Suchergebnisse

Information systems are facing conflicting requirements. On the one hand, systems need to be adaptive and self-managing to deal with rapidly changing circumstances. On the other hand, legislation such as the Sarbanes-Oxley Act, is putting increasing demands on monitoring activities and processes. As processes and systems become more flexible, both the need for, and the complexity of monitoring increases. Our earlier work on process mining has primarily focused on process discovery, i.e., automatically constructing models describing knowledge extracted from event logs. In this paper, we focus on a different problem complementing process discovery. Given an event log and some property, we want to verify whether the property holds. For this purpose we have developed a new language based on Linear Temporal Logic (LTL) and we combine this with a standard XML format to store event logs. Given an event log and an LTL property, our LTL Checker verifies whether the observed behavior matches the (un)expected/(un)desirable behavior.

Compliance checking is gaining importance as today's organizations need to show that operational processes are executed in a controlled manner while satisfying prede¿ned (legal) requirements. Deviations may be costly and expose the organization to severe risks. Compliance checking is of growing importance for the business process management and auditing communities. This paper presents an approach for checking compliance of observed process executions recorded in an event log to control-¿ow and temporal compliance requirements. We show a collection of 54 control ¿ow and 15 temporal compliance rules, distributed respectively over 10 and 7 categories. In addition we present how temporal compliance requirements discussed in literature can be uni¿ed and formalized using a generic temporal compliance rule. To check compliance with respect to a compliance rule, the event log describing the observed behavior is aligned with the corresponding rule. The alignment then shows which events occurred out of speci¿ed order and which events deviated by which amount of time from the prescribed behavior. The approach is ¿exible (easy to express new rules), and allowing for multi-perspective diagnostic information in case of compliance violations. The technique and corresponding tool support have been experimentally validated using a case study. Keywords: compliance checking, process mining, conformance checking, data-aware conformance checking, Petri-nets

Most organizations have process models describing how cases need to be handled. In fact, legislation and standardization (cf. the Sarbanes-Oxley Act, the Basel II Accord, and the ISO 9000 family of standards) are forcing organizations to document their processes. These processes are often not enforced by information systems. However, torrents of event data are recorded by today's information systems. These recorded events reflect how processes are really executed. Often reality deviates from the modeled behavior. Therefore, measuring the extent process executions conform to a predefined process model is increasingly important. In this paper, we propose an approach to measure the precision of a process model with respect to an event log. Unlike earlier approaches, we first align model and log thus making our approach more robust, even in case of deviations. The approach has been implemented in the ProM 6 tool and evaluated using both artificial and real life cases.