M Tech Dissertations

Permanent URI for this collectionhttp://drsr.daiict.ac.in/handle/123456789/3

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Subject: search.filters.subject.RDF Query Processing

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    Service level agreement parameter matching in cloud computing
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2011) Chauhan, Tejas; Chaudhary, Sanjay; Bise, Minal
    Cloud is a large pool of easily usable and accessible virtualized resources (such as hardware, development platforms and/or software services). It provides an on-demand, pay-asyo-ugo computing resources and had become an alternative to traditional IT infrastructure. As more and more consumers delegate their task to cloud providers, Service Level Agreement (SLA) between consumer and provider becomes an important aspect. Due to the dynamic nature of cloud the matching of service level agreement need to be dynamic and continuous monitoring of Quality of Service (QoS) is necessary to enforce SLAs. This complex nature of cloud warrants a sophisticated means of managing SLAs. SLA contains many parameters like cloud’s types of services, resources (physical memory, main memory, processor speed, ethernet speed etc.) and properties (availability, response time, server reboot time etc.). At present, actual Cloud SLAs are typically plain-text documents, and sometimes an informative document published online. Consumer needs to manually match application requirements with each and every cloud provider to identify compatible cloud provider. This work addresses the issue of matching SLA parameters to find best suitable cloud provider. Proposed algorithm identifies the compatible cloud provider by matching parameters of application requirements and cloud SLAs. It gives suggestion to a consumer in terms of number of matched parameters.
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    ItemOpen Access
    Service integration on social network
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2011) Patel, Mehul; Chaudhary, Sanjay; Bise, Minal
    Microblogging services are part of social network platforms, which allow people to exchange short messages. Social networks provide people to play an active role in collecting, analyzing and reporting news and information. People can use social network platform for marketing, buying and selling of their products. A sellers can tweet regarding product information including links of related photos, videos etc. A buyer can show interest in the product by means of tweets. Social network can be used as a mechanism to bring sellers and buyers closer. It provides a common platform for buyers and sellers to sell and buy their products. Microblogs can be parsed and analyzed to generate useful suggestions, e.g. sellers can be informed about potential buyers to get higher profit. Such information can be used to generate classified information to help users to take decision, e.g. minimum expected price of a crop that sellers expect in a given region. Microblogs can be written in different regional languages. Agro-produce marketing information can be processed and then stored in RDF/RDF(S) and OWL data store. SPARQL and conjunctive queries with pellet like reasoner or SPARQL-DL can be used to generate classified summarized information from RDF/RDF(S) and OWL data store.
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    SPARQL query optimization
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2011) Singh, Rohit Kumar; Chaudhary, Sanjay
    Query Optimization is the process of selecting the most efficient query evaluation plan among the many strategies possible for processing a given query, especially if the query is complex. The users are not expected to write their queries in such a way so that they can be processed efficiently; rather it is expected from system to construct a query evaluation plan that minimizes the cost of query evaluation. In any query optimization, the goal is to find the execution plan which is expected to return the result set without actually executing the query or subparts with optimal cost. Query engines for ontological data mostly execute user queries without considering any optimization. Especially for large ontologies,optimization techniques are required to ensure that query results are delivered within reasonable time. SPARQL can be used to express queries across diverse data sources, whether the data is stored natively as RDF or viewed as RDF via middleware. So, Query optimization may speed up SPARQL query answering by knowledge intensive reformulation. In our research work, we have proposed learning approach to solve this problem. In our approach, the learning is triggered by user queries. Then the system uses an inductive learning algorithm to generate semantic rules. This inductive learning algorithm can automatically select useful join paths and properties to construct rules from a ontology with many concepts. The learned semantic rules are effective for optimization of SPARQL query because they match query patterns and reflect data regularities.