DrugFusion - Retrieval Knowledge Management for Prediction of Adverse Drug Events

Galushka, Mykola; Gilani, Wasif

doi:10.1007/978-3-319-06695-0_2

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…The three logs BE1, BE2, and BE3 have been generated by us, using simulation on randomly created complex models with 20 activities (the models were created by performing a sequence of the operators described in Section 3 on initially sequential models with 20 activities). Log DF is extracted from an eHealth process which contains 18 activities [7], and FL A a is an excerpt of a loan application process log with ten activities used in the Business Process Intelligence Challenge 2012 (process "A").…”

Section: Discussionmentioning

confidence: 99%

Diversity Guided Evolutionary Mining of Hierarchical Process Models

Molka

Redlich

Drobek

et al. 2015

Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

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Easy-to-understand and up-to-date models of business processes are important for enterprises, as they aim to describe how work is executed in reality and provide a starting point for process analysis and optimization. With an increasing amount of event data logged by information systems today, the automatic discovery of process models from process logs has become possible. Whereas most existing techniques focus on the discovery of well-formalized models (e.g. Petri nets) which are popular among researchers, business analysts prefer business domain-specific models (such as Business Process Model Notation, BPMN) which are not well formally specified. We present and evaluate an approach for discovering the latter type of process models by formally specifying a hierarchical view on business process models and applying an evolution strategy on it. The evolution strategy efficiently finds process models which best represent a given event log by using fast methods for process model conformance checking, and is partly guided by the diversity of the process model population. The approach contributes to the field of evolutionary algorithms by showing that they can be successfully applied in the real-world use case of process discovery, and contributes to the process discovery domain by providing a promising alternative to existing methods.

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Section: Discussionmentioning

confidence: 99%

Diversity Guided Evolutionary Mining of Hierarchical Process Models

Molka

Redlich

Drobek

et al. 2015

Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

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“…Two types of Process Extractors have been developed in the project: (1) a statically operating genetic miner that works on the historic business process logs [14] and is suitable for long-life business processes that do not change over time, such as the one from Civil Infrastructure domain. However, due to the characteristics of the static genetic miner (non-deterministic as well as long and unpredictable execution time) it is not applicable for monitoring fast and dynamically changing processes such as the ones from the eHealth domain [3]. For these use-cases it is required to monitor the system for changes in the process which result in changes in the risk assessment and potentially entail a new preservation iteration.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Dynamic Construct Competition Miner for an eHealth System

Redlich

Galushka²,

Molka³

et al. 2015

Business Information Systems

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Abstract. Business processes of some domains are highly dynamic and increasingly complex due to their dependencies on a multitude of services provided by various providers. The quality of services directly impacts the business process's efficiency. A first prerequisite for any optimization initiative requires a better understanding of the deployed business processes. However, the business processes are either not documented at all or are only poorly documented. Since the actual behaviour of the business processes and underlying services can change over time it is required to detect the dynamically changing behaviour in order to carry out correct analyses. This paper presents and evaluates the integration of the Dynamic Construct Competition Miner (DCCM) as process monitor in the TIMBUS architecture. The DCCM discovers business processes and recognizes changes directly from an event stream at run-time. The evaluation is carried out in the context of an industrial use-case from the eHealth domain. We will describe the key aspects of the use-case and the DCCM as well as present the relevant evaluation results.

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