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Building socially-enabled event-enriched maps

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Abstract

With the advancement of social sensing technologies, digital maps have recently witnessed a tremendous evolution with the aim of integrating enriched semantic layers from heterogeneous and diverse data sources. Current generations of digital maps are often crowd-sourced, allow interactive route planning, and may contain live updates, such as traffic congestion states. Within this context, we believe that the next generation of maps will introduce the concept of extracting Events of Interest (EoI) from crowdsourced data, and displaying them at different spatial scales based on their significance. This paper introduces Hadath1, a scalable and efficient system that extracts social events from unstructured data streams, e.g. Twitter. Hadath applies natural language processing and multi-dimensional clustering techniques to extract relevant events of interest at different map scales, and to infer the spatio-temporal scope of detected events. Hadath also implements a hierarchical in-memory spatio-temporal indexing scheme to allow efficient and scalable access to raw data, as well as to extracted clusters of events. Initially, data packets are processed to discover events at a local scale, then, the proper spatio-temporal scope and the significance of detected events at a global scale is determined. As a result, live events can be displayed at different spatio-temporal resolutions, thus allowing a smooth and unique browsing experience. Finally, to validate our proposed system, we conducted experiments on real-time and historical social media streams.

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Notes

  1. http://www.4sqmap.com/checkins/map

  2. https://www.flickr.com/map

  3. https://www.mapd.com/demos/tweetmap/

  4. https://www.yelp.com/wordmap/

  5. https://cloud.google.com/dataflow/

  6. https://ibmstreams.github.io/

  7. https://aws.amazon.com/kinesis/

  8. https://spark.apache.org/streaming/

  9. https://storm.apache.org/

  10. https://www.elastic.co/

  11. https://kafka.apache.org/documentation/streams/

  12. https://flink.apache.org/

  13. Jaegwon Kim (1993) Supervenience and Mind, page 37, Cambridge University Press

  14. https://www.seleniumhq.org

  15. https://www.elastic.co/products/hadoop

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Acknowledgments

We kindly acknowledge Prof. Mohamed F. Mokbel from the University of Minnesota, for their useful suggestions. We would like to thank Shahbaz Atta for his development support in the big data processing. We extend our gratitude to King Abdul Aziz City for Science and Technology (KACST), Kingdom of Saudi Arabia for funding this research project through NSTIP grant number 14-INF2461-10.

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Authors and Affiliations

  1. Institute of Consulting Research and Studies, Umm Al-Qura University, Mecca, Saudi Arabia

    Faizan Ur Rehman

  2. LIG, University of Grenoble Alpes, Paris, France

    Faizan Ur Rehman & Ahmed Lbath

  3. Department of Computer Science, University of Sharjah, Sharjah, UAE

    Imad Afyouni

  4. Hazen.ai, Saudi Arabia

    Sohaib Khan

  5. College of Computer and Information Systems Umm Al-Qura University, Makkah, Saudi Arabia

    Saleh Basalamah

Authors
  1. Faizan Ur Rehman
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  2. Imad Afyouni
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  3. Ahmed Lbath
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  4. Sohaib Khan
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  5. Saleh Basalamah
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Correspondence to Faizan Ur Rehman.

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Hadath is an arabic word for an event or an unusual happening within a specified time and space.

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Rehman, F.U., Afyouni, I., Lbath, A. et al. Building socially-enabled event-enriched maps. Geoinformatica 24, 371–409 (2020). https://doi.org/10.1007/s10707-020-00394-y

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