PPT-Approximated Provenance for Complex Applications

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. 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. 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. 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. . . 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. 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. ECE 493/593 Big Data Security. Why overview (not much math details here)?. - This is still week 2, we need to have a big picture of this field. - Later on we will use 14 weeks to cover each detailed scheme.