IN 31 B1444 Temporal Metadata Modeling Semantic Representation

IN 31 B-1444 Temporal Metadata Modeling Semantic Representation of Temporal Metadata in a Virtual Observatory Han Wang 1 (wangh 17@rpi. edu), Eric Rozell 1 (rozele@rpi. edu), Patrick West 1 (westp@rpi. edu), Stephan Zednik 1 (zednis@rpi. edu), Peter Fox 1 (pfox@cs. rpi. edu) 1 Rensselaer Polytechnic Institute, 110 8 th St. , Troy, NY, United States Abstract The Virtual Solar-Terrestrial Observatory (VSTO) Portal at vsto. org (Fig. 1) provides a set of guided workflows to implement use cases designed for solar-terrestrial physics and upper atmospheric science. Semantics are used in VSTO to model abstract instrument and parameter classifications, providing data access to users without extended domain specific vocabularies. The temporal restrictions used in the workflows are currently possible via RESTful services made to a remote system with access to a SQL-based metadata catalog. Fig. 2. XML Schema Datatype date. Time Strings Fig. 3. Verbose OWL-Time Instances Fig. 2 shows a model that represents the start and end times for VSTO dataset records using only xsd: date. Time strings. Fig. 3 shows a model that represents the start and end times for VSTO dataset records using OWL-Time instances with a granularity of seconds. Fig. 4 show a model that represents the individual observations of VSTO dataset records using a notion of discrete intervals created within the Se. SF ontology. All of the three models above are actually not feasible solutions as they require the SPARQL engine to parse at least O(106) time instances to answer the queries for VSTO workflow with interactive responses (query response in less than 10 seconds). In order to provide a greater range of temporal reasoning and search capabilities for the user, we propose an alternative architecture design for the VSTO Portal, where the temporal metadata is integrated in the domain ontology. We achieve this integration by converting temporal metadata from the headers of raw data files into RDF using the OWL-Time vocabulary. This presentation covers our work with semantic temporal metadata, including: our representation using OWL-Time, issues that we have faced in persistent storage, and performance and scalability of semantic query. We conclude with discussions of the significance semantic temporal metadata has in virtual observatories. Motivations and Use Cases Limitations of relational database representations: • No mechanisms for inferring relationships given relationships that exit within the relational database. • No easy ways of inheriting relationships, as can be done with an ontology and knowledge base. Use case #1: Retrieve data where two or more instruments have coincident measurements within a temporal interval. Use case #2: Retrieve data in a non-contiguous time interval. Fig. 5. OWL-Time Instances with Date Coverages Fig. 5 illustrates a feasible solution for the temporal metadata modeling. It represents the start and end times for a VSTO dataset record using xsd: date. Time strings with a granularity of seconds, and it also includes the exact temporal range for that dataset to a granularity of days (as required by the use cases) using time: Date. Time. Interval. This modeling solution only increases the size of the data by approximately a factor of 5. We have achieved interactive response time for all scenarios in the original VSTO workflow by answering various SPARQL queries in the S 2 S Search Interface (Fig. 6), which is part of the Semantic e. Science Framework (Se. SF) project. Temporal Model Evaluation Table 1 lists 12 workflow tasks derived from the VSTO Portal. We generated SPARQL queries representing these tasks and executed them in a Virtuoso tripe store loaded with CEDAR datasets, which have approximately 20 million time instances (about 80 million triples). The average execution time of these queries all fall within 1 second, which either improves or is comparable to the task performance using RESTful service calls. The following shows a SPARQL query for Task 1, which takes about 5 seconds with the RESTful service. Instrument 175 is a instrument class called Space Craft, and it has the highest number of days covered. Fig. 1. VSTO Portal “Start by Dates” workflow Fig. 4. Discrete Interval Coverage in OWL-Time Task Number 1 2 3 4 5 6 7 8 9 10 11 12 Task Description Get all years a given instrument has data coverage Get all months a given instrument for given year has data coverage Get all days a given instrument for given year and month has data coverage Get all parameters measured by a given instrument in a given time interval Get all years any dataset has data coverage Get all months any dataset for given year has data coverage Get all days any dataset for given year and month has data coverage Get all instruments that have data coverage in a given time interval Get all years a given parameter has data coverage Get all months a given parameter for given year has data coverage Get all days a given parameter for given year and month has data coverage Get all instruments that measure a given parameter during a given time interval Table 1. A table of workflow tasks derived from the VSTO Portal PREFIX vsto: <http: //escience. rpi. edu/ontology/vsto/2/0/vsto. owl#> PREFIX cedar: <http: //escience. rpi. edu/ontology/vsto/2/0/cedar. owl#> PREFIX time: <http: //www. w 3. org/2006/time#> PREFIX xsd: <http: //www. w 3. org/2001/XMLSchema#> SELECT DISTINCT ? year WHERE { ? dataset vsto: is. From. Instrument cedar: cedar_instrument_175. ? dataset vsto: has. Date. Time. Coverage ? interval time: has. Date. Time. Description ? desc time: year ? year. Visit our working group page } at http: //goo. gl/q 9 mmn Glossary: Fig. 6. S 2 S Search Interface with the date facet RPI – Rensselaer Polytechnic Institute TWC – Tetherless World Constellation at Rensselaer Polytechnic Institute VSTO – Virtual Solar-Terrestrial Observatory CEDAR – Coupling Energetics and Dynamics of Atmospheric Regions Se. SF – Semantic e. Science Framework RESTful– Representational State Transfer Get the poster at http: //goo. gl/M 2 Uk. F Sponsors: NSF Office of Cyberinfrastructure (OCI) References: 1. 2. P. West, E. Rozell, S. Zednik, P. Fox, and D. Mc. Guinness, 2009, Semantically Enabled Temporal Reasoning in a Virtual Observatory, OWL Experiences and Directions, CEUR Workshop Proceedings, vol. 529. E. Rozell, P. West, and P. Fox, 2010, Experiences Integrating Temporal Metadata in a Domain Ontology, Technical Report.