Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
10.1145/2769493.2769526acmotherconferencesArticle/Chapter ViewAbstractPublication PagespetraConference Proceedingsconference-collections
short-paper

A framework for self-managing database support and parallel computing for assistive systems

Published: 01 July 2015 Publication History

Abstract

There is no doubt that assistive systems are and will be a great part of our everyday lives. Thus, it is not suprising that in recent years researchers all over the world have been putting a lot of effort into their development. One of the most challenging problems usually is the handling of enormous amounts of data, which often has been collected by numerous sensors. This data is the basis of models, e.g. for prediction of movement, which has been derived by statistical methods, e.g. machine learning. However, due to the massive amounts of data, conventional statistical tools suffer from performance issues. In this paper, we would like to introduce and discuss a framework that combines the popular, statistical development tool R, database technology and the widely known MapReduce framework. Our main focus is placed on user-friendliness, meaning that the user does not have to change anything in his R-script, but still benefits from parallel computation and the in- and output power of databases.

References

[1]
A. Ghoting, R. Krishnamurthy, E. Pednault, B. Reinwald, V. Sindhwani, S. Tatikonda, Y. Tian, S. Vaithyanathan. SystemML: Declarative Machine Learning on MapReduce. In 2011 IEEE 27th International Conference on Data Engineering.
[2]
Apache. Hadoop. http://hadoop.apache.org.
[3]
B. Huang, S. Babu, J. Yang. Cumulon: optimizing statistical data analysis in the cloud. In 2013 ACM SIGMOD/PODS.
[4]
H. Grunert. Distributed denial of privacy. In Jahrestagung der Gesellschaft für Informatik (INFORMATIK 2014).
[5]
S. Guha. Computing Environment for the statistical analysis of large and complex data. PhD thesis, Purdue University, 2010.
[6]
H. Wickham, R. Francois, RStudio. dplyr: A grammar of data manipulation, 2015.
[7]
J. Dean, S. Ghemawat. Mapreduce: A flexible data processing tool. Communications of the ACM, 53(1):66--71, January 2010.
[8]
J. Lajus, H. Mühleisen. Efficient data management and statistics with zero-copy integration. In International Conference on Scientific and Statistical Database Management, volume 24, 2014.
[9]
J. Li, X. Ma, S. Yoginath, G. Kora, N. F. Samatova. Transparent runtime parallelization of the R script language. Journal of Parallel and Distributed Computing, 2011.
[10]
M. Boehm, S. Tatikonda, B. Reinwald, P. Sen, Y. Tian, D. Burdick, S. Vaithyanathan. Hybrid Parallelization Strategies for Large-Scale Machine learning in systemml. Proceedings of the VLDB Endowment, 2014.
[11]
M. G. Ivanova, M. L. Kersten, N. J. Nes, R. A. P. Goncalves. An architecture for recycling intermediates in a column-store. ACM Trans. Database Syst., 35(4):24, 2010.
[12]
M. Stonebraker, D. Abadi, D. J. Dewitt, S. Madden, E. Paulson, A. Pavlo, A. Rasin. MapReduce and parallel DBMSs: Friends or foes? Communications of the ACM, 53(1):66--71, January 2010.
[13]
Paradigm4. SciDB. http://www.scidb.org.
[14]
R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, 2014.
[15]
S. Das, Y. Sismanis, K. S. Beyer, R. Gemulla, P. J. Haas, J. McPherson. Ricardo: Integrating R and Hadoop. In 2010 ACM SIGMOD.
[16]
S. Idreos, M. L. Kersten, S. Manegold. Self-organizing tuple reconstruction in column-stores. In Proceedings of the ACM SIGMOD International Conference on Management of Data, SIGMOD 2009, Providence, Rhode Island, USA, June 29 - July 2, 2009, pages 297--308, 2009.
[17]
Y. Zhang, H. Herodotou, J. Yang. RIOT: I/O-efficient numerical computing without SQL. In 4th Biennial Conference on Innovative Data Systems Research, 2009.

Cited By

View all

Index Terms

  1. A framework for self-managing database support and parallel computing for assistive systems

      Recommendations

      Comments

      Information & Contributors

      Information

      Published In

      cover image ACM Other conferences
      PETRA '15: Proceedings of the 8th ACM International Conference on PErvasive Technologies Related to Assistive Environments
      July 2015
      526 pages
      ISBN:9781450334525
      DOI:10.1145/2769493
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Sponsors

      • NSF: National Science Foundation
      • University of Texas at Austin: University of Texas at Austin
      • Univ. of Piraeus: University of Piraeus
      • NCRS: Demokritos National Center for Scientific Research
      • Ionian: Ionian University, GREECE

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 01 July 2015

      Permissions

      Request permissions for this article.

      Check for updates

      Author Tags

      1. MapReduce
      2. R
      3. assistive systems
      4. big data
      5. database
      6. machine learning

      Qualifiers

      • Short-paper

      Funding Sources

      Conference

      PETRA '15
      Sponsor:
      • NSF
      • University of Texas at Austin
      • Univ. of Piraeus
      • NCRS
      • Ionian

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • Downloads (Last 12 months)0
      • Downloads (Last 6 weeks)0
      Reflects downloads up to 08 Feb 2025

      Other Metrics

      Citations

      Cited By

      View all

      View Options

      Login options

      View options

      PDF

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      Figures

      Tables

      Media

      Share

      Share

      Share this Publication link

      Share on social media