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IRSS 2014 The SemaGrow project

IRSS 2014 The SemaGrow project. Vangelis Karkaletsis NCSR “Demokritos”. s upported by:. Presentation Outline. Introduction The SemaGrow Solution The POWDER W3C Recommendation SemaGrow Architecture The SemaGrow Stack. Introduction.

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IRSS 2014 The SemaGrow project

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  1. IRSS 2014The SemaGrow project Vangelis Karkaletsis NCSR “Demokritos” 2nd SemaGrow Hackathon (in conjunction with IRSS14) supported by:

  2. Presentation Outline Introduction The SemaGrow Solution The POWDER W3C Recommendation SemaGrow Architecture The SemaGrow Stack 2nd SemaGrow Hackathon (in conjunction with IRSS14)

  3. Introduction 2nd SemaGrow Hackathon (in conjunction with IRSS14) Semantic Infrastructures for Unified Access to Heterogeneous Data

  4. Developing Applications over Data Infrastructures • Applications are not required to manage the underlying physical resources • Applications must handle heterogeneity • Different Data Schemas / Vocabularies • Applications must handle source selection • Support access for all sources, even for non-necessary queries (waste of physical resources) 2nd SemaGrow Hackathon (in conjunction with IRSS14)

  5. Transitioning to Semantic Web Technologies How Many? Is it feasible? BigData Problem! 2nd SemaGrow Hackathon (in conjunction with IRSS14)

  6. SemaGrow 2nd SemaGrow Hackathon (in conjunction with IRSS14) What Semantic Web Brings in the Big Data Picture

  7. What Semantic Web can bring into the picture • Going beyond existing Distributed Triple Store Implementations • Link Heterogeneous but Semantically Connected Data • Index Extremely Large Information Volumes (Peta Sizes) • Improve Information Retrieval response • One Data Access Point for the entire Data Cloud • Choice between alternative Vocabularies / Thesauri / Ontologies • Enabling different application facets for different communities of users over the SAME data pool 2nd SemaGrow Hackathon (in conjunction with IRSS14) • Data (+Metadata) physically stored in Data Provider • No need for harvesting • Vocabularies / Thesauri / Ontologies of Data Provider choice • No need for aligning according to common schemas

  8. The SemaGrow Solution • Use POWDER to mass-annotate large-subspaces • Exploit naming convention regularities to compress the indexes used by the system • Partition triple patterns in the original query • Annotate each fragment with an ordered list of data sources most likely to contain relevant data • Distribute and transform the query fragments • Collect and align the results 2nd SemaGrow Hackathon (in conjunction with IRSS14)

  9. SemaGrow Architecture / Core Technologies Resource Discovery Query Decomposition 2nd SemaGrow Hackathon (in conjunction with IRSS14) Federated Endpoint Wrapper Data Summaries Endpoint

  10. Use Cases (DLO)Heterogeneous Data Collections & Streams • Big data: • Sensor data: soil data, weather • GIS data: land usage, forest and natural resources management data • Historical data: crop yield, economic data • Forecasts: climate change models • Problem: • Combine heterogeneous sources to analyze past food production and forecast future trends • Cannot clone and translate: large scale, live data streams • Cannot immediately and directly affect radical re-design of all sensing and processing currently in place 2nd SemaGrow Hackathon (in conjunction with IRSS14) 3rd Plenary & ESG Meeting

  11. Use Cases (FAO)Reactive Data Analysis • Big data: • Document collections: past experiences, analysis and research results • Databases: climate conditions and crop yield observations, economic data (land and food prices) • Problem: • Retrieving complete and accurate information to compile reports • Raw data and reports, scientific publications, etc. • Wastes human resources that could analyze data and synthesize useful knowledge and advice for food production • Too much time spent cross-relating responses from different sources • Too many different organizations and processes rely on the different schemas to make re-design viable • Cloning is inefficient: large and constantly updated stores 2nd SemaGrow Hackathon (in conjunction with IRSS14) 3rd Plenary & ESG Meeting

  12. Use Cases (AK)Reactive Resource Discovery • Big data: • Multimedia content about agriculture and biodiversity • Problem: • Real-time retrieval of relevant content • Used to compile educational activities • Schema heterogeneity: • Different providers (Oganicedunet, Europeana, VOA3R, etc.) • Too many different organizations and processes rely on the different schema to make re-design viable • Cloning is inefficient: large and constantly updated stores 2nd SemaGrow Hackathon (in conjunction with IRSS14) 3rd Plenary & ESG Meeting

  13. Project Info SemaGrow: Data intensive techniques to boost the real-time performance of global agricultural data infrastructures FP7-ICT-2011.4.4 (Intelligent Information Management) 2nd SemaGrow Hackathon (in conjunction with IRSS14)

  14. Thank you! Vangelis Karkaletsis NCSR “Demokritos” vangelis@iit.Demokritos.gr 2nd SemaGrow Hackathon (in conjunction with IRSS14)

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