Itseorganisoituvat kartat päätöksenteon tuessa: päätöksenteon tukijärjestelmän prototyyppi

Teollisuus ja liiketoiminta ovat täynnä monimutkaisia päätöksentekoprosesseja, joissa inhimillisen virheen mahdollisuus on suuri. Nämä prosessit ovat hyvin kriittisiä ydinvoimaloiden toiminnan ohjauksessa. Päätöksien laatua voidaan parantaa ja virheiden todennäköisyyttä vähentää antamalla tietokoneistettua päätöksenteon tukea päätöksen tekijälle. Itseorganisoituva kartta (SOM) on hyödyllinen tapa visualisoida moniulotteisia ja suuria data-aineistoja. Tämän työn tavoitteena on löytää tapoja SOM-menetelmän hyödyntämiseen ydinvoimalan operaattorin päätöksenteon tuessa ja analysoida, voiko kyseisiä tapoja käyttää myös muiden sovellusalueiden päätöksenteon tukeen. Työn tutkimusmenetelmät ovat kokeellinen prototyyppikehitys, tiedonlouhinta ja kirjallisuustutkimus. Tutkimuksessa on toteutettu prototyyppi (DERSI) päätöksenteon tukijärjestelmän (DSS) alustasta. Se hyödyntää päätöksenteon tuessa kokoelmaa erilaisia menetelmiä, kuten SOM kvantisointivirhettä, SOM U-matriisia, sumeaa logiikkaa, sääntöpohjaista päättelyä ja tapauspohjaista päättelyä. Prototyyppi on ohjelmoitu Matlabohjelmointikielellä ja se hyödyntää Matlabin SOM Toolbox -laajennusta. Siihen kuuluu myös graafinen käyttöliittymä, joka sisältää käytettyjen menetelmien visualisoinnit. Tutkimuksen alustalle on rakennettu kaksi päätöksenteon tukijärjestelmän prototyypiyksikköä. Yksi niistä hyödyntää tutkimuksen Simulink-simulaatiomallin dataa ja toinen Teollisuuden Voiman (TVO) ydinvoimalasimulaattorista saatua dataa. Nämä yksiköt demonstroivat prototyypin menetelmien mahdollisuuksia. Kirjallisuudessa esiintyi myös vaihtoehtoisia tapoja hyödyntää SOM-menetelmää päätöksenteon tuessa. Näitä verrattiin prototyypin menetelmiin ja lisäksi pohdittiin, voiko prototyyppialustaa hyödyntää muilla sovellusalueilla.Industry and business are full of complicated decision making processes in which there is a high probability of human error. These processes are most crucial in the operation of nuclear power plants. The quality of decisions can be increased and probability of errors can be reduced by providing computerized decision support for the decision maker. Self-Organizing Map (SOM) is a useful method for visualizing high-dimensional and large datasets. The aim of this work is to find approaches for using SOM in supporting the decision making processes of nuclear power plant operators, and to analyze whether these approaches can be used for decision support in other applications. The research methods are prototyping, data mining and survey of literature. A prototype of a decision support system (DSS) platform (DERSI) has been developed. The prototype uses a collection of methods for decision support, including SOM quantization error, SOM U-matrix, fuzzy logic, rule-based reasoning and case-based reasoning. It is programmed with the Matlab programming language and uses a SOM Toolbox add-on. It has a graphical user interface that contains visualizations of the methods. Two units of a DSS prototype have been built on this platform. One uses data from a Simulink simulation model and the other unit uses data from the Teollisuuden Voima (TVO) nuclear power plant simulator. These prototype units demonstrate the potential of the prototype methods. Other approaches for using SOM in decision support were found from literature. The thesis compares these approaches with the prototype methods and discusses the possible use of this prototype in other applications

Efficient Decision Support Systems

This series is directed to diverse managerial professionals who are leading the transformation of individual domains by using expert information and domain knowledge to drive decision support systems (DSSs). The series offers a broad range of subjects addressed in specific areas such as health care, business management, banking, agriculture, environmental improvement, natural resource and spatial management, aviation administration, and hybrid applications of information technology aimed to interdisciplinary issues. This book series is composed of three volumes: Volume 1 consists of general concepts and methodology of DSSs; Volume 2 consists of applications of DSSs in the biomedical domain; Volume 3 consists of hybrid applications of DSSs in multidisciplinary domains. The book is shaped upon decision support strategies in the new infrastructure that assists the readers in full use of the creative technology to manipulate input data and to transform information into useful decisions for decision makers

Combining SOA and BPM Technologies for Cross-System Process Automation

This paper summarizes the results of an industry case study that introduced a cross-system business process automation solution based on a combination of SOA and BPM standard technologies (i.e., BPMN, BPEL, WSDL). Besides discussing major weaknesses of the existing, custom-built, solution and comparing them against experiences with the developed prototype, the paper presents a course of action for transforming the current solution into the proposed solution. This includes a general approach, consisting of four distinct steps, as well as specific action items that are to be performed for every step. The discussion also covers language and tool support and challenges arising from the transformation

Data Mining Techniques to Understand Textual Data

More than ever, information delivery online and storage heavily rely on text. Billions of texts are produced every day in the form of documents, news, logs, search queries, ad keywords, tags, tweets, messenger conversations, social network posts, etc. Text understanding is a fundamental and essential task involving broad research topics, and contributes to many applications in the areas text summarization, search engine, recommendation systems, online advertising, conversational bot and so on. However, understanding text for computers is never a trivial task, especially for noisy and ambiguous text such as logs, search queries. This dissertation mainly focuses on textual understanding tasks derived from the two domains, i.e., disaster management and IT service management that mainly utilizing textual data as an information carrier. Improving situation awareness in disaster management and alleviating human efforts involved in IT service management dictates more intelligent and efficient solutions to understand the textual data acting as the main information carrier in the two domains. From the perspective of data mining, four directions are identified: (1) Intelligently generate a storyline summarizing the evolution of a hurricane from relevant online corpus; (2) Automatically recommending resolutions according to the textual symptom description in a ticket; (3) Gradually adapting the resolution recommendation system for time correlated features derived from text; (4) Efficiently learning distributed representation for short and lousy ticket symptom descriptions and resolutions. Provided with different types of textual data, data mining techniques proposed in those four research directions successfully address our tasks to understand and extract valuable knowledge from those textual data. My dissertation will address the research topics outlined above. Concretely, I will focus on designing and developing data mining methodologies to better understand textual information, including (1) a storyline generation method for efficient summarization of natural hurricanes based on crawled online corpus; (2) a recommendation framework for automated ticket resolution in IT service management; (3) an adaptive recommendation system on time-varying temporal correlated features derived from text; (4) a deep neural ranking model not only successfully recommending resolutions but also efficiently outputting distributed representation for ticket descriptions and resolutions

Correctness of services and their composition

We study correctness of services and their composition and investigate how the design of correct service compositions can be systematically supported. We thereby focus on the communication protocol of the service and approach these questions using formal methods and make contributions to three scenarios of SOC.Wir studieren die Korrektheit von Services und Servicekompositionen und untersuchen, wie der Entwurf von korrekten Servicekompositionen systematisch unterstützt werden kann. Wir legen dabei den Fokus auf das Kommunikationsprotokoll der Services. Mithilfe von formalen Methoden tragen wir zu drei Szenarien von SOC bei

Correctness of services and their composition

We study correctness of services and their composition and investigate how the design of correct service compositions can be systematically supported. We thereby focus on the communication protocol of the service and approach these questions using formal methods and make contributions to three scenarios of SOC.Wir studieren die Korrektheit von Services und Servicekompositionen und untersuchen, wie der Entwurf von korrekten Servicekompositionen systematisch unterstützt werden kann. Wir legen dabei den Fokus auf das Kommunikationsprotokoll der Services. Mithilfe von formalen Methoden tragen wir zu drei Szenarien von SOC bei

Fault Diagnosis in Enterprise Software Systems Using Discrete Monitoring Data

Success for many businesses depends on their information software systems. Keeping these systems operational is critical, as failure in these systems is costly. Such systems are in many cases sophisticated, distributed and dynamically composed. To ensure high availability and correct operation, it is essential that failures be detected promptly, their causes diagnosed and remedial actions taken. Although automated recovery approaches exists for specific problem domains, the problem-resolution process is in many cases manual and painstaking. Computer support personnel put a great deal of effort into resolving the reported failures. The growing size and complexity of these systems creates the need to automate this process. The primary focus of our research is on automated fault diagnosis and recovery using discrete monitoring data such as log files and notifications. Our goal is to quickly pinpoint the root-cause of a failure. Our contributions are: Modelling discrete monitoring data for automated analysis, automatically leveraging common symptoms of failures from historic monitoring data using such models to pinpoint faults, and providing a model for decision-making under uncertainty such that appropriate recovery actions are chosen. Failures in such systems are caused by software defects, human error, hardware failures, environmental conditions and malicious behaviour. Our primary focus in this thesis is on software defects and misconfiguration

Multidisciplinary perspectives on Artificial Intelligence and the law

This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics – and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the Católica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio

Improving data preparation for the application of process mining

Immersed in what is already known as the fourth industrial revolution, automation and data exchange are taking on a particularly relevant role in complex environments, such as industrial manufacturing environments or logistics. This digitisation and transition to the Industry 4.0 paradigm is causing experts to start analysing business processes from other perspectives. Consequently, where management and business intelligence used to dominate, process mining appears as a link, trying to build a bridge between both disciplines to unite and improve them. This new perspective on process analysis helps to improve strategic decision making and competitive capabilities. Process mining brings together data and process perspectives in a single discipline that covers the entire spectrum of process management. Through process mining, and based on observations of their actual operations, organisations can understand the state of their operations, detect deviations, and improve their performance based on what they observe. In this way, process mining is an ally, occupying a large part of current academic and industrial research. However, although this discipline is receiving more and more attention, it presents severe application problems when it is implemented in real environments. The variety of input data in terms of form, content, semantics, and levels of abstraction makes the execution of process mining tasks in industry an iterative, tedious, and manual process, requiring multidisciplinary experts with extensive knowledge of the domain, process management, and data processing. Currently, although there are numerous academic proposals, there are no industrial solutions capable of automating these tasks. For this reason, in this thesis by compendium we address the problem of improving business processes in complex environments thanks to the study of the state-of-the-art and a set of proposals that improve relevant aspects in the life cycle of processes, from the creation of logs, log preparation, process quality assessment, and improvement of business processes. Firstly, for this thesis, a systematic study of the literature was carried out in order to gain an in-depth knowledge of the state-of-the-art in this field, as well as the different challenges faced by this discipline. This in-depth analysis has allowed us to detect a number of challenges that have not been addressed or received insufficient attention, of which three have been selected and presented as the objectives of this thesis. The first challenge is related to the assessment of the quality of input data, known as event logs, since the requeriment of the application of techniques for improving the event log must be based on the level of quality of the initial data, which is why this thesis presents a methodology and a set of metrics that support the expert in selecting which technique to apply to the data according to the quality estimation at each moment, another challenge obtained as a result of our analysis of the literature. Likewise, the use of a set of metrics to evaluate the quality of the resulting process models is also proposed, with the aim of assessing whether improvement in the quality of the input data has a direct impact on the final results. The second challenge identified is the need to improve the input data used in the analysis of business processes. As in any data-driven discipline, the quality of the results strongly depends on the quality of the input data, so the second challenge to be addressed is the improvement of the preparation of event logs. The contribution in this area is the application of natural language processing techniques to relabel activities from textual descriptions of process activities, as well as the application of clustering techniques to help simplify the results, generating more understandable models from a human point of view. Finally, the third challenge detected is related to the process optimisation, so we contribute with an approach for the optimisation of resources associated with business processes, which, through the inclusion of decision-making in the creation of flexible processes, enables significant cost reductions. Furthermore, all the proposals made in this thesis are validated and designed in collaboration with experts from different fields of industry and have been evaluated through real case studies in public and private projects in collaboration with the aeronautical industry and the logistics sector