Streaming Knowledge Bases

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Streaming Knowledge Bases

• Onkar Walavalkar, Anupam JoshiTim Finin and
  Yelena Yesha
• University of Maryland, Baltimore County
• 27 October 2008

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Streaming Knowledge Bases

• Onkar Walavalkar, Anupam JoshiTim Finin and
  Yelena Yesha
• University of Maryland, Baltimore County
• 27 October 2008

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Streaming Knowledge Bases

• Onkar Walavalkar, Anupam JoshiTim Finin and
  Yelena Yesha
• University of Maryland, Baltimore County
• 27 October 2008

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Overview

• Motivation
• Streaming databases
• Streaming knowledge bases
• Experiments and results
• Conclusions

? Motivation ? Stream DBs ? Stream KBs ?
Experiments ? Conclusions ?
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Operating Room of the Future
drugs
RFID
RFID
ORF
tools
AwarePoint

WIFI
patient Monitors
Bluetooth
devices
staff

• 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

? Motivation ? Stream DBs ? Stream KBs ?
Experiments ? Conclusions ?
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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 ?
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Efficient Data Stream Management
Index
Queries
Index
Data
Traditional DBMS
Stream Management System

• Data is stored/indexed in system
• Queries applied to stored data as they stream
  through

• 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 ?
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? Motivation ? Stream DBs ? Stream KBs ?
Experiments ? Conclusions ?
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Whats 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 ?
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Whats 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 ?
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RDF Stream Processing
Query for Class of Concern
Input Triple Stream
Detected Instances
input stream handler
Special domainrules queries
Enhanced Stream
Static Data Store
RangeInfo
DomainInfo
Classtree
PropertyTree
InverseInfo
? Motivation ? Stream DBs ? Stream KBs ?
Experiments ? Conclusions ?
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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

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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

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Reasoning delay comparison for all approaches
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Reasoning delay comparison for all approaches
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Reasoning delay comparison for all approaches
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Reasoning delay comparison for all approaches
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VM Usage comparison of all 3 approaches
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VM Usage for Jena for different classes
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VM usage comparison for Hashtable and TCQ
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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.

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http//ebiquity.umbc.edu/