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ELAN: An Efficient Location-Aware Analytics System
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DOI：10.1016/j.bdr.2016.08.001.png)
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Abstract
En 中文
We demonstrate an Efficient Location-Aware aNalytics system (ELAN), aiming to provide users with location-aware data analytics services. For each user-selected spatial region, ELAN can instantly identify the most important functionality features of the region (e.g., business zones and residential areas) by efficiently analyzing the user-generated content (UGC) within the region. For each feature, ELAN can efficiently calculate the spatial boundary of the functional zone (denoted by a convex hull) in order to help users better understand the feature and furthermore we can identify the influential range of a certain feature. ELAN has many real-world applications, e.g., choosing business locations and popular regions discovery. There are two main challenges in designing a location-aware data analytics system. The first is to achieve high performance, as the region may contain a large amount of location-based UGC data. The second is to support continuous queries as users may continuously change the region by zooming in, zooming out, and panning the map. To address these challenges, we propose effective spatio-textual indexes and efficient incremental algorithms to support instant location-aware data analytics. We have implemented and deployed a system, which has been commonly used and widely accepted. (C) 2016 Published by Elsevier Inc.
Keywords:
Location-aware analytics
Spatial index
Incremental algorithm
Big data
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