[ecoop-info] 3rd CFP: 5th Workshop on Big Data Benchmarking (WBDB 2014) - Extended Submission Deadline
rami.akkad at sap.com
Mon May 19 09:28:04 CEST 2014
CALL FOR PAPERS
Fifth Workshop on Big Data Benchmarking (WBDB 2014)
August 5-6, 2014 - HPI, Potsdam, Germany
Sponsored by SAP, Intel, Mellanox, Pivotal, US National Science Foundation
**** Extended Submission Deadline - May 30, 2014 ****
The objective of the WBDB workshops is to make progress towards
development of industry standard application-level benchmarks for
evaluating hardware and software systems for big data applications.
Discussions from previous WBDB workshops have resulted in the formation
of the TPC-BigData subcommittee, which is currently working on the
TPCx-HS benchmark, for Hadoop Systems. There have also been
presentations on a variety of proposed benchmarks including, BigBench,
HiBench, BigDataBenchmark, BigDecision, and the Deep Analytics Pipeline.
To continue making progress towards big data benchmarking standards, the
workshop will explore a range of issues including:
- Data features: New feature sets of data including, high-dimensional
data, sparse data, event-based data, and enormous data sizes.
- System characteristics: System-level issues including, large-scale and
evolving system configurations, shifting loads, and heterogeneous
technologies for big data and cloud platforms.
- Implementation options: Different implementation options such as SQL,
NoSQL, Hadoop software ecosystem, and different implementations of HDFS.
- Workloads: Representative big data business problems and corresponding
benchmark implementations. Specification of benchmark applications that
represent the different modalities of big data, including graphs,
streams, scientific data, and document collections.
- Hardware options: Evaluation of new options in hardware including
different types of HDD, SSD, and main memory, and large-memory systems,
and new platform options that include dedicated commodity clusters and
- Synthetic data generation: Models and procedures for generating
large-scale synthetic data with requisite properties.
- Benchmark execution rules: E.g. data scale factors, benchmark
versioning to account for rapidly evolving workloads and system
configurations, benchmark metrics.
- Metrics for efficiency: Measuring the efficiency of the solution, e.g.
based on costs of acquisition, ownership, energy and/or other factors,
while encouraging innovation and avoiding benchmark escalations that
favor large inefficient configuration over small efficient configurations.
- Evaluation frameworks: Tool chains, suites and frameworks for
evaluating big data systems.
- Early implementations: E.g. of, say, BigBench, HiBench,
BigDataBenchmark, BigDecision, and the Deep Analytics Pipeline, and
lessons learned in benchmarking big data applications.
- Submission Date: May 30
- Notification Date: June 20
- Camera-ready: August 30
Chaitan Baru, San Diego Supercomputer Center (SDSC) UC San Diego
Tilmann Rabl, Middleware Services Research Group, University of Toronto
Kai Sachs, SAP AG
Matthias Uflacker, Hasso-Plattner-Institut
Henning Schmitz, SAP Innovation Center
Meikel Poess, Oracle
Milind Bhandarkar, Pivotal
Anja Bog, SAP Labs
Dhruba Borthakur, Facebook
Joos-Hendrik Böse, Amazon
Tobias Bürger, Payback
Tyson Condie, UCLA
Kshitij Doshi, Intel
Pedro Furtado, University of Coimbra
Bhaskar Gowda, Intel
Goetz Graefe, HP
Martin Grund, Exascale
Alfons Kemper, TU München
Donald Kossmann, ETH Zürich
Tim Kraska, Brown University
Wolfgang Lehner, TU Dresden
Christof Leng, UC Berkeley
Raghu Nambiar, Cisco
Manoj Karunakaran Nambiar, Tata Consulting Services
Glenn Paulley, Conestoga College
Scott Pearson, CLDS Industry Fellow
Andreas Polze, Hasso-Plattner-Institut
Alexander Reinefeld, Zuse Institute Berlin / HU Berlin
Berni Schiefer, IBM Labs Toronto
Saptak Sen, Hortonworks
Florian Stegmaier, University of Passau
Till Westmann, Oracle Labs
Jianfeng Zhan, Chinese Academy of Science
Contact Info: For questions please contact Chaitan Baru, baru at sdsc.edu<mailto:baru at sdsc.edu>
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