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JAVA 2013 IEEE DATAMINING PROJECT Elca evaluation for keyword search on probabilistic xml data
 

JAVA 2013 IEEE DATAMINING PROJECT Elca evaluation for keyword search on probabilistic xml data

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To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org

To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org

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    JAVA 2013 IEEE DATAMINING PROJECT Elca evaluation for keyword search on probabilistic xml data JAVA 2013 IEEE DATAMINING PROJECT Elca evaluation for keyword search on probabilistic xml data Document Transcript

    • ELCA: Evaluation for Keyword Search on Probabilistic XML Data ABSTRACT As probabilistic data management is becoming one of the main research focuses and keyword search is turning into a more popular query means, it is natural to think how to support keyword queries on probabilistic XML data. With regards to keyword query on deterministic XML documents, ELCA (Exclusive Lowest Common Ancestor) semantics allows more relevant fragments rooted at the ELCAs to appear as results and is more popular compared with other keyword query result semantics (such as SLCAs). In this paper, we investigate how to evaluate ELCA results for keyword queries on probabilistic XML documents. After defining probabilistic ELCA semantics in terms of possible world semantics, we propose an approach to compute ELCA probabilities without generating possible worlds. Then we develop an efficient stack-based algorithm that can find all probabilistic ELCA results and their ELCA probabilities for a given keyword query on a probabilistic XML document. Finally, we experimentally evaluate the proposed ELCA algorithm and compare it with its SLCA counterpart in aspects of result effectiveness, time and space efficiency, and scalability. GLOBALSOFT TECHNOLOGIES IEEE PROJECTS & SOFTWARE DEVELOPMENTS IEEE FINAL YEAR PROJECTS|IEEE ENGINEERING PROJECTS|IEEE STUDENTS PROJECTS|IEEE BULK PROJECTS|BE/BTECH/ME/MTECH/MS/MCA PROJECTS|CSE/IT/ECE/EEE PROJECTS CELL: +91 98495 39085, +91 99662 35788, +91 98495 57908, +91 97014 40401 Visit: www.finalyearprojects.org Mail to:ieeefinalsemprojects@gmail.com
    • Modules: Data storage and search: we describe an approach based on tree-based association rules(tars) mined rules, which provide approximate, intensional information on both the structure and the contents of xml documents and can be stored in xml format as well. There are two main approaches to xml document access: keyword- based search and query-answering. the idea of mining association rules to provide summarized representations of xml documents has been investigated in many proposals either by using languages xquery. file organization blacks We do not store the data in a single file because, in hadoop and mapreduce framework, a file is the smallest unit of input to a mapreduce job and, in the absence of caching, a file is always read from the disk. if we have all the data in one file, the whole file will be input to jobs for each query. Instead, we divide the data into multiple smaller files. User index based search: We introduce indexes on tars to further speed up the access to mined trees - and in general of intentional query answering. In general, path indexes are proposed to quickly answer queries that follow some frequent path template, and are built by indexing only those paths having highly frequent queries. We start from a different perspective: we want to provide quick, and often approximate, answers also to casual queries. Query plan generation: We define the query plan generation problem, and show that generating the best (i.e., least cost) query plan for the ideal model as well as for the practical
    • is computationally expensive. then, we will present a heuristic and a greedy approach to generate an approximate solution to generate the best plan. Running example: We will use the following query as a running example in this section. Running example select ?v, ?x, ?y, ?z where{ ?x xml : type ub : graduatestudent ?y xml : type ub : university ?z ?v ub : department ?x ub : memberof ?z ?x ub : undergraduatedegreefrom ?y } 5. Time Base Search: Then we develop an efficient stack-based algorithm that can find all probabilistic ELCA results and their ELCA probabilities for a given keyword query on a probabilistic XML document. Finally, we experimentally evaluate the proposed ELCA algorithm and compare it with its SLCA counterpart in aspects of result effectiveness, time.
    • Existing System: Semantic web technologies are being developed to present data in standardized way such that such data can be retrieved and understood by both human and machine. Historically, web pages are published in plain html files which are not suitable for reasoning. 1. No user data privacy 2. Existing commercial tools and technologies do not scale well in cloud 3. Computing settings. Proposed System: Integrates the functionalities proposed in our approach. Given an XML document, it enables users to extract intensional knowledge and compose traditional queries as well as queries over the intensional knowledge, receiving both extensional and intensional answers. Users formulate XQueries over the original data, and queries are automatically translated and executed on the intensional knowledge. Propose an approach to compute ELCA probabilities without generating possible worlds. Then we develop an efficient stack-based algorithm that can find all probabilistic ELCA results and their ELCA probabilities for a given keyword query on a probabilistic XML document. Finally, we experimentally evaluate the proposed ELCA algorithm and compare it with its SLCA counterpart in aspects of result effectiveness, time.
    • ALGORITHM: IN THIS SECTION, WE INTRODUCE AN ALGORITHM, PRELCA, TO PUT THE CONCEPTUAL IDEA IN THE PREVIOUS SECTION INTO PROCEDURAL COMPUTATION STEPS. WE START WITH INDEXING PROBABILISTIC XML DATA, AND THEN INTRODUCE PRELCA ALGORITHM, IN THE END, WE DISCUSS WHY IT IS RELUCTANT TO FIND EFFECTIVE UPPER BOUNDS FOR ELCA PROBABILITIES, AND IT TURNS OUT THAT PRELCA ALGORITHM MAY BE THE ONLY ACCEPTABLE SOLUTION.
    • System Requirements: Hardware Requirements: • System : Pentium IV 2.4 GHz. • Hard Disk : 40 GB. • Floppy Drive : 1.44 Mb. • Monitor : 15 VGA Colour. • Mouse : Sony. • Ram : 512 Mb. Software Requirements: • Operating system : Windows 7. • Coding Language : ASP.Net 4.0 with C# • Data Base : SQL Server 2008.