IBM streams processing language: analyzing big data in motion
dc.citation.epage | 7:11 | en_US |
dc.citation.issueNumber | 3-4 | en_US |
dc.citation.spage | 7:1 | en_US |
dc.citation.volumeNumber | 57 | en_US |
dc.contributor.author | Hirzel M. | en_US |
dc.contributor.author | Andrade, H. | en_US |
dc.contributor.author | Gedik, B. | en_US |
dc.contributor.author | Jacques-Silva, R. | en_US |
dc.contributor.author | Khandekar, R. | en_US |
dc.contributor.author | Kumar, V. | en_US |
dc.contributor.author | Mendell, M. | en_US |
dc.contributor.author | Nasgaard, H. | en_US |
dc.contributor.author | Schneider S. | en_US |
dc.contributor.author | Soule´, R. | en_US |
dc.contributor.author | Wu, K. L. | en_US |
dc.date.accessioned | 2019-02-01T12:55:33Z | |
dc.date.available | 2019-02-01T12:55:33Z | |
dc.date.issued | 2013-05-17 | en_US |
dc.department | Department of Computer Engineering | en_US |
dc.description.abstract | The IBM Streams Processing Language (SPL) is the programming language for IBM InfoSphere® Streams, a platform for analyzing Big Data in motion. By “Big Data in motion,” we mean continuous data streams at high data-transfer rates. InfoSphere Streams processes such data with both high throughput and short response times. To meet these performance demands, it deploys each application on a cluster of commodity servers. SPL abstracts away the complexity of the distributed system, instead exposing a simple graph-of-operators view to the user. SPL has several innovations relative to prior streaming languages. For performance and code reuse, SPL provides a code-generation interface to C++ and Java®. To facilitate writing well-structured and concise applications, SPL provides higher-order composite operators that modularize stream sub-graphs. Finally, to enable static checking while exposing optimization opportunities, SPL provides a strong type system and user-defined operator models. This paper provides a language overview, describes the implementation including optimizations such as fusion, and explains the rationale behind the language design. | en_US |
dc.description.provenance | Submitted by Betül Özen (ozen@bilkent.edu.tr) on 2019-02-01T12:55:33Z No. of bitstreams: 1 IBM_Streams_Processing.pdf: 1306393 bytes, checksum: 5a9a53dee7e9bf10c07f520b05955cb7 (MD5) | en |
dc.description.provenance | Made available in DSpace on 2019-02-01T12:55:33Z (GMT). No. of bitstreams: 1 IBM_Streams_Processing.pdf: 1306393 bytes, checksum: 5a9a53dee7e9bf10c07f520b05955cb7 (MD5) Previous issue date: 2013-05-17 | en |
dc.identifier.doi | 10.1147/JRD.2013.2243535 | en_US |
dc.identifier.eissn | 0018-8646 | en_US |
dc.identifier.issn | 0018-8646 | en_US |
dc.identifier.uri | http://hdl.handle.net/11693/48728 | en_US |
dc.language.iso | English | en_US |
dc.publisher | I B M Corp. | en_US |
dc.relation.isversionof | http://doi.org/10.1147/JRD.2013.2243535 | en_US |
dc.source.title | IBM Journal of Research and Development | en_US |
dc.subject | Data handling | en_US |
dc.subject | Information management | en_US |
dc.subject | Optimization | en_US |
dc.subject | Batch production systems | en_US |
dc.subject | Generators | en_US |
dc.subject | Syntactics | en_US |
dc.subject | Java | en_US |
dc.subject | Data processing | en_US |
dc.subject | Information processing | en_US |
dc.title | IBM streams processing language: analyzing big data in motion | en_US |
dc.type | Article | en_US |
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