Probabilistic Skyline Queries over Uncertain Moving Objects

Data uncertainty inherently exists in a large number of applications due to factors such as limitations of measuring equipments, update delay, and network bandwidth. Recently, modeling and querying uncertain data have attracted considerable attention from the database community. However, how to perform advanced analysis on uncertain data remains an interesting question. In this paper, we focus on the execution of skyline computation over uncertain moving objects. We propose a novel probabilistic skyline model where an uncertain object may take a probability to be in the skyline at a certain time point, therefore a p-t-skyline contains those moving objects whose skyline probabilities are at least p at time point t. Computing probabilistic skyline over a large number of uncertain moving objects is a daunting task in practice. In order to efficiently compute the probabilistic skyline query, we propose a discrete-and-conquer strategy, which follows the sampling-bounding-pruning-refining procedure. To further reduce the skyline computation cost, we propose an enhanced framework that is based on a multi-dimensional indexing structure combined with the discrete-and-conquer strategy. Through extensive experiments with synthetic datasets, we show that the framework can efficiently support skyline queries over uncertain moving object and is scalable on large data sets

Reporting Skyline on Uncertain Dimension with Query Interval

Naturally, users sometimes specify their preference in an imprecise way (i.e. query with an interval/range). To report results that satisfy the imprecise query as well as interesting would be easy on dataset with atomic values. The challenge is when the dataset being queried consists of both atomic values as well as continuous range of values. For a set of objects with uncertain dimension and given a query interval

Coping with new Challenges in Clustering and Biomedical Imaging

The last years have seen a tremendous increase of data acquisition in different scientific fields such as molecular biology, bioinformatics or biomedicine. Therefore, novel methods are needed for automatic data processing and analysis of this large amount of data. Data mining is the process of applying methods like clustering or classification to large databases in order to uncover hidden patterns. Clustering is the task of partitioning points of a data set into distinct groups in order to minimize the intra cluster similarity and to maximize the inter cluster similarity. In contrast to unsupervised learning like clustering, the classification problem is known as supervised learning that aims at the prediction of group membership of data objects on the basis of rules learned from a training set where the group membership is known. Specialized methods have been proposed for hierarchical and partitioning clustering. However, these methods suffer from several drawbacks. In the first part of this work, new clustering methods are proposed that cope with problems from conventional clustering algorithms. ITCH (Information-Theoretic Cluster Hierarchies) is a hierarchical clustering method that is based on a hierarchical variant of the Minimum Description Length (MDL) principle which finds hierarchies of clusters without requiring input parameters. As ITCH may converge only to a local optimum we propose GACH (Genetic Algorithm for Finding Cluster Hierarchies) that combines the benefits from genetic algorithms with information-theory. In this way the search space is explored more effectively. Furthermore, we propose INTEGRATE a novel clustering method for data with mixed numerical and categorical attributes. Supported by the MDL principle our method integrates the information provided by heterogeneous numerical and categorical attributes and thus naturally balances the influence of both sources of information. A competitive evaluation illustrates that INTEGRATE is more effective than existing clustering methods for mixed type data. Besides clustering methods for single data objects we provide a solution for clustering different data sets that are represented by their skylines. The skyline operator is a well-established database primitive for finding database objects which minimize two or more attributes with an unknown weighting between these attributes. In this thesis, we define a similarity measure, called SkyDist, for comparing skylines of different data sets that can directly be integrated into different data mining tasks such as clustering or classification. The experiments show that SkyDist in combination with different clustering algorithms can give useful insights into many applications. In the second part, we focus on the analysis of high resolution magnetic resonance images (MRI) that are clinically relevant and may allow for an early detection and diagnosis of several diseases. In particular, we propose a framework for the classification of Alzheimer's disease in MR images combining the data mining steps of feature selection, clustering and classification. As a result, a set of highly selective features discriminating patients with Alzheimer and healthy people has been identified. However, the analysis of the high dimensional MR images is extremely time-consuming. Therefore we developed JGrid, a scalable distributed computing solution designed to allow for a large scale analysis of MRI and thus an optimized prediction of diagnosis. In another study we apply efficient algorithms for motif discovery to task-fMRI scans in order to identify patterns in the brain that are characteristic for patients with somatoform pain disorder. We find groups of brain compartments that occur frequently within the brain networks and discriminate well among healthy and diseased people

Processing Rank-Aware Queries in Schema-Based P2P Systems

Effiziente Anfragebearbeitung in Datenintegrationssystemen sowie in P2P-Systemen ist bereits seit einigen Jahren ein Aspekt aktueller Forschung. Konventionelle Datenintegrationssysteme bestehen aus mehreren Datenquellen mit ggf. unterschiedlichen Schemata, sind hierarchisch aufgebaut und besitzen eine zentrale Komponente: den Mediator, der ein globales Schema verwaltet. Anfragen an das System werden auf diesem globalen Schema formuliert und vom Mediator bearbeitet, indem relevante Daten von den Datenquellen transparent für den Benutzer angefragt werden. Aufbauend auf diesen Systemen entstanden schließlich Peer-Daten-Management-Systeme (PDMSs) bzw. schemabasierte P2P-Systeme. An einem PDMS teilnehmende Knoten (Peers) können einerseits als Mediatoren agieren andererseits jedoch ebenso als Datenquellen. Darüber hinaus sind diese Peers autonom und können das Netzwerk jederzeit verlassen bzw. betreten. Die potentiell riesige Datenmenge, die in einem derartigen Netzwerk verfügbar ist, führt zudem in der Regel zu sehr großen Anfrageergebnissen, die nur schwer zu bewältigen sind. Daher ist das Bestimmen einer vollständigen Ergebnismenge in vielen Fällen äußerst aufwändig oder sogar unmöglich. In diesen Fällen bietet sich die Anwendung von Top-N- und Skyline-Operatoren, ggf. in Verbindung mit Approximationstechniken, an, da diese Operatoren lediglich diejenigen Datensätze als Ergebnis ausgeben, die aufgrund nutzerdefinierter Ranking-Funktionen am relevantesten für den Benutzer sind. Da durch die Anwendung dieser Operatoren zumeist nur ein kleiner Teil des Ergebnisses tatsächlich dem Benutzer ausgegeben wird, muss nicht zwangsläufig die vollständige Ergebnismenge berechnet werden sondern nur der Teil, der tatsächlich relevant für das Endergebnis ist. Die Frage ist nun, wie man derartige Anfragen durch die Ausnutzung dieser Erkenntnis effizient in PDMSs bearbeiten kann. Die Beantwortung dieser Frage ist das Hauptanliegen dieser Dissertation. Zur Lösung dieser Problemstellung stellen wir effiziente Anfragebearbeitungsstrategien in PDMSs vor, die die charakteristischen Eigenschaften ranking-basierter Operatoren sowie Approximationstechniken ausnutzen. Peers werden dabei sowohl auf Schema- als auch auf Datenebene hinsichtlich der Relevanz ihrer Daten geprüft und dementsprechend in die Anfragebearbeitung einbezogen oder ausgeschlossen. Durch die Heterogenität der Peers werden Techniken zum Umschreiben einer Anfrage von einem Schema in ein anderes nötig. Da existierende Techniken zum Umschreiben von Anfragen zumeist nur konjunktive Anfragen betrachten, stellen wir eine Erweiterung dieser Techniken vor, die Anfragen mit ranking-basierten Anfrageoperatoren berücksichtigt. Da PDMSs dynamische Systeme sind und teilnehmende Peers jederzeit ihre Daten ändern können, betrachten wir in dieser Dissertation nicht nur wie Routing-Indexe verwendet werden, um die Relevanz eines Peers auf Datenebene zu bestimmen, sondern auch wie sie gepflegt werden können. Schließlich stellen wir SmurfPDMS (SiMUlating enviRonment For Peer Data Management Systems) vor, ein System, welches im Rahmen dieser Dissertation entwickelt wurde und alle vorgestellten Techniken implementiert.In recent years, there has been considerable research with respect to query processing in data integration and P2P systems. Conventional data integration systems consist of multiple sources with possibly different schemas, adhere to a hierarchical structure, and have a central component (mediator) that manages a global schema. Queries are formulated against this global schema and the mediator processes them by retrieving relevant data from the sources transparently to the user. Arising from these systems, eventually Peer Data Management Systems (PDMSs), or schema-based P2P systems respectively, have attracted attention. Peers participating in a PDMS can act both as a mediator and as a data source, are autonomous, and might leave or join the network at will. Due to these reasons peers often hold incomplete or erroneous data sets and mappings. The possibly huge amount of data available in such a network often results in large query result sets that are hard to manage. Due to these reasons, retrieving the complete result set is in most cases difficult or even impossible. Applying rank-aware query operators such as top-N and skyline, possibly in conjunction with approximation techniques, is a remedy to these problems as these operators select only those result records that are most relevant to the user. Being aware that in most cases only a small fraction of the complete result set is actually output to the user, retrieving the complete set before evaluating such operators is obviously inefficient. Therefore, the questions we want to answer in this dissertation are how to compute such queries in PDMSs and how to do that efficiently. We propose strategies for efficient query processing in PDMSs that exploit the characteristics of rank-aware queries and optionally apply approximation techniques. A peer's relevance is determined on two levels: on schema-level and on data-level. According to its relevance a peer is either considered for query processing or not. Because of heterogeneity queries need to be rewritten, enabling cooperation between peers that use different schemas. As existing query rewriting techniques mostly consider conjunctive queries only, we present an extension that allows for rewriting queries involving rank-aware query operators. As PDMSs are dynamic systems and peers might update their local data, this dissertation addresses not only the problem of considering such structures within a query processing strategy but also the problem of keeping them up-to-date. Finally, we provide a system-level evaluation by presenting SmurfPDMS (SiMUlating enviRonment For Peer Data Management Systems) -- a system created in the context of this dissertation implementing all presented techniques

Robust subgroup discovery

We introduce the problem of robust subgroup discovery, i.e., finding a set of interpretable descriptions of subsets that 1) stand out with respect to one or more target attributes, 2) are statistically robust, and 3) non-redundant. Many attempts have been made to mine either locally robust subgroups or to tackle the pattern explosion, but we are the first to address both challenges at the same time from a global modelling perspective. First, we formulate the broad model class of subgroup lists, i.e., ordered sets of subgroups, for univariate and multivariate targets that can consist of nominal or numeric variables, and that includes traditional top-1 subgroup discovery in its definition. This novel model class allows us to formalise the problem of optimal robust subgroup discovery using the Minimum Description Length (MDL) principle, where we resort to optimal Normalised Maximum Likelihood and Bayesian encodings for nominal and numeric targets, respectively. Second, as finding optimal subgroup lists is NP-hard, we propose SSD++, a greedy heuristic that finds good subgroup lists and guarantees that the most significant subgroup found according to the MDL criterion is added in each iteration, which is shown to be equivalent to a Bayesian one-sample proportions, multinomial, or t-test between the subgroup and dataset marginal target distributions plus a multiple hypothesis testing penalty. We empirically show on 54 datasets that SSD++ outperforms previous subgroup set discovery methods in terms of quality and subgroup list size.Comment: For associated code, see https://github.com/HMProenca/RuleList ; submitted to Data Mining and Knowledge Discovery Journa

Efficient Retrieval of Web Services Using Prioritization and Clustering

WEB services are software entities that have a well defined interface and perform a specific task. Typical examples include services returning information to the user, such as news or weather forecast services. A web service is formally described in a standardized language (WSDL). The service description may include the parameters associated with web services like input , output and quality of service. As web services and service providers proliferate, there will be a large number of candidate, and likely competing, services for fulfilling a desired task. Hence, effective service discovery mechanisms are required for identifying and retrieving the most appropriate services. The main contributions of our paper are summarized as follows; we propose and implement a method for determining dominance relationships among service advertisements that simultaneously takes into consideration multiple PDM criteria. We introduce a method for prioritization and clustering web services based on similarity measures using efficient algorithms Keywords : Web Service , PDM , dominance score ,TKDD, clustering

Threshold interval indexing techniques for complicated uncertain data

Uncertain data is an increasingly prevalent topic in database research, given the advance of instruments which inherently generate uncertainty in their data. In particular, the problem of indexing uncertain data for range queries has received considerable attention. To efficiently process range queries, existing approaches mainly focus on reducing the number of disk I/Os. However, due to the inherent complexity of uncertain data, processing a range query may incur high computational cost in addition to the I/O cost. In this paper, I present a novel indexing strategy focusing on one-dimensional uncertain continuous data, called threshold interval indexing. Threshold interval indexing is able to balance I/O cost and computational cost to achieve an optimal overall query performance. A key ingredient of the proposed indexing structure is a dynamic interval tree. The dynamic interval tree is much more resistant to skew than R-trees, which are widely used in other indexing structures. This interval tree optimizes pruning by storing x-bounds, or pre-calculated probability boundaries, at each node. In addition to the basic threshold interval index, I present two variants, called the strong threshold interval index and the hyper threshold interval index, which leverage x-bounds not only for pruning but also for accepting results. Furthermore, I present a more efficient memory-loaded versions of these indexes, which reduce the storage size so the primary interval tree can be loaded into memory. Each index description includes methods for querying, parallelizing, updating, bulk loading, and externalizing. I perform an extensive set of experiments to demonstrate the effectiveness and efficiency of the proposed indexing strategies