2. Streaming Knowledge Bases Onkar Walavalkar, Anupam Joshi Tim Finin and Yelena Yesha University of Maryland, Baltimore County 27 October 2008

3. Streaming Knowledge Bases Onkar Walavalkar, Anupam Joshi Tim Finin and Yelena Yesha University of Maryland, Baltimore County 27 October 2008

4. Streaming Knowledge Bases Onkar Walavalkar, Anupam Joshi Tim Finin and Yelena Yesha University of Maryland, Baltimore County 27 October 2008

5. Overview Motivation Streaming databases Streaming knowledge bases Experiments and results Conclusions  Motivation  Stream DBs  Stream KBs  Experiments  Conclusions 

6. Operating Room of the Future ORs will be awash in low-level data, much of it noisy or incomplete Challenges include coping with the noise and interpreting the low- level data to recognize high-level events and activities ORF drugs patient Monitors staff tools RFID AwarePoint RFID Bluetooth WIFI devices  Motivation  Stream DBs  Stream KBs  Experiments  Conclusions 

7. Initial work in OR training UMD Mastri Center is experimenting with OR technologies and training environments The Human Patient Simulator from METI – Designed to react like a human – Responds to medical treatment Generates continuous streams of data, moderated by – Initial conditions (e.g. blunt trauma multiple injuries scenario) – human interactions  Motivation  Stream DBs  Stream KBs  Experiments  Conclusions 

8. Efficient Data Stream Management Data is stored/indexed in system Queries applied to stored data as they “stream through” Queries Index Results Data Query Index Results Data Traditional DBMSStream Management System Queries stored/indexed in system Data applied to stored queries as they “stream through” Several efforts: Tapestry, Aurora, TelegraphCQ  Motivation  Stream DBs  Stream KBs  Experiments  Conclusions 

9. Stream Processor (TelegraphCQ) Continuous Queries Patient Monitor RFID System Medicines Tools Staff Trend Analyzer Physiological Data Low-Level Event Processor Database Patient History Medical Supplies Staff Rule Base Assert facts Medical Encounter Record Video Clipper Assert facts Event Detection - Level 3 Event Detection - Level 2 Event Detection - Level 1 Events  Motivation  Stream DBs  Stream KBs  Experiments  Conclusions 

10. What’s wrong with this picture? We need to enhance this to support semantic interoperability for medical data & knowledge The medial community has a long history developing & using standard ontologies & metadata Incoming streams of data can be in rdf And reference terms in appropriate ontologies  Motivation  Stream DBs  Stream KBs  Experiments  Conclusions 

11. What’s wrong with this picture? Streaming Database systems use continuous queries specified over a sliding time window – e.g., [range by ‘30 seconds’ slide by ‘10 seconds’] Issues: – Where do we we do reasoning? – How do we answer queries against a sliding window of data?  Motivation  Stream DBs  Stream KBs  Experiments  Conclusions 

12. RDF Stream Processing Static Data Store RangeInfo PropertyTree DomainInfo InverseInfo Classtree input stream handler Special domain rules & queries Input Triple Stream Enhanced Stream Query for Class of Concern Detected Instances  Motivation  Stream DBs  Stream KBs  Experiments  Conclusions 

13. Experiments and results Three simple reasoners – Jena, in core – Pre-computed custom hash tables – Using tables in TelegraphCQ Various scenarios – Ontology size: 118 - 23.1 MB – Number of subclasses: 49 - 57,000 – Subclass depth: 2 - 9 – Data rate: 1 - 50 triples per second

14. Domain Example Monitor data stream looking for observations of invasive species from Bioblitz and eco-blogging data streams Uses our Ethan ontologies for ecoinformatics Tree of life (~340K taxons from ITIS and other sources) Species profiles Invasive species definitions Observation

15. Reasoning delay comparison for all approaches

19. VM Usage comparison of all 3 approaches

20. VM Usage for Jena for different classes

21. VM usage comparison for Hashtable and TCQ

22. Conclusions If the incoming triple data rate goes beyond a certain limit, the reasoning speed starts to lag and tends to slow down the incoming stream. The speedup achieved by using TCQ and a hashtable prove the value of pre-processing an ontology, particularly for fast streaming facts.

23. http://ebiquity.umbc.edu/