Inference Optimization using Relational Algebra

Exact inference procedures in Bayesian networks can be expressed using relational algebra; this provides a common ground for optimizations from the AI and database communities. Specifically, the ability to accomodate sparse representations of probability distributions opens up the way to optimize for their cardinality instead of the dimensionality; we apply this in a sensor data model.\u

Data Workflow - A Workflow Model for Continuous Data Processing

Online data or streaming data are getting more and more important for enterprise information systems, e.g. by integrating sensor data and workflows. The continuous flow of data provided e.g. by sensors requires new workflow models addressing the data perspective of these applications, since continuous data is potentially infinite while business process instances are always finite.\ud In this paper a formal workflow model is proposed with data driven coordination and explicating properties of the continuous data processing. These properties can be used to optimize data workflows, i.e., reducing the computational power for processing the workflows in an engine by reusing intermediate processing results in several workflows