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Improving Ocean Data Services with Semantics and Quick Index

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Abstract

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.

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References

  1. Agapiou A. Remote sensing heritage in a petabyte-scale: Satellite data and heritage earth engine© applications. Int. J. Digit. Earth, 2017, 10(1): 85-102. https://doi.org/10.1080/17538947.2016.1250829.

    Article  Google Scholar 

  2. Alotaibi R, Bursztyn D, Deutsch A, Manolescu I, Zampetakis S. Towards scalable hybrid stores: Constraint-based rewriting to the rescue. In Proc. the 2019 International Conference on Management of Data, Jun. 2019, pp.1660-1677. https://doi.org/10.1145/3299869.3319895.

  3. Mattson T, Rogers J, Elmore A J. The BigDAWG polystore system. In Making Databases Work: The Pragmatic Wisdom of Michael Stonebraker, Brodie M L (ed.), Association for Computing Machinery and Morgan & Claypool, 2019, pp.279-289. https://doi.org/10.1145/3226595.3226620.

  4. Elmore A J, Duggan J, Stonebraker M et al. A demonstration of the BigDAWG polystore system. Proc. VLDB Endow., 2015, 8(12): 1908-1911. https://doi.org/10.14778/2824032.2824098.

    Article  Google Scholar 

  5. Wilkinson M D, Sansone S A, Schultes E, Doorn P, Da Silva Santos L O B, Dumontier M. A design framework and exemplar metrics for FAIRness. Scientific Data, 2018, 5: Article No. 180118. https://doi.org/10.1038/sdata.2018.118.

  6. Tanhua T, Pouliquen S, Hausman J et al. Ocean FAIR data services. Front. Mar. Sci., 2019, 6: Article No. 440. https://doi.org/10.3389/fmars.2019.00440.

  7. Reed G. Project report: Marine environmental data inventory (MEDI). In Proc. the 19th Session of the IOC Committee on International Oceanographic Data and Information Exchange, March 2007.

  8. Buron M, Goasdoué F, Manolescu I, Mugnier M. Ontology-based RDF integration of heterogeneous data. In Proc. the 23rd International Conference on Extending Database Technology, March 30-April 2, 2020, pp.299-310. https://doi.org/10.5441/002/edbt.2020.27.

  9. Wilkinson M D, Dumontier M, Aalbersberg I J et al. The FAIR guiding principles for scientific data management and stewardship. Scientific Data, 2016, 3: Article No. 160018. https://doi.org/10.1038/sdata.2016.18.

  10. Ren K, Liu X, Chen J, Xiao N, Song J, Zhang W. A QSQL-based efficient planning algorithm for fully-automated service composition in dynamic service environments. In Proc. the 2008 IEEE International Conference on Services Computing, Jul. 2008, pp.301-308. https://doi.org/10.1109/SCC.2008.26.

  11. Crasso M, Mateos C, Zunino A, Campo M. Easysoc: Making web service outsourcing easier. Inf. Sci., 2014, 259: 452-473. https://doi.org/10.1016/j.ins.2010.01.013.

    Article  Google Scholar 

  12. Brabra H, Mtibaa A, Sliman L, Gaaloul W, Gargouri F. Semantic web technologies in cloud computing: A systematic literature review. In Proc. the 2016 IEEE International Conference on Services Computing, Jun. 27-Jul. 2, 2016, pp.744-751. https://doi.org/10.1109/SCC.2016.102.

  13. Imam F T. Application of ontologies in cloud computing: The state-of-the-art. arXiv:1610.02333, 2016. http://arxiv.org/abs/1610.02333, Jan. 2021.

  14. Janowicz K, Compton M. The stimulus-sensor-observation ontology design pattern and its integration into the semantic sensor network ontology. In Proc. the 3rd International Workshop on Semantic Sensor Networks, Nov. 2010, pp.64-78.

  15. Compton M, Barnaghi P M, Bermudez L et al. The SSN ontology of the W3C semantic sensor network incubator group. J. Web Semant., 2012, 17: 25-32. https://doi.org/10.1016/j.websem.2012.05.003.

    Article  Google Scholar 

  16. Zhou A, Ren K, Li X, Zhang W, Ren X. Building quick resource index list using WordNet and high-performance computing resource ontology towards efficient resource discovery. In Proc. the 21st IEEE International Conference on High Performance Computing and Communications, the 17th IEEE International Conference on Smart City and the 5th IEEE International Conference on Data Science and Systems, Aug. 2019, pp.885-892. https://doi.org/10.1109/HPCC/SmartCity/DSS.2019.00129.

  17. Castañé G G, Xiong H, Dong D, Morrison J P. An ontology for heterogeneous resources management interoperability and HPC in the cloud. Future Gener. Comput. Syst., 2018, 88: 373-384. https://doi.org/10.1016/j.future.2018.05.086.

    Article  Google Scholar 

  18. Sun L, Ma J, Wang H, Zhang Y, Yong J. Cloud service description model: An extension of USDL for cloud services. IEEE Trans. Serv. Comput., 2018, 11(2): 354-368. https://doi.org/10.1109/TSC.2015.2474386.

    Article  Google Scholar 

  19. Challita S, Paraiso F, Merle P. Towards formal-based semantic interoperability in multi-clouds: The FCLOUDS framework. In Proc. the 10th IEEE International Conference on Cloud Computing, Jun. 2017, pp.710-713. https://doi.org/10.1109/CLOUD.2017.98.

  20. Yongsiriwit K, Sellami M, Gaaloul W. A semantic framework supporting cloud resource descriptions interoperability. In Proc. the 9th IEEE International Conference on Cloud Computing, Jun. 27-Jul. 2, 2016, pp.585-592. https://doi.org/10.1109/CLOUD.2016.0083.

  21. Bermudez-Edo M, Elsaleh T, Barnaghi P M, Taylor K. IoT-Lite: A lightweight semantic model for the internet of things and its use with dynamic semantics. Pers. Ubiquitous Comput., 2017, 21(3): 475-487. https://doi.org/10.1007/s00779-017-1010-8.

    Article  Google Scholar 

  22. Elsaleh T, Enshaeifar S, Rezvani R, Acton S T, Janeiko V, Bermudez-Edo M. IoT-Stream: A lightweight ontology for Internet of Things data streams and its use with data analytics and event detection services. Sensors, 2020, 20(4): Article No. 953. https://doi.org/10.3390/s20040953.

  23. Cong Z, Fernández A, Billhardt H, Lujak M. Service discovery acceleration with hierarchical clustering. Inf. Syst. Frontiers, 2015, 17(4): 799-808. https://doi.org/10.1007/s10796-014-9525-2.

    Article  Google Scholar 

  24. Roman D, Kopecký J, Vitvar T, Domingue J, Fensel D. WSMO-Lite and hRESTS: Lightweight semantic annotations for Web services and RESTful APIs. J. Web Semant., 2015, 31: 39-58. https://doi.org/10.1016/j.websem.2014.11.006.

    Article  Google Scholar 

  25. Rodríguez-Mier P, Pedrinaci C, Lama M, Mucientes M. An integrated semantic web service discovery and composition framework. IEEE Trans. Serv. Comput., 2016, 9(4): 537-550. https://doi.org/10.1109/TSC.2015.2402679.

    Article  Google Scholar 

  26. Chen F, Li M, Wu H, Xie L. Web service discovery among large service pools utilising semantic similarity and clustering. Enterp. Inf. Syst., 2017, 11(3): 452-469. https://doi.org/10.1080/17517575.2015.1081987.

    Article  Google Scholar 

  27. Zhang N, Wang J, Ma Y, He K, Li Z, Liu X F. Web service discovery based on goal-oriented query expansion. J. Syst. Softw., 2018, 142: 73-91. https://doi.org/10.1016/j.jss.2018.04.046.

    Article  Google Scholar 

  28. Garriga M, Renzis A D, Lizarralde I, Flores A, Mateos C, Cechich A, Zunino A. A structural-semantic web service selection approach to improve retrievability of web services. Inf. Syst. Frontiers, 2018, 20(6): 1319-1344. https://doi.org/10.1007/s10796-016-9731-1.

    Article  Google Scholar 

  29. Paliwal A V, Shafiq B, Vaidya J, Xiong H, Adam N R. Semantics-based automated service discovery. IEEE Trans. Serv. Comput., 2012, 5(2): 260-275. https://doi.org/10.1109/TSC.2011.19.

    Article  Google Scholar 

  30. Ma S P, Chen Y J, Syu Y, Lin H J, FanJiang Y Y. TEST-Oriented RESTful service discovery with semantic interface compatibility. IEEE Trans. Serv. Comput.. https://doi.org/10.1109/TSC.2018.2871133.

  31. Dong X, Madhavan J, Halevy A Y. Mining structures for semantics. ACM SIGKDD Explorations Newsletter, 2004, 6(2): 53-60. https://doi.org/10.1145/1046456.1046463.

    Article  Google Scholar 

  32. Ren K, Xiao N, Chen J. Building quick service query list using WordNet and multiple heterogeneous ontologies toward more realistic service composition. IEEE Trans. Serv. Comput., 2011, 4(3): 216-229. https://doi.org/10.1109/TSC.2010.24.

    Article  Google Scholar 

  33. Miller G A. WordNet: A lexical database for English. Commun. ACM, 1995, 38(11): 39-41. https://doi.org/10.1145/219717.219748.

    Article  Google Scholar 

  34. Ren X, Li X, Deng K, Ren K, Zhou A, Song J. Bringing semantics to support ocean FAIR data services with ontologies. In Proc. the 2020 IEEE International Conference on Services Computing, Nov. 2020, pp.30-37. https://doi.org/10.1109/SCC49832.2020.00011.

  35. Bermudez L, Graybeal J, Arko R. A marine platforms ontology: Experiences and lessons. In Proc. the ISWC Workshop on Semantic Sensor Networks, November 2006.

  36. Graybeal J, Bermudez L, Bogden P, Miller S, Watson S. Marine metadata interoperability project: Leading to collaboration. In Proc. the IEEE International Symposium on Mass Storage Systems and Technology, Jun. 2005, pp.14-18. https://doi.org/10.1109/LGDI.2005.1612458.

  37. Lowry R, Leadbetter A. Semantically supporting data discovery, markup and aggregation in the European marine observation and data network (EMODnet). In Proc. the European Geosciences Union General Assembly, April 27-May 2, 2014.

  38. Bart A A, Churuksaeva V V, Fazliev A Z, Privezentsev A I, Gordov E P, Okladnikov I G, Titov A G. Ontological description of meteorological and climate data collections. In Proc. the 19th International Conference on Data Analytics and Management in Data Intensive Domains, Oct. 2017, pp.266-272.

  39. Plebani P, Pernici B. URBE: Web service retrieval based on similarity evaluation. IEEE Trans. Knowl. Data Eng., 2009, 21(11): 1629-1642. https://doi.org/10.1109/TKDE.2009.35.

    Article  Google Scholar 

  40. Wang Y, Lin X, Wu L, Zhang W. Effective multi-query expansions: Collaborative deep networks for robust landmark retrieval. IEEE Trans. Image Process., 2017, 26(3): 1393-1404. https://doi.org/10.1109/TIP.2017.2655449.

    Article  MathSciNet  MATH  Google Scholar 

  41. Rekik M, Boukadi K, Ben-Abdallah H. Cloud description ontology for service discovery and selection. In Proc. the 10th International Conference on Software Engineering and Applications, Jul. 2015, pp.26-36. https://doi.org/10.5220/0005556400260036.

  42. Parhi M, Pattanayak B K, Patra M R. An ontology-based cloud infrastructure service discovery and selection system. Int. J. Grid Util. Comput., 2018, 9(2): 108-119. https://doi.org/10.1504/IJGUC.2018.10012792.

    Article  Google Scholar 

  43. Calvanese D, Giacomo G D, Lembo D, Lenzerini M, Poggi A, Rodriguez-Muro M, Rosati R, Ruzzi M, Savo D F. The MASTRO system for ontology-based data access. Semantic Web, 2011, 2(1): 43-53. https://doi.org/10.3233/SW-2011-0029.

    Article  Google Scholar 

  44. Rodríguez-Muro M, Kontchakov R, Zakharyaschev M. Ontology-based data access: Ontop of databases. In Proc. the 12th International Semantic Web Conference, Oct. 2013, pp.558-573. https://doi.org/10.1007/978-3-642-41335-3_35.

  45. Pinto F D, Lembo D, Lenzerini M, Mancini R, Poggi A, Rosati R, Ruzzi M, Savo D F. Optimizing query rewriting in ontology-based data access. In Proc. the 16th International Conference on Extending Database Technology, Mar. 2013, pp.561-572. https://doi.org/10.1145/2452376.2452441.

  46. Hovland D, Kontchakov R, Skjæveland M G, Waaler A, Zakharyaschev M. Ontology-based data access to Slegge. In Proc. the 16th International Semantic Web Conference, Oct. 2017, pp.120-129. https://doi.org/10.1007/978-3-319-68204-4_12.

  47. Lanti D, Xiao G, Calvanese D. Cost-driven ontology-based data access. In Proc. the 16th International Semantic Web Conference, Oct. 2017, pp.452-470. https://doi.org/10.1007/978-3-319-68288-4_27.

  48. Botoeva E, Calvanese D, Cogrel B, Corman J, Xiao G. A generalized framework for ontology-based data access. In Proc. the 2018 International Conference of the Italian Association for Artificial Intelligence, Nov. 2018, pp.166-180. https://doi.org/10.1007/978-3-030-03840-3_13.

  49. Xiao G, Calvanese D, Kontchakov R, Lembo D, Poggi A, Rosati R, Zakharyaschev M. Ontology-based data access: A survey. In Proc. the 27th International Joint Conference on Artificial Intelligence, Jul. 2018, pp.5511-5519. https://doi.org/10.24963/ijcai.2018/777.

  50. Buron M, Goasdoué F, Manolescu I, Mugnier M. Reformulation-based query answering for RDF graphs with RDFS ontologies. In Proc. the 16th International Conference, Jun. 2019, pp.19-35. https://doi.org/10.1007/978-3-030-21348-0_2.

  51. Peng P, Zou L, Özsu M T, Chen L, Zhao D. Processing SPARQL queries over distributed RDF graphs. The VLDB J., 2016, 25(2): 243-268. https://doi.org/10.1007/s00778-015-0415-0.

    Article  Google Scholar 

  52. Quamar A, Lei C, Miller D, Özcan F, Kreulen J, Moore R J, Efthymiou V. An ontology-based conversation system for knowledge bases. In Proc. the 2020 International Conference on Management of Data, Jun. 2020, pp.361-376. https://doi.org/10.1145/3318464.3386139.

  53. Zhang N, Wang J, Ma Y. Mining domain knowledge on service goals from textual service descriptions. IEEE Trans. Serv. Comput., 2020, 13(3): 488-502. https://doi.org/10.1109/TSC.2017.2693147.

    Article  Google Scholar 

  54. Dividino R, Soares A, Matwin S, Isenor A W, Webb S, Brousseau M. Semantic integration of real-time heterogeneous data streams for ocean-related decision making. In Proc. the Specialists’ Meeting on Big Data and Artificial Intelligence for Military Decision Making, May 2018.

  55. Wilson W J, Yueh SH, Dinardo S J, Chazanoff S L, Kitiyakara A, Li F K, Rahmat-Samii Y. Passive active Land S-band (PALS) microwave sensor for ocean salinity and soil moisture measurements. IEEE Trans. Geosci. Remote Sens., 2001, 39(5): 1039-1048. https://doi.org/10.1109/36.921422.

    Article  Google Scholar 

  56. Loni Z M, Espinosa H G, Thiel D V. Floating monopole antenna on a tethered subsurface sensor at 433 MHz for ocean monitoring applications. IEEE Journal of Oceanic Engineering, 2017, 42(4): 818-825. https://doi.org/10.1109/JOE.2016.2639111.

    Article  Google Scholar 

  57. Liu S S, Sun L, Wu Q, Yang Y J. The responses of cyclonic and anticyclonic eddies to typhoon forcing: The vertical temperature-salinity structure changes associated with the horizontal convergence/divergence. Journal of Geophysical Research: Oceans, 2017, 122(6): 4974-4989. https://doi.org/10.1002/2017JC012814.

    Article  Google Scholar 

  58. Smits G, Pivert O, Jaudoin H, Paulus F. AGGREGO SEARCH: Interactive keyword query construction. In Proc. the 17th International Conference on Extending Database Technology, Mar. 2014, pp.636-639. https://doi.org/10.5441/002/edbt.2014.62.

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Acknowledgement(s)

We would like to thank the anonymous reviewers for their valuable and constructive comments. We thank Dr. Xiang Wang from National University of Defense Technology for the discussion and useful commentary on various drafts of this paper.

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Authors and Affiliations

  1. College of Computer Science and Technology, National University of Defense Technology, Changsha, 410073, China

    Xiao-Li Ren, Kai-Jun Ren, Ao-Long Zhou & Jun-Qiang Song

  2. College of Meteorology and Oceanography, National University of Defense Technology, Changsha, 410073, China

    Xiao-Li Ren, Kai-Jun Ren, Xiao-Yong Li, Ao-Long Zhou, Jun-Qiang Song & Ke-Feng Deng

  3. College of Computer Science and Technology, Nanchang University, Nanchang, 330031, China

    Zi-Chen Xu

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  1. Xiao-Li Ren
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Ren, XL., Ren, KJ., Xu, ZC. et al. Improving Ocean Data Services with Semantics and Quick Index. J. Comput. Sci. Technol. 36, 963–984 (2021). https://doi.org/10.1007/s11390-021-1374-0

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