LogLogics: A logic for history-dependent business processes

Hee, Kees van; Oanea, Olivia; Serebrenik, Alexander; Sidorova, Natalia; Voorhoeve, Marc

doi:10.1016/j.scico.2006.08.004

Cited by 8 publications

(4 citation statements)

References 16 publications

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“…A history of related events can therefore be a strong predictor and is readily available in event logs. However, non-local behavior of business processes already presents many challenges for process modeling [2,40]. For process discovery, the inclusion of such non-local, non-free choice in the hypothesis space of process mining algorithms raises difficulties with regard to search space complexity and hypothesis visualization [45].…”

Section: Challenging Problemsmentioning

confidence: 99%

Process discovery in event logs: An application in the telecom industry

Goedertier

Weerdt

Martens

et al. 2011

Applied Soft Computing

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Section: Challenging Problemsmentioning

confidence: 99%

Process discovery in event logs: An application in the telecom industry

Goedertier

Weerdt

Martens

et al. 2011

Applied Soft Computing

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“…Another example is the occurrence of history-based joins in the control flow of a business process (van der Aalst and ter van Hee et al, 2006b). This non-local behavior of business processes presents many challenges for process modeling (van Hee et al, 2006a). Consider as an example the process model ABCD in which each activity A,B, C and D can be performed at most once for each process instance.…”

Section: History-dependent Behaviormentioning

confidence: 99%

EM-BrA2CE v0.1: A Vocabulary and Execution Model for Declarative Business Process Modeling

Goedertier¹,

Haesen²,

Vanthienen

2007

SSRN Journal

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In management theory of the last decades, much importance has been attached to a process-oriented perspective on organizational (re)structuring. Yet to date, organizations still experience difficulties in applying this process-oriented perspective to the design and maintenance information systems. The root of the problem lies with a procedural representation of business processes that contains inadequate information for computer systems to provide flexible automated business process support. The counterpart of a procedural representation is a declarative one that explicitly takes into account the business concerns that govern business processes. Recently, a number of process modeling languages have appeared that could be identified as declarative languages. These modeling languages have very distinct knowledge representation backgrounds, often lack a formal execution model and often only model one aspect of the many business concerns that exist in reality. What is needed are meaningful ways to combine several kinds of expressions, called business rule types, independently of the used methods for knowledge representation and reasoning. In this paper, we present the EM-BrA 2 CE (Enterprise Modeling using Business Rules, Agents, Activities, Concepts and Events) Framework, a unifying vocabulary and execution model for declarative process modeling. The vocabulary is described in terms of the Semantics for Business Vocabulary and Rules (SBVR) standard and the execution model is presented as a Colored Petri Net (CP-Net). In addition, we show how declarative process models can contribute to the model-driven design of Service-Oriented Architectures.

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“…Another example is the occurrence of history-based joins in the control flow of a business process (van Hee et al, 2006b). This non-local behavior of business processes presents many challenges, not only for process mining but also for process modeling (Alur and Henzinger, 1994;van Hee et al, 2006a).…”

Section: History-dependent Processesmentioning

confidence: 99%

“…Too complex refinement steps might specify too large a hypothesis space making search inefficient. In (Alur and Henzinger, 1994;van Hee et al, 2006a), Linear Temporal Logic (LTL) language for representing non-local guard conditions is proposed. LTL is nonetheless unsuitable to make up the language bias of an ILP learner, because there is no immediate transformation of the LTL modal operators to first-order predicate logic.…”

Section: History-dependent Processesmentioning

confidence: 99%

A New Approach for Discovering Business Process Models from Event Logs

Goedertier¹,

Martens

Baesens

et al. 2007

SSRN Journal

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Process mining is the automated acquisition of process models from the event logs of information systems. Although process mining has many useful applications, not all inherent difficulties have been sufficiently solved. A first difficulty is that process mining is often limited to a setting of non-supervised learning since negative information is often not available. Moreover, state transitions in processes are often dependent on the traversed path, which limits the appropriateness of search techniques based on local information in the event log. Another difficulty is that case data and resource properties that can also influence state transitions are time-varying properties, such that they cannot be considered as cross-sectional. This article investigates the use of first-order, ILP classification learners for process mining and describes techniques for dealing with each of the above mentioned difficulties. To make process mining a supervised learning task, we propose to include negative events in the event log. When event logs contain no negative information, a technique is described to add artificial negative examples to a process log. To capture history-dependent behavior the article proposes to take advantage of the multi-relational nature of ILP classification learners. Multi-relational process mining allows to search for patterns among multiple event rows in the event log, effectively basing its search on global information. To deal with timevarying case data and resource properties, a closed-world version of the Event Calculus has to be added as background knowledge, transforming the event log effectively in a temporal database. First experiments on synthetic event logs show that first-order classification learners are capable of predicting the behavior with high accuracy, even under conditions of noise.

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LogLogics: A logic for history-dependent business processes

Cited by 8 publications

References 16 publications

Process discovery in event logs: An application in the telecom industry

Process discovery in event logs: An application in the telecom industry

EM-BrA2CE v0.1: A Vocabulary and Execution Model for Declarative Business Process Modeling

A New Approach for Discovering Business Process Models from Event Logs

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