Interestingness of traces in declarative process mining: The janus LTLPf Approach

Declarative process mining is the set of techniques aimed at extracting behavioural constraints from event logs. These constraints are inherently of a reactive nature, in that their activation restricts the occurrence of other activities. In this way, they are prone to the principle of ex falso quod libet: they can be satisfied even when not activated. As a consequence, constraints can be mined that are hardly interesting to users or even potentially misleading. In this paper, we build on the observation that users typically read and write temporal constraints as if-statements with an explicit indication of the activation condition. Our approach is called Janus, because it permits the specification and verification of reactive constraints that, upon activation, look forward into the future and backwards into the past of a trace. Reactive constraints are expressed using Linear-time Temporal Logic with Past on Finite Traces (LTLp f). To mine them out of event logs, we devise a time bi-directional valuation technique based on triplets of automata operating in an on-line fashion. Our solution proves efficient, being at most quadratic w.r.t. trace length, and effective in recognising interestingness of discovered constraints

Matching events and activities by integrating behavioral aspects and label analysis

Nowadays, business processes are increasingly supported by IT services that produce massive amounts of event data during the execution of a process. These event data can be used to analyze the process using process mining techniques to discover the real process, measure conformance to a given process model, or to enhance existing models with performance information. Mapping the produced events to activities of a given process model is essential for conformance checking, annotation and understanding of process mining results. In order to accomplish this mapping with low manual effort, we developed a semi-automatic approach that maps events to activities using insights from behavioral analysis and label analysis. The approach extracts Declare constraints from both the log and the model to build matching constraints to efficiently reduce the number of possible mappings. These mappings are further reduced using techniques from natural language processing, which allow for a matching based on labels and external knowledge sources. The evaluation with synthetic and real-life data demonstrates the effectiveness of the approach and its robustness toward non-conforming execution logs

Comprehensive process drift analysis with the visual drift detection tool

Recent research has introduced ideas from concept drift into process mining to enable the analysis of changes in business processes over time. This stream of research, however, has not yet addressed the challenges of drift categorization, drilling-down, and quantification. In this tool demonstration paper, we present a novel software tool to analyze process drifts, called Visual Drift Detection (VDD), which fulfills these requirements. The tool is of benefit to the researchers and practitioners in the business intelligence and process analytics area, and can constitute a valuable aid to those who are involved in business process redesign endeavors

Business process improvement with the AB-BPM methodology

A fundamental assumption of Business Process Management (BPM) is that redesign delivers refined and improved versions of business processes. This assumption, however, does not necessarily hold, and any required compensatory action may be delayed until a new round in the BPM life-cycle completes. Current approaches to process redesign face this problem in one way or another, which makes rapid process improvement a central research problem of BPM today. In this paper, we address this problem by integrating concepts from process execution with ideas from DevOps. More specifically, we develop a methodology called AB-BPM that offers process improvement validation in two phases: simulation and AB tests. Our simulation technique extracts decision probabilities and metrics from the event log of an existing process version and generates traces for the new process version based on this knowledge. The results of simulation guide us towards AB testing where two versions (A and B) are operational in parallel and any new process instance is routed to one of them. The routing decision is made at runtime on the basis of the achieved results for the registered performance metrics of each version. Our routing algorithm provides for ultimate convergence towards the best performing version, no matter if it is the old or the new version. We demonstrate the efficacy of our methodology and techniques by conducting an extensive evaluation based on both synthetic and real-life data

What Automated Planning Can Do for Business Process Management

Business Process Management (BPM) is a central element of today organizations. Despite over the years its main focus has been the support of processes in highly controlled domains, nowadays many domains of interest to the BPM community are characterized by ever-changing requirements, unpredictable environments and increasing amounts of data that influence the execution of process instances. Under such dynamic conditions, BPM systems must increase their level of automation to provide the reactivity and flexibility necessary for process management. On the other hand, the Artificial Intelligence (AI) community has concentrated its efforts on investigating dynamic domains that involve active control of computational entities and physical devices (e.g., robots, software agents, etc.). In this context, Automated Planning, which is one of the oldest areas in AI, is conceived as a model-based approach to synthesize autonomous behaviours in automated way from a model. In this paper, we discuss how automated planning techniques can be leveraged to enable new levels of automation and support for business processing, and we show some concrete examples of their successful application to the different stages of the BPM life cycle

BIOMOLECULAR IDENTIFICATION OF METHICILLIN-RESISTANT STRAINS OF STAPHYLOCOCCUS AUREUS (MRSA) ISOLATED FROM MEAT AND MEAT PROCESSING ENVIRONMENTS

371 samples from meat and meat-environments were collected and examined for the detection of methicillin-resistant Staphylococcus aureus (MRSA). The structural gene for penicillin-binding protein 2a (mecA gene), was amplified by PCR and detected by agarose gel electrophoresis. 96 samples (25.8%), contained S. aureus and 2 of them (2.08%) were mecA positive. Further assays are necessary to evaluate the spread of MRSA in food and food-environments

THE MANAGEMENT OF THE DOMESTIC REFRIGERATION: HYGIENIC AND SANITARY CHARACTERISTICS OF REFRIGERATORS FROM NORTHEN AND CENTRAL ITALY

This study aimed to provide information on the consumer management of refrigerated food. N° 469 interviews were carried out and the results obtained were subjected to descriptive statistical analysis and further processed with the Multiple Correspondence Analysis and Cluster Analysis. Five homogeneous groups were obtained. In each of them a significant number of refrigerators (60) were tested to assess the temperature and the microbiological status (TVC, Enterobacteriaceae, Salmonella spp. and Listeria spp.). Listeria monocytogenes and Salmonella spp. were not recovered; Listeria innocua was recovered (3.3%). Regarding the TVC values, the 30% of the tested refrigerators was classified as not appropriate (28.3%) or not acceptable (1.7%). Consumer education should be focused in order to reduce foodborne disease. Only safety-conscious consumers can become active partners within the food safety chain

THE MANAGEMENT OF THE DOMESTIC REFRIGERATION: HYGIENIC AND SANITARY CHARACTERISTICS OF REFRIGERATORS FROM NORTHEN AND CENTRAL ITALY

This study aimed to provide information on the consumer management of refrigerated food. N° 469 interviews were carried out and the results obtained were subjected to descriptive statistical analysis and further processed with the Multiple Correspondence Analysis and Cluster Analysis. Five homogeneous groups were obtained. In each of them a significant number of refrigerators (60) were tested to assess the temperature and the microbiological status (TVC, Enterobacteriaceae, Salmonella spp. and Listeria spp.). Listeria monocytogenes and Salmonella spp. were not recovered; Listeria innocua was recovered (3.3%). Regarding the TVC values, the 30% of the tested refrigerators was classified as not appropriate (28.3%) or not acceptable (1.7%). Consumer education should be focused in order to reduce foodborne disease. Only safety-conscious consumers can become active partners within the food safety chain

Towards a methodology for the engineering of event-driven process applications

Successful applications of the Internet of Things such as smart cities, smart logistics, and predictive maintenance, build on observing and analyzing business-related objects in the real world for business process execution and monitoring. In this context, complex event processing is increasingly used to integrate events from sensors with events stemming from business process management systems. This paper describes a methodology to combine the areas and engineer an event-driven logistics processes application. Thereby, we describe the requirements, use cases and lessons learned to design and implement such an architecture