Verslo procesų prognozavimo ir imitavimo taikant sisteminių įvykių žurnalų analizės metodus tyrimas

Business process (BP) analysis is one of the core activities in organisations that lead to improvements and achievement of a competitive edge. BP modelling and simulation are one of the most widely applied methods for analysing and improving BPs. The analysis requires to model BP and to apply analysis techniques to the models to answer queries leading to improvements. The input of the analysis process is BP models. The models can be in the form of BP models using industry-accepted BP modelling languages, mathematical models, simulation models and others. The model creation is the most important part of the BP analysis, and it is both time-consuming and costly activity. Nowadays most of the data generated in the organisations are electronic. Therefore, the re-use of such data can improve the results of the analysis. Thus, the main goal of the thesis is to improve BP analysis and simulation by proposing a method to discover a BP model from an event log and automate simulation model generation. The dissertation consists of an introduction, three main chapters and general conclusions. The first chapter discusses BP analysis methods. In addition, the process mining research area is presented, the techniques for automated model discovery, model validation and execution prediction are analysed. The second part of the chapter investigates the area of BP simula-tion. The second chapter of the dissertation presents a novel method which automatically discovers Bayesian Belief Network from an event log and, furthermore, automatically generates BP simulation model. The discovery of the Bayesian Belief Network consists of three steps: the discovery of a directed acyclic graph, generation of conditional probability tables and their combination. The BP simulation model is generated from the discovered directed acyclic graph and uses the belief network inferences during the simulation to infer the execution of the BP and to generate activity data dur-ing the simulation. The third chapter presents the experimental research of the proposed network and discusses the validity of the research and experiments. The experiments use selected logs that exhibit a wide array of behaviour. The experiments are performed in order to test the discovery of the graphs, the inference of the current process instance execution probability, the predic-tion of the future execution of the process instances and the correctness of the simulation. The results of the dissertation were published in 9 scientific publica-tions, 2 of which were in reviewed scientific journals indexed in Clarivate Analytics Science Citation Index

Business Process Event Log Transformation into Bayesian Belief Network

Business process (BP) mining has been recognized in business intelligence and reverse engineering fields because of the capabilities it has to discover knowledge about the implementation and execution of BP for analysis and improvement. Existing business knowledge extraction solutions in process mining context requires repeating analysis of event logs for each business knowledge extraction task. The probabilistic modelling could allow improved performance of BP analysis. Bayesian belief networks are a probabilistic modelling tool and the paper presents their application in BP mining. The paper shows that existing process mining algorithms are not suited for this, since they allow for loops in the extracted BP model that do not really exist in the event log,and presents a custom solution for directed acyclic graph extraction. The paper presents results of a synthetic log transformation into Bayesian belief network showing possible application in business intelligence extraction and improved decision support capabilities

Improvement of search process in electronic catalogues

The paper presents investigation on search in electronic catalogues. The chosen problem domain is the search system in the electronic catalogue of Lithuanian Academic Libraries. The catalogue uses ALEPH system with MARC21 bibliographic format. The article presents analysis of problems pertaining to the current search engine and user expectations related to the search system of the electronic catalogue of academic libraries. Subsequent to analysis, the research paper presents the architecture for a semantic search system in the electronic catalogue that uses search process designed to improve search results for users. Article in Lithuanian. Paieškos elektroniniuose kataloguose veiklos proceso tobulinimas Santrauka Straipsnyje analizuojama paieška elektroniniuose kataloguose. Pasirinkta dalykinė sritis – Lietuvos universitetų akademinių bibliotekų elektroninis katalogas. Straipsnyje pateikiamos esamos paieškos sistemos ALEPH problemos ir vartotojų poreikių tyrimo rezultatai. Išanalizuojamas MARC21 formatas ir galimi alternatyvūs paieškos būdai, naudojami bibliotekoje. Atlikus analizę siūloma sistemos architektūra ir paieškos procesas, kuriais bandoma padidinti paieškos elektroniniame kataloge efektyvumą ir užtikrinti vartotojų poreikių patenkinimą. Reikšminiai žodžiai: elektroninis katalogas, biblioteka, semantinė paieška, MARC21, ALEPH, ontologijos

Selection of activities in dynamic business process simulation / Veiklų pasirinkimas dinaminio verslo modelio simuliacijoje

Maintaining dynamicity of business processes is one of the core issues of today's business as it enables businesses to adapt to constantly changing environment. Upon changing the processes, it is vital to assess possible impact, which is achieved by using simulation of dynamic processes. In order to implement dynamicity in business processes, it is necessary to have an ability to change components of the process (a set of activities, a content of activity, a set of activity sequences, a set of rules, performers and resources) or dynamically select them during execution. This problem attracted attention of researches over the past few years; however, there is no proposed solution, which ensures the business process (BP) dynamicity. This paper proposes and specifies dynamic business process (DBP) simulation model, which satisfies all of the formulated DBP requirements. Santrauka  Šiuolaikiniam verslui svarbu vykdyti procesus dinamiškai, norint laiku prisitaikyti prie besikeičiančios aplinkos. Keičiant procesus reikia įvertinti keitimo pasekmes, o įvertinimui galima naudoti dinaminių procesų imitaciją. Siekiant realizuoti procesų dinamiką, reikia imitacijos metu turėti galimybę keisti proceso komponentus. Problema pritraukia daug dėmesio jau kelerius metus, tačiau vis dar nepasiūlytas sprendimas, kuris užtikrintų verslo proceso dinamiškumą. Šis straipsnis siūlo ir pateikia dinaminio verslo proceso imitacinį modelį, kuris atitinka anksčiau suformuotus dinaminio verslo proceso reikalavimus. Reikšminiai žodžiai: dinaminis veiklos procesas, dinamiškumas, imitacinis modelis

Decision-making in information systems based on new development framework and business process mining

The studies on software project failures have identified problems in capturing requirements, managing complexity and dynamic changes of the environment because of the using traditional software engineering, where requirement capturing is static and prolonged. This issue is especially important for decision-making in dynamically changing business. The paper offers modernization of information system development methods used for implementation of automated information-, rule-, knowledge-, model-based decision processes. The paper propose to assist processes by early separation and development of a business logic model and implementation of decision-making, knowledge discovery process models and business process analysis using probabilistic models by proposing an information systems development framework. The advantages of such approach are early separation and development of a business logic model and further support for business people for modification of business logic without involvement of software developers and minimizing their persistence in the latter exploitation stages. Finally, the paper presents experimental results for stochastic decision extraction from system database using process mining and probabilistic models to ease framework implementation