Sökning: "streaming graph"

Visar resultat 1 - 5 av 8 avhandlingar innehållade orden streaming graph.

  1. 1. Methods and Algorithms for Data-Intensive Computing : Streams, Graphs, and Geo-Distribution

    Författare :Hooman Peiro Sajjad; Vladimir Vlassov; Keijo Heljanko; KTH; []
    Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; stream processing; geo-distributed infrastructure; edge computing; streaming graph; dynamic graph; Informations- och kommunikationsteknik; Information and Communication Technology; Datalogi; Computer Science;

    Sammanfattning : Struggling with the volume and velocity of Big Data has attracted lots of interest towards stream processing paradigm, a paradigm in the area of data-intensive computing that provides methods and solutions to process data in motion. Today's Big Data includes geo-distributed data sources. LÄS MER

  3. 2. Enabling Enterprise Live Video Streaming with Reinforcement Learning and Graph Neural Networks

    Författare :Stefanos Antaris; Sarunas Girdzijauskas; Dimitrios Katsaros; KTH; []
    Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Graph Neural Networks; Reinforcement Learning; Meta-Learning; Knowledge Distillation; Enterprise Liveo Video Streaming;

    Sammanfattning : Over the last decade, video has vastly become the most popular way the world consumes content. Due to the increased popularity, video has been a strategic tool for enterprises. LÄS MER

  4. 3. Scalable Streaming Graph and Time Series Analysis Using Partitioning and Machine Learning

    Författare :Zainab Abbas; Vladimir Vlassov; Peter Van Roy; Paris Carbone; Vasiliki Kalavri; Vincenzo Massimiliano Gulisano; KTH; []
    Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Stream processing; graph processing; time series; big data; machine learning; Informations- och kommunikationsteknik; Information and Communication Technology;

    Sammanfattning : Recent years have witnessed a massive increase in the amount of data generated by the Internet of Things (IoT) and social media. Processing huge amounts of this data poses non-trivial challenges in terms of the hardware and performance requirements of modern-day applications. LÄS MER

  5. 4. Explainable and Resource-Efficient Stream Processing Through Provenance and Scheduling

    Författare :Dimitrios Palyvos-Giannas; Chalmers tekniska högskola; []
    Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Data Streaming; Scheduling; Provenance; Stream Processing;

    Sammanfattning : In our era of big data, information is captured at unprecedented volumes and velocities, with technologies such as Cyber-Physical Systems making quick decisions based on the processing of streaming, unbounded datasets. In such scenarios, it can be beneficial to process the data in an online manner, using the stream processing paradigm implemented by Stream Processing Engines (SPEs). LÄS MER

  7. 5. Scalable Machine Learning through Approximation and Distributed Computing

    Författare :Theodore Vasiloudis; Anders Holst; Henrik Boström; Seif Haridi; Daniel Gillblad; Indre Žliobaitė; KTH; []
    Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Online Learning; Distributed Computing; Graph Similarity; Decision Trees; Gradient Boosting;

    Sammanfattning : Machine learning algorithms are now being deployed in practically all areas of our lives. Part of this success can be attributed to the ability to learn complex representations from massive datasets. LÄS MER