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  • Automated Deployment of a Spark Cluster with Machine Learning Algorithm Integration
    Big Data Res. (IF 2.673) Pub Date : 2020-05-08
    A.M. Fernández; D. Gutiérrez-Avilés; A. Troncoso; F. Martínez–Álvarez

    The vast amount of data stored nowadays has turned big data analytics into a very trendy research field. The Spark distributed computing platform has emerged as a dominant and widely used paradigm for cluster deployment and big data analytics. However, to get started up is still a task that may take much time when manually done, due to the requisites that all nodes must fulfill. This work introduces

    更新日期:2020-05-08
  • PatSeg: A Sequential Patent Segmentation Approach
    Big Data Res. (IF 2.673) Pub Date : 2020-05-04
    Maryam Habibi; Astrid Rheinlaender; Wolfgang Thielemann; Robert Adams; Peter Fischer; Sylvia Krolkiewicz; David Luis Wiegandt; Ulf Leser

    Patents are an important source of information in industry and academia. However, quickly grasping the essence of a given patent is difficult as they typically are very long and written in a rather inaccessible style. These essential information, especially the invention itself and the experimental part of the invention, are usually contained in the description section. However, in many patents the

    更新日期:2020-05-04
  • Entity Resolution with Recursive Blocking
    Big Data Res. (IF 2.673) Pub Date : 2020-04-30
    Shao-Qing Yu

    Entity resolution is a well-known challenge in data management for the lack of unique identifiers of records and various errors hidden in the data, undermining the identifiability of entities they refer to. To reveal matching records, every record potentially needs to be compared with all other records in the database, which is computationally intractable even for moderately-sized databases. To circumvent

    更新日期:2020-04-30
  • A Hierarchical Dimension Reduction Approach for Big Data with Application to Fault Diagnostics
    Big Data Res. (IF 2.673) Pub Date : 2019-08-22
    R. Krishnan; V.A. Samaranayake; S. Jagannathan

    About four zetta bytes of data, which falls into the category of big data, is generated by complex manufacturing systems annually. Big data can be utilized to improve the efficiency of an aging manufacturing system, provided, several challenges are handled. In this paper, a novel methodology is presented to detect faults in manufacturing systems while overcoming some of these challenges. Specifically

    更新日期:2020-04-20
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