Explora: Interactive Querying of Multidimensional Data in the Context of Smart Cities

Area of Science:

• Computer Science
• Data Science
• Urban Planning

Background:

• Smart city initiatives require citizen engagement for sustainability.
• Providing timely data and tools to citizens is crucial for deriving valuable insights.
• The massive volume of smart city data presents challenges for real-time analysis and accessibility.

Purpose of the Study:

• To introduce Explora, a generic framework for interactive, low-latency visual exploratory applications on spatiotemporal data.
• To enable efficient querying of large-scale smart city datasets.
• To support citizen engagement by providing accessible data insights.

Main Methods:

• Leveraging stream processing for deriving synopsis data structures at ingestion time.
• Creating compacted datasets that capture spatial and temporal trends.
• Implementing and evaluating Explora on traditional database and distributed data processing setups.

Main Results:

• Achieved a decrease of up to 2 orders of magnitude in query latency compared to raw data queries.
• Maintained query accuracy with less than 10% loss.
• Reduced data footprint by 30%.

Conclusions:

• Explora framework is feasible for processing real smart city data.
• The approach enables fast, approximate answers to visual queries on spatiotemporal data.
• Facilitates enhanced citizen engagement through accessible and timely data insights.