a critical need that can greatly enhance the productivity and quality of scientific research. Most existing search tools are designed for exact string matching, but such matches are highly unlikely given the nature of metadata produced by instruments and a user’s inability to recall exact numbers to search in very large datasets. This paper presents research to locate metadata of interest within a range of values. To meet this goal, we leverage the use of XML in metadata description for scientific datasets, specifically the NeXus datasets generated by the SNS scientists. We have designed a scalable indexing structure for processing data reduction queries. Web semantics and ontology based methodologies are also employed to provide an elegant, intuitive, and powerful free-form query based data reduction interface to end users.

Semantic Framework for Free-Form Search of Grid Resources

If the model of free-form queries, which has proved successful for HTML-based search on the Web, is made available for grid services, it will serve as a powerful tool for scientists to retrieve information on resources, monitoring data, replica location sets, and meta-data on scientific data sets, etc., in an intuitive manner. To enable this vision, there is a critical need to design and develop tools that abstract away the fundamental complexity of XML-based grid specifications and toolkits, and provide an elegant, simple, and powerful free-form query-based invocation system to end users. Current implementations of XML-based grid service descriptions require end users to have intimate knowledge of service descriptions, related toolkits, and query languages. We present the design, implementation, and performance analysis of our ontological framework that employs matching algorithms, automated extension of ontologies, and optimizations to match free-form user queries with corresponding operations in grid services and resource information stored in RDF/OWL format. Our system uses Semantic Web concepts, ontologies, and lexical tools to automate discovery and matchmaking of grid services and resource information. We quantify the performance improvements due to knowledge acquisition and extension of ontology files with each missed query, and present precision and recall studies to measure the accuracy of the free-form search.

McGrid: framework for optimizing grid middleware on multi-core processors

The microprocessor industry is rapidly moving towards chip multi-processors (CMPs), commonly referred to as multi-core processors, where multiple cores can independently execute different threads. This change in computer architecture requires corresponding design modifications in programming paradigms, including grid middleware tools, to harness the opportunities presented by multi-core processors. Naive implementations of grid middleware on multi-core systems can severely impact performance because of limitations of shared bus bandwidth, cache size and coherency, and communication between threads. The goal of developing an optimized multi-threaded grid middleware for emerging multi-core processors will be realized only if researchers and developers have access to an in-depth analysis of the impact of several low level microarchitectural parameters on performance. None of the current grid simulators and emulators provide feedback at the microarchitectural level, which is essential for such an analysis. We describe the initial design and implementation of an emulation framework, Multi-core Grid (McGrid), to analyze and provide insightful feedback on the performance limitations, bottlenecks, and optimization opportunities for grid middleware on multi-core systems. We describe early performance results of emulating the processing of some representative XML based grid documents on multi-core nodes using the McGrid framework.

Automatic Creation of an Ontological Knowledge Base from Grid and Cloud-based Wikipages

As the size of experimental data, documents, and web pages grows larger, it will be difficult for scientists to search for appropriate data and grid web-services through keyword-based grid search portal interfaces. A well designed and dynamically updated knowledge base can help understand user queries when they are structurally different, but semantically correlated, with actual data stored in a grid or cloud. In this paper we discuss the implementation of a knowledge base creation tool for grid and cloud based wikipages. In order to achieve accurate results in the query match-making process, we store the knowledge base in the rich OWL format and update it automatically with subsequent inference of new facts. Our framework extracts grid and cloud related wikipages and web pages using syntactic Link Grammar Parser and creates core ontology models specific to the grid and cloud domain. In this paper we describe two core components of the ontology generation framework - an information extraction framework and a core ontology model. We present the accuracy of search in terms of precision and recall and its relationship with the dynamically updated ontological domain knowledge base.