1. NEESgrid Data Overview Charles Severance

2. Goals n Operates both as local and central (curated) repository using same toolset n Object oriented - referential integrity - data and metadata are supported together n Data migration and replication transparently including security, and metadata n Uses Grid Services throughout (OGSA/GT3) n Easily used access controls - Grid as “single sign on” n Data provenance n Data in support of research publication n Support for repeatable experiments n Data oriented research computation support n Support for workflow

3. Vision: data on the Grid studies Data Gathering Repository Extracting Mapping Meta Data

4. NEESgrid Data – Core Elements n Local Repository n Central Repository n JAVA APIs – Run locally on the same system as a repository or over OGSA Web Services –NEES File Management Services –NEES Meta Data Services n Data Viewers –Streaming (numeric, X/Y graph) –Stored (X/Y graph, 2-D structure, video)

5. NEESdata NEESpop Local Repository Core Elements API Central Repository Data Teamlets Data Acquisition Workstation API Data Teamlets API Data/MD Ingest Tools Data tools Data viewers Grid and Web Services

6. NEESgrid Data – Technologies n Grid –GRIDFTP is used for data transport –Grid Web Services are used to insure security and provide access control between systems over the Internet – also provide for credential passthrough –Grid credentials are used as part of login providing a single sign-on framework n CHEF –Provides a flexible mechanism for deploying GUI tools like the data viewers and data browsers.

7. A Simple Experimental Scenario DAQ System Glue Test Specimen Labview Developer System Researcher System

8. Simulation System Code Simulation System Code A Simulation Scenario Developer System Simulation System Code

9. The MOST Scenario n Part of the run-up to NEESPop 2.0 –Used Beta of NEESpop and Beta of CHEF –Tested the data ingestion –Tested the metadata capabilities –Developed sample metadata –Tested mapping capabilities n System still available at https://cee-nees.cee.uiuc.edu/chef/

10. NCSA NEESPop (1.1) Colorado NEESPop (1.1) Incoming FTP NEESMost (Win XP) UIUC/Newmark NEESPop (2.0) LabView DAQ MatLab Host And Real-Time Target Control System Sim Controller CO LabView DAQ NSDS UIUC Test Specimen Matlab Computational Model Shore-Western Test Specimen Incoming FTP NSDS Repository CO NCSA UIUC Meta Series of files Complete file (aggregated) NTCP Site / Location ComputerProcess NCSA Ingest NTCP Matlab NTCP Ftp NTCP Wires NFMS/NMDS NSDS File I/O Plug In Ingest UIUC MOST Data Flows

11. NEESpop 2.0 Alpha DAQ 0 2 3 5 3 3 5 3 4 0 3 4 0 6 8 Metadata 0.00 0.01 Time 3636 ch01 4848 ch02 The experiment was viewed using the standard NEES stored viewer with synchronized video and data and the ability to move back and forth

13. DAQ 0 3 4 0 6 8 0 3 4 0 6 8 University of Minnesota

14. NEES Markup Language (NEESML) n Provides an RDF-like structure capable of representing semantic information –XML is the syntax which is used –Logic is more “object oriented” Can define objects Can create objects Can reference objects

15. RDF/XML Versus NEESML n NEESml is topologically equivalent to RDF but more straightforward to use –A compromise between usability and functionality –Focused on solving the problems of ingesting types and data – rather than “cross-server ontology webs” –Used to build a reference set of ingestion tools –RDF is a moving target n Repository does not store either RDF or NEESML – It is an relational database tuned to store “three-tuples”

16. The Slide Metadata Data Data Viewers Data Mappers Data Ingestors There is a layer is where we develop tools which take advantage and begin to depend on of the “meaning” of the data – where we begin to depend on the meaning of a second. Where we make a viewer capable of viewing a certain type of object. This is where we build things which make use of knowledge. This layer will never be complete but it is a large focus of the coming months. Concepts Search

17. Looking for Data Models… Looking for Semantics …

18. Strategies n Take Existing Data Models and Adopt n Build tools and data models at the same time n Find existing tools that produce types of data we find interesting n Build meta data extractors for important file formats n Build converters for things like Excel

19. Why start with the ORST Model? n It provides good coverage of a well- understood scope n It has consensus across multiple sites n A prototype toolset exists around the model which is an important validation n http://nees.orst.edu/IT/data.model/ n We can adopt the core elements and extend as necessary

20. http://nees.orst.edu/IT/data.model/docs/v1.3a.july16.pdf

21. o:project o:experiment[s] o:role[s] o:acknowledgement[s] specialCondions title startDate o:experiment o:facility status title shortDescription o:facility longDescription title shortDescription

27. Go Forward – Core Elements n Implement Access Control n Implement Replication n Investigate RDF and its relationship to NEESML n Investigate provenance – would like to adopt from another project n Investigate mapping – Would like to adopt from another project

28. Go Forward - Tools n Evaluate the ORST interface and use it to implement experiment-based interface to meta data repository n Investigate tools to represent structural data (like SAC data) n Extend and improve viewers – publish API so that sites can extend the viewers n Improve notebook –Single signon using CHEF/Grid credentials –Integration with Metadata –Smother integration with CHEF n Explore automated synchronized video and data capture and after-experiment replay of synchronized video and data (ORST UMinn) n Explore the capture of high quality still images as data (UMinn) n Investigate adopting a data-editing tool (XMLSpy)

29. Go Forward – Data Models n Analyze the ORST model, determine core, convert to NEESML, pre-populate repositories with types, and develop usage documentation n Form core group between SI, ES, and CS to push data model issues forward – once groundwork is better defined – we can disperse into distributed teams n Use experiment based deployment to help us encounter new data needs over time

30. How to prioritize model exploration and development n Focus on the following areas: –Areas where we have or are building tools –Areas where we already have incoming data in some format –Build the model through experiment based deployment - solve real problems in an open way and see if (with some adaptation) the solutions apply more broadly (i.e. Minnesota )

31. What is in Release 2.0? October 7 n Groovy look and feel n Local Data Repository n Repository Browser in CHEF –Browse –Create objects –Upload / download data n API documentation n NEESML User Documentation n Extensible data mapping in Java n Data Viewer in CHEF –Improved visually –Configured by XML –Can read data from repository or from urls –Pre-populated with sample video and data formats n Local Repositories pre- populated with –SAC Data –MOST Data –ORST data model (subset)

32. NEESML Table 1: Primitive types in NEESML NameDescriptionExamples string Text “Hello, world.” “BN# 493-2584x” int Integer 3 -2 2147483647 long Long integer. Can exceed the size of an integer. -5782347562427 9223372036854775807 double Double precision floating point number. 523425.4568574636 -0.0000000435234 date A moment in time, represented as a date and time stamp in UTC with 1ms resolution. 2002-10-27 15:40:32.048 1969-01-12 00:03:48.774

33. Repository Browser

34. Ingestor

35. API Documentation

36. Configuring Events <event id="oregon" desc="Oregon Large Tank Test September 8, 2003" host=“/chef/org.nees.repo.data/retrieve-data?lfn=nacse_sample_01.txt& static=yes&mapping=nacse-" type="stored"> <video id="01" desc="Video of cylinder" url=“/chef/retrieve-data/static/nacse_sample_01.avi" /> We may be able to get a patch out to switch this to NEESml and provide a simple entry tool.

37. Mappings and the Data Viewer n NSDS (ISO 8601 Time channel) n Column data with time recorded as a column n Column – generate time n Column – generate time – trigger filter Channel units: g,g,in,kip Time ATL1 ATT1 2002-11-13T15:48:55.26499 -0.006409 0.004272 2002-11-13T15:48:55.36499 -0.005798 -0.003662 100.000 0.435 0.161 -1.016 -0.981 0.430 0.161 -1.016 -0.977 0.435 0.161 -1.016 -0.977 public class NEESDataMap { public static boolean repoMap(File mainFile, File mappingFile, String mapping) { // Code here }

38. Release 2.1 Data Aspects December 2003 n NEESpop –Notebook to metadata repository connection made –Closer integration of notebook into CHEF –First release of experiment tool (based on ORST) –Retool data viewers to be completely driven by Metadata objects rather than their own objects –More fine grained access control –Enhanced data models n Tools –Ingestion tools released –A limited set of pre-release video/image tools

39. Further releases n Release 2.2 – March 04 –Driven by your needs as we encounter them –Perhaps some “nice to haves” from the SI team n Release 3.0 – June 04 –Very limited new functionality – maybe almost nothing new in the core components of the NEESpop