PPT-A Provenance assisted Roadmap for

Life Sciences Linked Open Data Cloud Ali Hasnain et al Insight Center for Data Analytics National University of Ireland Galway Agenda Motivation Linked Life Sciences Roadmap Cataloguing and Linking. Key components of the business plan. Customers. Who will use your product. Who will see value in it. Channel. How you plan to reach these customers / users. Key resources / skills. What do you need to make sure that you can keep servicing your customer. Susan B. . Davidson. University of Pennsylvania. Eleanor . Ainy. , Daniel . Deutch. , . Tova. Milo. Tel Aviv University. The engagement of crowds of Web users for data procurement and knowledge creation.. Ontologies. . to Query Meteorological Data . . Thiago Marcos da Silva Barbosa. ,. Ednaldo. O. . Santos. , Gustavo . B. . Lyra,. Sérgio . Manuel Serra da . Cruz. LabData. Federal Rural . University. Middleware for Database Queries. , Updates, and . Transactions. Bahareh. Sadat . Arab. 1. , . Dieter . Gawlick. 2. , . Venkatesh. . Radhakrishnan. 2. , . Hao. . Guo. 1. , . Boris . Glavic. 1. . IIT DBGroup. Why – How . – . Where. What’s up with this presentation?. Cannibalized from:. Val . Tannen’s. EDBT and AMW 2010 tutorials. Peter . Buneman. and Wang-. Chiew. Tan’s SIGMOD 2007 tutorial. Julie and . . Session 6: Interoperability. Session 6: Aims. In this session, you will learn about:. Steps towards interoperability. Interoperability challenges. Next steps towards . achieving . interoperability. . Session 1: Background. Luc Moreau. L.Moreau@ecs.soton.ac.uk. University . of Southampton. Session 1: Aims. In this session, you will learn about:. The notion of provenance. The Open Provenance Vision. Susan B. Davidson. University of Pennsylvania. Joint work with . Zhuowei. . Bao. , . Xiaocheng. Huang and . Tova. Milo . Provenance in Scientific Workflows. Scientific workflow systems. Automate the execution of scientific workflows. Bibi . Raju, Todd Elsethagen, Eric . Stephan. 1. Pacific Northwest National Laboratory, Richland, WA. Provenance Goals. Provenance Overarching . Goals. Provide thorough results explanation. Reuse, repeat, or reproduce workflows. Todd J. Green. University of Pennsylvania. Spring 2009 . The Case for a Collaborative . Data Sharing System (CDSS). Scientists build data repositories, need to share with collaborators. Goal: import, transform, modify (curate) each other’s data. HITPC Consumer Empowerment Workgroup Meeting. July 28, 2013.  . . . . . . Joy Pritts, JD. Data Provenance. What is “Provenance”? . Origin of clinical information when first created. Information about the source of the data. 5/21/2018. 1. UW. Val Tannen. University of Pennsylvania. . 5/21/2018. 2. UW. . Collaborators. . ORCHESTRA. . . Raju, Todd Elsethagen, Eric . Stephan. 1. Pacific Northwest National Laboratory, Richland, WA. Provenance Goals. Provenance Overarching . Goals. Provide thorough results explanation. Reuse, repeat, or reproduce workflows. December 8, 2010. Dan Suciu -- CSEP544 Fall 2010. Outline. Database provenance. Slides based on Val . Tannen’s. Keynote talk at EDBT 2010. Data privacy. Slides from my UW colloquium talk in 2005. Dan Suciu -- CSEP544 Fall 2010.