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From Data Collection Merit to Data Connection Merit for Smart Sustainable Cities

Published: 08 June 2020 Publication History

Abstract

Smart data refers to the IoT data that has been processed to produce valuable data be turned into actionable information, which can be used for intelligence, planning, controlling and decision making efficiently and effectively by governments, industries and citizens. Unprecedentedly large amount and variety of sensory data can be collected to explore how these big data can become smart data and offer intelligence. Advanced data modeling and analytics, as well as data science solutions, are indispensable for transforming big data into smart data. For accelerating the utilization of smart data, NICT Real Space Information Analytics Project makes efforts to develop a cross-data analytics technology for utilizing data obtained from a variety of sensing technologies and different kinds of social big data to construct a platform that will help develop and expand smart services with a view towards smart sustainable cities. We are developing the xData Platform on NICT's Integrated Testbed, which is a platform implementing functions of a data loader for data collection, retrieval and conversion from a variety of data sources, association mining for spatiotemporal data integration and discovery of association rules, machine learning for prediction of spatiotemporal association patterns, and creation and distribution of prediction result data in a GIS format for route search and alert notification. For accelerating open innovation using the xData Platform, we conducted field experiments with participation of citizens.

References

[1]
NICT Big Data Analytics Laboratory, http://www2.nict.go.jp/bidal/en/ .
[2]
Rage Uday Kiran, Koji Zettsu, Masashi Toyoda, Philippe Fournier-Viger, P. Krishna Reddy and Masaru Kitsuregawa, 2019. Discovering Spatial High Utility Itemsets in Spatiotemporal Databases. In Proceedings of 31st International Conference on Scientific and Statistical Database Management (SSDBM 2019). Santa Cruz, CA, USA, 49--61.
[3]
Peijiang Zhao and Koji Zettsu. 2019. Decoder Transfer Learning for Predicting Personal Exposure to Air Pollution. In Proceedings of 2019 IEEE International Conference on Big Data (Big Data 2019) . Los Angeles, CA, USA, 5620--5629.
[4]
Minh-Son Dao, Ngoc-Thanh Nguyen and Koji Zettsu. 2019. Multi-time-horizon Traffic Risk Prediction using Spatio-Temporal Urban Sensing Data Fusion. In Proceedings of IEEE International Conference on Big Data (Big Data 2019). Los Angeles, CA, USA. 2205- 2214.
[5]
Sadanori Itoh and Koji Zettsu. 2019. Report on a Hackathon for Car Navigation Using Traffic Risk Data. In Proceedings of 3rd International Conference on Intelligent Traffic and Transportation. Amsterdam, The Netherlands.
[6]
Tomohiro Sato, Minh-Son Dao, Kota Kuribayashi and Koji Zettsu. 2019. SEPHLA: Challenges and Opportunities Within Environment-Personal Health Archives. In Proceedings of 25th International Conference on MultiMedia Modeling (MMM 2019). Thessaloniki, Greece, Lecture Notes in Computer Science, 11295, 325--337.

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cover image ACM Conferences
ICDAR '20: Proceedings of the 2020 ACM Workshop on Intelligent Cross-Data Analysis and Retrieval
June 2020
44 pages
ISBN:9781450375092
DOI:10.1145/3379174
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 June 2020

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Author Tags

  1. data mining
  2. deep learning
  3. event data warehouse
  4. smart data analytics
  5. smart sustainable cities
  6. xData platform

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