Convergence of Recognition, Mining, and Synthesis Workloads and Its Implications
This paper examines the growing need for a general-purpose ldquoanalytics enginerdquo that can enable next-generation processing platforms to effectively model events, objects, and concepts based on end-user input, and accessible datasets, along with an ability to iteratively refine the model in rea...
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| Veröffentlicht in: | Proceedings of the IEEE 2008-05, Vol.96 (5), p.790-807 |
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| container_title | Proceedings of the IEEE |
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| creator | Chen, Yen-Kuang Chhugani, Jatin Dubey, Pradeep Hughes, Christopher J. Kim, Daehyun Kumar, Sanjeev Lee, Victor W. Nguyen, Anthony D. Smelyanskiy, Mikhail |
| description | This paper examines the growing need for a general-purpose ldquoanalytics enginerdquo that can enable next-generation processing platforms to effectively model events, objects, and concepts based on end-user input, and accessible datasets, along with an ability to iteratively refine the model in real-time. We find such processing needs at the heart of many emerging applications and services. This processing is further decomposed in terms of an integration of three fundamental compute capabilities-recognition, mining, and synthesis (RMS). The set of RMS workloads is examined next in terms of usage, mathematical models, numerical algorithms, and underlying data structures. Our analysis suggests a workload convergence that is analyzed next for its platform implications. In summary, a diverse set of emerging RMS applications from market segments like graphics, gaming, media-mining, unstructured information management, financial analytics, and interactive virtual communities presents a relatively focused, highly overlapping set of common platform challenges. A general-purpose processing platform designed to address these challenges has the potential for significantly enhancing users' experience and programmer productivity. |
| doi_str_mv | 10.1109/JPROC.2008.917729 |
| format | Article |
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We find such processing needs at the heart of many emerging applications and services. This processing is further decomposed in terms of an integration of three fundamental compute capabilities-recognition, mining, and synthesis (RMS). The set of RMS workloads is examined next in terms of usage, mathematical models, numerical algorithms, and underlying data structures. Our analysis suggests a workload convergence that is analyzed next for its platform implications. In summary, a diverse set of emerging RMS applications from market segments like graphics, gaming, media-mining, unstructured information management, financial analytics, and interactive virtual communities presents a relatively focused, highly overlapping set of common platform challenges. A general-purpose processing platform designed to address these challenges has the potential for significantly enhancing users' experience and programmer productivity.</description><identifier>ISSN: 0018-9219</identifier><identifier>EISSN: 1558-2256</identifier><identifier>DOI: 10.1109/JPROC.2008.917729</identifier><identifier>CODEN: IEEPAD</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Algorithms ; Convergence ; Data structures ; emerging applications ; Graphics ; Heart ; Information analysis ; Information management ; Markets ; Mathematical model ; Mathematical models ; Mining ; parallel architectures ; Platforms ; Process design ; Productivity ; Programming profession ; Studies ; Synthesis ; Workload</subject><ispartof>Proceedings of the IEEE, 2008-05, Vol.96 (5), p.790-807</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2008</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c421t-f28c41c07e02b058ec2814f0d73aebcc92919df2e16c3acde8bd41fc11ed4e563</citedby><cites>FETCH-LOGICAL-c421t-f28c41c07e02b058ec2814f0d73aebcc92919df2e16c3acde8bd41fc11ed4e563</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/4479863$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27901,27902,54733</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/4479863$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Chen, Yen-Kuang</creatorcontrib><creatorcontrib>Chhugani, Jatin</creatorcontrib><creatorcontrib>Dubey, Pradeep</creatorcontrib><creatorcontrib>Hughes, Christopher J.</creatorcontrib><creatorcontrib>Kim, Daehyun</creatorcontrib><creatorcontrib>Kumar, Sanjeev</creatorcontrib><creatorcontrib>Lee, Victor W.</creatorcontrib><creatorcontrib>Nguyen, Anthony D.</creatorcontrib><creatorcontrib>Smelyanskiy, Mikhail</creatorcontrib><title>Convergence of Recognition, Mining, and Synthesis Workloads and Its Implications</title><title>Proceedings of the IEEE</title><addtitle>JPROC</addtitle><description>This paper examines the growing need for a general-purpose ldquoanalytics enginerdquo that can enable next-generation processing platforms to effectively model events, objects, and concepts based on end-user input, and accessible datasets, along with an ability to iteratively refine the model in real-time. We find such processing needs at the heart of many emerging applications and services. This processing is further decomposed in terms of an integration of three fundamental compute capabilities-recognition, mining, and synthesis (RMS). The set of RMS workloads is examined next in terms of usage, mathematical models, numerical algorithms, and underlying data structures. Our analysis suggests a workload convergence that is analyzed next for its platform implications. In summary, a diverse set of emerging RMS applications from market segments like graphics, gaming, media-mining, unstructured information management, financial analytics, and interactive virtual communities presents a relatively focused, highly overlapping set of common platform challenges. A general-purpose processing platform designed to address these challenges has the potential for significantly enhancing users' experience and programmer productivity.</description><subject>Algorithms</subject><subject>Convergence</subject><subject>Data structures</subject><subject>emerging applications</subject><subject>Graphics</subject><subject>Heart</subject><subject>Information analysis</subject><subject>Information management</subject><subject>Markets</subject><subject>Mathematical model</subject><subject>Mathematical models</subject><subject>Mining</subject><subject>parallel architectures</subject><subject>Platforms</subject><subject>Process design</subject><subject>Productivity</subject><subject>Programming profession</subject><subject>Studies</subject><subject>Synthesis</subject><subject>Workload</subject><issn>0018-9219</issn><issn>1558-2256</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2008</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNqF0cFv0zAUBnALgUQZ_AGIS8SB7bCU954dxz6iaoyioU0DxDFKnZfikdolTpH2389dEQcO28mS9fs-Wf6EeI0wRwT7_vPV9eViTgBmbrGuyT4RM6wqUxJV-qmYAaApLaF9Ll6kdAMAstJyJq4WMfzhcc3BcRH74ppdXAc_-RhOiy8--LA-LdrQFV9vw_STk0_Fjzj-GmLbpfv75ZSK5WY7eNfuQ-mleNa3Q-JXf88j8f3j2bfFp_Li8ny5-HBROkU4lT0Zp9BBzUArqAw7Mqh66GrZ8so5SxZt1xOjdrJ1HZtVp7B3iNwpzk8_EseH3u0Yf-84Tc3GJ8fD0AaOu9RYkFqClJjluwelVAo0yjrDkwch6hqlJEX6cQpEFipJkOnb_-hN3I0h_01jNBkyWsuM8IDcGFMauW-2o9-0421uavYDN_cDN_uBm8PAOfPmkPHM_M8rVVuTG-8A_B2gWA</recordid><startdate>20080501</startdate><enddate>20080501</enddate><creator>Chen, Yen-Kuang</creator><creator>Chhugani, Jatin</creator><creator>Dubey, Pradeep</creator><creator>Hughes, Christopher J.</creator><creator>Kim, Daehyun</creator><creator>Kumar, Sanjeev</creator><creator>Lee, Victor W.</creator><creator>Nguyen, Anthony D.</creator><creator>Smelyanskiy, Mikhail</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>8FD</scope><scope>L7M</scope><scope>F28</scope><scope>FR3</scope></search><sort><creationdate>20080501</creationdate><title>Convergence of Recognition, Mining, and Synthesis Workloads and Its Implications</title><author>Chen, Yen-Kuang ; Chhugani, Jatin ; Dubey, Pradeep ; Hughes, Christopher J. ; Kim, Daehyun ; Kumar, Sanjeev ; Lee, Victor W. ; Nguyen, Anthony D. ; Smelyanskiy, Mikhail</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c421t-f28c41c07e02b058ec2814f0d73aebcc92919df2e16c3acde8bd41fc11ed4e563</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2008</creationdate><topic>Algorithms</topic><topic>Convergence</topic><topic>Data structures</topic><topic>emerging applications</topic><topic>Graphics</topic><topic>Heart</topic><topic>Information analysis</topic><topic>Information management</topic><topic>Markets</topic><topic>Mathematical model</topic><topic>Mathematical models</topic><topic>Mining</topic><topic>parallel architectures</topic><topic>Platforms</topic><topic>Process design</topic><topic>Productivity</topic><topic>Programming profession</topic><topic>Studies</topic><topic>Synthesis</topic><topic>Workload</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chen, Yen-Kuang</creatorcontrib><creatorcontrib>Chhugani, Jatin</creatorcontrib><creatorcontrib>Dubey, Pradeep</creatorcontrib><creatorcontrib>Hughes, Christopher J.</creatorcontrib><creatorcontrib>Kim, Daehyun</creatorcontrib><creatorcontrib>Kumar, Sanjeev</creatorcontrib><creatorcontrib>Lee, Victor W.</creatorcontrib><creatorcontrib>Nguyen, Anthony D.</creatorcontrib><creatorcontrib>Smelyanskiy, Mikhail</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><jtitle>Proceedings of the IEEE</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Chen, Yen-Kuang</au><au>Chhugani, Jatin</au><au>Dubey, Pradeep</au><au>Hughes, Christopher J.</au><au>Kim, Daehyun</au><au>Kumar, Sanjeev</au><au>Lee, Victor W.</au><au>Nguyen, Anthony D.</au><au>Smelyanskiy, Mikhail</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Convergence of Recognition, Mining, and Synthesis Workloads and Its Implications</atitle><jtitle>Proceedings of the IEEE</jtitle><stitle>JPROC</stitle><date>2008-05-01</date><risdate>2008</risdate><volume>96</volume><issue>5</issue><spage>790</spage><epage>807</epage><pages>790-807</pages><issn>0018-9219</issn><eissn>1558-2256</eissn><coden>IEEPAD</coden><abstract>This paper examines the growing need for a general-purpose ldquoanalytics enginerdquo that can enable next-generation processing platforms to effectively model events, objects, and concepts based on end-user input, and accessible datasets, along with an ability to iteratively refine the model in real-time. We find such processing needs at the heart of many emerging applications and services. This processing is further decomposed in terms of an integration of three fundamental compute capabilities-recognition, mining, and synthesis (RMS). The set of RMS workloads is examined next in terms of usage, mathematical models, numerical algorithms, and underlying data structures. Our analysis suggests a workload convergence that is analyzed next for its platform implications. In summary, a diverse set of emerging RMS applications from market segments like graphics, gaming, media-mining, unstructured information management, financial analytics, and interactive virtual communities presents a relatively focused, highly overlapping set of common platform challenges. A general-purpose processing platform designed to address these challenges has the potential for significantly enhancing users' experience and programmer productivity.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/JPROC.2008.917729</doi><tpages>18</tpages></addata></record> |
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| source | IEEE Electronic Library (IEL) |
| subjects | Algorithms Convergence Data structures emerging applications Graphics Heart Information analysis Information management Markets Mathematical model Mathematical models Mining parallel architectures Platforms Process design Productivity Programming profession Studies Synthesis Workload |
| title | Convergence of Recognition, Mining, and Synthesis Workloads and Its Implications |
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