Journal of Computer Science and Technology ›› 2021, Vol. 36 ›› Issue (5): 963-984.doi: 10.1007/s11390-021-1374-0

Special Issue: Computer Architecture and Systems; Data Management and Data Mining

• Special Section of APPT 2021 (Part 1) • Previous Articles     Next Articles

Improving Ocean Data Services with Semantics and Quick Index

Xiao-Li Ren1,2, Member, CCF, Kai-Jun Ren1,2,*, Member, CCF Zi-Chen Xu3,*, Senior Member, CCF, Member, ACM, IEEE, Xiao-Yong Li2, Senior Member, CCF Ao-Long Zhou1,2, Jun-Qiang Song1,2, and Ke-Feng Deng2, Member, CCF        

  1. 1 College of Computer Science and Technology, National University of Defense Technology, Changsha 410073, China;
    2 College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, China;
    3 College of Computer Science and Technology, Nanchang University, Nanchang 330031, China
  • Received:2021-02-11 Revised:2021-08-20 Online:2021-09-30 Published:2021-09-30
  • About author:Xiao-Li Ren received her B.S. degree in computer science and technology from Central South University, Changsha, in 2008, and her M.S. degree in computer application technology from Dalian University of Technology, Dalian, in 2011. Currently she is a Ph.D. candidate in the College of Computer Science and Technology at National University of Defense Technology, Changsha. Her research interests include service-oriented computing and ocean big data. She is a member of CCF.
  • Supported by:
    This work was partially supported by the National Key Research and Development Program of China under Grant No. 2018YFB0203801, and the National Natural Science Foundation of China under Grant Nos. 61702529 and 61802424.

Massive ocean data acquired by various observing platforms and sensors poses new challenges to data management and utilization. Typically, it is difficult to find the desired data from the large amount of datasets efficiently and effectively. Most of existing methods for data discovery are based on the keyword retrieval or direct semantic reasoning, and they are either limited in data access rate or do not take the time cost into account. In this paper, we creatively design and implement a novel system to alleviate the problem by introducing semantics with ontologies, which is referred to as Data Ontology and List-Based Publishing (DOLP). Specifically, we mainly improve the ocean data services in the following three aspects. First, we propose a unified semantic model called OEDO (Ocean Environmental Data Ontology) to represent heterogeneous ocean data by metadata and to be published as data services. Second, we propose an optimized quick service query list (QSQL) data structure for storing the pre-inferred semantically related services, and reducing the service querying time. Third, we propose two algorithms for optimizing QSQL hierarchically and horizontally, respectively, which aim to extend the semantics relationships of the data service and improve the data access rate. Experimental results prove that DOLP outperforms the benchmark methods. First, our QSQL-based data discovery methods obtain a higher recall rate than the keyword-based method, and are faster than the traditional semantic method based on direct reasoning. Second, DOLP can handle more complex semantic relationships than the existing methods.

Key words: data service; ocean data; ontology; semantic representation;

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