Abstract
Citizen engagement is one of the key factors for smart city initiatives to remain sustainable over time. This in turn entails providing citizens and other relevant stakeholders with the latest data and tools that enable them to derive insights that add value to their day-to-day life. The massive volume of data being constantly produced in these smart city environments makes satisfying this requirement particularly challenging. This paper introduces Explora, a generic framework for serving interactive low-latency requests, typical of visual exploratory applications on spatiotemporal data, which leverages the stream processing for deriving—on ingestion time—synopsis data structures that concisely capture the spatial and temporal trends and dynamics of the sensed variables and serve as compacted data sets to provide fast (approximate) answers to visual queries on smart city data. The experimental evaluation conducted on proof-of-concept implementations of Explora, based on traditional database and distributed data processing setups, accounts for a decrease of up to 2 orders of magnitude in query latency compared to queries running on the base raw data at the expense of less than 10% query accuracy and 30% data footprint. The implementation of the framework on real smart city data along with the obtained experimental results prove the feasibility of the proposed approach.
Highlights
The increasing pervasiveness of data in the world is currently leading to a new era of human progress, which has been referred to as the Fourth Industrial Revolution
The work reported in this paper aims at answering the research question on how to serve common data exploration tasks over live smart city data coming from nonstationary sensors under interactive time constraints
Since street-block views rely on a finer spatial fragmentation strategy than that used for tile views, the number of data summaries placed into the former views is larger in proportion to the amount of raw sensor observations
Summary
The increasing pervasiveness of data in the world is currently leading to a new era of human progress, which has been referred to as the Fourth Industrial Revolution. Harrison et al [2] argue how, by building on the advances in IT, the traditional physical city infrastructure is extended to an integrated framework allowing cities to gather, process, analyze, and make decisions based on detailed operational data. These authors define smart cities through three IT aspects: Sensors 2020, 20, 2737; doi:10.3390/s20092737 www.mdpi.com/journal/sensors . Intelligent systems able to analyze, model, and visualize the above interconnected data and to derive from the valuable insights that drive decisions and actions to optimize the operation of the city’s services and infrastructure
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