The topic of managing uncertain data has been explored in many ways. Different methodologies for data storage and query processing have been proposed. As the availability of management systems grows, the research on analytics of uncertain data is gaining in importance. Similar to the challenges faced in the field of data management, algorithms for uncertain data mining also have a high performance degradation compared to their certain algorithms. To overcome the problem of performance degradation, the MCDB approach was developed for uncertain data management based on the possible world scenario. As this methodology shows significant performance and scalability enhancement, we adopt this method for the field of mining on uncertain data. In this paper, we introduce a clustering methodology for uncertain data and illustrate current issues with this approach within the field of clustering uncertain data.

Im Herbst 2012 feiert der Lehrstuhl Datenbanken an der Technischen Universität Dresden sein 10-jähriges Bestehen unter der Leitung von Wolfgang Lehner. In diesem Zeitraum wurde die inhaltliche Ausrichtung im Bereich der Datenbankunterstützung zur Auswertung großer Datenbestände weiter fokussiert sowie auf Systemebene deutlich ausgeweitet. Die Forschungsgruppe um Wolfgang Lehner ist dabei sowohl auf internationaler Ebene durch Publikationen und Kooperationen sichtbar als auch in Forschungsverbünden auf regionaler Ebene aktiv, um sowohl an der extrem jungen und agilen Software-Industrie in Dresden zu partizipieren und, soweit eine Forschungsgruppe dies zu leisten vermag, auch unterstützend zu wirken. [Aus: Einleitung]

In data-centric environments, for example, in the field of scientific computing, the transmission of large amount of structured data to Web services is required. In service-oriented environments (SOA), the Simple Object Access Protocol (SOAP) is commonly used as the main transport protocol. However, the resulting 'by value' data transmission approach is not efficiently applicable in data-centric environments. One challenging bottleneck of SOAP arises from the XML serialization and deserialization when processing large SOAP messages. In this paper, we present an extended Web service framework which explicitly considers the data aspects of functional Web services. Aside from the possibility to integrate specialized data transfer methods in SOA, this framework allows the efficient and scalable data handling and processing within Web services. In this case, we combine the advantages of the functional perspective (SOA) and the data perspective to efficiently support data-centric environments.

Modern Web or ¿Eternal-Beta¿ applications necessitate a flexible and easy-to-use data management platform that allows the evolutionary development of databases and applications. The classical approach of relational database systems following strictly the ACID properties has to be extended by an extensible and easy-to-use persistency layer with specialized DB features. Using the underlying concept of Software as a Service (SaaS) also enables an economic advantage based on the ¿economy of the scale¿, where application and system environments only need to be provided once but can be used by thousands of users. Within this tutorial, we are looking at the current state-of-the-art from different perspectives. We outline foundations and techniques to build database services based on the SaaS-paradigm. We discuss requirements from a programming perspective, show different dimensions in the context of consistency and reliability, and also describe different non-functional properties under the umbrella of Service-Level agreements (SLA).