SEARCH

SEARCH BY CITATION

REFERENCES

  • 1
    Sebot J, Drach-Temam N.Memory bandwidth: The true bottleneck of SIMD multimedia performance on a superscalar processor. In Euro-Par 2001 Parallel Processing, Vol. 2150, Lecture Notes in Computer Science. Springer: Berlin/Heidelberg, 2001; 439447, DOI: 10.1007/3-540-44681-8_63.
  • 2
    Drepper U.What every programmer should know about memory, 2007. Available from: http://www.akkadia.org/drepper/cpumemory.pdf [last accessed August 2012].
  • 3
    Manegold S, Kersten ML, Boncz P.Database architecture evolution: Mammals flourished long before dinosaurs became extinct. Proceedings of the VLDB Endowment 2009; 2(2):16481653.
  • 4
    Zukowski M.Balancing vectorized query execution with bandwidth-optimized storage. Ph.D. Thesis, Universiteit van Amsterdam, 2009.
  • 5
    Harizopoulos S, Liang V, Abadi DJ, Madden S.Performance tradeoffs in read-optimized databases. Proceedings of the 32nd International Conference on Very Large Data Bases, VLDB ’06, VLDB Endowment, Seoul, Korea, 2006; 487498.
  • 6
    Westmann T, Kossmann D, Helmer S, Moerkotte G.The implementation and performance of compressed databases. SIGMOD Record 2000; 29(3):5567.
  • 7
    Abadi D, Madden S, Ferreira M.Integrating compression and execution in column-oriented database systems. Proceedings of the 2006 ACM SIGMOD International Conference on Management of Data, SIGMOD ’06, ACM: New York, NY, USA, 2006; 671682. DOI: 10.1145/1142473.1142548.
  • 8
    Büttcher S, Clarke CLA.Index compression is good, especially for random access. Proceedings of the 16th ACM Conference on Information and Knowledge Management, CIKM ’07, ACM: New York, NY, USA, 2007; 761770. DOI: 10.1145/1321440.1321546.
  • 9
    Anh VN, Moffat A.Inverted index compression using word-aligned binary codes. Information Retrieval 2005; 8(1):151166.
  • 10
    Yan H, Ding S, Suel T.Inverted index compression and query processing with optimized document ordering. Proceedings of the 18th International Conference on World Wide Web, WWW ’09, ACM: New York, NY, USA, 2009; 401410. DOI: 10.1145/1526709.1526764.
  • 11
    Popov P.Basic optimizations: Talk at the YaC (Yet Another Conference) held by Yandex (in Russian), (2010). Available from: http://yac2011.yandex.com/archive2010/topics/ [last accessed September 2012].
  • 12
    Stepanov AA, Gangolli AR, Rose DE, Ernst RJ, Oberoi PS.SIMD-based decoding of posting lists. Proceedings of the 20th ACM International Conference on Information and Knowledge Management, CIKM ’11, ACM: New York, NY, USA, 2011; 317326. DOI: 10.1145/2063576.2063627.
  • 13
    Dean J.Challenges in building large-scale information retrieval systems: invited talk. Proceedings of the Second ACM International Conference on Web Search and Data Mining, WSDM ’09, ACM: New York, NY, USA, 2009; 11, DOI: 10.1145/1498759.1498761. Author's slides: http://static.googleusercontent.com/external_content/untrusted_dlcp/research.google.com/en/us/people/jeff/WSDM09-keynote.pdf [last checked December 2012].
  • 14
    Lemke C, Sattler KU, Faerber F, Zeier A.Speeding up queries in column stores: A case for compression. Proceedings of the 12th International Conference on Data Warehousing and Knowledge Discovery, DaWaK’10, Springer-Verlag: Berlin, Heidelberg, 2010; 117129, DOI: 10.1007/978-3-642-15105-7_10.
  • 15
    Binnig C, Hildenbrand S, Färber F.Dictionary-based order-preserving string compression for main memory column stores. Proceedings of the 2009 ACM SIGMOD International Conference on Management of Data, Seoul, Korea, ACM: New York, NY, USA, 2009; 283296, DOI: 10.1145/1559845.1559877.
  • 16
    Poess M, Potapov D.Data compression in Oracle. VLDB’03, Proceedings of the 29th International Conference on Very Large Data Bases, Morgan Kaufmann: San Francisco, CA, USA, 2003; 937947.
  • 17
    Hall A, Bachmann O, Büssow R, Gänceanu S, Nunkesser M.Processing a trillion cells per mouse click. Proceedings of the VLDB Endowment 2012; 5(11):14361446.
  • 18
    Raman V, Swart G.How to wring a table dry: Entropy compression of relations and querying of compressed relations. Proceedings of the 32nd International Conference on Very Large Data Bases, VLDB ’06, VLDB Endowment, 2006; 858869.
  • 19
    Lemire D, Kaser O.Reordering columns for smaller indexes. Information Sciences 2011; 181(12):25502570.
  • 20
    Bjørklund TA, Grimsmo N, Gehrke J, Torbjørnsen O.Inverted indexes vs. bitmap indexes in decision support systems. Proceedings of the 18th ACM Conference on Information and Knowledge Management, CIKM ’09, ACM: New York, NY, USA, 2009; 15091512, DOI: 10.1145/1645953.1646158.
  • 21
    Holloway AL, Raman V, Swart G, DeWitt DJ.How to barter bits for chronons: Compression and bandwidth trade offs for database scans. Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data, ACM: New York, NY, USA, 2007; 389400.
  • 22
    Holloway AL, DeWitt DJ.Read-optimized databases, in depth. Proceedings of the VLDB Endowment 2008; 1(1):502513.
  • 23
    Anh VN, Moffat A.Index compression using 64-bit words. Software: Practice and Experience 2010; 40(2):131147.
  • 24
    Silvestri F, Venturini R.VSEncoding: Efficient coding and fast decoding of integer lists via dynamic programming. Proceedings of the 19th ACM International Conference on Information and Knowledge Management, CIKM ’10, ACM: New York, NY, USA, 2010; 12191228, DOI: 10.1145/1871437.1871592.
  • 25
    Zhang J, Long X, Suel T.Performance of compressed inverted list caching in search engines. Proceedings of the 17th International Conference on World Wide Web, WWW ’08, ACM: New York, NY, USA, 2008; 387396, DOI: 10.1145/1367497.1367550.
  • 26
    Zukowski M, Heman S, Nes N, Boncz P.Super-scalar RAM-CPU cache compression. Proceedings of the 22nd International Conference on Data Engineering, ICDE ’06, IEEE Computer Society: Washington, DC, USA, 2006; 5971, DOI: 10.1109/ICDE.2006.150.
  • 27
    Witten IH, Moffat A, Bell TC.Managing Gigabytes (2nd ed.): Compressing and Indexing Documents and Images. Morgan Kaufmann Publishers Inc.: San Francisco, CA, USA, 1999.
  • 28
    Rice R, Plaunt J.Adaptive variable-length coding for efficient compression of spacecraft television data. IEEE Transactions on Communication Technology 1971; 19(6):889897.
  • 29
    Elias P.Universal codeword sets and representations of the integers. IEEE Transactions on Information Theory 1975; 21(2):194203.
  • 30
    Büttcher S, Clarke C, Cormack GV.Information Retrieval: Implementing and Evaluating Search Engines. The MIT Press: Cambridge, Massachusetts, 2010.
  • 31
    Transier F, Sanders P.Engineering basic algorithms of an in-memory text search engine. ACM Transactions on Information Systems 2010; 29(1):2:12:37.
  • 32
    Moffat A, Stuiver L.Binary interpolative coding for effective index compression. Information Retrieval 2000; 3(1):2547.
  • 33
    Walder J, Krátký M, Bača R, Platoš J, Snášel V.Fast decoding algorithms for variable-lengths codes. Information Sciences 2012; 183(1):6691.
  • 34
    Schlegel B, Gemulla R, Lehner W.Fast integer compression using SIMD instructions. Proceedings of the Sixth International Workshop on Data Management on New Hardware, DaMoN ’10, ACM: New York, NY, USA, 2010; 3440, DOI: 10.1145/1869389.1869394.
  • 35
    Cutting D, Pedersen J.Optimization for dynamic inverted index maintenance. Proceedings of the 13th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’90, ACM: New York, NY, USA, 1990; 405411, DOI: 10.1145/96749.98245.
  • 36
    Williams H, Zobel J.Compressing integers for fast file access. The Computer Journal 1999; 42(3):193201.
  • 37
    Thiel L, Heaps H.Program design for retrospective searches on large data bases. Information Storage and Retrieval 1972; 8(1):120.
  • 38
    Williams R.An extremely fast Ziv-Lempel data compression algorithm. Proceedings of the 1st Data Compression Conference, DCC ’91, Snowbird, Utah, 1991; 362371.
  • 39
    Goldstein J, Ramakrishnan R, Shaft U.Compressing relations and indexes. Proceedings of the Fourteenth International Conference on Data Engineering, ICDE ’98, IEEE Computer Society: Washington, DC, USA, 1998; 370379.
  • 40
    Ng WK, Ravishankar CV.Block-oriented compression techniques for large statistical databases. IEEE Transactions on Knowledge and Data Engineering 1997; 9(2):314328.
  • 41
    Delbru R, Campinas S, Tummarello G.Searching web data: An entity retrieval and high-performance indexing model. Web Semantics 2012; 10:3358.
  • 42
    Deveaux JP, Rau-Chaplin A, Zeh N.Adaptive tuple differential coding. In Database and Expert Systems Applications, Vol. 4653, Lecture Notes in Computer Science. Springer: Berlin/Heidelberg, 2007; 109119, DOI: 10.1007/978-3-540-74469-6_12.
  • 43
    Ao N, Zhang F, Wu D, Stones DS, Wang G, Liu X, Liu J, Lin S.Efficient parallel lists intersection and index compression algorithms using graphics processing units. Proceedings of the VLDB Endowment 2011; 4(8):470481.
  • 44
    Baeza-Yates R, Jonassen S.Modeling static caching in web search engines. In Advances in Information Retrieval, Vol. 7224, Lecture Notes in Computer Science. Springer, Berlin/Heidelberg, 2012; 436446, DOI: 10.1007/978-3-642-28997-2_37.
  • 45
    Jonassen S, Bratsberg S.Intra-query concurrent pipelined processing for distributed full-text retrieval. In Advances in Information Retrieval, Vol. 7224, Lecture Notes in Computer Science. Springer: Berlin/Heidelberg, 2012; 413425, DOI: 10.1007/978-3-642-28997-2_35.
  • 46
    Jones DM.The New C Standard: A Cultural and Economic Commentary. Addison Wesley Longman Publishing Co., Inc.: Redwood City, CA, USA, 2003.
  • 47
    Willhalm T, Popovici N, Boshmaf Y, Plattner H, Zeier A, Schaffner J.SIMD-scan: Ultra fast in-memory table scan using on-chip vector processing units. Proceedings of the VLDB Endowment 2009; 2(1):385394.
  • 48
    Zhou J, Ross KA.Implementing database operations using SIMD instructions. Proceedings of the 2002 ACM SIGMOD International Conference on Management of Data, SIGMOD ’02, ACM: New York, NY, USA, 2002; 145156, DOI: 10.1145/564691.564709.
  • 49
    Inoue H, Moriyama T, Komatsu H, Nakatani T.A high-performance sorting algorithm for multicore single-instruction multiple-data processors. Software: Practice and Experience 2012; 42(6):753777.
  • 50
    Wassenberg J.Lossless asymmetric single instruction multiple data codec. Software: Practice and Experience 2012; 42(9):10951106.
  • 51
    Boystov L.Clueweb09 posting list data set, 2012. Available from: http://boytsov.info/datasets/clueweb09gap/ [last checked August 2012].
  • 52
    Brenes DJ, Gayo-Avello D.Stratified analysis of AOL query log. Information Sciences 2009; 179(12):18441858.
  • 53
    Pass G, Chowdhury A, Torgeson C.A picture of search. Proceedings of the 1st International Conference on Scalable Information Systems, InfoScale ’06, ACM: New York, NY, USA, 2006, DOI: 10.1145/1146847.1146848.
  • 54
    Fusco F, Stoecklin MP, Vlachos M.NET-FLi: On-the-fly compression, archiving and indexing of streaming network traffic. Proceedings of the VLDB Endowment 2010; 3:13821393.
  • 55
    Baeza-Yates R, Salinger A.Experimental analysis of a fast intersection algorithm for sorted sequences. Proceedings of the 12th International Conference on String Processing and Information Retrieval, SPIRE'05, Springer-Verlag: Berlin, Heidelberg, 2005; 1324.
  • 56
    Ding B, König AC.Fast set intersection in memory. Proceedings of the VLDB Endowment 2011; 4(4):255266.
  • 57
    Vigna S.Quasi-succinct indices. Proceedings of the Sixth ACM International Conference on Web Search and Data Mining, WSDM ’13, ACM: New York, NY, USA, 2013; 8392.
  • 58
    Schlegel B, Willhalm T, Lehner W.Fast sorted-set intersection using SIMD instructions. ADMS Workshop, Seattle, WA, 2011.
  • 59
    Aksyonoff A.Introduction to Search with Sphinx: From Installation to Relevance Tuning. O'Reilly Media: Sebastopol, California, 2011.