Discovery of temporal patterns from process instances

Discovery of temporal patterns from process instances

Existing work in process mining focuses on the discovery of the underlying process model from their instances. In this paper, we do not assume the existence of a single process model to which all process instances comply, and the goal is to discover a set of frequently occurring temporal patterns. Discovery of temporal patterns can be applied to various application domains to support crucial bu...

On the discovery of process models from their instances

A thorough understanding of the way in which existing business processes currently practice is essential from the perspectives of both process reengineering and workflow management. In this paper, we present a framework and algorithms that derive the underlying process model from past executions. The process model employs a directed graph for representing the control dependencies among activiti...

Discovery of Spatio-Temporal Patterns from Location Based Social Networks

Location Based Social Networks (LBSN) like Twitter or Instagram are a good source for user spatio-temporal behavior. These networks collect data from users in such a way that they can be seen as a set of collective and distributed sensors of a geographical area. A low rate sampling of user’s location information can be obtained during large intervals of time that can be used to discover complex...

Optimal Approach for Temporal Patterns Discovery

This paper presents new technique for discovering temporal patterns when considered primitives are intervals. Apriori technique is the most used one to deal with temporal patterns using point primitives. An extension of this technique is proposed by Höppner to deal with interval primitives. In this paper, we show that it is not necessary to discover all patterns, instead it is sufficient to dis...

Mastering the Spatio - Temporal Knowledge Discovery Process