Self-indexing inverted files for fast text retrieval
ACM Transactions on Information Systems (TOIS)
Integrating structured data and text: a relational approach
Journal of the American Society for Information Science
Managing gigabytes (2nd ed.): compressing and indexing documents and images
Managing gigabytes (2nd ed.): compressing and indexing documents and images
A framework for sparse matrix code synthesis from high-level specifications
Proceedings of the 2000 ACM/IEEE conference on Supercomputing
Compression of inverted indexes For fast query evaluation
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
On the Mapping of Index Compression Techniques on CSR Information Retrieval
ITCC '03 Proceedings of the International Conference on Information Technology: Computers and Communications
Enterprise Text Processing: A Sparse Matrix Approach
ITCC '01 Proceedings of the International Conference on Information Technology: Coding and Computing
On Improving the Performance of Sparse Matrix-Vector Multiplication
HIPC '97 Proceedings of the Fourth International Conference on High-Performance Computing
Information Retrieval: Algorithms and Heuristics (The Kluwer International Series on Information Retrieval)
Enhanced Algorithm for Extracting the Root of Arabic Words
CGIV '09 Proceedings of the 2009 Sixth International Conference on Computer Graphics, Imaging and Visualization
Efficient assembly of sparse matrices using hashing
PARA'06 Proceedings of the 8th international conference on Applied parallel computing: state of the art in scientific computing
A comparison study of some Arabic root finding algorithms
Journal of the American Society for Information Science and Technology
| Hi-index | 0.00 |
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.