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International Conference on Advanced Information Networking and Applications

AINA 2021: Advanced Information Networking and Applications pp 133–145Cite as

A Big Data Science Solution for Analytics on Moving Objects

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Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 226))

Abstract

Biodiversity data (e.g., for aquatic organisms, marine creatures and terrestrial animals) and environmental data (e.g., air pollution statistics, water supply and sanitation information, soil contamination data) are examples of big data. Embedded in these big data are implicit, previously unknown and potentially useful information and knowledge that could help improve the ecosystem. As such, data science solutions for big data analytics and mining are in demand. In this paper, we present a data science solution for biodiversity informatics, environmental analytics and sustainability analysis. Specifically, our solution analyzes and mines both biodiversity data and environmental data to examine the impacts of pollution to moving objects. The convex-hull-based method in our solution estimates the pollution exposure to these objects. For evaluation, we conducted case studies on analyzing, mining and visualizing both marine biodiversity data and plastic exposure data to examine the impacts of the plastic exposure to marine creatures. Knowledge discovered by our solution help decision and policy makers to take appropriate actions in building and maintaining a sustainable environment.

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Notes

  1. 1.

    https://www.oecd.org/gov/digital-government/open-government-data.htm.

  2. 2.

    https://open.canada.ca/en/maps/open-data-canada.

  3. 3.

    https://ccadi.ca/.

  4. 4.

    https://lwbin.cc.umanitoba.ca/.

  5. 5.

    https://www.gbif.org/.

  6. 6.

    https://obis.org/.

  7. 7.

    https://www.polardata.ca/.

  8. 8.

    https://www.marineconservation.org.au/calls-for-endangered-listing-of-tiger-sharks-as-new-study-reports-a-71-decline-in-three-decades-along-east-coast-australia/.

  9. 9.

    https://litterbase.awi.de/.

  10. 10.

    https://portal.aodn.org.au/.

References

  1. Ahmad, A., et al.: Defining human behaviors using big data analytics in social Internet of Things. In: IEEE AINA 2016, pp. 1101–1107 (2016)

    Google Scholar 

  2. Barbieru, C., Pop, F.: Soft real-time Hadoop scheduler for big data processing in smart cities. In: IEEE AINA 2016, pp. 863–870 (2016)

    Google Scholar 

  3. Jiang, F., Leung, C.K.: A data analytic algorithm for managing, querying, and processing uncertain big data in cloud environments. Algorithms 8(4), 1175–1194 (2015)

    Article  Google Scholar 

  4. Leung, C.K.: Big data analysis and mining. In: Encyclopedia of Information Science and Technology, 4e, pp. 338–348 (2018)

    Google Scholar 

  5. Susanto, H., et al.: Revealing storage and speed transmission emerging technology of big data. In: AINA 2019. AISC, vol. 926, pp. 571–583 (2019)

    Google Scholar 

  6. Gadelha, L.M.R., et al.: A survey of biodiversity informatics: concepts, practices, and challenges. WIREs DMKD 11(1), e1394:1-e1394:41 (2021)

    Google Scholar 

  7. Ibraheam, M., et al.: Animal species recognition using deep learning. In: AINA 2020. AISC, vol. 1151, pp. 523–532 (2020)

    Google Scholar 

  8. Ali, S., et al.: A blockchain-based secure data storage and trading model for wireless sensor networks. In: AINA 2020. AISC, vol. 1151, pp. 499–511 (2020)

    Google Scholar 

  9. Kobusinska, A., et al.: Emerging trends, issues and challenges in Internet of Things, big data and cloud computing. FGCS 87, 416–419 (2018)

    Article  Google Scholar 

  10. Fariha, A., et al.: Mining frequent patterns from human interactions in meetings using directed acyclic graphs. In: PAKDD 2013, Part I. LNCS (LNAI), vol. 7818, pp. 38–49 (2013)

    Google Scholar 

  11. Jiang, F., et al.: Finding popular friends in social networks. In: CGC 2012, pp. 501–508 (2012)

    Google Scholar 

  12. Leung, C.K., Jiang, F.: Big data analytics of social networks for the discovery of “following” patterns. In: DaWaK 2015. LNCS, vol. 9263, pp. 123–135 (2015)

    Google Scholar 

  13. Souza, J., et al.: An innovative big data predictive analytics framework over hybrid big data sources with an application for disease analytics. In: AINA 2020. AISC, vol. 1151, pp. 669–680 (2020)

    Google Scholar 

  14. Chen, Y., et al.: Temporal data analytics on COVID-19 data with ubiquitous computing. In: IEEE ISPA-BDCloud-SocialCom-SustainCom 2020, pp. 958–965 (2020). https://doi.org/10.1109/ISPA-BDCloud-SocialCom-SustainCom51426.2020.00146

  15. Gupta, P., et al.: Vertical data mining from relational data and its application to COVID-19 data. In: Big Data Analyses, Services, and Smart Data. AISC, vol. 899, pp. 106–116 (2021)

    Google Scholar 

  16. Leung, C.K., et al.: Big data science on COVID-19 data. In: IEEE BigDataSE 2020, pp. 14–21 (2020). https://doi.org/10.1109/BigDataSE50710.2020.00010

  17. Liu, Q., et al.: A two-dimensional sparse matrix profile DenseNet for COVID-19 diagnosis using chest CT images. IEEE Access 8, 213718–213728 (2020)

    Article  Google Scholar 

  18. Shang, S., et al.: Spatial data science of COVID-19 data. In: IEEE HPCC-SmartCity-DSS 2020 (2020)

    Google Scholar 

  19. Camara, R.C., et al.: Fuzzy logic-based data analytics on predicting the effect of hurricanes on the stock market. In: FUZZ-IEEE 2018, pp. 576–583 (2018)

    Google Scholar 

  20. Chanda, A.K., et al.: A new framework for mining weighted periodic patterns in time series databases. ESWA 79, 207–224 (2017)

    Google Scholar 

  21. Leung, C.K., et al.: A machine learning approach for stock price prediction. In: IDEAS 2014, pp. 274–277 (2014)

    Google Scholar 

  22. De Guia, J., et al.: DeepGx: deep learning using gene expression for cancer classification. In: IEEE/ACM ASONAM 2019, pp. 913–920 (2019)

    Google Scholar 

  23. Leung, C.K., et al.: Predictive analytics on genomic data with high-performance computing. In: IEEE BIBM 2020, pp. 2187–2194 (2020). https://doi.org/10.1109/BIBM49941.2020.9312982

  24. Pawliszak, T., et al.: Operon-based approach for the inference of rRNA and tRNA evolutionary histories in bacteria. BMC Genomics 21(Suppl. 2), 252:1–252:14 (2020)

    Google Scholar 

  25. Sarumi, O.A., et al.: Spark-based data analytics of sequence motifs in large omics data. Procedia Comput. Sci. 126, 596–605 (2018)

    Article  Google Scholar 

  26. Sarumi, O.A., Leung, C.K.: Adaptive machine learning algorithm and analytics of big genomic data for gene prediction. In: Tracking and Preventing Diseases with Artificial Intelligence (2021)

    Google Scholar 

  27. Balbin, P.P.F., et al.: Predictive analytics on open big data for supporting smart transportation services. Procedia Comput. Sci. 176, 3009–3018 (2020)

    Article  Google Scholar 

  28. Chowdhury, N.K., Leung, C.K.: Improved travel time prediction algorithms for intelligent transportation systems. In: KES 2011, Part II. LNCS (LNAI), vol. 6882, pp. 355–365 (2011)

    Google Scholar 

  29. Leung, C.K., et al.: Conceptual modeling and smart computing for big transportation data. In: IEEE BigComp 2021, pp. 260–267 (2021). https://doi.org/10.1109/BigComp51126.2021.00055

  30. Leung, C.K., et al.: Urban analytics of big transportation data for supporting smart cities. In: DaWaK 2019. LNCS, vol. 11708, pp. 24–33 (2019)

    Google Scholar 

  31. Cox, T.S., et al.: An accurate model for hurricane trajectory prediction. In: IEEE COMPSAC 2018, vol. 2, pp. 534–539 (2018)

    Google Scholar 

  32. Mateo, M.A.F., Leung, C.K.: CHARIOT: a comprehensive data integration and quality assurance model for agro-meteorological data. In: Data Quality and High-Dimensional Data Analysis, pp. 21–41 (2009)

    Google Scholar 

  33. Sassi, M.S.H., Fourati, L.C.: Architecture for visualizing indoor air quality data with augmented reality based cognitive Internet of Things. In: AINA 2020. AISC, vol. 1151, pp. 405–418 (2020)

    Google Scholar 

  34. Cao, L.: Data science: a comprehensive overview. ACM CSUR 50(3), 43:1–43:42 (2017)

    Google Scholar 

  35. Dierckens, K.E., et al.: A data science and engineering solution for fast k-means clustering of big data. In: IEEE TrustCom-BigDataSE-ICESS 2017, pp. 925–932 (2017)

    Google Scholar 

  36. Leung, C.K., Jiang, F.: A data science solution for mining interesting patterns from uncertain big data. In: IEEE BDCloud 2014, pp. 235–242 (2014)

    Google Scholar 

  37. Chen, Y., et al.: Mining opinion leaders in big social network. In: IEEE AINA 2017, pp. 1012–1018 (2017)

    Google Scholar 

  38. Leung, C.K.: Uncertain frequent pattern mining. In: Frequent Pattern Mining, pp. 417–453 (2014)

    Google Scholar 

  39. Leung, C.K., et al.: Distributed uncertain data mining for frequent patterns satisfying anti-monotonic constraints. In: IEEE AINA Workshops 2014, pp. 1–6 (2014)

    Google Scholar 

  40. Casagrande, L.C., et al.: DeepScheduling: grid computing job scheduler based on deep reinforcement learning. In: AINA 2020. AISC, vol. 1151, pp. 1032–1044 (2020)

    Google Scholar 

  41. Leung, C.K., et al.: Explainable machine learning and mining of influential patterns from sparse web. In: IEEE/WIC/ACM WI-IAT 2020 (2020)

    Google Scholar 

  42. Leung, C.K., et al.: Machine learning and OLAP on big COVID-19 data. In: IEEE BigData 2020, pp. 5118–5127 (2020)

    Google Scholar 

  43. Min, B., et al.: Image classification for agricultural products using transfer learning. In: BigDAS 2020, pp. 48–52 (2020)

    Google Scholar 

  44. Leung, C.K.: Mathematical model for propagation of influence in a social network. In: Encyclopedia of Social Network Analysis and Mining, 2e, pp. 1261–1269 (2018)

    Google Scholar 

  45. Lee, W., et al.: Reducing noises for recall-oriented patent retrieval. In: IEEE BDCloud 2014, pp. 579–586 (2014)

    Google Scholar 

  46. Leung, C.K., et al.: Information technology-based patent retrieval model. In: Springer Handbook of Science and Technology Indicators, pp. 859–874 (2019)

    Google Scholar 

  47. Barkwell, K.E., et al.: Big data visualisation and visual analytics for music data mining. In: IV 2018, pp. 235–240 (2018)

    Google Scholar 

  48. Braun, P., et al.: Game data mining: clustering and visualization of online game data in cyber-physical worlds. Procedia Comput. Sci. 112, 2259–2268 (2017)

    Article  Google Scholar 

  49. Carmichael, C.L., et al.: Visually contrast two collections of frequent patterns. In: IEEE ICDM Workshops 2011, pp. 1128–1135 (2011)

    Google Scholar 

  50. Dubois, P.M.J., et al.: An interactive circular visual analytic tool for visualization of web data. In: IEEE/WIC/ACM WI 2016, pp. 709–712 (2016)

    Google Scholar 

  51. Leung, C.K., Carmichael, C.L.: FpVAT: a visual analytic tool for supporting frequent pattern mining. ACM SIGKDD Explor. 11(2), 39–48 (2009)

    Article  Google Scholar 

  52. Munzner, T., et al.: Visual mining of power sets with large alphabets. Technical report TR-2005-25, Computer Science, UBC, Canada (2005). https://www.cs.ubc.ca/tr/2005/tr-2005-25

  53. Audu, A.A., et al.: An intelligent predictive analytics system for transportation analytics on open data towards the development of a smart city. In: CISIS 2019. AISC, vol. 993, pp. 224–236 (2019)

    Google Scholar 

  54. Perovich, L.J., et al.: Chemicals in the Creek: designing a situated data physicalization of open government data with the community. IEEE TVCG 27(2), 913–923 (2021)

    Google Scholar 

  55. Freinkel, S.: Plastic: A Toxic Love Story (2011)

    Google Scholar 

  56. Beckman, E.: The world’s plastic problem in numbers. World Economic Forum (2018). https://www.weforum.org/agenda/2018/08/the-world-of-plastics-in-numbers

  57. Jambek, J., et al.: Plastic waste inputs from land into the ocean. Science 347, 768–771 (2015)

    Article  Google Scholar 

  58. Wright, S.L., et al.: The physical impacts of microplastics on marine organisms: a review. Environ. Poll. 178, 483–492 (2013)

    Article  Google Scholar 

  59. Fossi, M.C., et al.: Large filter feeding marine organisms as indicators of microplastic in the pelagic environment: the case studies of the Mediterranean basking shark (Cetorhinus maximus) and fin whale (Balaenoptera physalus). Mar. Environ. Res. 100, 17–24 (2014)

    Article  Google Scholar 

  60. Germanov, E., et al.: Microplastics on the menu: plastics pollute Indonesian Manta ray and whale shark feeding grounds. Front. Mar. Sci. 6, 679:1–679:21 (2019)

    Google Scholar 

  61. Hueter, R.E., et al.: Evidence of philopatry in sharks and implications for the management of shark fisheries. J. Northwest Atlantic Fish. Sci. 35, 239–247 (2005)

    Google Scholar 

  62. Queiroz, N., et al.: Ocean-wide tracking of pelagic sharks reveals extent of overlap with longline fishing hotspots. PNAS 113(6), 1582–1587 (2016)

    Article  Google Scholar 

  63. Heithaus, M.R., et al.: Long-term movements of tiger sharks satellite-tagged in Shark Bay, Western Australia. Mar. Biol. 151, 1455–1461 (2007)

    Article  Google Scholar 

  64. Hoenner, X., et al.: Australia’s continental-scale acoustic tracking database and its automated quality control process. Sci. Data 5, 170206:1–170206:10 (2018). https://doi.org/10.1038/sdata.2017.206

  65. Cuzzocrea, A.: Improving range-sum query evaluation on data cubes via polynomial approximation. DKE 56(2), 85–121 (2006)

    Article  Google Scholar 

  66. Cuzzocrea, A., et al.: A hierarchy-driven compression technique for advanced OLAP visualization of multidimensional data cubes. In: DaWaK 2006. LNCS, vol. 4081, pp. 106–119 (2006)

    Google Scholar 

  67. Cuzzocrea, A., et al.: OLAP*: effectively and efficiently supporting parallel OLAP over big data. In: MEDI 2013. LNCS, vol. 8216, pp. 38–49 (2013)

    Google Scholar 

  68. Cuzzocrea, A., Leung, C.K.: Efficiently compressing OLAP data cubes via R-tree based recursive partitions. In: ISMIS 2012. LNCS (LNAI), vol. 7661, pp. 455–465 (2012)

    Google Scholar 

  69. Cuzzocrea, A., Matrangolo, U.: Analytical synopses for approximate query answering in OLAP environments. In: DEXA 2004. LNCS, vol. 3180, pp. 359–370 (2004)

    Google Scholar 

  70. Cuzzocrea, A., Serafino, P.: LCS-Hist: taming massive high-dimensional data cube compression. In: EDBT 2009, pp. 768–779 (2009)

    Google Scholar 

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Acknowledgments

This project is partially supported by (a) Natural Sciences and Engineering Research Council of Canada (NSERC) and (b) University of Manitoba.

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Authors and Affiliations

  1. University of Manitoba, Winnipeg, MB, Canada

    Isabelle M. Anderson-Grégoire, Kaitlyn A. Horner, Carson K. Leung, Delica S. Leboe-McGowan, Anifat M. Olawoyin & Beni Reydman

  2. University of Calabria, Rende, CS, Italy

    Alfredo Cuzzocrea

Authors
  1. Isabelle M. Anderson-Grégoire
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  2. Kaitlyn A. Horner
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  3. Carson K. Leung
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  4. Delica S. Leboe-McGowan
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  5. Anifat M. Olawoyin
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  6. Beni Reydman
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  7. Alfredo Cuzzocrea
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Corresponding author

Correspondence to Carson K. Leung .

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Editors and Affiliations

  1. Department of Information and Communication Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. Department of Computer Science, Ryerson University, Toronto, ON, Canada

    Isaac Woungang

  3. Faculty of Business Administration, Rissho University, Tokyo, Japan

    Tomoya Enokido

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Anderson-Grégoire, I.M. et al. (2021). A Big Data Science Solution for Analytics on Moving Objects. In: Barolli, L., Woungang, I., Enokido, T. (eds) Advanced Information Networking and Applications. AINA 2021. Lecture Notes in Networks and Systems, vol 226. Springer, Cham. https://doi.org/10.1007/978-3-030-75075-6_11

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