[1] Jeffrey Dean and Sanjay Ghemawat. Mapreduce: Simplified data processing on large clusters. In OSDI 2004, pages 137-150, 2004. [ bib | .html ]
[2] Jeffrey Dean and Sanjay Ghemawat. Mapreduce: a flexible data processing tool. Commun. ACM, 53(1):72-77, 2010. [ bib | DOI | http ]
[3] Jeffrey Dean. Experiences with mapreduce, an abstraction for large-scale computation. In PACT '06: Proceedings of the 15th international conference on Parallel architectures and compilation techniques, page 1, New York, NY, USA, 2006. ACM. [ bib | DOI | http ]
[4] Jeffrey Dean. Handling large datasets at google: Current systems and future designs., 2008. [ bib | .pdf ]
[5] Luiz André Barroso, Jeffrey Dean, and Urs Hölzle. Web search for a planet: The google cluster architecture. IEEE Micro, 23(2):22-28, 2003. [ bib ]
[6] Jeffrey Dean. Challenges in building large-scale information retrieval systems: invited talk. In WSDM '09: Proceedings of the Second ACM International Conference on Web Search and Data Mining, pages 1-1, New York, NY, USA, 2009. ACM. [ bib | DOI | .pdf ]
[7] Rob Pike, Sean Dorward, Robert Griesemer, and Sean Quinlan. Interpreting the data: Parallel analysis with sawzall. Sci. Program., 13(4):277-298, October 2005. [ bib | http ]
[8] Christopher Olston, Benjamin Reed, Utkarsh Srivastava, Ravi Kumar, and Andrew Tomkins. Pig latin: a not-so-foreign language for data processing. In SIGMOD '08: Proceedings of the 2008 ACM SIGMOD international conference on Management of data, pages 1099-1110, New York, NY, USA, 2008. ACM. [ bib | DOI | http ]
[9] Alfred V. Aho, Brian W. Kernighan, and Peter J. Weinberger. The AWK programming language. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA, 1987. [ bib ]
[10] Amy N. Langville and Carl D. Meyer. Google's PageRank and Beyond: The Science of Search Engine Rankings. Princeton University Press, Princeton, NJ, USA, 2006. [ bib ]
[11] Sanjay Ghemawat, Howard Gobioff, and Shun T. Leung. The google file system. SIGOPS Oper. Syst. Rev., 37(5):29-43, December 2003. [ bib | DOI | http ]
[12] Cheng T. Chu, Sang K. Kim, Yi A. Lin, Yuanyuan Yu, Gary R. Bradski, Andrew Y. Ng, and Kunle Olukotun. Map-reduce for machine learning on multicore. In Bernhard Schölkopf, John C. Platt, and Thomas Hoffman, editors, NIPS, pages 281-288. MIT Press, 2006. [ bib | .pdf ]
[13] Michael Stonebraker, Daniel Abadi, David J. DeWitt, Sam Madden, Erik Paulson, Andrew Pavlo, and Alexander Rasin. Mapreduce and parallel dbmss: friends or foes? Commun. ACM, 53(1):64-71, 2010. [ bib | DOI | http ]
[14] David J. DeWitt and Michael Stonebraker. Mapreduce: A major step backwards. [ bib | http ]
[15] John Darlington, Yi ke Guo, Hing Wing To, Jin Yang, Hing Wing, and To Jin Yang. Parallel skeletons for structured composition. page pages. ACM Press, 1995. [ bib ]
[16] R. Kessler, H. Carr, L. Stollner, and M. Swanson. Implementing concurrent scheme for the mayfly distributed parallel processing system. LISP AND SYMBOLIC COMPUTATION: An International Journal, 5, 1992. [ bib ]
[17] Hung-chih Yang, Ali Dasdan, Ruey-Lung Hsiao, and D. Stott Parker. Map-reduce-merge: simplified relational data processing on large clusters. In SIGMOD '07: Proceedings of the 2007 ACM SIGMOD international conference on Management of data, pages 1029-1040, New York, NY, USA, 2007. ACM. [ bib | DOI ]
[18] Ralf Lämmel. Google's mapreduce programming model - revisited. Science of Computer Programming, 70(1):1 - 30, 2008. [ bib | DOI | http ]
[19] Jimmy Lin and Chris Dyer. Data-Intensive Text Processing with MapReduce. [ bib | .html ]
[20] Jimmy Lin. Brute force and indexed approaches to pairwise document similarity comparisons with mapreduce. In SIGIR '09: Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval, pages 155-162, New York, NY, USA, 2009. ACM. [ bib | DOI | http ]
[21] Colby Ranger, Ramanan Raghuraman, Arun Penmetsa, Gary Bradski, and Christos Kozyrakis. Evaluating mapreduce for multi-core and multiprocessor systems. In HPCA '07: Proceedings of the 2007 IEEE 13th International Symposium on High Performance Computer Architecture, volume 0, pages 13-24, Washington, DC, USA, February 2007. IEEE Computer Society. [ bib | DOI | http ]
[22] Gene M. Amdahl. Validity of the single processor approach to achieving large scale computing capabilities. pages 79-81, 2000. [ bib ]
[23] Richard M. Yoo, Anthony Romano, and Christos Kozyrakis. Phoenix rebirth: Scalable mapreduce on a large-scale shared-memory system. In IISWC, pages 198-207. IEEE, 2009. [ bib | .pdf ]
[24] B.F. Cooper, A. Silberstein, Ramakrishnan Tam, E., and R. R., Sears. Benchmarking cloud serving systems with ycsb, 2010. [ bib | http ]
[25] Dan Orban Huan liu. Cloud mapreduce - a mapreduce implementation on amazon cloud os, 2009. [ bib | .pdf ]
[26] Matei Zaharia, Andy Konwinski, Anthony D. Joseph, Y. Katz, and Ion Stoica. Improving mapreduce performance in heterogeneous environments. [ bib | http ]
[27] Chao Tian, Haojie Zhou, Yongqiang He, and Li Zha. A dynamic mapreduce scheduler for heterogeneous workloads. In GCC '09: Proceedings of the 2009 Eighth International Conference on Grid and Cooperative Computing, pages 218-224, Washington, DC, USA, 2009. IEEE Computer Society. [ bib | DOI | http ]
[28] Michael C. Schatz. Cloudburst: highly sensitive read mapping with mapreduce. Bioinformatics (Oxford, England), 25(11):1363-1369, June 2009. [ bib | DOI | http ]
[29] Tyson Condie, Neil Conway, Peter Alvaro, Joseph M. Hellerstein, Khaled Elmeleegy, and Russell Sears. Mapreduce online. Technical Report UCB/EECS-2009-136, EECS Department, University of California, Berkeley, Oct 2009. [ bib | .html ]
[30] Jaliya Ekanayake, Shrideep Pallickara, and Geoffrey Fox. Mapreduce for data intensive scientific analyses. eScience, IEEE International Conference on, 0:277-284, 2008. [ bib | http ]
[31] Andrew Pavlo, Erik Paulson, Alexander Rasin, Daniel J. Abadi, David J. DeWitt, Samuel Madden, and Michael Stonebraker. A comparison of approaches to large-scale data analysis. In SIGMOD '09: Proceedings of the 35th SIGMOD international conference on Management of data, pages 165-178, New York, NY, USA, 2009. ACM. [ bib | DOI ]
[32] Andrew McCallum, Kamal Nigam, and Lyle H. Ungar. Efficient clustering of high-dimensional data sets with application to reference matching, 2000. [ bib ]
[33] Makho Ngazimbi. Data clustering with mapreduce. Master's thesis, Boise State University, 2009. [ bib | .pdf ]
[34] Mike Cafarella and Doug Cutting. Building nutch: Open source search: A case study in writing an open source search engine. ACM Queue, 2(2), April 2004. [ bib | http ]
[35] Tom White. Hadoop: The Definitive Guide. O'Reilly Media, 1 edition, June 2009. [ bib ]
[36] Kiyoung Kim, Kyungho Jeon, Hyuck Han, Shin G. Kim, Hyungsoo Jung, and Heon Y. Yeom. Mrbench: A benchmark for mapreduce framework. In ICPADS '08: Proceedings of the 2008 14th IEEE International Conference on Parallel and Distributed Systems, pages 11-18, Washington, DC, USA, 2008. IEEE Computer Society. [ bib | DOI | http ]
[37] Jonathan Cohen. Graph twiddling in a mapreduce world. Computing in Science and Engineering, 11(4):29-41, July 2009. [ bib | DOI | http ]
[38] Grzegorz Malewicz, Matthew H. Austern, Aart J.C. Bik, James C. Dehnert, Ilan Horn, Naty Leiser, and Grzegorz Czajkowski. Pregel: a system for large-scale graph processing. In SPAA '09: Proceedings of the twenty-first annual symposium on Parallelism in algorithms and architectures, pages 48-48, New York, NY, USA, 2009. ACM. [ bib | DOI ]
[39] Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schtze. Introduction to Information Retrieval. Cambridge University Press, New York, NY, USA, 2008. [ bib ]
[40] M. Livny, R. Ramakrishnan, K. Beyer, G. Chen, D. Donjerkovic, S. Lawande, J. Myllymaki, and K. Wenger. Devise: integrated querying and visual exploration of large datasets. In SIGMOD '97: Proceedings of the 1997 ACM SIGMOD international conference on Management of data, pages 301-312, New York, NY, USA, 1997. ACM. [ bib | DOI ]
[41] F. Delaglio, S. Grzesiek, G.W. Vuister, G. Zhu, J. Pfeifer, and A. Bax. Nmrpipe: A multidimensional spectral processing system based on unix pipes. Journal of Biomolecular NMR, 6(3):277-293, 1995. [ bib | http ]
[42] Leslie G. Valiant. A bridging model for parallel computation. Commun. ACM, 33(8):103-111, 1990. [ bib | DOI ]
[43] John L. Hennessy and David A. Patterson. Computer Architecture - A Quantitative Approach. Morgan Kaufmann, fourth edition, 2007. [ bib ]
[44] David Culler, J. P. Singh, and Anoop Gupta. Parallel Computer Architecture: A Hardware/Software Approach (The Morgan Kaufmann Series in Computer Architecture and Design). Morgan Kaufmann, August 1998. [ bib | http ]
[45] Nancy A. Lynch. Distributed Algorithms. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 1996. [ bib ]
[46] Barry Wilkinson and Michael Allen. Parallel programming: techniques and applications using networked workstations and parallel computers. Prentice-Hall, Inc., Upper Saddle River, NJ, USA, 1999. [ bib ]
[47] William Gropp, Ewing Lusk, and Anthony Skjellum. Using MPI: portable parallel programming with the message-passing interface. MIT Press, Cambridge, MA, USA, 1994. [ bib ]
[48] Jorg Keller, Christopher Kessler, and Jesper Larsson Traeff. Practical Pram Programming. John Wiley & Sons, Inc., New York, NY, USA, 2000. [ bib ]
[49] Foto N. Afrati and Jeffrey D. Ullman. A new computation model for cluster computing. Technical report, National Tecghnical Univ. of Athens/Stanford University, December 2009. [ bib | http ]
[50] Foto N. Afrati and Jeffrey D. Ullman. Optimizing joins in a mapreduce environment, 2010. [ bib | .pdf ]
[51] Jerome Boulon et al. Chukwa, a large-scale monitoring system. In Cloud Computing and its Applications, pages 1-5, 2008. [ bib | .pdf ]
[52] Jiaqi Tan, Xinghao Pan, Soila Kavulya, Rajeev G, and Priya Narasimhan. Salsa: Analyzing logs as state machines 1. 2009. [ bib | http ]
[53] Oliver J. Haggarty, William J. Knottenbelt, and Jeremy T. Bradley. Distributed response time analysis of gspn models with mapreduce. SIMULATION, 85(8):497-509, August 2009. [ bib | DOI | http ]
[54] Bryan Catanzaro, Narayanan Sundaram, and Kurt Keutzer. A map reduce framework for programming graphics processors. In In Workshop on Software Tools for MultiCore Systems, 2008. [ bib | http ]
[55] Bingsheng He, Wenbin Fang, Qiong Luo, Naga K. Govindaraju, and Tuyong Wang. Mars: a mapreduce framework on graphics processors. In PACT '08: Proceedings of the 17th international conference on Parallel architectures and compilation techniques, pages 260-269, New York, NY, USA, 2008. ACM. [ bib | DOI ]
[56] Jeremy Archuleta, Yong Cao, Tom Scogland, and Wu chun Feng. Multi-dimensional characterization of temporal data mining on graphics processors. Parallel and Distributed Processing Symposium, International, 0:1-12, 2009. [ bib | DOI ]
[57] Yi Shan, Bo Wang, Jing Yan, Yu Wang, Ningyi Xu, and Huazhong Yang. Fpmr: Mapreduce framework on fpga. In FPGA '10: Proceedings of the 18th annual ACM/SIGDA international symposium on Field programmable gate arrays, pages 93-102, New York, NY, USA, 2010. ACM. [ bib | DOI ]
[58] Javier Tordable. Mapreduce for integer factorization. Jan 2010. [ bib | arXiv | http ]
[59] Christopher Dyer, Aaron Cordova, Alex Mont, and Jimmy Lin. Fast, easy, and cheap: construction of statistical machine translation models with mapreduce. In StatMT '08: Proceedings of the Third Workshop on Statistical Machine Translation, pages 199-207, Morristown, NJ, USA, 2008. Association for Computational Linguistics. [ bib ]
[60] Johannes Gehrke. Technical perspective data stream processing: when you only get one look. Commun. ACM, 52(10):96-96, 2009. [ bib | DOI ]
[61] Azza Abouzeid, Kamil Bajda-Pawlikowski, Daniel Abadi, Avi Silberschatz, and Alexander Rasin. Hadoopdb: an architectural hybrid of mapreduce and dbms technologies for analytical workloads. Proc. VLDB Endow., 2(1):922-933, 2009. [ bib ]
[62] Chao Jin and Rajkumar Buyya. Mapreduce programming model for .net-based cloud computing. In Henk Sips, Dick Epema, and Hai-Xiang Lin, editors, Euro-Par 2009 Parallel Processing, volume 5704, chapter 41, pages 417-428. Springer Berlin Heidelberg, Berlin, Heidelberg, 2009. [ bib | DOI | http ]
[63] Edward Y. Chang, Kaihua Zhu, Hao Wang, Hongjie Bai, Jian Li, Zhihuan Qiu, and Hang Cui. Psvm: Parallelizing support vector machines on distributed computers. 2009. [ bib | http ]
[64] Hans Peter Graf, Eric Cosatto, Leon Bottou, Igor Durdanovic, and Vladimir Vapnik. Parallel support vector machines: The cascade svm. In In Advances in Neural Information Processing Systems, pages 521-528. MIT Press, 2005. [ bib ]
[65] Richard E. Ladner and Michael J. Fischer. Parallel prefix computation. J. ACM, 27(4):831-838, 1980. [ bib | DOI ]
[66] Fay Chang, Jeffrey Dean, Sanjay Ghemawat, Wilson C. Hsieh, Deborah A. Wallach, Mike Burrows, Tushar Chandra, Andrew Fikes, and Robert E. Gruber. Bigtable: A distributed storage system for structured data. ACM Trans. Comput. Syst., 26(2):1-26, June 2008. [ bib | DOI | .html ]
[67] Lars George. Hbase architecture, 2009. [ bib | .html ]
[68] Schubert Zhang. Hfile: A block-indexed file format to store sorted key-value pairs, 2009. [ bib | http ]
[69] Ioannis Konstantinou, Evangelos Angelou, Dimitrios Tsoumakos, and Nectarios Koziris. Distributed indexing of web scale datasets for the cloud. In MDAC '10: Proceedings of the 2010 Workshop on Massive Data Analytics on the Cloud, pages 1-6, New York, NY, USA, 2010. ACM. [ bib | DOI ]
[70] Ning Li, Jun Rao, Eugene Shekita, and Sandeep Tata. Leveraging a scalable row store to build a distributed text index. In CloudDB '09: Proceeding of the first international workshop on Cloud data management, pages 29-36, New York, NY, USA, 2009. ACM. [ bib | DOI ]
[71] Jimmy Lin, Donald Metzler, Tamer Elsayed, and Lidan Wang. Of ivory and smurfs: Loxodontan mapreduce experiments for web search. [ bib ]
[72] Ashish Thusoo, Joydeep Sen Sarma, Namit Jain, Zheng Shao, Prasad Chakka, Suresh Anthony, Hao Liu, Pete Wyckoff, and Raghotham Murthy. Hive: a warehousing solution over a map-reduce framework. Proc. VLDB Endow., 2(2):1626-1629, 2009. [ bib ]
[73] Aaron Kimball and Sierra Michels-slettvet. Abstract cluster computing for web-scale data processing. [ bib ]
[74] Anthony Tomasic and Hector Garcia-molina. Performance of inverted indices in shared-nothing distributed text document information retrieval systems. In In Proceedings of the Second International Conference on Parallel and Distributed Information Systems, pages 8-17, 1993. [ bib ]
[75] Eric A. Brewer. Towards robust distributed systems (abstract). In PODC '00: Proceedings of the nineteenth annual ACM symposium on Principles of distributed computing, page 7, New York, NY, USA, 2000. ACM. [ bib | DOI ]
[76] Seth Gilbert and Nancy Lynch. Brewer's conjecture and the feasibility of consistent available partition-tolerant web services. In In ACM SIGACT News, page 2002, 2002. [ bib ]
[77] Werner Vogels. Eventually consistent, 2008. [ bib | http ]
[78] Michael Isard, Mihai Budiu, Yuan Yu, Andrew Birrell, and Dennis Fetterly. Dryad: distributed data-parallel programs from sequential building blocks. In EuroSys '07: Proceedings of the 2nd ACM SIGOPS/EuroSys European Conference on Computer Systems 2007, pages 59-72, New York, NY, USA, 2007. ACM. [ bib | DOI | http ]
[79] Giuseppe Decandia, Deniz Hastorun, Madan Jampani, Gunavardhan Kakulapati, Avinash Lakshman, Alex Pilchin, Swaminathan Sivasubramanian, Peter Vosshall, and Werner Vogels. Dynamo: amazon's highly available key-value store. SIGOPS Oper. Syst. Rev., 41(6):205-220, 2007. [ bib | DOI | .pdf ]
[80] Hector Garcia-Molina, Jennifer Widom, and Jeffrey D. Ullman. Database System Implementation. Prentice-Hall, Inc., Upper Saddle River, NJ, USA, 1999. [ bib ]
[81] M. Tamer Ozsu and Patrick Valduriez. Principles of Distributed Database Systems (2nd Edition). Prentice Hall, 2 edition, January 1999. [ bib | http ]
[82] Brian D. Halligan, Joey F. Geiger, Andrew K. Vallejos, Andrew S. Greene, and Simon N. Twigger. Low cost, scalable proteomics data analysis using amazon's cloud computing services and open source search algorithms. Journal of Proteome Research, 8(6):3148-3153, June 2009. [ bib | DOI | http ]
[83] Yan Varley. No Relation: The Mixed Blessings of Non-Relational Databases. PhD thesis, The University of Texas at Austin, 2009. [ bib | .pdf ]
[84] Jon Feldman, S. Muthukrishnan, Anastasios Sidiropoulos, Cliff Stein, and Zoya Svitkina. On the complexity of processing massive, unordered, distributed data, May 2007. [ bib | arXiv | http ]
[85] Yunhong G. Robert Grossman. Data mining using high performance data clouds: Experimental studies using sector and sphere. [ bib ]
[86] Guy Blelloch. Vector models for data-parallel computing. MIT Press, Cambridge, MA, USA, 1990. [ bib ]
[87] Guy Blelloch. Scans as primitive parallel operations. IEEE Transactions on Computers, 38:1526-1538, 1987. [ bib | http ]
[88] Guy Blelloch. Programming parallel algorithms. Commun. ACM, 39(3):85-97, 1996. [ bib | DOI ]
[89] Magnus Ekman and Per Stenstrom. A robust main-memory compression scheme. SIGARCH Comput. Archit. News, 33(2):74-85, 2005. [ bib | DOI | .PDF ]
[90] Anurag Acharya, Mustafa Uysal, and Joel Saltz. Active disks: programming model, algorithms and evaluation. SIGPLAN Not., 33(11):81-91, 1998. [ bib | DOI ]
[91] Michael Zeller, Robert Grossman, Christoph Lingenfelder, Michael R. Berthold, Erik Marcade, Rick Pechter, Mike Hoskins, Wayne Thompson, and Rich Holada. Open standards and cloud computing: Kdd-2009 panel report. In KDD '09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 11-18, New York, NY, USA, 2009. ACM. [ bib | DOI ]
[92] U. Kang, Charalampos E. Tsourakakis, and Christos Faloutsos. Pegasus: A peta-scale graph mining system. Data Mining, IEEE International Conference on, 0:229-238, 2009. [ bib | DOI ]
[93] Malcolm P. Atkinson, Jano I. van Hemert, Liangxiu Han, Ally Hume, and Chee Sun Liew. A distributed architecture for data mining and integration. In DADC '09: Proceedings of the second international workshop on Data-aware distributed computing, pages 11-20, New York, NY, USA, 2009. ACM. [ bib | DOI ]
[94] Robert L. Grossman and Yunhong Gu. On the varieties of clouds for data intensive computing. IEEE Data Eng. Bull., 32(1):44-50, 2009. [ bib ]
[95] Roman Dementiev. Algorithm engineering for large data sets. 2006. [ bib | http ]
[96] M. Zukowski, P. A. Boncz, N. Nes, and S. Heman. MonetDB/X100 - A DBMS In The CPU Cache. IEEE Data Engineering Bulletin, 28(2):17-22, June 2005. [ bib | http ]
[97] Anastassia Ailamaki, David J. DeWitt, Mark D. Hill, and Marios Skounakis. Weaving relations for cache performance, 2001. [ bib ]
[98] P. Boncz. Monet: A Next-Generation DBMS Kernel for Query-Intensive Applications. PhD thesis. [ bib | .pdf ]
[99] Daniel J. Abadi, Peter A. Boncz, and Stavros Harizopoulos. Column-oriented database systems. Proc. VLDB Endow., 2(2):1664-1665, 2009. [ bib | http ]
[100] Daniel Abadi, Samuel Madden, and Miguel Ferreira. Integrating compression and execution in column-oriented database systems. In SIGMOD '06: Proceedings of the 2006 ACM SIGMOD international conference on Management of data, pages 671-682, New York, NY, USA, 2006. ACM. [ bib | DOI ]
[101] Marcin Zukowski, Sándor Héman, Niels Nes, and Peter Boncz. Cooperative scans: Dynamic bandwidth sharing in a dbms. In In Proc. of the 33 rd Intl. Conf. on Very Large Databases (VLDB, pages 723-734, 2007. [ bib ]
[102] Lin Qiao, Vijayshankar Raman, Frederick Reiss, Peter J. Haas, and Guy M. Lohman. Main-memory scan sharing for multi-core cpus. Proc. VLDB Endow., 1(1):610-621, 2008. [ bib | DOI ]
[103] Marcin Zukowski, Sandor Heman, Niels Nes, and Peter Boncz. Super-scalar ram-cpu cache compression. In ICDE '06: Proceedings of the 22nd International Conference on Data Engineering, page 59, Washington, DC, USA, 2006. IEEE Computer Society. [ bib | DOI ]
[104] Dimitris Tsirogiannis, Stavros Harizopoulos, Mehul A. Shah, Janet L. Wiener, and Goetz Graefe. Query processing techniques for solid state drives. In SIGMOD '09: Proceedings of the 35th SIGMOD international conference on Management of data, pages 59-72, New York, NY, USA, 2009. ACM. [ bib | DOI ]
[105] Mike Stonebraker, Daniel J. Abadi, Adam Batkin, Xuedong Chen, Mitch Cherniack, Miguel Ferreira, Edmond Lau, Amerson Lin, Sam Madden, Elizabeth O'Neil, Pat O'Neil, Alex Rasin, Nga Tran, and Stan Zdonik. C-store: a column-oriented dbms. In VLDB '05: Proceedings of the 31st international conference on Very large data bases, pages 553-564. VLDB Endowment, 2005. [ bib ]
[106] George P. Copeland and Setrag N. Khoshafian. A decomposition storage model. In SIGMOD '85: Proceedings of the 1985 ACM SIGMOD international conference on Management of data, pages 268-279, New York, NY, USA, 1985. ACM. [ bib | DOI ]
[107] Daniel J. Abadi, Samuel R. Madden, and Nabil Hachem. Column-stores vs. row-stores: how different are they really? In SIGMOD '08: Proceedings of the 2008 ACM SIGMOD international conference on Management of data, pages 967-980, New York, NY, USA, 2008. ACM. [ bib | DOI ]
[108] Daniel J. Abadi, Daniel S. Myers, David J. DeWitt, and Samuel Madden. Materialization strategies in a column-oriented dbms. In ICDE, volume 0, pages 466-475, Los Alamitos, CA, USA, 2007. IEEE. [ bib | DOI | http ]
[109] David J DeWitt, Randy H Katz, Frank Olken, Leonard D Shapiro, Michael R Stonebraker, and David Wood. Implementation techniques for main memory database systems. SIGMOD Rec., 14(2):1-8, 1984. [ bib | DOI ]
[110] Ion Stoica, Robert Morris, David Karger, M. Frans Kaashoek, and Hari Balakrishnan. Chord: A scalable peer-to-peer lookup service for internet applications. pages 149-160, 2001. [ bib ]
[111] Tushar Chandra, Robert Griesemer, and Joshua Redstone. Paxos made live: an engineering perspective. In In Proc. of PODC, pages 398-407. ACM Press, 2007. [ bib ]
[112] Michael Stonebraker University and Michael Stonebraker. The case for shared nothing. Database Engineering, 9:4-9, 1986. [ bib ]
[113] John MacCormick, Nick Murphy, Marc Najork, Chandramohan A. Thekkath, and Lidong Zhou. Boxwood: Abstractions as the foundation for storage infrastructure, 2004. [ bib ]
[114] S. Muthukrishnan. Data streams: Algorithms and applications, 2003. [ bib ]
[115] Monika Rauch Henzinger, Prabhakar Raghavan, and Sridar Rajagopalan. Computing on data streams, 1998. [ bib ]
[116] James Ahrens, Kristi Brislawn, Ken Martin, Berk Geveci, C. Charles Law, and Michael Papka. Large-scale data visualization using parallel data streaming. IEEE Computer Graphics and Applications, 21:34-41, 2001. [ bib | DOI ]
[117] Open problems in data stream research, 2006. [ bib | .pdf ]
[118] Christos Faloutsos, M. Ranganathan, and Yannis Manolopoulos. Fast subsequence matching in time-series databases. SIGMOD Rec., 23(2):419-429, 1994. [ bib | DOI ]
[119] Henrik André-Jönsson and Dushan Z. Badal. Using signature files for querying time-series data. In PKDD '97: Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery, pages 211-220, London, UK, 1997. Springer-Verlag. [ bib ]
[120] André-Jönsson. Indexing Strategies for Time Series Data. PhD thesis, Linköping University, 2002. [ bib | .pdf ]
[121] Jin Shieh and Eamonn Keogh. isax: indexing and mining terabyte sized time series. In KDD '08: Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 623-631, New York, NY, USA, 2008. ACM. [ bib | DOI ]
[122] Dennis Shasha and Yunyue Zhu. High Performance Discovery In Time Series: Techniques And Case Studies (Monographs in Computer Science). SpringerVerlag, 2004. [ bib ]
[123] Norbert Wiener. Extrapolation, Interpolation, and Smoothing of Stationary Time Series. The MIT Press, 1964. [ bib ]
[124] Peter J. Brockwell and Richard A. Davis. Introduction to Time Series and Forecasting. Springer, March 2002. [ bib | http ]
[125] Saeid Sanei and J. A. Chambers. EEG Signal Processing. Wiley-Interscience, Sep 2007. [ bib | http ]
[126] Catherine A. Schevon, A. J. Trevelyan, C. E. Schroeder, R. R. Goodman, G. McKhann, and R. G. Emerson. Spatial characterization of interictal high frequency oscillations in epileptic neocortex. [ bib | http ]
[127] Boaz Porat. Digital processing of random signals: theory and methods. Prentice-Hall, Inc., Upper Saddle River, NJ, USA, 1994. [ bib ]
[128] Sergios Theodoridis and Konstantinos Koutroumbas. Pattern Recognition, Third Edition. Academic Press, February 2006. [ bib | http ]
[129] Alan V. Oppenheim, Ronald W. Schafer, and John R. Buck. Discrete-time signal processing (2nd ed.). Prentice-Hall, Inc., Upper Saddle River, NJ, USA, 1999. [ bib ]
[130] Don H. Johnson and Dan E. Dudgeon. Array Signal Processing: Concepts and Techniques. Simon & Schuster, 1992. [ bib ]
[131] Harry L. Van Trees. Detection, Estimation, and Modulation Theory: Radar-Sonar Signal Processing and Gaussian Signals in Noise. Krieger Publishing Co., Inc., Melbourne, FL, USA, 1992. [ bib ]
[132] Steven M. Kay. Fundamentals of statistical signal processing: estimation theory. Prentice-Hall, Inc., Upper Saddle River, NJ, USA, 1993. [ bib ]
[133] Ian H. Witten, Alistair Moffat, and Timothy C. Bell. Managing gigabytes (2nd ed.): compressing and indexing documents and images. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 1999. [ bib ]
[134] Ian H. Witten Timothy C. Bell, John G. Cleary. Text compression. Prentice Hall, 1990. [ bib ]
[135] David Salomon. Data Compression : The Complete Reference. Springer, February 2004. [ bib | http ]
[136] Allen Gersho and Robert M. Gray. Vector quantization and signal compression. Kluwer Academic Publishers, Norwell, MA, USA, 1991. [ bib ]
[137] Jon Bentley and Douglas McIlroy. Data compression using long common strings. In DCC '99: Proceedings of the Conference on Data Compression, page 287, Washington, DC, USA, 1999. IEEE Computer Society. [ bib ]
[138] Jacob Ziv and Abraham Lempel. Compression of individual sequences via variable-rate coding, 1978. [ bib ]
[139] Shu Lin and Daniel J. Costello. Error Control Coding, Second Edition. Prentice-Hall, Inc., Upper Saddle River, NJ, USA, 2004. [ bib ]
[140] David J. C. Mackay. Information Theory, Inference & Learning Algorithms. Cambridge University Press, 1st edition, June 2002. [ bib | http ]
[141] Christopher M. Bishop. Pattern Recognition and Machine Learning (Information Science and Statistics). Springer, 1st ed. 2006. corr. 2nd printing edition, October 2007. [ bib | http ]
[142] T. H. Cormen, C. E. Leiserson, R. L. Rivest, and C. Stein. Introduction to Algorithms. MIT Press, 2001. [ bib ]
[143] Dan Gusfield. Algorithms on strings, trees, and sequences : computer science and computational biology. Cambridge Univ. Press, January 2007. [ bib | http ]
[144] Hanan Samet. Foundations of Multidimensional and Metric Data Structures (The Morgan Kaufmann Series in Computer Graphics and Geometric Modeling). Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 2005. [ bib ]
[145] Gene H. Golub and Charles F. Van Loan. Matrix Computations. The Johns Hopkins University Press, 3rd edition, 1996. [ bib ]
[146] Lars Eldén. Matrix Methods in Data Mining and Pattern Recognition. Society for Industrial and Applied Mathematics, Philadelphia, PA, USA, 2007. [ bib ]
[147] Lloyd N. Trefethen and David Bau. Numerical Linear Algebra. SIAM: Society for Industrial and Applied Mathematics, June 1997. [ bib | http ]
[148] Alfred V. Aho, John E. Hopcroft, and Jeffrey D. Ullman. The Design and Analysis of Computer Algorithms. Addison-Wesley, Reading, Mass., 1974. [ bib ]
[149] Anne Greenbaum. Iterative methods for solving linear systems, volume 17 of Frontiers in Applied Mathematics. Society for Industrial and Applied Mathematics (SIAM), 1997. [ bib ]
[150] Dimitri P. Bertsekas and John N. Tsitsiklis. Parallel and distributed computation: numerical methods. Prentice-Hall, Inc., Upper Saddle River, NJ, USA, 1989. [ bib ]
[151] James W. Demmel, Michael T. Heath, and Henk A. van der Vorst. Parallel numerical linear algebra. In Society for Industrial and Applied Mathematics. SIAM, 1997. [ bib ]
[152] Jack J. Dongarra, Lain S. Duff, Danny C. Sorensen, and Henk A. Vander Vorst. Numerical Linear Algebra for High Performance Computers. Society for Industrial and Applied Mathematics, Philadelphia, PA, USA, 1998. [ bib ]
[153] William H. Press, Saul A. Teukolsky, William T. Vetterling, and Brian P. Flannery. Numerical Recipes 3rd Edition: The Art of Scientific Computing. Cambridge University Press, 3 edition, August 2007. [ bib | http ]
[154] E. Anderson, Z. Bai, C. Bischof, J. Demmel, J. Dongarra, J. Du Croz, A. Greenbaum, S. Hammarling, A. McKenney, S. Ostrouchov, and D. Sorensen. LAPACK's user's guide. Society for Industrial and Applied Mathematics, Philadelphia, PA, USA, 1992. [ bib ]
[155] Laura Susan Blackford, J. Choi, A. Cleary, A. Petitet, R. C. Whaley, J. Demmel, I. Dhillon, K. Stanley, J. Dongarra, S. Hammarling, G. Henry, and D. Walker. Scalapack: a portable linear algebra library for distributed memory computers - design issues and performance. In Supercomputing '96: Proceedings of the 1996 ACM/IEEE conference on Supercomputing (CDROM), page 5, Washington, DC, USA, 1996. IEEE Computer Society. [ bib | DOI ]
[156] A. Cleary and J. Dongarra. Implementation in scalapack of divide-and-conquer algorithms for banded and tridiagonal linear systems. Technical report, In: EuroPar'96 - Parallel Processing, L, 1997. [ bib ]
[157] L. S. Blackford, J. Choi, A. Cleary, E. D'Azeuedo, J. Demmel, I. Dhillon, S. Hammarling, G. Henry, A. Petitet, K. Stanley, D. Walker, and R. C. Whaley. ScaLAPACK user's guide. Society for Industrial and Applied Mathematics, Philadelphia, PA, USA, 1997. [ bib ]

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