ºÝºÝߣshows by User: eXascaleInfolab / http://www.slideshare.net/images/logo.gif ºÝºÝߣshows by User: eXascaleInfolab / Wed, 22 Apr 2020 05:59:00 GMT ºÝºÝߣShare feed for ºÝºÝߣshows by User: eXascaleInfolab Beyond Triplets: Hyper-Relational Knowledge Graph Embedding for Link Prediction /slideshow/beyond-triplets-hyperrelational-knowledge-graph-embedding-for-link-prediction/232404290 rossopresentation-200422055900
WWW 2020 presentation Paolo Rosso, eXascale Infolab]]>
WWW 2020 presentation Paolo Rosso, eXascale Infolab]]> Wed, 22 Apr 2020 05:59:00 GMT /slideshow/beyond-triplets-hyperrelational-knowledge-graph-embedding-for-link-prediction/232404290 eXascaleInfolab@slideshare.net(eXascaleInfolab) Beyond Triplets: Hyper-Relational Knowledge Graph Embedding for Link Prediction eXascaleInfolab WWW 2020 presentation Paolo Rosso, eXascale Infolab <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/rossopresentation-200422055900-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> WWW 2020 presentation Paolo Rosso, eXascale Infolab
Beyond Triplets: Hyper-Relational Knowledge Graph Embedding for Link Prediction from eXascale Infolab
]]> 297 0 https://cdn.slidesharecdn.com/ss_thumbnails/rossopresentation-200422055900-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 It Takes Two: Instrumenting the Interaction between In-Memory Databases and Solid-State Drives CIDR 2020 presentation /slideshow/it-takes-two-instrumenting-the-interaction-between-inmemory-databases-and-solidstate-drives-cidr-2020-presentation/227529104 ittakestwo-cidrpresentation-200210135236
It Takes Two: Instrumenting the Interaction between In-Memory Databases and Solid-State Drives CIDR 2020 presentation]]>
It Takes Two: Instrumenting the Interaction between In-Memory Databases and Solid-State Drives CIDR 2020 presentation]]> Mon, 10 Feb 2020 13:52:36 GMT /slideshow/it-takes-two-instrumenting-the-interaction-between-inmemory-databases-and-solidstate-drives-cidr-2020-presentation/227529104 eXascaleInfolab@slideshare.net(eXascaleInfolab) It Takes Two: Instrumenting the Interaction between In-Memory Databases and Solid-State Drives CIDR 2020 presentation eXascaleInfolab It Takes Two: Instrumenting the Interaction between In-Memory Databases and Solid-State Drives CIDR 2020 presentation <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ittakestwo-cidrpresentation-200210135236-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> It Takes Two: Instrumenting the Interaction between In-Memory Databases and Solid-State Drives CIDR 2020 presentation
It Takes Two: Instrumenting the Interaction between In-Memory Databases and Solid-State Drives CIDR 2020 presentation from eXascale Infolab
]]> 173 1 https://cdn.slidesharecdn.com/ss_thumbnails/ittakestwo-cidrpresentation-200210135236-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Representation Learning on Complex Graphs /slideshow/representation-learning-on-complex-graphs/161218021 invitedtalkdl4gpcm-190805132752
Representation Learning on Graphs with Complex Structures Invited talk, Deep Learning for Graphs and Structured Data Embedding Workshop WWW2019, San Francisco, May 13, 2019]]>
Representation Learning on Graphs with Complex Structures Invited talk, Deep Learning for Graphs and Structured Data Embedding Workshop WWW2019, San Francisco, May 13, 2019]]> Mon, 05 Aug 2019 13:27:52 GMT /slideshow/representation-learning-on-complex-graphs/161218021 eXascaleInfolab@slideshare.net(eXascaleInfolab) Representation Learning on Complex Graphs eXascaleInfolab Representation Learning on Graphs with Complex Structures Invited talk, Deep Learning for Graphs and Structured Data Embedding Workshop WWW2019, San Francisco, May 13, 2019 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/invitedtalkdl4gpcm-190805132752-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Representation Learning on Graphs with Complex Structures Invited talk, Deep Learning for Graphs and Structured Data Embedding Workshop WWW2019, San Francisco, May 13, 2019
Representation Learning on Complex Graphs from eXascale Infolab
]]> 561 3 https://cdn.slidesharecdn.com/ss_thumbnails/invitedtalkdl4gpcm-190805132752-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 A force directed approach for offline gps trajectory map /eXascaleInfolab/a-force-directed-approach-for-offline-gps-trajectory-map aforce-directedapproachforofflinegpstrajectorymap-final2-181203141215
SIGSPATIAL 2018 paper A Force-Directed Approach for Offline GPS Trajectory Map Matching Efstratios Rappos (University of Applied Sciences of Western Switzerland (HES-SO)), Stephan Robert (University of Applied Sciences of Western Switzerland (HES-SO)), Philippe Cudré-Mauroux (University of Fribourg)]]>
SIGSPATIAL 2018 paper A Force-Directed Approach for Offline GPS Trajectory Map Matching Efstratios Rappos (University of Applied Sciences of Western Switzerland (HES-SO)), Stephan Robert (University of Applied Sciences of Western Switzerland (HES-SO)), Philippe Cudré-Mauroux (University of Fribourg)]]> Mon, 03 Dec 2018 14:12:15 GMT /eXascaleInfolab/a-force-directed-approach-for-offline-gps-trajectory-map eXascaleInfolab@slideshare.net(eXascaleInfolab) A force directed approach for offline gps trajectory map eXascaleInfolab SIGSPATIAL 2018 paper A Force-Directed Approach for Offline GPS Trajectory Map Matching Efstratios Rappos (University of Applied Sciences of Western Switzerland (HES-SO)), Stephan Robert (University of Applied Sciences of Western Switzerland (HES-SO)), Philippe Cudré-Mauroux (University of Fribourg) <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/aforce-directedapproachforofflinegpstrajectorymap-final2-181203141215-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> SIGSPATIAL 2018 paper A Force-Directed Approach for Offline GPS Trajectory Map Matching Efstratios Rappos (University of Applied Sciences of Western Switzerland (HES-SO)), Stephan Robert (University of Applied Sciences of Western Switzerland (HES-SO)), Philippe Cudré-Mauroux (University of Fribourg)
A force directed approach for offline gps trajectory map from eXascale Infolab
]]> 459 2 https://cdn.slidesharecdn.com/ss_thumbnails/aforce-directedapproachforofflinegpstrajectorymap-final2-181203141215-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Cikm 2018 /slideshow/cikm-2018/120943895 cikm2018-181027162647
Are Meta-Paths Necessary? Revisiting Heterogeneous Graph Embeddings Full paper @CIKM 2018 Rana Hussein, Dingqi Yang and Philippe Cudre-Mauroux ]]>
Are Meta-Paths Necessary? Revisiting Heterogeneous Graph Embeddings Full paper @CIKM 2018 Rana Hussein, Dingqi Yang and Philippe Cudre-Mauroux ]]> Sat, 27 Oct 2018 16:26:47 GMT /slideshow/cikm-2018/120943895 eXascaleInfolab@slideshare.net(eXascaleInfolab) Cikm 2018 eXascaleInfolab Are Meta-Paths Necessary? Revisiting Heterogeneous Graph Embeddings Full paper @CIKM 2018 Rana Hussein, Dingqi Yang and Philippe Cudre-Mauroux <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/cikm2018-181027162647-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Are Meta-Paths Necessary? Revisiting Heterogeneous Graph Embeddings Full paper @CIKM 2018 Rana Hussein, Dingqi Yang and Philippe Cudre-Mauroux
Cikm 2018 from eXascale Infolab
]]> 916 4 https://cdn.slidesharecdn.com/ss_thumbnails/cikm2018-181027162647-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 HistoSketch: Fast Similarity-Preserving Sketching of Streaming Histograms with Concept Drift /slideshow/histosketch-fast-similaritypreserving-sketching-of-streaming-histograms-with-concept-drift/82330172 histosketch-171119214242
Presented at ICDM 2017]]>
Presented at ICDM 2017]]> Sun, 19 Nov 2017 21:42:42 GMT /slideshow/histosketch-fast-similaritypreserving-sketching-of-streaming-histograms-with-concept-drift/82330172 eXascaleInfolab@slideshare.net(eXascaleInfolab) HistoSketch: Fast Similarity-Preserving Sketching of Streaming Histograms with Concept Drift eXascaleInfolab Presented at ICDM 2017 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/histosketch-171119214242-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Presented at ICDM 2017
HistoSketch: Fast Similarity-Preserving Sketching of Streaming Histograms with Concept Drift from eXascale Infolab
]]> 797 4 https://cdn.slidesharecdn.com/ss_thumbnails/histosketch-171119214242-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 SwissLink: High-Precision, Context-Free Entity Linking Exploiting Unambiguous Labels /slideshow/swisslink-highprecision-contextfree-entity-linking-exploiting-unambiguous-labels/82322331 swisslink-171119170429
Presented at SEMANTICS 2017]]>
Presented at SEMANTICS 2017]]> Sun, 19 Nov 2017 17:04:29 GMT /slideshow/swisslink-highprecision-contextfree-entity-linking-exploiting-unambiguous-labels/82322331 eXascaleInfolab@slideshare.net(eXascaleInfolab) SwissLink: High-Precision, Context-Free Entity Linking Exploiting Unambiguous Labels eXascaleInfolab Presented at SEMANTICS 2017 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/swisslink-171119170429-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Presented at SEMANTICS 2017
SwissLink: High-Precision, Context-Free Entity Linking Exploiting Unambiguous Labels from eXascale Infolab
]]> 1217 5 https://cdn.slidesharecdn.com/ss_thumbnails/swisslink-171119170429-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Dependency-Driven Analytics: A Compass for Uncharted Data Oceans /slideshow/dependencydriven-analytics-a-compass-for-uncharted-data-oceans/71497355 p59-mavlyutov-cidr17-slides-170128195800
ºÝºÝߣs of Carlo's CIDR 2017 talk on GUIDER Paper is here: https://exascale.info/assets/pdf/cidr2017_dependency-driven-analytics.pdf ]]>
ºÝºÝߣs of Carlo's CIDR 2017 talk on GUIDER Paper is here: https://exascale.info/assets/pdf/cidr2017_dependency-driven-analytics.pdf ]]> Sat, 28 Jan 2017 19:58:00 GMT /slideshow/dependencydriven-analytics-a-compass-for-uncharted-data-oceans/71497355 eXascaleInfolab@slideshare.net(eXascaleInfolab) Dependency-Driven Analytics: A Compass for Uncharted Data Oceans eXascaleInfolab ºÝºÝߣs of Carlo's CIDR 2017 talk on GUIDER Paper is here: https://exascale.info/assets/pdf/cidr2017_dependency-driven-analytics.pdf <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/p59-mavlyutov-cidr17-slides-170128195800-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> ºÝºÝߣs of Carlo&#39;s CIDR 2017 talk on GUIDER Paper is here: https://exascale.info/assets/pdf/cidr2017_dependency-driven-analytics.pdf
Dependency-Driven Analytics: A Compass for Uncharted Data Oceans from eXascale Infolab
]]> 688 4 https://cdn.slidesharecdn.com/ss_thumbnails/p59-mavlyutov-cidr17-slides-170128195800-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Crowd scheduling www2016 /slideshow/crowd-scheduling-www2016/61576837 crowdschedulingwww2016-160502121450
ºÝºÝߣs from the paper: "Scheduling Human Intelligence Tasks in Multi-Tenant Crowd-Powered Systems."]]>
ºÝºÝߣs from the paper: "Scheduling Human Intelligence Tasks in Multi-Tenant Crowd-Powered Systems."]]> Mon, 02 May 2016 12:14:50 GMT /slideshow/crowd-scheduling-www2016/61576837 eXascaleInfolab@slideshare.net(eXascaleInfolab) Crowd scheduling www2016 eXascaleInfolab ºÝºÝߣs from the paper: "Scheduling Human Intelligence Tasks in Multi-Tenant Crowd-Powered Systems." <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/crowdschedulingwww2016-160502121450-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> ºÝºÝߣs from the paper: &quot;Scheduling Human Intelligence Tasks in Multi-Tenant Crowd-Powered Systems.&quot;
Crowd scheduling www2016 from eXascale Infolab
]]> 1244 5 https://cdn.slidesharecdn.com/ss_thumbnails/crowdschedulingwww2016-160502121450-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 SANAPHOR: Ontology-based Coreference Resolution /slideshow/sanaphor-ontologybased-coreference-resolution/53905011 iswc2015romanprokofyev-151014014104-lva1-app6891
Presented at International Semantic Web Conference, 2015. Bethlehem, PA.]]>
Presented at International Semantic Web Conference, 2015. Bethlehem, PA.]]> Wed, 14 Oct 2015 01:41:04 GMT /slideshow/sanaphor-ontologybased-coreference-resolution/53905011 eXascaleInfolab@slideshare.net(eXascaleInfolab) SANAPHOR: Ontology-based Coreference Resolution eXascaleInfolab Presented at International Semantic Web Conference, 2015. Bethlehem, PA. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/iswc2015romanprokofyev-151014014104-lva1-app6891-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Presented at International Semantic Web Conference, 2015. Bethlehem, PA.
SANAPHOR: Ontology-based Coreference Resolution from eXascale Infolab
]]> 1104 7 https://cdn.slidesharecdn.com/ss_thumbnails/iswc2015romanprokofyev-151014014104-lva1-app6891-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Efficient, Scalable, and Provenance-Aware Management of Linked Data /slideshow/efficient-scalable-and-provenanceaware-management-of-linked-data/53601795 pdfid1qk81or5u8gkk0gvdgnwr-pqajotpxcxtodrgq-mwkipageidga15fe47db00attachmentfalseoptssb1tsnspp1ss365-151006143450-lva1-app6892
The proliferation of heterogeneous Linked Data on the Web requires data management systems to constantly improve their scalability and efficiency. Despite recent advances in distributed Linked Data management, efficiently processing large amounts of Linked Data in a scalable way is still very challenging. In spite of their seemingly simple data models, Linked Data actually encode rich and complex graphs mixing both instance and schema level data. At the same time, users are increasingly interested in investigating or visualizing large collections of online data by performing complex analytic queries. The heterogeneity of Linked Data on the Web also poses new challenges to database systems. The capacity to store, track, and query provenance data is becoming a pivotal feature of Linked Data Management Systems. In this thesis, we tackle issues revolving around processing queries on big, unstructured, and heterogeneous Linked Data graphs.]]>
The proliferation of heterogeneous Linked Data on the Web requires data management systems to constantly improve their scalability and efficiency. Despite recent advances in distributed Linked Data management, efficiently processing large amounts of Linked Data in a scalable way is still very challenging. In spite of their seemingly simple data models, Linked Data actually encode rich and complex graphs mixing both instance and schema level data. At the same time, users are increasingly interested in investigating or visualizing large collections of online data by performing complex analytic queries. The heterogeneity of Linked Data on the Web also poses new challenges to database systems. The capacity to store, track, and query provenance data is becoming a pivotal feature of Linked Data Management Systems. In this thesis, we tackle issues revolving around processing queries on big, unstructured, and heterogeneous Linked Data graphs.]]> Tue, 06 Oct 2015 14:34:50 GMT /slideshow/efficient-scalable-and-provenanceaware-management-of-linked-data/53601795 eXascaleInfolab@slideshare.net(eXascaleInfolab) Efficient, Scalable, and Provenance-Aware Management of Linked Data eXascaleInfolab The proliferation of heterogeneous Linked Data on the Web requires data management systems to constantly improve their scalability and efficiency. Despite recent advances in distributed Linked Data management, efficiently processing large amounts of Linked Data in a scalable way is still very challenging. In spite of their seemingly simple data models, Linked Data actually encode rich and complex graphs mixing both instance and schema level data. At the same time, users are increasingly interested in investigating or visualizing large collections of online data by performing complex analytic queries. The heterogeneity of Linked Data on the Web also poses new challenges to database systems. The capacity to store, track, and query provenance data is becoming a pivotal feature of Linked Data Management Systems. In this thesis, we tackle issues revolving around processing queries on big, unstructured, and heterogeneous Linked Data graphs. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/pdfid1qk81or5u8gkk0gvdgnwr-pqajotpxcxtodrgq-mwkipageidga15fe47db00attachmentfalseoptssb1tsnspp1ss365-151006143450-lva1-app6892-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> The proliferation of heterogeneous Linked Data on the Web requires data management systems to constantly improve their scalability and efficiency. Despite recent advances in distributed Linked Data management, efficiently processing large amounts of Linked Data in a scalable way is still very challenging. In spite of their seemingly simple data models, Linked Data actually encode rich and complex graphs mixing both instance and schema level data. At the same time, users are increasingly interested in investigating or visualizing large collections of online data by performing complex analytic queries. The heterogeneity of Linked Data on the Web also poses new challenges to database systems. The capacity to store, track, and query provenance data is becoming a pivotal feature of Linked Data Management Systems. In this thesis, we tackle issues revolving around processing queries on big, unstructured, and heterogeneous Linked Data graphs.
Efficient, Scalable, and Provenance-Aware Management of Linked Data from eXascale Infolab
]]> 715 10 https://cdn.slidesharecdn.com/ss_thumbnails/pdfid1qk81or5u8gkk0gvdgnwr-pqajotpxcxtodrgq-mwkipageidga15fe47db00attachmentfalseoptssb1tsnspp1ss365-151006143450-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Entity-Centric Data Management /slideshow/entitycentric-data-management/52553230 icdar2015-150908191613-lva1-app6892
Keynote @ ICDAR 2015 on Entity-Centric Data Management]]>
Keynote @ ICDAR 2015 on Entity-Centric Data Management]]> Tue, 08 Sep 2015 19:16:13 GMT /slideshow/entitycentric-data-management/52553230 eXascaleInfolab@slideshare.net(eXascaleInfolab) Entity-Centric Data Management eXascaleInfolab Keynote @ ICDAR 2015 on Entity-Centric Data Management <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/icdar2015-150908191613-lva1-app6892-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Keynote @ ICDAR 2015 on Entity-Centric Data Management
Entity-Centric Data Management from eXascale Infolab
]]> 1161 9 https://cdn.slidesharecdn.com/ss_thumbnails/icdar2015-150908191613-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 SSSW 2015 Sense Making /slideshow/sssw2015-sense-making/50377170 ssswsensemaking2015combined-150710063629-lva1-app6891
Sense Making Tutorial @ SSSW 2015 Axel-Cyrille Ngonga Ngomo & Philippe Cudré-Mauroux]]>
Sense Making Tutorial @ SSSW 2015 Axel-Cyrille Ngonga Ngomo & Philippe Cudré-Mauroux]]> Fri, 10 Jul 2015 06:36:29 GMT /slideshow/sssw2015-sense-making/50377170 eXascaleInfolab@slideshare.net(eXascaleInfolab) SSSW 2015 Sense Making eXascaleInfolab Sense Making Tutorial @ SSSW 2015 Axel-Cyrille Ngonga Ngomo & Philippe Cudré-Mauroux <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ssswsensemaking2015combined-150710063629-lva1-app6891-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Sense Making Tutorial @ SSSW 2015 Axel-Cyrille Ngonga Ngomo &amp; Philippe Cudré-Mauroux
SSSW 2015 Sense Making from eXascale Infolab
]]> 2524 9 https://cdn.slidesharecdn.com/ss_thumbnails/ssswsensemaking2015combined-150710063629-lva1-app6891-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 LDOW2015 - Uduvudu: a Graph-Aware and Adaptive UI Engine for Linked Data /slideshow/ldow2015-uduvudu-a-graphaware-and-adaptive-ui-engine-for-linked-data/49208026 uduvuduleonardo-150610073323-lva1-app6891
Uduvudu exploits the semantic and structured nature of Linked Data to generate the best possible representation for a human based on a catalog of available Matchers and Templates. Matchers and Templates are designed that they can be build through an intuitive editor interface.]]>
Uduvudu exploits the semantic and structured nature of Linked Data to generate the best possible representation for a human based on a catalog of available Matchers and Templates. Matchers and Templates are designed that they can be build through an intuitive editor interface.]]> Wed, 10 Jun 2015 07:33:23 GMT /slideshow/ldow2015-uduvudu-a-graphaware-and-adaptive-ui-engine-for-linked-data/49208026 eXascaleInfolab@slideshare.net(eXascaleInfolab) LDOW2015 - Uduvudu: a Graph-Aware and Adaptive UI Engine for Linked Data eXascaleInfolab Uduvudu exploits the semantic and structured nature of Linked Data to generate the best possible representation for a human based on a catalog of available Matchers and Templates. Matchers and Templates are designed that they can be build through an intuitive editor interface. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/uduvuduleonardo-150610073323-lva1-app6891-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Uduvudu exploits the semantic and structured nature of Linked Data to generate the best possible representation for a human based on a catalog of available Matchers and Templates. Matchers and Templates are designed that they can be build through an intuitive editor interface.
LDOW2015 - Uduvudu: a Graph-Aware and Adaptive UI Engine for Linked Data from eXascale Infolab
]]> 4093 3 https://cdn.slidesharecdn.com/ss_thumbnails/uduvuduleonardo-150610073323-lva1-app6891-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Executing Provenance-Enabled Queries over Web Data /eXascaleInfolab/triple-prov-www2015 tripleprovwww2015-150521152930-lva1-app6891
The proliferation of heterogeneous Linked Data on the Web poses new challenges to database systems. In particular, because of this heterogeneity, the capacity to store, track, and query provenance data is becoming a pivotal feature of modern triple stores. In this paper, we tackle the problem of efficiently executing provenance-enabled queries over RDF data. We propose, implement and empirically evaluate five different query execution strategies for RDF queries that incorporate knowledge of provenance. The evaluation is conducted on Web Data obtained from two different Web crawls (The Billion Triple Challenge, and the Web Data Commons). Our evaluation shows that using an adaptive query materialization execution strategy performs best in our context. Interestingly, we find that because provenance is prevalent within Web Data and is highly selective, it can be used to improve query processing performance. This is a counterintuitive result as provenance is often associated with additional overhead.]]>
The proliferation of heterogeneous Linked Data on the Web poses new challenges to database systems. In particular, because of this heterogeneity, the capacity to store, track, and query provenance data is becoming a pivotal feature of modern triple stores. In this paper, we tackle the problem of efficiently executing provenance-enabled queries over RDF data. We propose, implement and empirically evaluate five different query execution strategies for RDF queries that incorporate knowledge of provenance. The evaluation is conducted on Web Data obtained from two different Web crawls (The Billion Triple Challenge, and the Web Data Commons). Our evaluation shows that using an adaptive query materialization execution strategy performs best in our context. Interestingly, we find that because provenance is prevalent within Web Data and is highly selective, it can be used to improve query processing performance. This is a counterintuitive result as provenance is often associated with additional overhead.]]> Thu, 21 May 2015 15:29:30 GMT /eXascaleInfolab/triple-prov-www2015 eXascaleInfolab@slideshare.net(eXascaleInfolab) Executing Provenance-Enabled Queries over Web Data eXascaleInfolab The proliferation of heterogeneous Linked Data on the Web poses new challenges to database systems. In particular, because of this heterogeneity, the capacity to store, track, and query provenance data is becoming a pivotal feature of modern triple stores. In this paper, we tackle the problem of efficiently executing provenance-enabled queries over RDF data. We propose, implement and empirically evaluate five different query execution strategies for RDF queries that incorporate knowledge of provenance. The evaluation is conducted on Web Data obtained from two different Web crawls (The Billion Triple Challenge, and the Web Data Commons). Our evaluation shows that using an adaptive query materialization execution strategy performs best in our context. Interestingly, we find that because provenance is prevalent within Web Data and is highly selective, it can be used to improve query processing performance. This is a counterintuitive result as provenance is often associated with additional overhead. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/tripleprovwww2015-150521152930-lva1-app6891-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> The proliferation of heterogeneous Linked Data on the Web poses new challenges to database systems. In particular, because of this heterogeneity, the capacity to store, track, and query provenance data is becoming a pivotal feature of modern triple stores. In this paper, we tackle the problem of efficiently executing provenance-enabled queries over RDF data. We propose, implement and empirically evaluate five different query execution strategies for RDF queries that incorporate knowledge of provenance. The evaluation is conducted on Web Data obtained from two different Web crawls (The Billion Triple Challenge, and the Web Data Commons). Our evaluation shows that using an adaptive query materialization execution strategy performs best in our context. Interestingly, we find that because provenance is prevalent within Web Data and is highly selective, it can be used to improve query processing performance. This is a counterintuitive result as provenance is often associated with additional overhead.
Executing Provenance-Enabled Queries over Web Data from eXascale Infolab
]]> 1521 2 https://cdn.slidesharecdn.com/ss_thumbnails/tripleprovwww2015-150521152930-lva1-app6891-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 The Dynamics of Micro-Task Crowdsourcing /slideshow/m-turk-dynamics/48406667 mturkdynamics-150520213413-lva1-app6892
Micro-task crowdsourcing is rapidly gaining popularity among research communities and businesses as a means to leverage Human Computation in their daily operations. Unlike any other service, a crowdsourcing platform is in fact a marketplace subject to human factors that affect its performance, both in terms of speed and quality. Indeed, such factors shape the dynamics of the crowdsourcing market. For example, a known behavior of such markets is that increasing the reward of a set of tasks would lead to faster results. However, it is still unclear how different dimensions interact with each other: reward, task type, market competition, requester reputation, etc. In this paper, we adopt a data-driven approach to (A) perform a long-term analysis of a popular micro-task crowdsourcing platform and understand the evolution of its main actors (workers, requesters, and platform). (B) We leverage the main findings of our five year log analysis to propose features used in a predictive model aiming at determining the expected performance of any batch at a specific point in time. We show that the number of tasks left in a batch and how recent the batch is are two key features of the prediction. (C) Finally, we conduct an analysis of the demand (new tasks posted by the requesters) and supply (number of tasks completed by the workforce) and show how they affect task prices on the marketplace.]]>
Micro-task crowdsourcing is rapidly gaining popularity among research communities and businesses as a means to leverage Human Computation in their daily operations. Unlike any other service, a crowdsourcing platform is in fact a marketplace subject to human factors that affect its performance, both in terms of speed and quality. Indeed, such factors shape the dynamics of the crowdsourcing market. For example, a known behavior of such markets is that increasing the reward of a set of tasks would lead to faster results. However, it is still unclear how different dimensions interact with each other: reward, task type, market competition, requester reputation, etc. In this paper, we adopt a data-driven approach to (A) perform a long-term analysis of a popular micro-task crowdsourcing platform and understand the evolution of its main actors (workers, requesters, and platform). (B) We leverage the main findings of our five year log analysis to propose features used in a predictive model aiming at determining the expected performance of any batch at a specific point in time. We show that the number of tasks left in a batch and how recent the batch is are two key features of the prediction. (C) Finally, we conduct an analysis of the demand (new tasks posted by the requesters) and supply (number of tasks completed by the workforce) and show how they affect task prices on the marketplace.]]> Wed, 20 May 2015 21:34:13 GMT /slideshow/m-turk-dynamics/48406667 eXascaleInfolab@slideshare.net(eXascaleInfolab) The Dynamics of Micro-Task Crowdsourcing eXascaleInfolab Micro-task crowdsourcing is rapidly gaining popularity among research communities and businesses as a means to leverage Human Computation in their daily operations. Unlike any other service, a crowdsourcing platform is in fact a marketplace subject to human factors that affect its performance, both in terms of speed and quality. Indeed, such factors shape the dynamics of the crowdsourcing market. For example, a known behavior of such markets is that increasing the reward of a set of tasks would lead to faster results. However, it is still unclear how different dimensions interact with each other: reward, task type, market competition, requester reputation, etc. In this paper, we adopt a data-driven approach to (A) perform a long-term analysis of a popular micro-task crowdsourcing platform and understand the evolution of its main actors (workers, requesters, and platform). (B) We leverage the main findings of our five year log analysis to propose features used in a predictive model aiming at determining the expected performance of any batch at a specific point in time. We show that the number of tasks left in a batch and how recent the batch is are two key features of the prediction. (C) Finally, we conduct an analysis of the demand (new tasks posted by the requesters) and supply (number of tasks completed by the workforce) and show how they affect task prices on the marketplace. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/mturkdynamics-150520213413-lva1-app6892-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Micro-task crowdsourcing is rapidly gaining popularity among research communities and businesses as a means to leverage Human Computation in their daily operations. Unlike any other service, a crowdsourcing platform is in fact a marketplace subject to human factors that affect its performance, both in terms of speed and quality. Indeed, such factors shape the dynamics of the crowdsourcing market. For example, a known behavior of such markets is that increasing the reward of a set of tasks would lead to faster results. However, it is still unclear how different dimensions interact with each other: reward, task type, market competition, requester reputation, etc. In this paper, we adopt a data-driven approach to (A) perform a long-term analysis of a popular micro-task crowdsourcing platform and understand the evolution of its main actors (workers, requesters, and platform). (B) We leverage the main findings of our five year log analysis to propose features used in a predictive model aiming at determining the expected performance of any batch at a specific point in time. We show that the number of tasks left in a batch and how recent the batch is are two key features of the prediction. (C) Finally, we conduct an analysis of the demand (new tasks posted by the requesters) and supply (number of tasks completed by the workforce) and show how they affect task prices on the marketplace.
The Dynamics of Micro-Task Crowdsourcing from eXascale Infolab
]]> 1637 4 https://cdn.slidesharecdn.com/ss_thumbnails/mturkdynamics-150520213413-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Fixing the Domain and Range of Properties in Linked Data by Context Disambiguation /slideshow/fixing-the-domain-and-range-of-properties-in-linked-data-by-context-disambiguation/48327315 ldow2015-150519102812-lva1-app6891
Presentation given at LDOW2015]]>
Presentation given at LDOW2015]]> Tue, 19 May 2015 10:28:12 GMT /slideshow/fixing-the-domain-and-range-of-properties-in-linked-data-by-context-disambiguation/48327315 eXascaleInfolab@slideshare.net(eXascaleInfolab) Fixing the Domain and Range of Properties in Linked Data by Context Disambiguation eXascaleInfolab Presentation given at LDOW2015 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ldow2015-150519102812-lva1-app6891-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Presentation given at LDOW2015
Fixing the Domain and Range of Properties in Linked Data by Context Disambiguation from eXascale Infolab
]]> 3181 4 https://cdn.slidesharecdn.com/ss_thumbnails/ldow2015-150519102812-lva1-app6891-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 CIKM14: Fixing grammatical errors by preposition ranking /slideshow/cikm14-fixing-grammatical-errors-by-preposition-ranking/41058034 cikm2014romanprokofyev-141103101713-conversion-gate01
The detection and correction of grammatical errors still represent very hard problems for modern error-correction systems. As an example, the top-performing systems at the preposition correction challenge CoNLL-2013 only achieved a F1 score of 17%. In this paper, we propose and extensively evaluate a series of approaches for correcting prepositions, analyzing a large body of high-quality textual content to capture language usage. Leveraging n-gram statistics, association measures, and machine learning techniques, our system is able to learn which words or phrases govern the usage of a specific preposition. Our approach makes heavy use of n-gram statistics generated from very large textual corpora. In particular, one of our key features is the use of n-gram association measures (e.g., Pointwise Mutual Information) between words and prepositions to generate better aggregated preposition rankings for the individual n-grams. We evaluate the effectiveness of our approach using cross-validation with different feature combinations and on two test collections created from a set of English language exams and StackExchange forums. We also compare against state-of-the-art supervised methods. Experimental results from the CoNLL-2013 test collection show that our approach to preposition correction achieves ~30% in F1 score which results in 13% absolute improvement over the best performing approach at that challenge.]]>
The detection and correction of grammatical errors still represent very hard problems for modern error-correction systems. As an example, the top-performing systems at the preposition correction challenge CoNLL-2013 only achieved a F1 score of 17%. In this paper, we propose and extensively evaluate a series of approaches for correcting prepositions, analyzing a large body of high-quality textual content to capture language usage. Leveraging n-gram statistics, association measures, and machine learning techniques, our system is able to learn which words or phrases govern the usage of a specific preposition. Our approach makes heavy use of n-gram statistics generated from very large textual corpora. In particular, one of our key features is the use of n-gram association measures (e.g., Pointwise Mutual Information) between words and prepositions to generate better aggregated preposition rankings for the individual n-grams. We evaluate the effectiveness of our approach using cross-validation with different feature combinations and on two test collections created from a set of English language exams and StackExchange forums. We also compare against state-of-the-art supervised methods. Experimental results from the CoNLL-2013 test collection show that our approach to preposition correction achieves ~30% in F1 score which results in 13% absolute improvement over the best performing approach at that challenge.]]> Mon, 03 Nov 2014 10:17:13 GMT /slideshow/cikm14-fixing-grammatical-errors-by-preposition-ranking/41058034 eXascaleInfolab@slideshare.net(eXascaleInfolab) CIKM14: Fixing grammatical errors by preposition ranking eXascaleInfolab The detection and correction of grammatical errors still represent very hard problems for modern error-correction systems. As an example, the top-performing systems at the preposition correction challenge CoNLL-2013 only achieved a F1 score of 17%. In this paper, we propose and extensively evaluate a series of approaches for correcting prepositions, analyzing a large body of high-quality textual content to capture language usage. Leveraging n-gram statistics, association measures, and machine learning techniques, our system is able to learn which words or phrases govern the usage of a specific preposition. Our approach makes heavy use of n-gram statistics generated from very large textual corpora. In particular, one of our key features is the use of n-gram association measures (e.g., Pointwise Mutual Information) between words and prepositions to generate better aggregated preposition rankings for the individual n-grams. We evaluate the effectiveness of our approach using cross-validation with different feature combinations and on two test collections created from a set of English language exams and StackExchange forums. We also compare against state-of-the-art supervised methods. Experimental results from the CoNLL-2013 test collection show that our approach to preposition correction achieves ~30% in F1 score which results in 13% absolute improvement over the best performing approach at that challenge. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/cikm2014romanprokofyev-141103101713-conversion-gate01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> The detection and correction of grammatical errors still represent very hard problems for modern error-correction systems. As an example, the top-performing systems at the preposition correction challenge CoNLL-2013 only achieved a F1 score of 17%. In this paper, we propose and extensively evaluate a series of approaches for correcting prepositions, analyzing a large body of high-quality textual content to capture language usage. Leveraging n-gram statistics, association measures, and machine learning techniques, our system is able to learn which words or phrases govern the usage of a specific preposition. Our approach makes heavy use of n-gram statistics generated from very large textual corpora. In particular, one of our key features is the use of n-gram association measures (e.g., Pointwise Mutual Information) between words and prepositions to generate better aggregated preposition rankings for the individual n-grams. We evaluate the effectiveness of our approach using cross-validation with different feature combinations and on two test collections created from a set of English language exams and StackExchange forums. We also compare against state-of-the-art supervised methods. Experimental results from the CoNLL-2013 test collection show that our approach to preposition correction achieves ~30% in F1 score which results in 13% absolute improvement over the best performing approach at that challenge.
CIKM14: Fixing grammatical errors by preposition ranking from eXascale Infolab
]]> 1691 7 https://cdn.slidesharecdn.com/ss_thumbnails/cikm2014romanprokofyev-141103101713-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds presentation White http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 OLTP-Bench /slideshow/oltpbench/38795983 oltpbenchvldb-140907112311-phpapp02
OLTPBenchmark is a multi-threaded load generator. The framework is designed to be able to produce variable rate, variable mixture load against any JDBC-enabled relational database. The framework also provides data collection features, e.g., per-transaction-type latency and throughput logs. Together with the framework we provide the following OLTP/Web benchmarks: TPC-C Wikipedia Synthetic Resource Stresser Twitter Epinions.com TATP AuctionMark SEATS YCSB JPAB (Hibernate) CH-benCHmark Voter (Japanese "American Idol") SIBench (Snapshot Isolation) SmallBank LinkBench CH-benCHmark]]>
OLTPBenchmark is a multi-threaded load generator. The framework is designed to be able to produce variable rate, variable mixture load against any JDBC-enabled relational database. The framework also provides data collection features, e.g., per-transaction-type latency and throughput logs. Together with the framework we provide the following OLTP/Web benchmarks: TPC-C Wikipedia Synthetic Resource Stresser Twitter Epinions.com TATP AuctionMark SEATS YCSB JPAB (Hibernate) CH-benCHmark Voter (Japanese "American Idol") SIBench (Snapshot Isolation) SmallBank LinkBench CH-benCHmark]]> Sun, 07 Sep 2014 11:23:11 GMT /slideshow/oltpbench/38795983 eXascaleInfolab@slideshare.net(eXascaleInfolab) OLTP-Bench eXascaleInfolab OLTPBenchmark is a multi-threaded load generator. The framework is designed to be able to produce variable rate, variable mixture load against any JDBC-enabled relational database. The framework also provides data collection features, e.g., per-transaction-type latency and throughput logs. Together with the framework we provide the following OLTP/Web benchmarks: TPC-C Wikipedia Synthetic Resource Stresser Twitter Epinions.com TATP AuctionMark SEATS YCSB JPAB (Hibernate) CH-benCHmark Voter (Japanese "American Idol") SIBench (Snapshot Isolation) SmallBank LinkBench CH-benCHmark <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/oltpbenchvldb-140907112311-phpapp02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> OLTPBenchmark is a multi-threaded load generator. The framework is designed to be able to produce variable rate, variable mixture load against any JDBC-enabled relational database. The framework also provides data collection features, e.g., per-transaction-type latency and throughput logs. Together with the framework we provide the following OLTP/Web benchmarks: TPC-C Wikipedia Synthetic Resource Stresser Twitter Epinions.com TATP AuctionMark SEATS YCSB JPAB (Hibernate) CH-benCHmark Voter (Japanese &quot;American Idol&quot;) SIBench (Snapshot Isolation) SmallBank LinkBench CH-benCHmark
OLTP-Bench from eXascale Infolab
]]> 2284 4 https://cdn.slidesharecdn.com/ss_thumbnails/oltpbenchvldb-140907112311-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 An Introduction to Big Data /eXascaleInfolab/an-introduction-to-big-data cusobigdataintro-140812132013-phpapp01
An Introduction to Big Data CUSO Seminar on Big Data, Switzerland Prof. Philippe Cudre-Mauroux eXascale Infolab http://exascale.info/]]>
An Introduction to Big Data CUSO Seminar on Big Data, Switzerland Prof. Philippe Cudre-Mauroux eXascale Infolab http://exascale.info/]]> Tue, 12 Aug 2014 13:20:13 GMT /eXascaleInfolab/an-introduction-to-big-data eXascaleInfolab@slideshare.net(eXascaleInfolab) An Introduction to Big Data eXascaleInfolab An Introduction to Big Data CUSO Seminar on Big Data, Switzerland Prof. Philippe Cudre-Mauroux eXascale Infolab http://exascale.info/ <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/cusobigdataintro-140812132013-phpapp01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> An Introduction to Big Data CUSO Seminar on Big Data, Switzerland Prof. Philippe Cudre-Mauroux eXascale Infolab http://exascale.info/
An Introduction to Big Data from eXascale Infolab
]]> 2803 9 https://cdn.slidesharecdn.com/ss_thumbnails/cusobigdataintro-140812132013-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 https://cdn.slidesharecdn.com/profile-photo-eXascaleInfolab-48x48.jpg?cb=1696467995 eXascale Infolab University of Fribourg Switzerland http://exascale.info/ https://cdn.slidesharecdn.com/ss_thumbnails/rossopresentation-200422055900-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/beyond-triplets-hyperrelational-knowledge-graph-embedding-for-link-prediction/232404290 Beyond Triplets: Hyper... https://cdn.slidesharecdn.com/ss_thumbnails/ittakestwo-cidrpresentation-200210135236-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/it-takes-two-instrumenting-the-interaction-between-inmemory-databases-and-solidstate-drives-cidr-2020-presentation/227529104 It Takes Two: Instrume... https://cdn.slidesharecdn.com/ss_thumbnails/invitedtalkdl4gpcm-190805132752-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/representation-learning-on-complex-graphs/161218021 Representation Learnin...