Article
VTMine Framework as Applied to Process Mining Modeling
Process mining is a research area dealing with, inter alia, the construction of models of various types from event logs. Fuzzy maps are an example of such models produced by different process mining tools, such as ProM and Disco. We proposed a new approach to mining fuzzy models which is based on logs representation in the form of relation databases. Fast and effective SQL queries to such logs are made as a part of a DPMine workflow model. Resulting datasets are processed and visualized by a special DPMine component working tightly integrated with VTMine modeling framework. The paper discusses the suggested approach in the context of customization aspects of VTMine framework with an embedded DPM engine.
Process mining techniques relate observed behavior to modeled behavior, e.g., the automatic discovery of a process model based on an event log. Process mining is not limited to process discovery and also includes conformance checking and model enhancement. Conformance checking techniques are used to diagnose the deviations of the observed behavior as recorded in the event log from some process model. Model enhancement allows to extend process models using additional perspectives, conformance and performance information. In recent years, BPMN (Business Process Model and Notation) 2.0 has become a de facto standard for modeling business processes in industry. This paper presents the BPMN support current in ProM. ProM is the most known and used open-source process mining framework. ProM’s functionalities of discovering, analyzing and enhancing BPMN models are discussed. Support of the BPMN 2.0 standard will help ProM users to bridge the gap between formal models (such as Petri nets, causal nets and others) and process models used by practitioners.
The issue contains the papers presented at the 7th Spring/Summer Young Researchers' Соllоquium оn Software Engineering (SYRCoSE 2013) held in Kazan, Russia on 30th and З1st оf Мay, 2013. Paper selection was based on a competitive peer review process being done by the program committee. Both regular and reseаrсh-in-рrogrеss papers were соnsidered ассeрtable for the colloquium.
The topics of the colloquium include modeling of computer systems, software testing and verification, parallel and distributed systems, information search and data mining, image and speech processing and others.
ISBN 978-5-91474-020-4
Process mining is a new direction in the field of modeling and analysis of processes, where using information from event logs, describing the history of the system behavior, plays an important role. Methods and approaches used in the process mining are often based on various heuristics, and experiments with large event logs are crucial for the study and comparison of the developed methods and algorithms. Such experiments are very time consuming, so automation of experiments is an important task in the field of process mining. This paper presents the language DPMine developed specifically to describe and carry out experiments on the discovery and analysis of process models. The basic concepts of the DPMine language, as well as principles and mechanisms of its extension are described. Ways of integration of the DPMine language as dynamically loaded components into the VTMine modeling tool are considered. An illustrating example of an experiment to build a fuzzy model of the process discovered from the log data stored in a normalized database is given.
To large organizations, business intelligence (BI) promises the capability of collecting and analyzing internal and external data to generate knowledge and value, thus providing decision support at the strategic, tactical, and operational levels. BI is now impacted by the “Big Data” phenomena and the evolution of society and users. In particular, BI applications must cope with additional heterogeneous (often Web-based) sources, e.g., from social networks, blogs, competitors’, suppliers’, or distributors’ data, governmental or NGO-based analysis and papers, or from research publications. In addition, they must be able to provide their results also on mobile devices, taking into account location-based or time-based environmental data. The lectures held at the Third European Business Intelligence Summer School (eBISS), which are presented here in an extended and refined format, cover not only established BI and BPM technologies, but extend into innovative aspects that are important in this new environment and for novel applications, e.g., pattern and process mining, business semantics, Linked Open Data, and large-scale data management and analysis. Combining papers by leading researchers in the field, this volume equips the reader with the state-of-the-art background necessary for creating the future of BI. It also provides the reader with an excellent basis and many pointers for further research in this growing field.
Seit ihrem Entwurf im Jahr 1962 sind Petrinetze in ganz unterschiedlichen Bereichen eingesetzt worden. Obwohl sie graphisch dargestellt werden und intuitiv einfach verständlich sind, haben Petrinetze eine formal eindeutige Semantik mit einer Vielzahl mathematischer Analysetechniken. Sie reichen vom Model Checking und der Strukturellen Analyse über das Process Mining bis zur Performanz-Analyse. Im Lauf der Zeit haben Petrinetze solide Grundlagen für die Forschung zum Geschäftsprozess-Management (BPM) beigetragen. Sie umfassen Methoden, Techniken und Werkzeuge um Geschäftsprozesse zu entwerfen, implementieren, verwalten und zu analysieren. Die etablierten Modellierungsmethoden und Workflow-Managementsysteme verwenden Token-basierte, von Petrinetzen entlehnte Beschreibungen. Nutzer moderner BPM-Analysetechniken wissen oft gar nicht, dass ihre Geschäfts- prozesse intern als Petrinetze repräsentiert werden. Dieser Beitrag zeigt die grundlegende Rolle von Petrinetzen im BPM.
Process mining is a relatively new field of computer science which deals with process discovery and analysis based on event logs. In this work we consider the problem of discovering workflow nets with cancellation regions from event logs. Cancellations occur in the majority of real-life event logs. In spite of huge amount of process mining techniques little has been done on cancellation regions discovery. We show that the state-based region algorithm gives labeled Petri nets with overcomplicated control flow structure for logs with cancellations. We propose a novel method to discover cancellation regions from the transition systems built on event logs and show the way to construct equivalent workflow net with reset arcs to simplify the control flow structure.
Process-aware information systems (PAIS) enable developing models for interaction of processes, monitoring accuracy of their execution and checking if they interact with each other properly. PAIS can generate large data logs that contain the information about the interaction of processes in time. Studying PAIS logs with the purpose of data mining and modeling lies within the scope of Process Mining. There is a number of tools developed for Process Mining, including the most ubiquitous ProM, whose functionality is extended by plugins. To perform an object-aware experiment one has to sequentially run multiple plugins. This process becomes extremely time-consuming in the case of large-scale experiments involving a large number of plugins. The paper proposes a concept of DPMine/P language of process modeling and analysis to be implemented in ProM. The language under development aims at joining separate stages of the experiment into a single sequence, that is an experiment model. The implementation of the basic semantics of the language is done through the concept of blocks, ports, connectors and schemes. These items are discussed in detail in the paper, and examples of their use for specific tasks are presented ibid.
Process mining is a relatively new field of computer science which deals with process discovery and analysis based on event logs. In this work we consider the problem of discovering workflow nets with cancellation regions from event logs. Cancellations occur in the majority of real-life event logs. In spite of huge amount of process mining techniques little has been done on cancellation regions discovery. We show that the state-based region algorithm gives labeled Petri nets with overcomplicated control flow structure for logs with cancellations. We propose a novel method to discover cancellation regions from the transition systems built on event logs and show the way to construct equivalent workflow net with reset arcs to simplify the control flow structure.
A model for organizing cargo transportation between two node stations connected by a railway line which contains a certain number of intermediate stations is considered. The movement of cargo is in one direction. Such a situation may occur, for example, if one of the node stations is located in a region which produce raw material for manufacturing industry located in another region, and there is another node station. The organization of freight traffic is performed by means of a number of technologies. These technologies determine the rules for taking on cargo at the initial node station, the rules of interaction between neighboring stations, as well as the rule of distribution of cargo to the final node stations. The process of cargo transportation is followed by the set rule of control. For such a model, one must determine possible modes of cargo transportation and describe their properties. This model is described by a finite-dimensional system of differential equations with nonlocal linear restrictions. The class of the solution satisfying nonlocal linear restrictions is extremely narrow. It results in the need for the “correct” extension of solutions of a system of differential equations to a class of quasi-solutions having the distinctive feature of gaps in a countable number of points. It was possible numerically using the Runge–Kutta method of the fourth order to build these quasi-solutions and determine their rate of growth. Let us note that in the technical plan the main complexity consisted in obtaining quasi-solutions satisfying the nonlocal linear restrictions. Furthermore, we investigated the dependence of quasi-solutions and, in particular, sizes of gaps (jumps) of solutions on a number of parameters of the model characterizing a rule of control, technologies for transportation of cargo and intensity of giving of cargo on a node station.
Event logs collected by modern information and technical systems usually contain enough data for automated process models discovery. A variety of algorithms was developed for process models discovery, conformance checking, log to model alignment, comparison of process models, etc., nevertheless a quick analysis of ad-hoc selected parts of a journal still have not get a full-fledged implementation. This paper describes an ROLAP-based method of multidimensional event logs storage for process mining. The result of the analysis of the journal is visualized as directed graph representing the union of all possible event sequences, ranked by their occurrence probability. Our implementation allows the analyst to discover process models for sublogs defined by ad-hoc selection of criteria and value of occurrence probability
The geographic information system (GIS) is based on the first and only Russian Imperial Census of 1897 and the First All-Union Census of the Soviet Union of 1926. The GIS features vector data (shapefiles) of allprovinces of the two states. For the 1897 census, there is information about linguistic, religious, and social estate groups. The part based on the 1926 census features nationality. Both shapefiles include information on gender, rural and urban population. The GIS allows for producing any necessary maps for individual studies of the period which require the administrative boundaries and demographic information.
It is well-known that the class of sets that can be computed by polynomial size circuits is equal to the class of sets that are polynomial time reducible to a sparse set. It is widely believed, but unfortunately up to now unproven, that there are sets in EXPNP, or even in EXP that are not computable by polynomial size circuits and hence are not reducible to a sparse set. In this paper we study this question in a more restricted setting: what is the computational complexity of sparse sets that are selfreducible? It follows from earlier work of Lozano and Torán (in: Mathematical systems theory, 1991) that EXPNP does not have sparse selfreducible hard sets. We define a natural version of selfreduction, tree-selfreducibility, and show that NEXP does not have sparse tree-selfreducible hard sets. We also construct an oracle relative to which all of EXP is reducible to a sparse tree-selfreducible set. These lower bounds are corollaries of more general results about the computational complexity of sparse sets that are selfreducible, and can be interpreted as super-polynomial circuit lower bounds for NEXP.