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Journal on Big Data

About the Journal

Journal on Big Data is launched in a new area when the engineering features of big data are setting off upsurges of explorations in algorithms, raising challenges on big data, and industrial development integration; and novel paradigms in this cross–disciplinary field need to be constructed by translating complex innovative ideas from various fields.

  • An Importance Assessment Model of Open-Source Community Java Projects Based on Domain Knowledge Graph
  • Abstract With the rise of open-source software, the social development paradigm occupies an indispensable position in the current software development process. This paper puts forward a variant of the PageRank algorithm to build the importance assessment model, which provides quantifiable importance assessment metrics for new Java projects based on Java open-source projects or components. The critical point of the model is to use crawlers to obtain relevant information about Java open-source projects in the GitHub open-source community to build a domain knowledge graph. According to the three dimensions of the Java opensource project’s project influence, project activity and project popularity, the… More
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  • A Novel Framework for Biomedical Text Mining
  • Abstract Text mining has emerged as an effective method of handling and extracting useful information from the exponentially growing biomedical literature and biomedical databases. We developed a novel biomedical text mining model implemented by a multi-agent system and distributed computing mechanism. Our distributed system, TextMed, comprises of several software agents, where each agent uses a reinforcement learning method to update the sentiment of relevant text from a particular set of research articles related to specific keywords. TextMed can also operate on different physical machines to expedite its knowledge extraction by utilizing a clustering technique. We collected the biomedical textual data from… More
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  • An Improved Distributed Query for Large-Scale RDF Data
  • Abstract The rigid structure of the traditional relational database leads to data redundancy, which seriously affects the efficiency of the data query and cannot effectively manage massive data. To solve this problem, we use distributed storage and parallel computing technology to query RDF data. In order to achieve efficient storage and retrieval of large-scale RDF data, we combine the respective advantage of the storage model of the relational database and the distributed query. To overcome the disadvantages of storing and querying RDF data, we design and implement a breadth-first path search algorithm based on the keyword query on a distributed platform.… More
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  • Multi-Scale Blind Image Quality Predictor Based on Pyramidal Convolution
  • Abstract Traditional image quality assessment methods use the hand-crafted features to predict the image quality score, which cannot perform well in many scenes. Since deep learning promotes the development of many computer vision tasks, many IQA methods start to utilize the deep convolutional neural networks (CNN) for IQA task. In this paper, a CNN-based multi-scale blind image quality predictor is proposed to extract more effectivity multi-scale distortion features through the pyramidal convolution, which consists of two tasks: A distortion recognition task and a quality regression task. For the first task, image distortion type is obtained by the fully connected layer. For… More
  •   Views:79       Downloads:51        Download PDF