Date stream mining techniques can be used in tracking user behaviors as they attempt to achieve their goals. Quality metrics over stream-mined models identify potential changes in user goal attainment. When the quality of some data mined models varies significantly from nearby models—as defined by quality metrics—then the user’s behavior is automatically flagged as a potentially significant behavioral change. Decision tree, sequence pattern and Hidden Markov modeling being used in this study. These three types of modeling can expose different aspect of user’s behavior. In case of decision tree modeling, the specific changes in user behavior can automatically characterized by differencing the data-mined decision-tree models. The sequence pattern modeling can shed light on how the user changes his sequence of actions and Hidden Markov modeling can identifies the learning transition points. This research describes how model-quality monitoring and these three types of modeling as a generic framework can aid recognition and diagnoses of behavioral changes in a case study of cognitive rehabilitation via emailing. The date stream mining techniques mentioned are used to monitor patient goals as part of a clinical plan to aid cognitive rehabilitation. In this context, real time data mining aids clinicians in tracking user behaviors as they attempt to achieve their goals. This generic framework can be widely applicable to other real-time data-intensive analysis problems. In order to illustrate this fact, the similar Hidden Markov modeling is being used for analyzing the transactional behavior of a telecommunication company for fraud detection. Fraud similarly can be considered as a potentially significant transaction behavioral change.

Ein Vierteljahrhundert nach der Wiedervereinigung Deutschlands gibt es zwischen Ost- und Westdeutschland nach wie vor differierende Anteile von Personen, die eine schulische Hochschulzugangsberechtigung erlangen und diese anschließend durch den Übergang an eine Hochschule einlösen. Mittels eines selbst entwickelten Modells, das sich an die soziologische Wert-Erwartungs-Theorie von Erikson & Jonsson sowie den lebensverlaufstheoretischen Ansatz von Mayer anlehnt, werden verschiedene individuelle und kontextuelle Einflussfaktoren der Studienentscheidung erstmals in einem Kohortenvergleich seit 1990 betrachtet. Neben der Schwelle Hochschulzugang werden zudem erstmals die Bildungsverläufe von zwei Studienberechtigtenkohorten in ihrer Gesamtheit, also unter Berücksichtigung von Fortbildung, Erwerbs- und Familienverläufen über einen Zeitraum von zehneinhalb Jahren vergleichend in den Blick genommen und mittels Sequenzmusteranalysen jeweils typische Verlaufsmuster für ost- und westdeutsche Studienberechtigte ermittelt. Zur Untersuchung der zentralen Forschungsfrage nach Konvergenzen, Divergenzen und dauerhaften Unterschieden in den individuellen und kontextuellen Einflussfaktoren der Studienentscheidung sowie den nachschulischen Bildungsverläufen seit 1990 in Ost- und Westdeutschland werden Daten der DZHW-Studienberechtigtenpanel 1990, 1994, 1999, 2002 und 2006 verwendet. Zusammenfassend werden zahlreiche Konvergenzen und Gemeinsamkeiten identifiziert, insbesondere bei den individuellen Einflussfaktoren für eine Studienentscheidung. Charakteristische Ost-West-Unterschiede zeigen sich u.a. bei der Bedeutung der bisherigen Bildungsbiografie und der antizipierten Studienkosten für die Studienentscheidung sowie der Hochschulentfernung. Die nachschulischen Bildungs- und Lebensverläufe weisen bereits bei der Kohorte 1990 erhebliche Gemeinsamkeiten auf. Bei der Kohorte 1999 haben sich neue Unterschiede bei der Bedeutung von Arbeitslosigkeit und Familientätigkeit herausgebildet. / A quarter of a century has passed since the reunification of Germany. The proportion of young people who acquire a university entrance qualification and those who attend university subsequently differ in part considerably between East and West Germany. This survey examines different individual and contextual factors, which influence the decision to attend university, for the first time by contrasting cohorts since 1990, using a specifically developed model, which closely follows the rational choice model by Erikson & Jonsson and the life-course theory approach by Mayer. The transition to a university is one threshold in the complete educational after-school career. Therefore, the educational careers of two cohorts entitled to study are for the first time examined as a whole, regarding further training, as well as occupational trajectories and family development over a period of ten and a half years after schooldays; by means of sequence pattern analyses, typical sequential patterns of school leavers from East and respectively West Germany are identified. For the examination of the central research question concerning convergences, divergences and permanent differences with regard to the individual and contextual factors, which have influenced study decisions and after-school educational careers since 1990 in East and West Germany, this investigation uses data from the DZHW panels about persons entitled to study from 1990, 1994, 1999, 2002, and 2006. To sum up, numerous convergences and commonalities can be identified, especially regarding the individual factors, which influence the decision to attend university. But typical East-West differences appear with regard to the importance of the previous educational career and the anticipated costs to study, but also the distance of university. The after-school educational and life courses have already many common features in the cohort from 1990. New differences have developed in the cohort from 1999.

Protein sequences are essential for encoding molecular structures and functions. Consequently, biologists invest substantial resources and time discovering functional patterns in proteins. Using high-throughput technologies, biologists are generating an increasing amount of data. Thus, the major challenge in biosequencing today is the ability to conduct data analysis in an effi cient and productive manner. Conserved amino acids in proteins reveal important functional domains within protein families. Conversely, less conserved amino acid variations within these protein sequence patterns reveal areas of evolutionary and functional divergence. Exploring protein families using existing methods such as multiple sequence alignment is computationally expensive, thus pattern search is used. However, at present, combinatorial methods of pattern search generate a large set of solutions, and probabilistic methods require richer representations. They require biological ground truth of the input sequences, such as gene name or taxonomic species, as class labels based on traditional classi fication practice to train a model for predicting unknown sequences. However, these algorithms are inherently biased by mislabelling and may not be able to reveal class characteristics in a detailed and succinct manner. A novel pattern representation called an Aligned Pattern Cluster (AP Cluster) as developed in this dissertation is compact yet rich. It captures conservations and variations of amino acids and covers more sequences with lower entropy and greatly reduces the number of patterns. AP Clusters contain statistically signi cant patterns with variations; their importance has been confi rmed by the following biological evidences: 1) Most of the discovered AP Clusters correspond to binding segments while their aligned columns correspond to binding sites as verifi ed by pFam, PROSITE, and the three-dimensional structure. 2) By compacting strong correlated functional information together, AP Clusters are able to reveal class characteristics for taxonomical classes, gene classes and other functional classes, or incorrect class labelling. 3) Co-occurrence of AP Clusters on the same homologous protein sequences are spatially close in the protein's three-dimensional structure. These results demonstrate the power and usefulness of AP Clusters. They bring in similar statistically signifi cance patterns with variation together and align them to reveal protein regional functionality, class characteristics, binding and interacting sites for the study of protein-protein and protein-drug interactions, for diff erentiation of cancer tumour types, targeted gene therapy as well as for drug target discovery.