Search for dissertations about: "distributed and parallel processing"
Showing result 1 - 5 of 48 swedish dissertations containing the words distributed and parallel processing.
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1. Parallel and Distributed Processing in the Context of Fog Computing: High Throughput Pattern Matching and Distributed Monitoring
Abstract : With the introduction of the Internet of Things (IoT), physical objects now have cyber counterparts that create and communicate data. Extracting valuable information from that data requires timely and accurate processing, which calls for more efficient, distributed approaches. READ MORE
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2. Efficient Data Streaming Analytic Designs for Parallel and Distributed Processing
Abstract : Today, ubiquitously sensing technologies enable inter-connection of physical objects, as part of Internet of Things (IoT), and provide massive amounts of data streams. In such scenarios, the demand for timely analysis has resulted in a shift of data processing paradigms towards continuous, parallel, and multitier computing. READ MORE
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3. Efficient Approximate Big Data Clustering: Distributed and Parallel Algorithms in the Spectrum of IoT Architectures
Abstract : Clustering, the task of grouping together similar items, is a frequently used method for processing data, with numerous applications. Clustering the data generated by sensors in the Internet of Things, for instance, can be useful for monitoring and making control decisions. READ MORE
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4. Hardware-Aware Algorithm Designs for Efficient Parallel and Distributed Processing
Abstract : The introduction and widespread adoption of the Internet of Things, together with emerging new industrial applications, bring new requirements in data processing. Specifically, the need for timely processing of data that arrives at high rates creates a challenge for the traditional cloud computing paradigm, where data collected at various sources is sent to the cloud for processing. READ MORE
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5. Clustering in the Big Data Era: methods for efficient approximation, distribution, and parallelization
Abstract : Data clustering is an unsupervised machine learning task whose objective is to group together similar items. As a versatile data mining tool, data clustering has numerous applications, such as object detection and localization using data from 3D laser-based sensors, finding popular routes using geolocation data, and finding similar patterns of electricity consumption using smart meters. READ MORE