1. CIMA and Semantic Interoperability for Networked Instruments and Sensors Donald F. (Rick) McMullen Pervasive Technology Labs at Indiana University mcmullen@indiana.edu Open Collaboration: Networking Semantic Interoperability Across Distributed Organizations and Their Ontologies National Science Foundation August 15, 2006 Donald F. (Rick) McMullen Pervasive Technology Labs at Indiana University mcmullen@indiana.edu Open Collaboration: Networking Semantic Interoperability Across Distributed Organizations and Their Ontologies National Science Foundation August 15, 2006

2. CIMA Project Goals Project supported by the NSF Middleware Initiative to: Integrate instruments and sensors as real-time data sources into grid computing environments through a Service Oriented Architecture Improve accessibility and throughput in instrumentation investments Promote sharing across institutions and disciplines Develop a methodology for describing instrument capabilities and functions and embedding these in the hardware to improve flexibility and lifetime of data acquisition and analysis applications Towards a Semantic Web for instruments and sensors? Move production of metadata as close to instruments as possible and facilitate the automatic production of metadata Improve data management, provenance and reuse Next generation “smart context” for national research challengs Project supported by the NSF Middleware Initiative to: Integrate instruments and sensors as real-time data sources into grid computing environments through a Service Oriented Architecture Improve accessibility and throughput in instrumentation investments Promote sharing across institutions and disciplines Develop a methodology for describing instrument capabilities and functions and embedding these in the hardware to improve flexibility and lifetime of data acquisition and analysis applications Towards a Semantic Web for instruments and sensors? Move production of metadata as close to instruments as possible and facilitate the automatic production of metadata Improve data management, provenance and reuse Next generation “smart context” for national research challengs

3. CIMA Components Service architecture Instrument representative (IR) code (device independent) Drivers (Plug-ins, device dependent) Service life cycle, high level protocol, communications, self description, discovery, security Proxy service and embeddable code Client coding practices Communications protocol Transport neutral Standards based Support for synchronous and asynchronous interactions between client and IR Instrument and sensor description Ontology-based with static and dynamic information Clients should be able to build functional model from description Description instance development parallels plug-in development Ontology should be extensible as a community effort Service architecture Instrument representative (IR) code (device independent) Drivers (Plug-ins, device dependent) Service life cycle, high level protocol, communications, self description, discovery, security Proxy service and embeddable code Client coding practices Communications protocol Transport neutral Standards based Support for synchronous and asynchronous interactions between client and IR Instrument and sensor description Ontology-based with static and dynamic information Clients should be able to build functional model from description Description instance development parallels plug-in development Ontology should be extensible as a community effort

4. CIMA Reference Implementation Applications Synchrotron X-Ray crystallography Argonne APS ChemMatCARS & DND-CAT Also lab systems through CrystalGrid (global network of crystallography labs) TOF-MS Identification of proteins and other macromolecules Robotic telescopes Star variability Looking for killer asteroids? Sensor networks Ecological observation Crossbow MOTE sensor package Synchrotron X-Ray crystallography Argonne APS ChemMatCARS & DND-CAT Also lab systems through CrystalGrid (global network of crystallography labs) TOF-MS Identification of proteins and other macromolecules Robotic telescopes Star variability Looking for killer asteroids? Sensor networks Ecological observation Crossbow MOTE sensor package

5. Instrument description and metadata Enables a “smart context” for research starting at the source of observations Physical and logical attributes of the hardware and data produced by it Maps sensors and actuators defined in Plug-ins to the semantics of control functions and data outputs of the instrument Applications can query the instrument description to build an operational model of the instrument on the fly Description instance based on ontology which can be implemented as OWL-DL Enables a “smart context” for research starting at the source of observations Physical and logical attributes of the hardware and data produced by it Maps sensors and actuators defined in Plug-ins to the semantics of control functions and data outputs of the instrument Applications can query the instrument description to build an operational model of the instrument on the fly Description instance based on ontology which can be implemented as OWL-DL

6. Example: Research process in structure determination of macromolecules using X-ray diffraction crystallography Metadata requirements differ at each step but depend on prior actions and transformations. Need ontologies that span the process but are appropriate at each step to relevant actors.

7. General Interoperability Goals Hardware level Design evolution Multiple designs/vendors Basis for metadata languages Within a community Different perspectives: roles, goals Annotation across all transformations in discovery process Awareness of services and data Across communities Awareness Shared understanding of data semantics leads directly to the hardware level and semantics of processes involved in creating transformation products. Fusion across discipline (e.g. physical+biological systems) Re-purposing and re-visiting data; decision support Hardware level Design evolution Multiple designs/vendors Basis for metadata languages Within a community Different perspectives: roles, goals Annotation across all transformations in discovery process Awareness of services and data Across communities Awareness Shared understanding of data semantics leads directly to the hardware level and semantics of processes involved in creating transformation products. Fusion across discipline (e.g. physical+biological systems) Re-purposing and re-visiting data; decision support

9. Ontologies for interoperability of resources within a community of interest (finding information and services)

10. Shared or aligned ontologies support sharing of resources and fusion of information across CoIs and disciplines, from sensor through analysis, simulation, and model building

11. CIMA Ontology for Instruments and Sensors Key Concepts Data products (what does this thing do?) names, units, conversion, calibration Location (where is it?) physical and network location, organizational ownership Functional model of hardware (how does it work?) Response models of sensors and actuators Access as network service (how do I use it?) Domain-specific metadata Hardware life-cycle calibration, validation, faults, maintenance Current version of core (data products and location) consists of 74 concepts, 70 properties and individuals. Key Concepts Data products (what does this thing do?) names, units, conversion, calibration Location (where is it?) physical and network location, organizational ownership Functional model of hardware (how does it work?) Response models of sensors and actuators Access as network service (how do I use it?) Domain-specific metadata Hardware life-cycle calibration, validation, faults, maintenance Current version of core (data products and location) consists of 74 concepts, 70 properties and individuals.

12. Development tools and Methodology Protégé IHMC Cmap tool for visual analysis of ontology RDQL - Pellet reasoner to test ontology and instances with use cases (competency questions) A qualitative research toolbox (Think of ontology development as an exercise in ethnography, case study and/or grounded theory; additional insights through activity theory.) Protégé IHMC Cmap tool for visual analysis of ontology RDQL - Pellet reasoner to test ontology and instances with use cases (competency questions) A qualitative research toolbox (Think of ontology development as an exercise in ethnography, case study and/or grounded theory; additional insights through activity theory.)

13. OWL Tools used in CIMA Protégé for Instance Editing

14. Tools - IHMC CMap A subset of the CIMA ontology visualized as a concept map:

15. Consistency Checking and Queries Pellet to check for consistency and to process RDQL queries. Query: Give me all instruments in Bloomington, Indiana. Query result: Pellet to check for consistency and to process RDQL queries. Query: Give me all instruments in Bloomington, Indiana. Query result: SELECT ?x WHERE (?x cima:hasLocation cima:BloomingtonIN), (?x rdf:type cima:Instrument) USING cima FOR, owl FOR, rdf FOR, rdfs FOR

16. Thanks! Questions? (mcmullen@indiana.edu) Support for this work provided by the National Science Foundation is gratefully acknowledged. (SCI 0330568, DBI 0446802) NSF Middleware Initiative: www.nsf-middleware.org CIMA project: www.instrument-middleware.org