Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Aggregating Linked Sensor Data


Published on

This presentation was given at the Semantic Sensor Networks Workshop at the International Semantic Web Conference (ISWC) 2011. It shows our approach for Aggregating Linked Sensor Data.

Published in: Education, Technology
  • Be the first to comment

  • Be the first to like this

Aggregating Linked Sensor Data

  1. 1. Aggregating Linked Sensor Data Christoph Stasch, Sven Schade, Alejandro Llaves, Krysztof Janowicz, Arne Bröring Institute for Geoinformatics Westfälische Wilhelms-Universität Münster 3rd Workshop on Semantic Sensor Networks Bonn, 2011 Christoph Stasch –
  2. 2. Introduction Christoph Stasch –
  3. 3. Aggregation in Linked Sensor Data Christoph Stasch – 15°C 16°C 17°C 14°C Adding new links: Belongs the observation value to that feature? Spatial Aggregation 15,5°C Linking the aggregated observation
  4. 4. Spatio-temporal and Thematic Aggregation Christoph Stasch –
  5. 5. Aggregation <ul><li>Aggregation: </li></ul><ul><ul><ul><li>An aggregation process computes a value, an aggregate , for a group of attribute values by means of an aggregation function . The attribute values are grouped by a partitioning predicate . </li></ul></ul></ul><ul><li>Aggregation Function: </li></ul><ul><ul><ul><li>Function used to compute the aggregate. </li></ul></ul></ul><ul><li>Partitioning Predicate: </li></ul><ul><ul><ul><li>Predicate used to group objects before aggregating the values attached to these objects. </li></ul></ul></ul>Christoph Stasch –
  6. 6. Spatio-temporal vs. Thematic Aggregation <ul><li>Spatio-temporal Aggregation: </li></ul><ul><ul><li>Partitionining predicate is spatial and/or temporal </li></ul></ul><ul><li>Thematic Aggregation: </li></ul><ul><ul><li>Partitioning predicate operates on attribute values </li></ul></ul>Christoph Stasch –
  7. 7. Previous Work Christoph Stasch –
  8. 8. Linked Sensor Data <ul><li>World Wide Web is for websites / documents </li></ul><ul><ul><li>HTTP </li></ul></ul><ul><ul><li>HTML </li></ul></ul><ul><ul><li>... </li></ul></ul><ul><li>Sensor Web is for sensors </li></ul><ul><ul><li>SOS </li></ul></ul><ul><ul><li>O&M </li></ul></ul><ul><ul><li>... </li></ul></ul><ul><li>Linked Data Web is for linked data </li></ul><ul><ul><li>RDF </li></ul></ul><ul><li>Linked Sensor Data (e.g. Page 2009) </li></ul>Christoph Stasch –
  9. 9. RESTful SOS Proxy <ul><li>Proxy service for Sensor Observation Services </li></ul><ul><li>Linked data model + URI scheme for observation resources </li></ul>Christoph Stasch – Janowicz, K., Bröring, A., Stasch, C., Schade, S., Everding, T., and Llaves, A. (2011): A RESTful Proxy and Data Model for Linked Sensor Data. International Journal of Digital Earth. DOI:10.1080/17538947.2011.614698, pp. 1-22
  10. 10. Spatio-Temporal Aggregation Service (STAS) Christoph Stasch – Stasch, C., Autermann, C., Foerster, T., Pebesma, E.: Towards a Spatiotemporal Aggregation Service in the Sensor Web. Poster Presentation. In: The 14th AGILE International Conference on Geographic Information Science. (2011)
  11. 11. Aggregating Linked Sensor Data Christoph Stasch –
  12. 12. Aggregating Linked Sensor Data <ul><li>Linked Data Model: </li></ul><ul><ul><li>Extending the SSO pattern to allow aggregated observations </li></ul></ul><ul><li>Effects on Links from and To Observations </li></ul><ul><ul><li>How do links change during aggregation? </li></ul></ul><ul><li>Provenance </li></ul><ul><ul><li>Information is contained in Linked Data Model; can be mapped to Open Provenance Model or Provenance Vocabulary </li></ul></ul>Christoph Stasch –
  13. 13. Extended SSO Design Pattern Christoph Stasch –
  14. 14. Effects on Links from and to Observations Christoph Stasch – 15°C 16°C 17°C 14°C FOI1 FOI2 FOI3 FOI4 Spatial Aggregation 15,5°C
  15. 15. Effects from and to Observations Christoph Stasch –
  16. 16. Provenance Christoph Stasch –
  17. 17. Provenance Information <ul><li>Common approaches: </li></ul><ul><ul><li>Open Provenance Model </li></ul></ul><ul><ul><ul><li>Nodes and edges to define provenance graphs </li></ul></ul></ul><ul><ul><li>Provenance Vocabulary </li></ul></ul><ul><li>Provenance in Sensor Data: </li></ul><ul><ul><li>Information about the source of the data as well as transformations applied </li></ul></ul><ul><ul><li>Approaches </li></ul></ul><ul><ul><ul><li>Provenance in Linked Sensor Data </li></ul></ul></ul><ul><ul><ul><li>Using OPM for sensor data </li></ul></ul></ul><ul><ul><ul><li>Defining own provenance models </li></ul></ul></ul>Christoph Stasch –
  18. 18. Provenance Christoph Stasch – DUL = Dolce Ultra Light ldm = Linked Sensor Data Model opmv = Open Provenance Model Vocabulary prv = Provenance Vocabulary
  19. 19. Conclusions & Outlook Christoph Stasch –
  20. 20. Conclusions <ul><li>Aggregation helps: </li></ul><ul><ul><li>Establishing new links </li></ul></ul><ul><ul><li>Fusing datasets </li></ul></ul><ul><li>Extended SSO pattern </li></ul><ul><ul><li>Allows for aggregated observations and aggregation processes </li></ul></ul><ul><ul><li>Retracing aggregated Observations back to original observations  mapping to OPM and Provenance Vocabulary </li></ul></ul><ul><li>Effects of aggregation on links from and to observations </li></ul>Christoph Stasch –
  21. 21. Outlook <ul><li>Formalize effects of aggregation on links </li></ul><ul><li>Enable Spatio-temporal Aggregation Service for linked sensor data </li></ul><ul><li>Integrate with approaches for sensor plug‘n‘play and linked sensor streams </li></ul><ul><li>Utilize semantics of aggregation processes </li></ul><ul><li>Integrate uncertainty/quality information </li></ul>Christoph Stasch –
  22. 22. Discussion Christoph Stasch –
  23. 23. Discussion <ul><li>To what aggregation level can we speak of observations? </li></ul><ul><li>Virtual sensors vs. Physical Sensors? </li></ul><ul><li>Common aggregation mechanisms in Linked Data? </li></ul>Christoph Stasch –
  24. 24. Thank you! RESTful SOS: STAS: Christoph Stasch – http://