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  • Cartography and geographic base data  (2)
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  • Cartography and geographic base data  (2)
  • 1
    Online Resource
    Online Resource
    A Density-Peak-Based Clustering Method for Multiple Densities Dataset
    MDPI AG ; 2021
    In:  ISPRS International Journal of Geo-Information Vol. 10, No. 9 ( 2021-09-06), p. 589-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 10, No. 9 ( 2021-09-06), p. 589-
    Abstract: Clustering methods in data mining are widely used to detect hotspots in many domains. They play an increasingly important role in the era of big data. As an advanced algorithm, the density peak clustering (DPC) algorithm is able to deal with arbitrary datasets, although it does not perform well when the dataset includes multiple densities. The parameter selection of cut-off distance dc is normally determined by users’ experience and could affect clustering result. In this study, a density-peak-based clustering method is proposed to detect clusters from datasets with multiple densities and shapes. Two improvements are made regarding the limitations of existing clustering methods. First, DPC finds it difficult to detect clusters in a dataset with multiple densities. Each cluster has a unique shape and the interior includes different densities. This method adopts a step by step merging approach to solve the problem. Second, high densities of points can automatically be selected without manual participation, which is more efficient than the existing methods, which require user-specified parameters. According to experimental results, the clustering method can be applied to various datasets and performs better than traditional methods and DPC.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi10090589
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2655790-3
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    Online Resource
  • 2
    Online Resource
    Online Resource
    Optimized Spatiotemporal Data Scheduling Based on Maximum Flow for Multilevel Visualization Tasks
    MDPI AG ; 2020
    In:  ISPRS International Journal of Geo-Information Vol. 9, No. 9 ( 2020-08-28), p. 518-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 9, No. 9 ( 2020-08-28), p. 518-
    Abstract: Massive spatiotemporal data scheduling in a cloud environment play a significant role in real-time visualization. Existing methods focus on preloading, prefetching, multithread processing and multilevel cache collaboration, which waste hardware resources and cannot fully meet the different scheduling requirements of diversified tasks. This paper proposes an optimized spatiotemporal data scheduling method based on maximum flow for multilevel visualization tasks. First, the spatiotemporal data scheduling framework is designed based on the analysis of three levels of visualization tasks. Second, the maximum flow model is introduced to construct the spatiotemporal data scheduling topological network, and the calculation algorithm of the maximum data flow is presented in detail. Third, according to the change in the data access hotspot, the adaptive caching algorithm and maximum flow model parameter switching strategy are devised to achieve task-driven spatiotemporal data optimization scheduling. Compared with two typical methods of first come first serve (FCFS) and priority scheduling algorithm (PSA) by simulating visualization tasks at three levels, the proposed maximum flow scheduling (MFS) method has been proven to be more flexible and efficient in adjusting each spatiotemporal data flow type as needed, and the method realizes spatiotemporal data flow global optimization under limited hardware resources in the cloud environment.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi9090518
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2655790-3
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
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