1,721,171 research outputs found
Aspect oriented business process modeling with precedence
No full textComplexity is a major concern which is aimed to be overcome by people through modeling. One way of reducing complexity is separation of concerns, e.g. separation of business process from applications. One sort of concerns are cross-cutting concerns i.e. concerns which are scattered and tangled through one of several models. In business process management, examples of such concerns are security and privacy policies. To deal with these cross-cutting concerns, the aspect orientated approach was introduced in the software development area and recently also in the business process management area. The work presented in this paper elaborates on aspect oriented process modelling. It extends earlier work by defining a mechanism for capturing multiple concerns and specifying a precedence order according to which they should be handled in a process. A formal syntax of the notation is presented precisely capturing the extended concepts and mechanisms. Finally, the relevant of the approach is demonstrated through a case study
BPMN research : what we know and what we don’t know
Full text linkIn this short keynote paper, I will briefly explore the current state of research and practice surrounding the BPMN standard. On basis of this analysis I will offer a personal outlook into the key emerging areas where I believe more research will be required to further understand BPMN, its premise and promise, and how we can shape – and join together – the landscape of BPMN practice and development in academia and industry
Fast and accurate business process drift detection
No full textBusiness processes are prone to continuous and unexpected changes. Process workers may start executing a process differently in order to adjust to changes in workload, season, guidelines or regulations for example. Early detection of business process changes based on their event logs – also known as business process drift detection – enables analysts to identify and act upon changes that may otherwise affect process performance. Previous methods for business process drift detection are based on an exploration of a potentially large feature space and in some cases they require users to manually identify the specific features that characterize the drift. Depending on the explored feature set, these methods may miss certain types of changes. This paper proposes a fully automated and statistically grounded method for detecting process drift. The core idea is to perform statistical tests over the distributions of runs observed in two consecutive time windows. By adaptively sizing the window, the method strikes a trade-off between classification accuracy and drift detection delay. A validation on synthetic and real-life logs shows that the method accurately detects typical change patterns and scales up to the extent it is applicable for online drift detection
Embracing Change: Incremental Updates of Discovered Event Queries
No full textIn complex event processing (CEP), queries are evaluated continuously over streams of events to detect situations of interest, thereby facilitating reactive applications. However, users often lack insights into the precise event pattern that characterizes the situation, which renders the definition of the respective queries challenging. Once a database of finite, historic streams, each containing a materialization of the situation of interest, is available, query discovery supports users in the definition of the desired queries. It constructs the queries that match a certain share of the given streams, as determined by a support threshold. Yet, upon changes in the database or changes of the support threshold, existing algorithms need to construct the resulting queries from scratch, neglecting the queries obtained in previous runs. In this paper, we aim to avoid the resulting inefficiencies by techniques for incremental query discovery. We first provide a theoretical analysis of the problem context, before presenting algorithmic solutions to cope with changes in the stream database or the adopted support threshold. Our experiments using real-world data show that our incremental query discovery reduces the runtimes by up to three orders of magnitude compared to a baseline solution
Preface to BPM 2015
No full textThis special issue contains extended versions of the outstanding papers presented at the 13th International Conference on Business Process Management (BPM) that took place in Innsbruck, Austria on August 31–September 3, 2015
Survey on BPM Conference impression
No full textIn May/June 2014, the Program Committee Chairs of BPM’15 conducted a survey with present and past attendees and submitters to the BPM conference to gather feedback on the general perception of the conference. The survey is available at http://survey.qut.edu.au/f/180586/6bb1/. In particular, the survey included questions about the reputation of the conference, the reasons why survey participants submitted papers, whether they plan to submit to BPM’15, and soliciting input on a number of suggested changes and additions to the conduct of the conference series
Complex Event Recognition Languages: Tutorial
No full textComplex event recognition (CER) refers to the detection of events in Big Data streams. The paper presents a summary of the most prominent models and algorithms for CER, and discusses the main conceptual links and the differences between them.info:eu-repo/semantics/publishe
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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