Formal analysis of executions of organizational scenarios based on process-oriented specifications

Abstract This paper presents various formal techniques for analysis of executions of organizational scenarios based on specifications of organizations. Organizational specifications describe (prescribe) ordering and timing relations on organizational processes, modes of use of resources, allocations of actors to processes, etc. The actual execution may diverge from scenarios (pre)defined by a specification. A part of techniques proposed in this paper is dedicated to establishing the correspondence between a formalized execution (i.e., a trace) and the corresponding specification. Other techniques proposed in this paper provide the analyst with wide possibilities to evaluate organizational performance and to identify bottlenecks and other inefficiencies in the organizational operation. For the proposed formal analysis the order-sorted predicate Temporal Trace Language (TTL) is used and it is supported by the dedicated software tool TTL Checker. The analysis approaches considered in this paper are illustrated by a case study in the context of an organization from the security domain. © Springer Science+Business Media, LLC 2009

A Model Based Slicing Technique for Process Mining Healthcare Information

Process mining is a powerful technique which uses an organization’s event data to extract and analyse process flow information and develop useful process models. However, it is difficult to apply process mining techniques to healthcare information due to factors relating to the complexity inherent in the healthcare domain and associated information systems. There are also challenges in understanding and meaningfully presenting results of process mining and problems relating to technical issues among the users. We propose a model based slicing approach based on dimensional modeling and ontological hierarchies that can be used to raise the level of abstraction during process mining, thereby more effectively dealing with the complexity and other issues. We also present a structural property of the proposed slicing technique for process mining

Machine Learning-Based Framework for Log-Lifting in Business Process Mining Applications

Real-life event logs are typically much less structured and more complex than the predefined business activities they refer to. Most of the existing process mining techniques assume that there is a one-to-one mapping between process model activities and events recorded during process execution. Unfortunately, event logs and process model activities are defined at different levels of granularity. The challenges posed by this discrepancy can be addressed by means of log-lifting. In this work, we develop a machine-learning-based framework aimed at bridging the abstraction level gap between logs and process models. The proposed framework operates in two main phases: log segmentation and machine-learning-based classification. The purpose of the segmentation phase is to identify the potential segment separators in a flow of low-level events, in which each segment corresponds to an unknown high-level activity. For this, we propose a segmentation algorithm based on maximum likelihood with n-gram analysis. In the second phase, event segments are mapped into their corresponding high-level activities using a supervised machine learning technique. Several machine learning classification methods are explored including ANNs, SVMs, and random forest. We demonstrate the applicability of our framework using a real-life event log provided by the SAP company. The results obtained show that a machine learning approach based on the random forest algorithm outperforms the other methods with an accuracy of 96.4%. The testing time was found to be around 0.01s, which makes the algorithm a good candidate for real-time deployment scenarios

Reusable data visualization patterns for clinical practice

Among clinical psychologists involved in guided internet-facilitated interventions, there is an overarching need to understand patients symptom development and learn about patients need for treatment support. Data visualizations is a technique for managing enormous amounts of data and extract useful information, and is often used in developing digital tool support for decision-making. Although there exists numerous data visualisation and analytical reasoning techniques available through interactive visual interfaces, it is a challenge to develop visualizations that are relevant and suitable in a healthcare context, and can be used in clinical practice in a meaningful way. For this purpose it is necessary to identify actual needs of healthcare professionals and develop reusable data visualization components according to these needs. In this paper we present a study of decision support needs of psychologists involved in online internet-facilitated cognitive behavioural therapy. Based on these needs, we provide a library of reusable visual components using a model-based approach. The visual components are featured with mechanisms for investigating data using various levels of abstraction and causal analysis

Mutational analysis of a cohort with clinical diagnosis of familial hypercholesterolemia: considerations for genetic diagnosis improvement

PURPOSE: Familial hypercholesterolemia (FH) is a common autosomal dominant disorder of lipid metabolism caused by mutations in LDLR, APOB, and PCSK9. To fulfill the World Health Organization recommendation, the Portuguese FH Study was established. Here, we report the results of the past 15 years and present practical considerations concerning the genetic diagnosis of FH based on our experience. METHODS: Our approach comprises a biochemical and molecular study and is divided into five phases, including the study of whole APOB and functional assays. RESULTS: A total of 2,122 individuals were enrolled. A putative pathogenic variant was identified in 660 heterozygous patients: LDLR (623), APOB (33), and PCSK9 (4); 8 patients presented with homozygous FH. A detection rate of 41.5% was observed. A stricter biochemical criteria was shown to improve patient identification. Overall, we have identified 3.4% and 80% of all heterozygous and homozygous patients, respectively, estimated to exist in our country. CONCLUSION: The Portuguese FH Study has established the genetic diagnosis of FH in Portugal and is committed to continue the investigation of the genetic complexity of FH. Genetic diagnosis of FH should be expanded to include the study of all coding/flanking regions of APOB and functional in vitro studies, to improve the correct patient identification, and to avoid misdiagnosis.Funding was obtained from the Portuguese Cardiology Society (project grant D13123) and Science and Technology Foundation (project grant PIC/IC/83333/2007). A.C.A. was supported by a PhD student grant (SFRH/BD/27990/2006). A.M.M. was supported by a research grant from the National Institute of Health Doutor Ricardo Jorge (BRJ-DPS/2012)