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SOLA: Stream OLAP-based Analytical Framework for Roadway Maintenance

Published: 07 November 2017 Publication History

Abstract

Maintaining infrastructures (e.g., roadway) is a critical issue for local governments. Data from physical devices and reports from citizens through social networks are helpful to observe conditions of infrastructures. This paper proposes a framework called SOLA for integrating and analysing data from multiple sources including streaming data and static data for roadway management. The framework integrates data from multiple sources in the way of stream OLAP architecture, and analyses the integrated data in terms of OLAP analysis. This paper applies the framework to support roadway managements of local governments, and develops the application called SOLAR. SOLAR aims at providing historical views of roadway patrols as well as roadway statuses for assisting in determining roadway patrolling schedules. The real-world use case on a city exhibits the applicability of SOLAR with positive feedbacks from city officers. SOLA is a promising framework for big data analysis and smart city applications, as the number, amount, and speed of generating data increase in the era of big data and smart city.

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Cited By

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  • (2019)StreamingCube-Based Analytical Framework for Environmental Data Analysis2019 IEEE International Conference on Big Data and Smart Computing (BigComp)10.1109/BIGCOMP.2019.8679149(1-8)Online publication date: Feb-2019

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cover image ACM Other conferences
MEDES '17: Proceedings of the 9th International Conference on Management of Digital EcoSystems
November 2017
299 pages
ISBN:9781450348959
DOI:10.1145/3167020
Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 07 November 2017

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Author Tags

  1. Data integration
  2. Real-time OLAP
  3. Roadway management
  4. Spatio-temporal OLAP
  5. Stream OLAP

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MEDES '17 Paper Acceptance Rate 41 of 65 submissions, 63%;
Overall Acceptance Rate 267 of 682 submissions, 39%

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Cited By

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  • (2019)StreamingCube-Based Analytical Framework for Environmental Data Analysis2019 IEEE International Conference on Big Data and Smart Computing (BigComp)10.1109/BIGCOMP.2019.8679149(1-8)Online publication date: Feb-2019

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