Sökning: "Distributed Data Stream Processing"
Visar resultat 1 - 5 av 17 avhandlingar innehållade orden Distributed Data Stream Processing.
1. Scalable and Reliable Data Stream Processing
Sammanfattning : Data-stream management systems have for long been considered as a promising architecture for fast data management. The stream processing paradigm poses an attractive means of declaring persistent application logic coupled with state over evolving data. LÄS MER
2. Scalable Scientific Stream Query Processing
Sammanfattning : Scientific applications require processing of high-volume on-line streams of numerical data from instruments and simulations. In order to extract information and detect interesting patterns in these streams scientists need to perform on-line analyses including advanced and often expensive numerical computations. LÄS MER
3. Scalable Validation of Data Streams
Sammanfattning : In manufacturing industries, sensors are often installed on industrial equipment generating high volumes of data in real-time. For shortening the machine downtime and reducing maintenance costs, it is critical to analyze efficiently this kind of streams in order to detect abnormal behavior of equipment. LÄS MER
4. Efficient Data Streaming Analytic Designs for Parallel and Distributed Processing
Sammanfattning : 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. LÄS MER
5. Towards Unifying Stream Processing over Central and Near-the-Edge Data Centers
Sammanfattning : In this thesis, our goal is to enable and achieve effective and efficient real-time stream processing in a geo-distributed infrastructure, by combining the power of central data centers and micro data centers. Our research focus is to address the challenges of distributing the stream processing applications and placing them closer to data sources and sinks. LÄS MER