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Showing 1 to 11 of 11 for “"Big Data Systems"”.

  1. Tuning Big Data Systems Via Deep Learning

    Modern database systems, including IBM Db2 have numerous parameters, “knobs,” that require precise configuration to achieve optimal workload performance. Even for experts, manually “tuning” these knobs is a challenging process. We present Db2une, an automatic query-aware tuning system that …

    york Repository record for Tuning Big Data Systems Via Deep Learning (opens in a new tab)

  2. Resource Management In Cloud And Big Data Systems

    … and they can handle the computing needs of big data analytics. The ever-growing demand for cloud services arises in many areas including healthcare, transportation, energy systems, and manufacturing. However, cloud resources such as computing power, storage, energy, dollars for …

    wayne-thes Repository record for Resource Management In Cloud And Big Data Systems (opens in a new tab)

  3. Screening and Engineering Phenotypes using Big Data Systems Biology

    Biological systems display remarkable complexity that is not properly accounted for in small, reductionistic models. Increasingly, big data approaches using genomics, proteomics, metabolomics etc. are being applied to predicting and modifying the emergent phenotypes produced by complex biological …

    vt Repository record for Screening and Engineering Phenotypes using Big Data Systems Biology (opens in a new tab)

  4. Secure and high-performance big-data systems in the cloud

    Cloud computing and big data technology continue to revolutionize how computing and data analysis are delivered today and in the future. To store and process the fast-changing big data, various scalable systems (e.g. key-value stores and MapReduce) have recently emerged in industry. However, there …

    gatech Repository record for Secure and high-performance big-data systems in the cloud (opens in a new tab)

  5. An Experimental Comparison of Complex Objects Implementations in Big Data Systems

    Many data management and analytics systems support complex objects. Dataflow platforms such as Spark and Flink allow programmers to manipulate sets consisting of objects from a host programming language, often Java. Document databases such as MongoDB make use of hierarchical interchange …

    rice Repository record for An Experimental Comparison of Complex Objects Implementations in Big Data Systems (opens in a new tab)

  6. Cost-effective batch-based migration strategies for NewSQL-based big data systems

    lethbridge

  7. Supporting Virtualisation Management through an Object Mapping Declarative Language Framework

    … vast scale of virtualised cloud computing systems, management of the numerous physical and virtual components that make up their underlying infrastructure may become unwieldy. Many software packages that have historically been installed on desktops / workstations for years are slowly but …

    liverpool-jm Repository record for Supporting Virtualisation Management through an Object Mapping Declarative Language Framework (opens in a new tab)

  8. BlueFlash : a reconfigurable flash controller for BlueDBM

    … over traditional hard disks. However, in modern Big Data systems, simply replacing disks with flash does not yield proportional performance gains. This is because of bottlenecks in various levels of the system stack: I/O interface, network, file system and processor. Introduced in 2012, BlueDBM …

    mit Repository record for BlueFlash : a reconfigurable flash controller for BlueDBM (opens in a new tab)

  9. Machine learning and coresets for automated real-time data segmentation and summarization

    In this thesis, we develop a family of real-time data reduction algorithms for large data streams, by computing a compact and meaningful representation of the data called a coreset. This representation can then be used to enable efficient analysis such as segmentation, summarization, …

    mit Repository record for Machine learning and coresets for automated real-time data segmentation and summarization (opens in a new tab)

  10. Approximate failure recovery in distributed graph processing systems

    Distributed graph processing systems are an emerging area of big data systems. As graphs continue to grow in size and prevalence, these systems must become faster and more scalable. However, after failures, distributed graph processing systems either largely rely on proactive fault tolerance …

    uiuc Repository record for Approximate failure recovery in distributed graph processing systems (opens in a new tab)

  11. A framework for strategic planning of data analytics in the educational sector

    The field of big data and data analysis is not a new one. Big data systems have been investigated with respect to the volume of the data and how it is stored, the data velocity and how it is subject to change, variety of data to be analysed and data veracity referring to integrity and quality. …

    middlesex Repository record for A framework for strategic planning of data analytics in the educational sector (opens in a new tab)