Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/203742
Title: Hybrid partitioning and distribution of RDF data
Researcher: Padiya, Trupti
Guide(s): Bhise, Minal
Keywords: DWAHP
Data-Aware Hybrid Partitioning
RDF Data Storage
Data Structures and Algorithms
University: Dhirubhai Ambani Institute of Information and Communication Technology (DA-IICT)
Completed Date: April 2018
Abstract: quotRDF is a standard model by W3C specifically designed for data interchange on the web. RDF was established and used for the development of the semantic web. However, nowadays RDF data is being used for diverse domains and is not limited to the semantic web. Tremendous increase is witnessed in RDF data due to its applications in various domains. With growing RDF data it is vital to manage this data efficiently. The thesis aims at efficient storage and faster querying of RDF data using various data partitioning techniques. newline newlineThe thesis studies the problem of basic data partitioning techniques for RDF data storage and proposes the use of hybrid data partitioning in centralized and distributed environment as a part of the solution to store and query RDF data. The dissertation emphasizes on efficient data storage and faster query execution for stationary RDF data. It demonstrates basic data partitioning techniques like PT (Property Table), BT (Binary Table), HP (Horizontally Partitioned Table), and use of MV (Materialized Views) over BT. Even though basic data partitioning techniques outperforms TT (Triple Table) they suffer from various performances issues. newline newlineThe thesis gives a detailed insight into advantages and disadvantages of basic data partitioning techniques. Consequently, it proposes hybrid solutions for data partitioning by exploiting the best of available techniques. It proposes three hybrid data partitioning techniques namely DAHP (Data-Aware Hybrid Partitioning), DASIVP (Data-Aware Structure Indexed Vertical Partitioning) and WAHP (Workload-Aware Hybrid Partitioning). DAHP and WAHP are a combination of PT and BT whereas DASIVP combines structure index partitioning with BT. DAHP and DASIVP consider a data-aware approach and WAHP considers a workload-aware approach. Data-aware approach stores RDF data based on how the data is related to each other in the dataset and workload-aware approach stores RDF data based on how the data that is queried together.
Pagination: xvi, 101p.
URI: http://hdl.handle.net/10603/203742
Appears in Departments:Department of Information and Communication Technology

Files in This Item:
File Description SizeFormat 
01_title.pdfAttached File62.91 kBAdobe PDFView/Open
02_declaration and certificate.pdf117.81 kBAdobe PDFView/Open
03_acknowledgements.pdf102.36 kBAdobe PDFView/Open
04_abstract.pdf61.65 kBAdobe PDFView/Open
05_contents.pdf170.06 kBAdobe PDFView/Open
06_list of acronyms and symbols.pdf142.89 kBAdobe PDFView/Open
07_list of tables.pdf149.71 kBAdobe PDFView/Open
08_chapter 1.pdf167.67 kBAdobe PDFView/Open
09_chapter 2.pdf264.04 kBAdobe PDFView/Open
10_chapter 3.pdf399.8 kBAdobe PDFView/Open
11_chapter 4.pdf404.01 kBAdobe PDFView/Open
12_chapter 5.pdf724.18 kBAdobe PDFView/Open
13_chapter 6.pdf190.19 kBAdobe PDFView/Open
14_appendix.pdf520.11 kBAdobe PDFView/Open
15_references.pdf250.28 kBAdobe PDFView/Open


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