Discovering duplicate tasks in transition systems for the simplification of process models

This work presents a set of methods to improve the understandability of process models. Traditionally, simplification methods trade off quality metrics, such as fitness or precision. Conversely, the methods proposed in this paper produce simplified models while preserving or even increasing fidelity metrics. The first problem addressed in the paper is the discovery of duplicate tasks. A new method is proposed that avoids overfitting by working on the transition system generated by the log. The method is able to discover duplicate tasks even in the presence of concurrency and choice. The second problem is the structural simplification of the model by identifying optional and repetitive tasks. The tasks are substituted by annotated events that allow the removal of silent tasks and reduce the complexity of the model. An important feature of the methods proposed in this paper is that they are independent from the actual miner used for process discovery.Peer ReviewedPostprint (author's final draft

Finding suitable activity clusters for decomposed process discovery

Event data can be found in any information system and provide the starting point for a range of process mining techniques. The widespread availability of large amounts of event data also creates new challenges. Existing process mining techniques are often unable to handle "big event data" adequately. Decomposed process mining aims to solve this problem by decomposing the process mining problem into many smaller problems which can be solved in less time, using less resources, or even in parallel. Many decomposed process mining techniques have been proposed in literature. Analysis shows that even though the decomposition step takes a relatively small amount of time, it is of key importance in finding a high-quality process model and for the computation time required to discover the individual parts. Currently there is no way to assess the quality of a decomposition beforehand. We define three quality notions that can be used to assess a decomposition, before using it to discover a model or check conformance with. We then propose a decomposition approach that uses these notions and is able to find a high-quality decomposition in little time. Keywords: decomposed process mining, decomposed process discovery, distributed computing, event lo

Resilience Analysis of Service Oriented Collaboration Process Management systems

Collaborative business process management allows for the automated coordination of processes involving human and computer actors. In modern economies it is increasingly needed for this coordination to be not only within organizations but also to cross organizational boundaries. The dependence on the performance of other organizations should however be limited, and the control over the own processes is required from a competitiveness perspective. The main objective of this work is to propose an evaluation model for measuring a resilience of a Service Oriented Architecture (SOA) collaborative process management system. In this paper, we have proposed resilience analysis perspectives of SOA collaborative process systems, i.e. overall system perspective, individual process model perspective, individual process instance perspective, service perspective, and resource perspective. A collaborative incident and maintenance notification process system is reviewed for illustrating our resilience analysis. This research contributes to extend SOA collaborative business process management systems with resilience support, not only looking at quantification and identification of resilience factors, but also considering ways of improving the resilience of SOA collaborative process systems through measures at design and run-time

Exploiting Event Log Event Attributes in RNN Based Prediction

In predictive process analytics, current and historical process data in event logs are used to predict future. E.g., to predict the next activity or how long a process will still require to complete. Recurrent neural networks (RNN) and its subclasses have been demonstrated to be well suited for creating prediction models. Thus far, event attributes have not been fully utilized in these models. The biggest challenge in exploiting them in prediction models is the potentially large amount of event attributes and attribute values. We present a novel clustering technique which allows for trade-offs between prediction accuracy and the time needed for model training and prediction. As an additional finding, we also find that this clustering method combined with having raw event attribute values in some cases provides even better prediction accuracy at the cost of additional time required for training and prediction.Peer reviewe

Towards an Entropy-based Analysis of Log Variability

Rules, decisions, and workflows are intertwined components depicting the overall process. So far imperative workflow modelling languages have played the major role for the description and analysis of business processes. Despite their undoubted efficacy in representing sequential executions, they hide circumstantial information leading to the enactment of activities, and obscure the rationale behind the verification of requirements, dependencies, and goals. This workshop aimed at providing a platform for the discussion and introduction of new ideas related to the development of a holistic approach that encompasses all those aspects. The objective was to extend the reach of the business process management audience towards the decisions and rules community and increase the integration between different imperative, declarative and hybrid modelling perspectives. Out of the high-quality submitted manuscripts, three papers were accepted for publication, with an acceptance rate of 50%. They contributed to foster a fruitful discussion among the participants about the respective impact and the interplay of decision perspective and the process perspective

ERP Conceptual Ecology

The technological evolution of recent years has made that information systems frequently adapt to the market realities to fulfill the improvements of the company’s organizational processes. In this context, new paradigms, approaches, and concepts were disseminated through the new realities of information systems. This study aims to verify how ERP (Enterprise Resource Planning) has been related to other information systems within its ecosystem. For this purpose, we have reviewed the literature based on 650 publications whose central theme was the ERP. The data were treated through a graphical analysis, inspired by SNA (Social Network Analysis), represented by related ERP concepts. The study results, determine the connection degree between the concepts that emerged with the technological evolution and the ERP, thus representing the ERP interoperability tendencies, over the last years. The study concludes that ERPs have been improving and substantially increasing the conditions of nteroperability with other information systems and with new organizational concepts that have emerged through the technological availability. This fact led to a better organizational process’s adoption and more organizational performance.info:eu-repo/semantics/publishedVersio

Antihypertensive and antioxidant effects of dietary black sesame meal in pre-hypertensive humans

<p>Abstract</p> <p>Background</p> <p>It has been known that hypertension is an independent risk factor for cardiovascular disease (CVD). CVD is the major cause of morbidity and mortality in developed and developing countries. Elevation of blood pressure (BP) increases the adverse effect for cardiovascular outcomes. Prevention of increased BP plays a crucial role in a reduction of those outcomes, leading to a decrease in mortality. Therefore, the purpose of this study was to investigate the effects of dietary black sesame meal on BP and oxidative stress in individuals with prehypertension.</p> <p>Methods</p> <p>Twenty-two women and eight men (aged 49.8 ± 6.6 years) with prehypertension were randomly divided into two groups, 15 subjects per group. They ingested 2.52 g black sesame meal capsules or placebo capsules each day for 4 weeks. Blood samples were obtained after overnight fasting for measurement of plasma lipid, malondialdehyde (MDA) and vitamin E levels. Anthropometry, body composition and BP were measured before and after 4-week administration of black sesame meal or a placebo.</p> <p>Results</p> <p>The results showed that 4-week administration of black sesame meal significantly decreased systolic BP (129.3 ± 6.8 vs. 121.0 ± 9.0 mmHg, <it>P </it>< 0.05) and MDA level (1.8 ± 0.6 vs. 1.2 ± 0.6 μmol/L, <it>P </it>< 0.05), and increased vitamin E level (29.4 ± 6.0 vs. 38.2 ± 7.8 μmol/L, <it>P </it>< 0.01). In the black sesame meal group, the change in SBP tended to be positively related to the change in MDA (<it>R = 0.50, P </it>= 0.05), while the change in DBP was negatively related to the change in vitamin E (<it>R = -0.55, P </it>< 0.05). There were no correlations between changes in BP and oxidative stress in the control group.</p> <p>Conclusions</p> <p>These results suggest the possible antihypertensive effects of black sesame meal on improving antioxidant status and decreasing oxidant stress. These data may imply a beneficial effect of black sesame meal on prevention of CVD.</p

Perceived stressors of climate vulnerability across scales in the Savannah zone of Ghana: a participatory approach

Smallholder farmers in sub-Saharan Africa are confronted with climatic and non-climatic stressors. Research attention has focused on climatic stressors, such as rainfall variability, with few empirical studies exploring non-climatic stressors and how these interact with climatic stressors at multiple scales to affect food security and livelihoods. This focus on climatic factors restricts understanding of the combinations of stressors that exacerbate the vulnerability of farming households and hampers the development of holistic climate change adaptation policies. This study addresses this particular research gap by adopting a multi-scale approach to understand how climatic and non-climatic stressors vary, and interact, across three spatial scales (household, community and district levels) to influence livelihood vulnerability of smallholder farming households in the Savannah zone of northern Ghana. This study across three case study villages utilises a series of participatory tools including semi-structured interviews, key informant interviews and focus group discussions. The incidence, importance, severity and overall risk indices for stressors are calculated at the household, community, and district levels. Results show that climatic and non-climatic stressors were perceived differently; yet, there were a number of common stressors including lack of money, high cost of farm inputs, erratic rainfall, cattle destruction of crops, limited access to markets and lack of agricultural equipment that crossed all scales. Results indicate that the gender of respondents influenced the perception and severity assessment of stressors on rural livelihoods at the community level. Findings suggest a mismatch between local and district level priorities that have implications for policy and development of agricultural and related livelihoods in rural communities. Ghana’s climate change adaptation policies need to take a more holistic approach that integrates both climatic and non-climatic factors to ensure policy coherence between national climate adaptation plans and District development plans