Web Mining for Web Personalization

Web personalization is the process of customizing a Web site to the needs of specific users, taking advantage of the knowledge acquired from the analysis of the user\u27s navigational behavior (usage data) in correlation with other information collected in the Web context, namely, structure, content, and user profile data. Due to the explosive growth of the Web, the domain of Web personalization has gained great momentum both in the research and commercial areas. In this article we present a survey of the use of Web mining for Web personalization. More specifically, we introduce the modules that comprise a Web personalization system, emphasizing the Web usage mining module. A review of the most common methods that are used as well as technical issues that occur is given, along with a brief overview of the most popular tools and applications available from software vendors. Moreover, the most important research initiatives in the Web usage mining and personalization areas are presented

Predicting potential customer needs and wants for agile design and manufacture in an industry 4.0 environment

Manufacturing is currently experiencing a paradigm shift in the way that products are designed, produced and serviced. Such changes are brought about mainly by the extensive use of the Internet and digital technologies. As a result of this shift, a new industrial revolution is emerging, termed “Industry 4.0” (i4), which promises to accommodate mass customisation at a mass production cost. For i4 to become a reality, however, multiple challenges need to be addressed, highlighting the need for design for agile manufacturing and, for this, a framework capable of integrating big data analytics arising from the service end, business informatics through the manufacturing process, and artificial intelligence (AI) for the entire manufacturing value chain. This thesis attempts to address these issues, with a focus on the need for design for agile manufacturing. First, the state of the art in this field of research is reviewed on combining cutting-edge technologies in digital manufacturing with big data analysed to support agile manufacturing. Then, the work is focused on developing an AI-based framework to address one of the customisation issues in smart design and agile manufacturing, that is, prediction of potential customer needs and wants. With this framework, an AI-based approach is developed to predict design attributes that would help manufacturers to decide the best virtual designs to meet emerging customer needs and wants predictively. In particular, various machine learning approaches are developed to help explain at least 85% of the design variance when building a model to predict potential customer needs and wants. These approaches include k-means clustering, self-organizing maps, fuzzy k-means clustering, and decision trees, all supporting a vector machine to evaluate and extract conscious and subconscious customer needs and wants. A model capable of accurately predicting customer needs and wants for at least 85% of classified design attributes is thus obtained. Further, an analysis capable of determining the best design attributes and features for predicting customer needs and wants is also achieved. As the information analysed can be utilized to advise the selection of desired attributes, it is fed back in a closed-loop of the manufacturing value chain: design → manufacture → management/service → → → design... For this, a total of 4 case studies are undertaken to test and demonstrate the efficacy and effectiveness of the framework developed. These case studies include: 1) an evaluation model of consumer cars with multiple attributes including categorical and numerical ones; 2) specifications of automotive vehicles in terms of various characteristics including categorical and numerical instances; 3) fuel consumptions of various car models and makes, taking into account a desire for low fuel costs and low CO2 emissions; and 4) computer parts design for recommending the best design attributes when buying a computer. The results show that the decision trees, as a machine learning approach, work best in predicting customer needs and wants for smart design. With the tested framework and methodology, this thesis overall presents a holistic attempt to addressing the missing gap between manufacture and customisation, that is meeting customer needs and wants. Effective ways of achieving customization for i4 and smart manufacturing are identified. This is achieved through predicting potential customer needs and wants and applying the prediction at the product design stage for agile manufacturing to meet individual requirements at a mass production cost. Such agility is one key element in realising Industry 4.0. At the end, this thesis contributes to improving the process of analysing the data to predict potential customer needs and wants to be used as inputs to customizing product designs agilely

PersoBOX: A Personalization Engine between ERP System and Web Frontend

The demand for personalization functions in e-shops is increasing steadily. In order to fulfil customer requirements best and to stimulate the customer’s buying experience positively, companies are aiming at an easy technical solution to the integration of ERP master data, CRM data, and transactional data from web shops. The current paper presents the state of the art in personalization in e-commerce and summarizes remaining problems. An integrated toolset, the so called PersoBOX, is introduced as a solution which connects the realm of ERP systems with web shops. We present a schematic architecture of the PersoBOX describing the data flows, as well as processes and functions to be implemented. The presentation of the architecture is a preliminary result of an ongoing research project in the area of personalization

Intelligent products: the grace experience

Product intelligence is a new industrial manufacturing control paradigm aligned with the context of cyber-physical systems and addressing the current requirements of flexibility, reconfigurability and responsiveness. This paradigm introduces benefits in terms of improvement of the entire product׳s life-cycle, and particularly the product quality and customization, aiming the customer satisfaction. This paper presents an implementation of a system of intelligent products, developed under the scope of the GRACE project, where an agent-based solution was deployed in a factory plant producing laundry washing machines. The achieved results show an increase of the production and energy efficiency, an increase of the product quality and customization, as well as a reduction of the scrap costs.info:eu-repo/semantics/publishedVersio

Laboratorion tiedonhallintajärjestelmäpalvelun integraatio tuotannon toimintojen hallintajärjestelmään

Laboratory information management systems (LIMS) enhance the productivity of the laboratories as the data throughput and the market demand increase. Integrating the laboratory with a manufacturing operations management system (MOMS) is the key to improve the productivity in the whole production chain. In the field of the mining and metal industry the laboratory can be located at a different location. Thus a software module functioning as an integrator transferring and scheduling the data between the laboratory and the process is a justifiable investment. This Master's Thesis documents the feature design and specification of a LIMS software module by following the method of the system development life cycle. This Master's Thesis consists of the following parts: the theory of the method, the overview of LIMS systems, the designing of the LIMS module and finally documenting the specifications and realization of the features and the requirements. A comprehensive description of a complex information system like LIMS requires use of different models describing the system from several perspectives. The models used in this Master's Thesis are the user journey map, the requirement specification list, the database diagram and the system interface diagram. The research problem stated, that various process plants with a laboratory have some LIMS functionalities that are yet not uniform. The question was about finding methods and results of the set of features and architecture for a platform independent, uniform LIMS module that can be used as the actual LIMS itself as well. The feature and requirement specification was designed by studying the functionality of the existing systems and by interviewing the key users and the stakeholders. The platform independency was solved with a microservice architecture, that is a set of coherent smaller services, that can be added seamlessly into the LIMS module and connected to any external system

Eine Analyse der Literatur zur Referenzmodellierung im Geschäftsprozessmanagement unter Berücksichtigung quantitativer Methoden

Im Geschäftsprozessmanagement nimmt die Referenzmodellierung bei der Gestaltung von Geschäftsprozessen eine große Bedeutung ein, da auf bereits existierende Modelle zurückgegriffen werden kann. So kann Zeit für die Entwicklung der Prozesse eingespart und von bereits etabliertem Wissen profitiert werden. Die vorliegende Masterarbeit analysiert die Literatur im Bereich der Referenzmodellierung im Geschäftsprozessmanagement unter Berücksichtigung quantitativer Methoden. Es werden insbesondere die Forschungsrichtungen bzw. Themenbereiche, Entwicklungen und der aktuelle Stand der Literatur in diesem Bereich ermittelt. Zunächst werden deutsch- und englischsprachige Artikel nach bestimmten Kriterien ausgewählt. Anschließend folgt eine quantitativ orientierte Analyse der Literatur. Dabei kommt die Latente Semantische Analyse zum Einsatz, mit deren Hilfe Themenbereiche ermittelt werden und die einzelnen Beiträge den ermittelten Themenbereichen zugeordnet werden können. Darüber hinaus wird die Entwicklung der Anzahl der Artikel in den Themenbereichen im Zeitverlauf betrachtet und auf Unterschiede zwischen der deutsch- und englischsprachigen Literatur eingegangen. In der darauf folgenden qualitativ orientierten Analyse werden die Artikel der einzelnen Themenbereiche inhaltlich analysiert und der aktuelle Stand der Forschung dargestellt. Nicht zuletzt werden die Ergebnisse der qualitativen Analyse in Bezug zu den Ergebnissen der quantitativen Analyse gesetzt

MANAGING THE FLOOD OF INFORMATION: THE ROLE OF INFORMATION PROCESSING IN CONTEMPORARY ORGANIZATIONS

Ph.DDOCTOR OF PHILOSOPH