�ݺ�ߣshows by User: asudeh

�ݺ�ߣshows by User: asudeh / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: asudeh / Fri, 02 Jun 2017 03:08:17 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: asudeh Efficient Computation of�Regret-ratio Minimizing Set:�A Compact Maxima Representative /slideshow/efficient-computation-ofregretratio-minimizing-seta-compact-maxima-representative/76579851 rrms-sigmod-170602030817
The slides used for SIGMOD 2017 presentation of the paper]]>
The slides used for SIGMOD 2017 presentation of the paper]]> Fri, 02 Jun 2017 03:08:17 GMT /slideshow/efficient-computation-ofregretratio-minimizing-seta-compact-maxima-representative/76579851 asudeh@slideshare.net(asudeh) Efficient Computation of�Regret-ratio Minimizing Set:�A Compact Maxima Representative asudeh The slides used for SIGMOD 2017 presentation of the paper <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/rrms-sigmod-170602030817-thumbnail.jpg?width=120&height=120&fit=bounds" /> The slides used for SIGMOD 2017 presentation of the paper

Efficient Computation of Regret-ratio Minimizing Set: A Compact Maxima Representative from Abolfazl Asudeh

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0 Query Reranking As A Service /slideshow/query-reranking-as-a-service/65988153 queryreranking-slides-160913184829
In Proceedings of the VLDB Endowment (PVLDB) Vol. 9 No. 11 www.vldb.org/pvldb/vol9/p888-asudeh.pdf -- The ranked retrieval model has rapidly become the de facto way for search query processing in client-server databases, especially those on the web. Despite of the extensive efforts in the database community on designing better ranking functions/mechanisms, many such databases in practice still fail to address the diverse and sometimes contradicting preferences of users on tuple ranking, perhaps (at least partially) due to the lack of expertise and/or motivation for the database owner to design truly effective ranking functions. This paper takes a different route on addressing the issue by defining a novel {\em query reranking problem}, i.e., we aim to design a third-party service that uses nothing but the public search interface of a client-server database to enable the on-the-fly processing of queries with any user-specified ranking functions (with or without selection conditions), no matter if the ranking function is supported by the database or not. We analyze the worst-case complexity of the problem and introduce a number of ideas, e.g., on-the-fly indexing, domination detection and virtual tuple pruning, to reduce the average-case cost of the query reranking algorithm. We also present extensive experimental results on real-world datasets, in both offline and live online systems, that demonstrate the effectiveness of our proposed techniques.]]>
In Proceedings of the VLDB Endowment (PVLDB) Vol. 9 No. 11 www.vldb.org/pvldb/vol9/p888-asudeh.pdf -- The ranked retrieval model has rapidly become the de facto way for search query processing in client-server databases, especially those on the web. Despite of the extensive efforts in the database community on designing better ranking functions/mechanisms, many such databases in practice still fail to address the diverse and sometimes contradicting preferences of users on tuple ranking, perhaps (at least partially) due to the lack of expertise and/or motivation for the database owner to design truly effective ranking functions. This paper takes a different route on addressing the issue by defining a novel {\em query reranking problem}, i.e., we aim to design a third-party service that uses nothing but the public search interface of a client-server database to enable the on-the-fly processing of queries with any user-specified ranking functions (with or without selection conditions), no matter if the ranking function is supported by the database or not. We analyze the worst-case complexity of the problem and introduce a number of ideas, e.g., on-the-fly indexing, domination detection and virtual tuple pruning, to reduce the average-case cost of the query reranking algorithm. We also present extensive experimental results on real-world datasets, in both offline and live online systems, that demonstrate the effectiveness of our proposed techniques.]]> Tue, 13 Sep 2016 18:48:29 GMT /slideshow/query-reranking-as-a-service/65988153 asudeh@slideshare.net(asudeh) Query Reranking As A Service asudeh In Proceedings of the VLDB Endowment (PVLDB) Vol. 9 No. 11 www.vldb.org/pvldb/vol9/p888-asudeh.pdf -- The ranked retrieval model has rapidly become the de facto way for search query processing in client-server databases, especially those on the web. Despite of the extensive efforts in the database community on designing better ranking functions/mechanisms, many such databases in practice still fail to address the diverse and sometimes contradicting preferences of users on tuple ranking, perhaps (at least partially) due to the lack of expertise and/or motivation for the database owner to design truly effective ranking functions. This paper takes a different route on addressing the issue by defining a novel {\em query reranking problem}, i.e., we aim to design a third-party service that uses nothing but the public search interface of a client-server database to enable the on-the-fly processing of queries with any user-specified ranking functions (with or without selection conditions), no matter if the ranking function is supported by the database or not. We analyze the worst-case complexity of the problem and introduce a number of ideas, e.g., on-the-fly indexing, domination detection and virtual tuple pruning, to reduce the average-case cost of the query reranking algorithm. We also present extensive experimental results on real-world datasets, in both offline and live online systems, that demonstrate the effectiveness of our proposed techniques. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/queryreranking-slides-160913184829-thumbnail.jpg?width=120&height=120&fit=bounds" /> In Proceedings of the VLDB Endowment (PVLDB) Vol. 9 No. 11 www.vldb.org/pvldb/vol9/p888-asudeh.pdf -- The ranked retrieval model has rapidly become the de facto way for search query processing in client-server databases, especially those on the web. Despite of the extensive efforts in the database community on designing better ranking functions/mechanisms, many such databases in practice still fail to address the diverse and sometimes contradicting preferences of users on tuple ranking, perhaps (at least partially) due to the lack of expertise and/or motivation for the database owner to design truly effective ranking functions. This paper takes a different route on addressing the issue by defining a novel {\em query reranking problem}, i.e., we aim to design a third-party service that uses nothing but the public search interface of a client-server database to enable the on-the-fly processing of queries with any user-specified ranking functions (with or without selection conditions), no matter if the ranking function is supported by the database or not. We analyze the worst-case complexity of the problem and introduce a number of ideas, e.g., on-the-fly indexing, domination detection and virtual tuple pruning, to reduce the average-case cost of the query reranking algorithm. We also present extensive experimental results on real-world datasets, in both offline and live online systems, that demonstrate the effectiveness of our proposed techniques.

Query Reranking As A Service from Abolfazl Asudeh

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0 [�ݺ�ߣs] Crowdsourcing Pareto-Optimal Object Finding By Pairwise Comparisons /asudeh/slides-crowdsourcing-paretooptimal-object-finding-by-pairwise-comparisons crowdpareto-cikm15-li-slides-151026183557-lva1-app6891
Authors: Abolfazl Asudeh, Gensheng Zhang, Naeemul Hassan, Chengkai Li, Gergely V. Záruba Proceedings of the 24th {ACM} International on Conference on Information and Knowledge Management, {CIKM} 2015, Melbourne, VIC, Australia, October 19 - 23, 2015 http://doi.acm.org/10.1145/2806416.2806451]]>
Authors: Abolfazl Asudeh, Gensheng Zhang, Naeemul Hassan, Chengkai Li, Gergely V. Záruba Proceedings of the 24th {ACM} International on Conference on Information and Knowledge Management, {CIKM} 2015, Melbourne, VIC, Australia, October 19 - 23, 2015 http://doi.acm.org/10.1145/2806416.2806451]]> Mon, 26 Oct 2015 18:35:57 GMT /asudeh/slides-crowdsourcing-paretooptimal-object-finding-by-pairwise-comparisons asudeh@slideshare.net(asudeh) [�ݺ�ߣs] Crowdsourcing Pareto-Optimal Object Finding By Pairwise Comparisons asudeh Authors: Abolfazl Asudeh, Gensheng Zhang, Naeemul Hassan, Chengkai Li, Gergely V. Záruba Proceedings of the 24th {ACM} International on Conference on Information and Knowledge Management, {CIKM} 2015, Melbourne, VIC, Australia, October 19 - 23, 2015 http://doi.acm.org/10.1145/2806416.2806451 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/crowdpareto-cikm15-li-slides-151026183557-lva1-app6891-thumbnail.jpg?width=120&height=120&fit=bounds" /> Authors: Abolfazl Asudeh, Gensheng Zhang, Naeemul Hassan, Chengkai Li, Gergely V. Záruba Proceedings of the 24th {ACM} International on Conference on Information and Knowledge Management, {CIKM} 2015, Melbourne, VIC, Australia, October 19 - 23, 2015 http://doi.acm.org/10.1145/2806416.2806451

[�ݺ�ߣs] Crowdsourcing Pareto-Optimal Object Finding By Pairwise Comparisons from Abolfazl Asudeh

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0 MapReduce : Simplified Data Processing on Large Clusters /slideshow/mapreduce-simplified-data-processing-on-large-clusters-23929036/23929036 mapreduce-130704230506-phpapp01
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]]> Thu, 04 Jul 2013 23:05:06 GMT /slideshow/mapreduce-simplified-data-processing-on-large-clusters-23929036/23929036 asudeh@slideshare.net(asudeh) MapReduce : Simplified Data Processing on Large Clusters asudeh <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/mapreduce-130704230506-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" />

MapReduce : Simplified Data Processing on Large Clusters from Abolfazl Asudeh

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0 GBLENDER: Towards blending visual query formulation and query processing in graph databases /slideshow/9-gblender/18700746 9-gblender-130412174209-phpapp01
I created the slides for presenting the following paper in the class: http://dl.acm.org/citation.cfm?id=1807182]]>
I created the slides for presenting the following paper in the class: http://dl.acm.org/citation.cfm?id=1807182]]> Fri, 12 Apr 2013 17:42:09 GMT /slideshow/9-gblender/18700746 asudeh@slideshare.net(asudeh) GBLENDER: Towards blending visual query formulation and query processing in graph databases asudeh I created the slides for presenting the following paper in the class: http://dl.acm.org/citation.cfm?id=1807182 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/9-gblender-130412174209-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> I created the slides for presenting the following paper in the class: http://dl.acm.org/citation.cfm?id=1807182

GBLENDER: Towards blending visual query formulation and query processing in graph databases from Abolfazl Asudeh

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0 Using incompletely cooperative game theory in wireless sensor networks /slideshow/using-incompletely-cooperative-game-theory-in-wireless-sensor-networks-18647020/18647020 usingincompletelycooperativegametheoryinwirelesssensornetworks-130411220121-phpapp01
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]]> Thu, 11 Apr 2013 22:01:21 GMT /slideshow/using-incompletely-cooperative-game-theory-in-wireless-sensor-networks-18647020/18647020 asudeh@slideshare.net(asudeh) Using incompletely cooperative game theory in wireless sensor networks asudeh <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/usingincompletelycooperativegametheoryinwirelesssensornetworks-130411220121-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" />

Using incompletely cooperative game theory in wireless sensor networks from Abolfazl Asudeh

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0 PREGEL a system for large scale graph processing /asudeh/2pregel-a-system-for-large-scale-graph-processing 2-pregelasystemforlarge-scalegraphprocessing-130411124901-phpapp01
Pregel is a framework for processing Large Scale Graphs efficiently. The slides are created for presenting the following paper in the class: http://dl.acm.org/citation.cfm?id=1807184 ]]>
Pregel is a framework for processing Large Scale Graphs efficiently. The slides are created for presenting the following paper in the class: http://dl.acm.org/citation.cfm?id=1807184 ]]> Thu, 11 Apr 2013 12:49:01 GMT /asudeh/2pregel-a-system-for-large-scale-graph-processing asudeh@slideshare.net(asudeh) PREGEL a system for large scale graph processing asudeh Pregel is a framework for processing Large Scale Graphs efficiently. The slides are created for presenting the following paper in the class: http://dl.acm.org/citation.cfm?id=1807184 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/2-pregelasystemforlarge-scalegraphprocessing-130411124901-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Pregel is a framework for processing Large Scale Graphs efficiently. The slides are created for presenting the following paper in the class: http://dl.acm.org/citation.cfm?id=1807184

PREGEL a system for large scale graph processing from Abolfazl Asudeh

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0 https://cdn.slidesharecdn.com/profile-photo-asudeh-48x48.jpg?cb=1718825352 asudeh.github.io https://cdn.slidesharecdn.com/ss_thumbnails/rrms-sigmod-170602030817-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/efficient-computation-ofregretratio-minimizing-seta-compact-maxima-representative/76579851 Efficient Computation ... https://cdn.slidesharecdn.com/ss_thumbnails/queryreranking-slides-160913184829-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/query-reranking-as-a-service/65988153 Query Reranking As A S... https://cdn.slidesharecdn.com/ss_thumbnails/crowdpareto-cikm15-li-slides-151026183557-lva1-app6891-thumbnail.jpg?width=320&height=320&fit=bounds asudeh/slides-crowdsourcing-paretooptimal-object-finding-by-pairwise-comparisons [�ݺ�ߣs] Crowdsourcing...