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Comparing Different Sparse Matrix Storage Structures as Index Structure for Arabic Text Collection

Author

Listed:
  • Basel Bani-Ismail

    (Department of Computer Science, Sultan Qaboos University, Muscat, Oman)

  • Ghassan Kanaan

    (Department of Computer Science, Amman Arab University, Amman, Jordan)

Abstract

In the authors’ study they evaluate and compare the storage efficiency of different sparse matrix storage structures as index structure for Arabic text collection and their corresponding sparse matrix-vector multiplication algorithms to perform query processing in any Information Retrieval (IR) system. The study covers six sparse matrix storage structures including the Coordinate Storage (COO), Compressed Sparse Row (CSR), Compressed Sparse Column (CSC), Block Coordinate (BCO), Block Sparse Row (BSR), and Block Sparse Column (BSC). Evaluation depends on the storage space requirements for each storage structure and the efficiency of the query processing algorithm. The experimental results demonstrate that CSR is more efficient in terms of storage space requirements and query processing time than the other sparse matrix storage structures. The results also show that CSR requires the least amount of disk space and performs the best in terms of query processing time compared with the other point entry storage structures (COO, CSC). The results demonstrate that BSR requires the least amount of disk space and performs the best in terms of query processing time compared with the other block entry storage structures (BCO, BSC).

Suggested Citation

  • Basel Bani-Ismail & Ghassan Kanaan, 2012. "Comparing Different Sparse Matrix Storage Structures as Index Structure for Arabic Text Collection," International Journal of Information Retrieval Research (IJIRR), IGI Global, vol. 2(2), pages 52-67, April.
    • Handle: RePEc:igg:jirr00:v:2:y:2012:i:2:p:52-67
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