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The database group at the University of Washington

Published: 13 May 2014 Publication History
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References

[1]
AstroDB: methods and tools for Big Data astronomy. http://db.cs.washington.edu/astrodb/
[2]
P. Beame, P. Koutris, and D. Suciu. Communication steps for parallel query processing. In PODS, pages 273--284, 2013.
[3]
Y. Bu, B. Howe, M. Balazinska, and M. Ernst. The HaLoop approach to large-scale iterative data analysis. VLDB Journal (Special Issue: Best of VLDB 2010), 21(2), 2012.
[4]
N. N. Dalvi and D. Suciu. The dichotomy of probabilistic inference for unions of conjunctive queries. J. ACM, 59(6):30, 2012.
[5]
P. Domingos and D. Lowd. Markov Logic: An Interface Layer for Artificial Intelligence. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool Publishers, 2009.
[6]
University of Washington eScience Instittute. http://www.escience.washington.edu
[7]
D. Halperin, K. Weitz, B. Howe, F. Ribalet, M. A. Saito, and E. V. Armbrust. Real-time collaborative analysis with (almost) pure SQL: A case study in biogeochemical oceanography. In SSDBM, 2013.
[8]
B. Howe, G. Cole, E. Souroush, P. Koutris, A. Key, N. Khoussainova, and L. Battle. Database-as-a-service for long tail science. In SSDBM, 2011.
[9]
B. Howe, F. Ribalet, D. Halperin, S. Chitnis, and E. V. Armbrust. Collaborative science workflows in SQL. Computing in Science & Engineering, Special Issue on Science Data Management, 15(2), May/June 2013.
[10]
Big Data U: PhD Program in Big Data and Data Science. http://data.washington.edu/
[11]
N. Khoussainova, M. Balazinska, and D. Suciu. Perfxplain: Debugging MapReduce job performance. PVLDB, 5(7):598--609, 2012.
[12]
N. Khoussainova, Y. Kwon, M. Balazinska, and D. Suciu. SnipSuggest: Context-aware autocompletion for SQL. PVLDB, 4(1):22--33, 2010.
[13]
Y. Kwon, M. Balazinska, B. Howe, and J. Rolia. SkewTune: Mitigating skew in MapReduce applications. In SIGMOD, 2012.
[14]
Myria: Big Data management as a Cloud service. http://myria.cs.washington.edu/
[15]
Nuage: Scientific data management in the cloud. http://nuage.cs.washington.edu/
[16]
J. Ortiz, V. T. de Almeida, and M. Balazinska. A vision for personalized service level agreements in the cloud. In Proc. of the DanaC Workshop, 2013.
[17]
The RFID Ecosystem. http://rfid.cs.washington.edu/
[18]
S. Roy and D. Suciu. A formal approach to finding explanations for database queries. In SIGMOD, 2014. To appear.
[19]
SQL Share. http://sqlshare.escience.washington.edu
[20]
UW SciDB Branch. http://scidb.cs.washington.edu/
[21]
D. Suciu, D. Olteanu, C. Ré, and C. Koch. Probabilistic Databases. Synthesis Lectures on Data Management. Morgan & Claypool Publishers, 2011.
[22]
P. Upadhyaya, N. R. Anderson, M. Balazinska, B. Howe, R. Kaushik, R. Ramamurthy, and D. Suciu. The power of data use management in action. In SIGMOD, pages 1117--1120, 2013.
[23]
J. Vanderplas, E. Soroush, S. Krughoff, M. Balazinska, and A. Connolly. Squeezing a big orange into little boxes: The AscotDB system for parallel processing of data on a sphere. IEEE Data Eng. Bulletin, 36(4):11--20, 2013.
[24]
E. Welbourne, K. Koscher, E. Soroush, M. Balazinska, and G. Borriello. Longitudinal study of a building-wide RFID Ecosystem. In MOBISYS, 2009.

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    Published In

    cover image ACM SIGMOD Record
    ACM SIGMOD Record  Volume 43, Issue 1
    March 2014
    71 pages
    ISSN:0163-5808
    DOI:10.1145/2627692
    Issue’s Table of Contents

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 13 May 2014
    Published in SIGMOD Volume 43, Issue 1

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