published_or_final_version / abstract / toc / Computer Science and Information Systems / Master / Master of Philosophy

published_or_final_version / Computer Science / Master / Master of Philosophy

Γνωριμία με την διαδικασία εξόρυξης γνώσης από δεδομένα και εφαρμογή των τεχνικών εξόρυξης γνώσης σε ιατρικά δεδομένα ασθενών με την χρήση της πλατφόρμας αλγορίθμων μηχανικής μάθησης Weka. / Getting to the process of data mining and applying data mining techniques in medical data of patients with the use of machine learning algorithms platform Weka.

Archeologists have used material artifacts found in a physical space to gain an understanding about the people who occupied that space. Likewise, as users wander through a digital library, they leave behind data-based artifacts of their activity in the virtual space. Digital library archeologists can gather these artifacts and employ inductive techniques, such as bibliomining, to create generalizations. These generalizations are the basis for hypotheses, which are tested to gain understanding about library services and users. In this article, the development of traditional archeological methods is presented and used to create a conceptual framework for the artifact-based evaluation in digital libraries.

Artificial Intelligence Lab, Department of MIS, University of Arizona / Observations of Information Retrieval (IR) system user experiences reveal a strong desire for collaborative search while at the same time suggesting that collaborative capabilities are rarely, and then only in a limited fashion, supported by current searching and visualization tools. Equally interesting is the fact that observations of user experiences with Group Support Systems (GSS) reveal that although access to external information and the ability to search for relevant material is often vital to the progress of GSS sessions, integrated support for collaborative searching and visualization of results is lacking in GSS systems. After reviewing both user experiences described in IR and GSS literature and observing and interviewing users of existing IR and GSS commercial and prototype systems, the authors conclude that there is an obvious demand for systems supporting multi-user IR.. It is surprising to the authors that very little attention has been given to the common ground shared by these two important research domains. With this in mind, our paper describes how user experiences with IR and GSS systems has shed light on a promising new area of collaborative research and led to the development of a prototype that merges the two paradigms into a Collaborative Information Retrieval Environment (CIRE). Finally the paper presents theory developed from initial user experiences with our prototype and describes plans to test the efficacy of this new paradigm empirically through controlled experimentation.

Artificial Intelligence Lab, Department of MIS, University of Arizona / Valuable criminal-justice data in free texts such as police narrative reports are currently difficult to be accessed and used by intelligence investigators in crime analyses. It would be desirable to automatically identify from text reports meaningful entities, such as person names, addresses, narcotic drugs, or vehicle names to facilitate crime investigation. In this paper, we report our work on a neural network-based entity extractor, which applies named-entity extraction techniques to identify useful entities from police narrative reports. Preliminary evaluation results demonstrated that our approach is feasible and has some potential values for real-life applications. Our system achieved encouraging precision and recall rates for person names and narcotic drugs, but did not perform well for addresses and personal properties. Our future work includes conducting larger-scale evaluation studies and enhancing the system to capture human knowledge interactively.

Artificial Intelligence Lab, Department of MIS, University of Arizona / To access the content of digital texts efficiently, it is necessary to provide more sophisticated access than keyword based searching. Genescene provides biomedical researchers with research findings and background relations automatically extracted from text and experimental data. These provide a more detailed overview of the information available. The extracted relations were evaluated by qualified researchers and are precise. A qualitative ongoing evaluation of the current online interface indicates that this method to search the literature is more useful and efficient than keyword based searching.

No description available.

Η εκρηκτική αύξηση του μεγέθους των δεδομένων που είναι διαθέσιμα στο Δίκτυο καθώς και η ποικιλία των εργασιών οι οποίες μπορούν να πραγματοποιηθούν μέσω του Δικτύου έχουν αυξήσει σημαντικά την νομισματική αξία της κίνησης στο Δίκτυο. Για να κερδίσουν από αυτή την αναπτυσσόμενη αγορά, οι διαχειριστές ιστοσελίδων προσπαθούν να αυξήσουν την κίνηση χρηστών στην ιστοσελίδα τους, διαμορφώνοντάς τη κατάλληλα έτσι ώστε να ικανοποιεί τις ανάγκες συγκεκριμένων χρηστών. Η διαμόρφωση και η παραμετροποίηση των ιστοσελίδων παρουσιάζει δύο μεγάλες προκλήσεις: την αποτελεσματική αναγνώριση των ενδιαφερόντων κάθε χρήστη και την ενσωμάτωση των ενδιαφερόντων του στην παρουσίαση και στο περιεχόμενο της ιστοσελίδας. Σε αυτή την εργασία μελετάται ο τρόπος με τον οποίο μπορούμε να αναγνωρίσουμε με ακρίβεια τα ενδιαφέροντα ενός χρήστη χρησιμοποιώντας τα μοτίβα πλοήγησης και παρουσιάζεται ένας καινοτόμος μηχανισμός προτάσεων ο οποίος εφαρμόζει τεχνικές εξόρυξης στο Δίκτυο για να πραγματοποιήσει τη συσχέτιση μεταξύ των ενδιαφερόντων του χρήστη και του περιεχομένου και της δομής της ιστοσελίδας. Στόχος της τεχνικής που παρουσιάζεται είναι να παραμετροποιηθεί η σελίδα για κάθε συγκεκριμένο χρήστη με βάση τα ενδιαφέροντά του. Η πειραματική αξιολόγηση αποδεικνύει ότι είναι δυνατόν να συμπεράνουμε με ακρίβεια τα ενδιαφέροντα ενός χρήστη από την συμπεριφορά του κατά την πλοήγηση και ότι ο μηχανισμός προτάσεων, ο οποίος χρησιμοποιεί τα συμπεράσματα για τα ενδιαφέροντα του χρήστη, έχει σαν αποτέλεσμα σημαντικές βελτιώσεις στη λειτουργικότητα μιας ιστοσελίδας. / The explosive growth of online data and the diversity of goals that may be pursued over the web have significantly increased the monetary value of the web traffic. To tap into this accelerating market, web site operators try to increase their traffic by customizing their sites to the needs of specific users. Web site customization involves two great challenges: the effective identification of the user interests and the encapsulation of those interests into the sites’ presentation and content. In this paper, we study how we can effectively detect the user interests that are hidden behind navigational patterns and we introduce a novel recommendation mechanism that employs web mining techniques for correlating the identified interests to the sites’ semantic content, in order to customize them to specific users. Our experimental evaluation shows that the user interests can be accurately detected from their navigational behavior and that our recommendation mechanism, which uses the identified interests, yields significant improvements in the sites’ usability.

Contemporary organizations live in an environment of networks: internally, they manage the networks of employees, information resources, and knowledge assets to enhance productivity and improve efficiency; externally, they form alliances with strategic partners, suppliers, buyers, and other stakeholders to conserve resources, share risks, andgain market power. Many managerial and strategic decisions are made by organizations based on their understanding of the structure of these networks. This dissertation is devoted to network structure mining, a new research topic on knowledge discovery indatabases (KDD) for supporting knowledge management and decision making in organizations.A comprehensive computational framework is developed to provide a taxonomy and summary of the theoretical foundations, major research questions, methodologies,techniques, and applications in this new area based on extensive literature review. Research in this new area is categorized into static structure mining and dynamic structure mining. The major research questions of static mining are locating criticalresources in networks, reducing network complexity, and capturing topological properties of large-scale networks. An inventory of techniques developed in multiple reference disciplines such as social network analysis and Web mining are reviewed. These techniques have been used in mining networks in various applications including knowledge management, marketing, Web mining, and intelligence and security. Dynamic pattern mining is concerned with network evolution and major findings are reviewed.A series of case studies are presented in this dissertation to demonstrate how network structure mining can be used to discover valuable knowledge from various networks ranging from criminal networks to patent citation networks. Several techniques aredeveloped and employed in these studies. Performance evaluation results are provided to demonstrate the usefulness and potential of this new research field in supporting knowledge management and decision making in real applications.