Traffic Analytics using Probabilistic Graphical Models Enhanced with Knowledge Bases

Pramod Anantharam, Krishnaprasad Thirunarayan, Amit P. Sheth

Research output: Contribution to conferencePresentation

Abstract

Graphical models have been successfully used to deal with uncertainty, incompleteness, and dynamism within many domains. These models built from data often ignore preexisting declarative knowledge about the domain in the form of ontologies and Linked Open Data (LOD) that is increasingly available on the web. In this paper, we present an approach to leverage such 'top-down' domain knowledge to enhance 'bottom-up' building of graphical models. Specifically, we propose three operations on the graphical model structure to enrich it with nodes, edges, and edge directions. We illustrate the enrichment process using traffic data from 511.org and declarative knowledge from ConceptNet. The resulting enriched graphical model can potentially lead to better predictions of traffic delays.

Original languageAmerican English
StatePublished - Jan 1 2013
EventProceedings of the 2nd International Workshop on Analytics for Cyber-Physical Systems (ACS-2013) -
Duration: Jan 1 2013 → …

Conference

ConferenceProceedings of the 2nd International Workshop on Analytics for Cyber-Physical Systems (ACS-2013)
Period1/1/13 → …

Keywords

  • Cyber physical systems
  • Declarative knowledge
  • Graphical models
  • Probabilistic modeling
  • Traffic analytics

Disciplines

  • Bioinformatics
  • Communication
  • Communication Technology and New Media
  • Computer Sciences
  • Databases and Information Systems
  • Life Sciences
  • OS and Networks
  • Physical Sciences and Mathematics
  • Science and Technology Studies
  • Social and Behavioral Sciences

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