Article
System Runs Analysis with Process Mining
Information systems (IS) produce numerous traces and logs at runtime. In context of SOA-based (service-oriented architecture) IS, these logs contain details about sequences of process and service calls. Modern application monitoring and error tracking tools provide only rather straightforward log search and filtering functionality. However, ``clever'' analysis of the logs is highly useful, since it can provide valuable insights into the system architecture, interaction of business domains and services.
Here we took runs event logs (trace data) of a big booking system and discovered architectural guidelines violations and common anti-patterns. We applied mature process mining techniques for discovery and analysis of these logs. Process mining aims to discover, analyze, and improve processes on the basis of IS behavior recorded as event logs. In several specific examples, we show successful applications of process mining to system runtime analysis and motivate further research in this area.
Proceedings of ISP RAS are a double-blind peer-reviewed journal publishing scientific articles in the areas of system programming, software engineering, and computer science. The journal's goal is to develop a respected network of knowledge in the mentioned above areas by publishing high quality articles on open access. The journal is intended for researchers, students, and practitioners.
Process mining is a new emerging discipline related to process management, formal process models, and data mining. One of the main tasks of process mining is the model synthesis (discovery) based on the event logs. A wide range of algorithms for process model discovery, analysis, and enhancement are developed. The real-life event logs often contain noise of different types. In this paper we describe the main causes of noise in the event logs, and study the effect of noise on the performance of process discovery algorithms. The experimental results of application of the main process discovery algorithms to artificial event logs with noise are provided. Specially generated event logs with noise of different types were processed using the four basic discovery techniques. Although modern algorithms can cope with some types of noise, in most cases, their use does not lead to obtaining a satisfactory result. Thus, there is a need for more sophisticated algorithms to deal with noise of different types.
Modern information systems produce tremendous amounts of event data. The area of process mining deals with extracting knowledge from this data. Real-life processes can be e ectively discovered, analyzed and optimized with the help of mature process mining techniques. There is a variety of process mining case studies and experience reports from such business areas as healthcare, public, transportation and education. Although nowadays, these techniques are mostly used for discovering business processes. However, process mining can be applied to software too. In the area of software design and development, process models and user interface workflows underlie the functional speci cation of almost every substantial software system. When the system is utilized, user interaction with the system can be recorded in event logs. After applying process mining methods to logs, we can derive process and user interface flow models. These models provide insights regarding the real usage of the software and can enable usability improvements and software redesign. In this industrial paper we present several process mining examples of di erent productive software systems used in the touristic domain. With the help of these examples we demonstrate that process mining enables new forms of software analysis. The user interaction with almost every software system can be mined in order to improve the software and to monitor and measure its real usage.
Article No. 57
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.
Since their inception in 1962, Petri nets have been used in a wide variety of application domains. Although Petri nets are graphical and easy to understand, they have formal semantics and allow for analysis techniques ranging from model checking and structural analysis to process mining and performance analysis. Over time Petri nets emerged as a solid foundation for Business Process Management (BPM) research. The BPM discipline develops methods, techniques, and tools to support the design, enactment, management, and analysis of operational business processes. Mainstream business process modeling notations and workflow management systems are using token-based semantics borrowed from Petri nets. Moreover, state-of-the-art BPM analysis techniques are using Petri nets as an internal representation. Users of BPM methods and tools are often not aware of this. This paper aims to unveil the seminal role of Petri nets in BPM.
Companies from various domains record their operational behavior in a form of event logs. These event logs can be analyzed and relevant process models representing the real companies’ behavior can be discovered. One of the main advantages of the process discovery methods is that they commonly produce models in a form of graphs which can be easily visualized giving an intuitive view of the executed processes. Moreover, the graph-based representation opens new challenging perspectives for the application of graph comparison methods to find and explicitly visualize differences between discovered process models (representing real behavior) and reference process models (representing expected behavior). Another important area where graph comparison algorithms can be used is the recognition of process modeling patterns. Unfortunately, exact graph comparison algorithms are computationally expensive. In this paper, we adapt an inexact tabu search algorithm to find differences between BPMN (Business Process Model and Notation) models. The tabu search and greedy algorithms were implemented within the BPMNDiffViz tool and were tested on BPMN models discovered from synthetic and real-life event logs. It was experimentally shown that inexact tabu search algorithm allows to find a solution which is close to the optimal in most of the cases. At the same, its computational complexity is significantly lower than the complexity of the exact A search algorithm investigated earlier.
Modern companies continue investing more and more in the creation, maintenance and change of software systems, but the proper specification and design of such systems continues to be a challenge. The majority of current approaches either ignore real user and system runtime behavior or consider it only informally. This leads to a rather prescriptive top-down approach to software development. In this paper, we propose a bottom-up approach, which takes event logs (e.g., trace data) of a software system for the analysis of the user and system runtime behavior and for improving the software. We use well-established methods from the area of process mining for this analysis. Moreover, we suggest embedding process mining into the agile development lifecycle. The goal of this position paper is to motivate the need for foundational research in the area of software process mining (applying process mining to software analysis) by showing the relevance and listing open challenges. Our proposal is based on our experiences with analyzing a big productive touristic system. This system was developed using agile methods and process mining could be effectively integrated into the development lifecycle.
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.
Existing approaches suggest that IT strategy should be a reflection of business strategy. However, actually organisations do not often follow business strategy even if it is formally declared. In these conditions, IT strategy can be viewed not as a plan, but as an organisational shared view on the role of information systems. This approach generally reflects only a top-down perspective of IT strategy. So, it can be supplemented by a strategic behaviour pattern (i.e., more or less standard response to a changes that is formed as result of previous experience) to implement bottom-up approach. Two components that can help to establish effective reaction regarding new initiatives in IT are proposed here: model of IT-related decision making, and efficiency measurement metric to estimate maturity of business processes and appropriate IT. Usage of proposed tools is demonstrated in practical cases.
I give the explicit formula for the (set-theoretical) system of Resultants of m+1 homogeneous polynomials in n+1 variables