English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

In recent years, there has been an increased need for the use of activesystems - systems required to act automatically based on events, or changes inthe environment. Such systems span many areas, from active databases toapplications that drive the core business processes of today's enterprises.However, in many cases, the events to which the system must respond are notgenerated by monitoring tools, but must be inferred from other events based oncomplex temporal predicates. In addition, in many applications, such inferenceis inherently uncertain. In this paper, we introduce a formal framework forknowledge representation and reasoning enabling such event inference. Based onprobability theory, we define the representation of the associated uncertainty.In addition, we formally define the probability space, and show how therelevant probabilities can be calculated by dynamically constructing a Bayesiannetwork. To the best of our knowledge, this is the first work that enablestaking such uncertainty into account in the context of active systems.herefore, our contribution is twofold: We formally define the representationand semantics of event composition for probabilistic settings, and show how toapply these extensions to the quantification of the occurrence probability ofevents. These results enable any active system to handle such uncertainty.

A Model for Reasoning with Uncertain Rules in Event Composition Systems