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A Services Oriented Architecture for Water Resources Data

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  1. A Services Oriented Architecture for Water Resources Data David R. Maidment and Timothy L. Whiteaker Center for Research in Water Resources University of Texas at Austin

  2. Collaborators • San Diego Supercomputer Center • Ilya Zaslavsky, David Valentine, Tom Whitenack • Utah State University • David Tarboton, Jeff Horsburgh, Kim Schreuders • Drexel University • Michael Piasecki, Bora Beran, Yoori Choi • University of South Carolina • Jon Goodall

  3. A Services Oriented Architecture for Water Resources Data • WATERS Network Information System • Observations data model • Data Services

  4. A Services Oriented Architecture for Water Resources Data • WATERS Network Information System • Hydrologic Information Server • Data Services

  5. Waters Network Testbed Sites

  6. 16 observation networks (some testbeds have more than one network) Provides data from 1246 sites Of these, 167 sites are operated by WATERS investigators Waters Observation Networks

  7. Florida – Santa Fe Watershed Nitrate Nitrogen (mg/L) Millpond Spring PI: Wendy Graham, ….; DM: Kathleen McKee, Mark Newman

  8. North Carolina – Albemarle Pamlico Sound Salinity Mod Mon and Ferry Mon networks PI: Hans Paerl; DM: Rodney Guajardo

  9. Chesapeake Information Management System (Johns Hopkins, Drexel, Penn State Universities) http://www.hydroseek.org PI: Michael Piasecki, Bill Ball, Kevin Dressler, Chris Duffy, Pat Reed; DM: Bora Beran, Yoori Choi

  10. Baltimore — Gwynns Falls Watershed 15-min Precipitation at Carroll Park PI: Claire Welty, …..; DM: Mike McGuire

  11. Susquehanna – Upper Juniata Basin Net Radiation (W/m2) Oct 05 May 06 PI: Chris Duffy, Pat Reed; DM: Bora Beran, Yoori Choi

  12. Iowa – Clear Creek Watershed Uses streaming data loader Precipitation PI: Craig Just, Marian Muste, Anton Kruger; DM: Marian Muste, Dong Su Kim, Nick Arnold

  13. Minnesota – Minnehaha Creek Nitrate Nitrogen (mg/L) PI: Miki Hondzo, Bill Arnold, …. DM: Jim Kang, Sung-Chul Kim

  14. Montana – Crown of the Continent Snow Depth (m) Sperry glacier on ice weather station 4 0 2007: July August PI: Johnnie Moore, … DM: Toby Meirbachtol, Aaron Deskins

  15. Utah – Little Bear River and Mud Lake Turbidity David Stevens, Jeff Horsburgh, David Tarboton, Nancy Mesner, Kim Schreuders

  16. Sierra Nevada – San Joaquin River Transect of measurements across the river PI: Roger Bales, Tom Harmon DM: Xiande Meng

  17. Corpus Christi Bay - Hypoxia DO (mg/L) PI: Barbara Minsker, Paul Montagna, Jim Bonner, Ben Hodges; DM: Kevin Nelson

  18. A Services Oriented Architecture for Water Resources Data • WATERS Network Information System • Observations data model • Data Services

  19. GetSites GetSiteInfo GetVariables GetVariableInfo GetValues Hydrologic Information Server WaterOneFlow services DASH – data access system for hydrology ArcGISServer Geospatial Data Observations Data Microsoft SQLServer Relational Database

  20. Hydrologic Information Server Deployment National Hydrologic Information Server San Diego Supercomputer Center metadata for national datasets: NWIS, Storet, Snotel WATERS testbed server

  21. Point Observations Information Model http://www.cuahsi.org/his/webservices.html Utah State Univ Data Source GetSites Little Bear River Network GetSiteInfo Little Bear River at Mendon Rd Sites GetVariables GetVariableInfo Dissolved Oxygen Variables GetValues 9.78 mg/L, 1 October 2007, 6PM Values {Value, Time, Qualifier, Offset} • A data source operates an observation network • A network is a set of observation sites • A site is a point location where one or more variables are measured • A variable is a property describing the flow or quality of water • A value is an observation of a variable at a particular time • A qualifier is a symbol that provides additional information about the value • An offset allows specification of measurements at various depths in water

  22. CUAHSI Observations Data Model http://www.cuahsi.org/his/odm.html

  23. Loading Data into ODM MyDB ODDataLoader Database

  24. New Methods for Data Loading SQL/Server Integration Services DataTurbine Streaming Data Loader

  25. A Services Oriented Architecture for Water Resources Data • Waters Network Information System • Observations Data Model • Data Services

  26. Definition The CUAHSI Hydrologic Information System (HIS) is a geographically distributed network of data sources and functions that are integrated using a web services architecture so that they operate as a connected whole.

  27. Services Oriented Architecture (from Wikipedia) • Service-oriented Architecture (SOA) is an architectural design pattern that concerns itself with defining loosely-coupled relationships between producers and consumers. • A major focus of Web services is to make functional building blocks accessible over standard Internet protocols that are independent from platforms and programming languages. • The Web Services Description Language (WSDL, pronounced 'wiz-dəl' or spelled out, 'W-S-D-L') is an XML-based language that provides a model for describing Web services. Defined by the World Wide Web Consortium (W3C)

  28. Web Pages and Web Services http://www.safl.umn.edu/ Uses Hypertext Markup Language (HTML) Uses WaterML (an eXtended Markup Language for water data)

  29. Locations Variable Codes Date Ranges WaterML and WaterOneFlow STORET Data GetSiteInfo GetVariableInfo GetValues Data NAM NWIS WaterML Data WaterOneFlow Web Service Data Repositories Client EXTRACT TRANSFORM LOAD WaterML is an XML language for communicating water data WaterOneFlow is a set of web services based on WaterML

  30. Set of query functions Returns data in WaterML WaterOneFlow Ilya Zaslavsky and David Valentine, SDSC

  31. Syntactic mediation Heterogeneity of format Use WaterML to get data into the same format Semantic mediation Heterogeneity of meaning Each water data source uses its own vocabulary Match these up with a common controlled vocabulary Make standard scientific data queries and have these automatically translated into specific queries on each data source Data Heterogeneity

  32. request return return request NAWQA request return return request NAM-12 request return NWIS request return request return return request NARR Objective • Search multiple heterogeneous data sources simultaneously regardless of semantic or structural differences between them What we are doing now ….. Michael Piasecki Drexel University

  33. NAWQA NWIS NARR HODM Semantic Mediator What we would like to do ….. GetValues GetValues GetValues GetValues generic request GetValues GetValues Michael Piasecki Drexel University GetValues GetValues

  34. Hydroseekhttp://www.hydroseek.org Bora Beran, Drexel Supports search by location and type of data across multiple observation networks including NWIS and Storet

  35. HydroTagger Ontology: A hierarchy of concepts Each Variable in your data is connected to a corresponding Concept

  36. HIS to Google Earthdeveloped by Peter Fitch, CSIRO, Australia http://www.watersnet.org/wtbs/ODMKMLGatway.html A web application housed in Canberra, Australia, that operates over the WATERS Network data services

  37. Conclusion: Web services work! The CUAHSI Hydrologic Information System (HIS) is a geographically distributed network of hydrologic data sources and functions that are integrated using a web services architecture so that they function as a connected whole. For more information: http://www.cuahsi.org/his.html

  38. Conclusions • Hydrologic Information Server is functioning at all testbed sites • Data are published in a consistent format (WaterML) and are thematically synthesized in Hydroseek with water agency data • Applications and analyses can operate seamlessly over the Waters Network data services • A lot more to be done – GIS, weather and climate, remote sensing, simulation modeling, interpretive analysis, ….. Digital Watershed development!