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Towards scalable RDF graph analytics on MapReduce

Authors:

Padmashree Ravindra,

Vikas V. Deshpande,

Kemafor AnyanwuAuthors Info & Claims

MDAC '10: Proceedings of the 2010 Workshop on Massive Data Analytics on the Cloud

Article No.: 5, Pages 1 - 6

https://doi.org/10.1145/1779599.1779604

Published: 26 April 2010 Publication History

Abstract

In order to exploit the growing amount of RDF data in decision-making, there is an increasing demand for analytics-style processing of such data. RDF data is modeled as a labeled graph that represents a collection of binary relations (triples). In this context, analytical queries can be interpreted as consisting of three main constructs namely pattern matching, grouping and aggregation, and require several join operations to reassemble them into n-ary relations relevant to the given query, unlike traditional OLAP systems where data is suitably organized. MapReduce-based parallel processing systems like Pig have gained success in processing scalable analytical workloads. However, these systems offer only relational algebra style operators which would require an iterative n-tuple reassembly process in which intermediate results need to be materialized. This leads to high I/O costs that negatively impacts performance. In this paper, we propose UDFs that (i) re-factor analytical processing on RDF graphs in a way that enables more parallelized processing (ii) perform a look-ahead processing to reduce the cost of subsequent operators in the query execution plan. These functions have been integrated into the Pig Latin function library and the experimental results show up to 50% improvement in execution times for certain classes of queries. An important impact of this work is that it could serve as the foundation for additional physical operators in systems such as Pig for more efficient graph processing.

References

[1]

Weiss, C., Karras, P., Bernstein, A.: Hexastore: Sextuple Indexing for Semantic Web Data Management. VLDB 2008

Digital Library

[2]

Dean, J., Ghemawat, S.: MapReduce: Simplified Data Processing on Large Clusters. In: Proc. Of OSDI 2004

Digital Library

[3]

Olston, C., Reed, B., Srivastava, U., Kumar, R., Tomkins, A.: Pig Latin: a not-so-foreign language for data processing. ACM SIGMOD 2008

Digital Library

[4]

Abadi, D. J., Marcus, A., Madden, S. R., Hollenbach, K.: Scalable Semantic Web Data Management Using Vertical Partitioning. VLDB 2007

Digital Library

[5]

Yang, H., Dasdan, A., Hsiao, R., Parker Jr., D. S.: Map-reducemerge: simplified relational data processing on large clusters. SIGMOD 2007

Digital Library

[6]

Chaiken, R., Jenkins, B., Larson, P., Ramsey, B., Shakib, D., Weaver, S., Zhou, J.: SCOPE: easy and efficient parallel processing of massive data sets. PVLDB 2008

Digital Library

[7]

Yu, Y., Isard, M., Fetterly, D., Badiu, M., Erlingsson, U., Gunda, P. K., and Currey, J.: DryadLINQ: A system for general-purpose distributed data-parallel computing using a high-level language. OSDI 2008

Digital Library

[8]

Pike, R., Dorward, S., Griesemer, R., Quinlan, S.: Interpreting the data: Parallel analysis with Sawzall. Scientific Programming 2005

Digital Library

[9]

Abouzeid, A., Bajda-Pawlikowski, K., Abadi, D., Silberschatz, A.: HadoopDB: An Architectural Hybrid of MapReduce and DBMS Technologies for Analytical Workloads. VLDB 2009

Digital Library

[10]

Afrati, Foto N. and Ullman, Jeffrey D.: Optimizing Joins in a Map-Reduce Environment. EDBT 2010

Digital Library

[11]

Neumann, T., Weikum, G.: Scalable join processing on very large RDF graphs. SIGMOD 2009

Digital Library

[12]

Urbani, J., Kotoulas, S., Oren, E., van Harmelen, F.: Scalable Distributed Reasoning using MapReduce, ISWC 2009

Digital Library

[13]

Newman, A., Li, Y-F., and Hunter, J.: Scalable Semantics - the Silver Lining of Cloud Computing, 4th IEEE International Conference on e-Science, 2008

Digital Library

[14]

Newman, A., Hunter, J., Li, Y-F., Bouton, C., Davis, M.: A Scale-Out RDF Molecule Store for Distributed Processing of Biomedical Data. HCLS'08 at WWW 2008

[15]

Sridhar, R., Ravindra, P., Anyanwu, K.: RAPID: Enabling scalable ad-hoc analytics on the semantic web. ISWC 2009

Digital Library

[16]

Pavlo, A., Paulson, E., Rasin, A., Abadi, D. J., DeWitt, D. J., Madden, S., Stonebraker M.: A Comparison of Approaches to Large-Scale Data Analysis. SIGMOD 2009

Digital Library

[17]

Prud'hommeaux, E., Seaborne, A.: SPARQL query language for RDF. Technical report, World Wide Web Consortium (2005) http://www.w3.org/TR/rdf-sparql-query

[18]

Apache Projects Proceedings, http://hadoop.apache.org/core/

[19]

VCL Setup at NC State University, https://vcl.ncsu.edu/

[20]

JAQL, http://code.google.com/p/jaql

Cited By

Papadaki MTzitzikas Y(2024)Unifying Faceted Search and Analytics over RDF Knowledge GraphsKnowledge and Information Systems10.1007/s10115-024-02076-966:7(3921-3958)Online publication date: 24-Mar-2024
https://doi.org/10.1007/s10115-024-02076-9
Papadaki MTzitzikas YMountantonakis M(2023)A Brief Survey of Methods for Analytics over RDF Knowledge GraphsAnalytics10.3390/analytics20100042:1(55-74)Online publication date: 17-Jan-2023
https://doi.org/10.3390/analytics2010004
Freire NProença D(2020)RDF Reasoning on Large Ontologies: A Study on Cultural Heritage and WikidataArtificial Intelligence Applications and Innovations10.1007/978-3-030-49161-1_32(381-393)Online publication date: 29-May-2020
https://doi.org/10.1007/978-3-030-49161-1_32
Show More Cited By

Index Terms

Towards scalable RDF graph analytics on MapReduce
1. Information systems
  1. Data management systems
    1. Query languages
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Database theory
      1. Database query languages (principles)

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cover image ACM Other conferences

MDAC '10: Proceedings of the 2010 Workshop on Massive Data Analytics on the Cloud

April 2010

53 pages

ISBN:9781605589916

DOI:10.1145/1779599

Conference Chairs:
Ullas Nambiar
IBM India Research Lab, New Delhi, India
,
John McPherson
IBM Almaden Research Center
,
David Konopnicki
IBM Haifa Research Lab, Israel

Copyright © 2010 ACM.

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]

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Association for Computing Machinery

New York, NY, United States

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Published: 26 April 2010

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MDAC '10: WWW 2010 Workshop on Massive Data Analytics over the Cloud

April 26, 2010

North Carolina, Raleigh, USA

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Cited By

Papadaki MTzitzikas Y(2024)Unifying Faceted Search and Analytics over RDF Knowledge GraphsKnowledge and Information Systems10.1007/s10115-024-02076-966:7(3921-3958)Online publication date: 24-Mar-2024
https://doi.org/10.1007/s10115-024-02076-9
Papadaki MTzitzikas YMountantonakis M(2023)A Brief Survey of Methods for Analytics over RDF Knowledge GraphsAnalytics10.3390/analytics20100042:1(55-74)Online publication date: 17-Jan-2023
https://doi.org/10.3390/analytics2010004
Freire NProença D(2020)RDF Reasoning on Large Ontologies: A Study on Cultural Heritage and WikidataArtificial Intelligence Applications and Innovations10.1007/978-3-030-49161-1_32(381-393)Online publication date: 29-May-2020
https://doi.org/10.1007/978-3-030-49161-1_32
Sakr SWylot MMutharaju RLe Phuoc DFundulaki ISakr SWylot MMutharaju RLe Phuoc DFundulaki I(2018)Distributed RDF Query ProcessingLinked Data10.1007/978-3-319-73515-3_4(51-83)Online publication date: 2-Mar-2018
https://doi.org/10.1007/978-3-319-73515-3_4
Hauwirth MWylot MGrund MSakr SCudré-Mauroux P(2017)Non-native RDF Storage EnginesHandbook of Big Data Technologies10.1007/978-3-319-49340-4_10(339-364)Online publication date: 26-Feb-2017
https://doi.org/10.1007/978-3-319-49340-4_10
Hashi YMatsumoto KSeki YHiji MAbe TSuganuma T(2015)Design and Implementation of Data Management Scheme to Enable Efficient Analysis of Sensing DataProceedings of the 2015 IEEE International Conference on Autonomic Computing10.1109/ICAC.2015.58(319-324)Online publication date: 7-Jul-2015
https://dl.acm.org/doi/10.1109/ICAC.2015.58
Hashi YMatsumoto KSeki YHiji MAbe TSuganuma T(2015)Data Management Scheme to Enable Efficient Analysis of Sensing Data for Smart CommunityProceedings of the 2015 IEEE 39th Annual Computer Software and Applications Conference - Volume 0310.1109/COMPSAC.2015.233(182-187)Online publication date: 1-Jul-2015
https://dl.acm.org/doi/10.1109/COMPSAC.2015.233
Kritikos KRousakis YKotzinos D(2015)A Cloud-Based, Geospatial Linked Data Management SystemTransactions on Large-Scale Data- and Knowledge-Centered Systems XX10.1007/978-3-662-46703-9_3(59-89)Online publication date: 18-Mar-2015
https://doi.org/10.1007/978-3-662-46703-9_3
Benhamed SNait-Bahloul S(2014)Classification of knowledge processing by MapReduce2014 4th International Symposium ISKO-Maghreb: Concepts and Tools for knowledge Management (ISKO-Maghreb)10.1109/ISKO-Maghreb.2014.7033463(1-8)Online publication date: Nov-2014
https://doi.org/10.1109/ISKO-Maghreb.2014.7033463
Hayakawa ANishiyama H(2013)Efficient Query Processing of Semantic Data Using Graph Contraction on RDBMSProceedings of the 2013 International Conference on Signal-Image Technology & Internet-Based Systems10.1109/SITIS.2013.155(958-965)Online publication date: 2-Dec-2013
https://dl.acm.org/doi/10.1109/SITIS.2013.155
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