Frequent pattern mining from uncertain data allows data analysts to mine frequent patterns from probabilistic databases, within which each item is associated with an existential probability representing the likelihood of the presence of the item in the transaction. When compared with precise data, the solution space for mining uncertain data is often much larger due to the probabilistic nature of uncertain databases. Thus, uncertain data mining algorithms usually take substantially more time to execute. Recent studies show that the MapReduce programming model yields significant performance gains for data mining algorithms, which can be mapped to the map and reduce execution phases of MapReduce. An attractive feature of MapReduce is fault-tolerance, which permits detecting and restarting failed jobs on working machines. In this M.Sc. thesis, I explore the feasibility of applying MapReduce to frequent pattern mining of uncertain data. Specifically, I propose two algorithms for mining frequent patterns from uncertain data with MapReduce.

Business processes are instrumental to manage work in organisations. To study the interdependencies between business processes, Business Process Architectures have been introduced. These express trigger and message ow relations between business processes. When we investigate real world Business Process Architectures, we find complex interdependencies, involving multiple process instances. These aspects have not been studied in detail so far, especially concerning correctness properties. In this paper, we propose a modular transformation of BPAs to open nets for the analysis of behavior involving multiple business processes with multiplicities. For this purpose we introduce intermediary nets to portray semantics of multiplicity specifications. We evaluate our approach on a use case from the public sector.

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The purpose of this research is to document data on the implementation and integration of computer networking capabilities and activities in regular classrooms, looking specifically at the expectations and concerns of those involved (school administrators and teachers), and at the issue of integrating the networking capabilities with the regular curriculum. The outcomes of the research will be recommendations on practices that produce successful or unsuccessful implementation of network-based classroom activities. Particular attention is given to Internet-based K--12 classroom activities and to the creation of electronic communities of learners.

This thesis is an examination of a population genetic database as both a social and scientific entity. Science and social science usually operate in a dichotomy this is a synergy of the two. The thesis examines practices and processes, and reveals how the formation of the Generation Scotland assemblage is the producer of multiple disconnections and connections layered in the science, technology, objects, people and places. The story is based on a multi‐sited ethnography that moves from the medical setting of blood sample and data collection, through the practices and processes of the laboratory, to end up in the much more diffuse settings of computer analysis. The blood sample is transformed into digital genetic data, and then connected to diverse other data for research. It traces the transformation and aggregation of heterogeneous elements which will become fixed in the population genetic database through scientific ordering and relationships which will be rendered immutable by the technology. In the processes described here, people’s bodies, and information about them, are explicitly rendered as research ‘resources’. The thesis contributes to the growing knowledge of population genetic databases, and it is a response to calls from social science to understand better the science and technology that are currently changing the shape of the social world. Disconnections and connections are creating a framework of new referents between health and illness, identity and relationships in a way that rearticulates the body and the population.

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