Optimizing Continuous kNN Queries over Large-Scale Spatial-Textual Data Streams

Abstract

The continuous k-Nearest Neighbor queries over spatial-textual data streams (abbr. CkQST) retrieve and continuously monitor at most k nearest neighbor (abbr. kNN) objects to the user-specified location containing all the user-specified keywords, which is the core operation of numerous location-based publish/subscribe systems. Such a system is usually subscribed with a massive number of CkQST and evaluated simultaneously whenever new objects are incoming and old objects are expiring. The approach to evaluating CkQST is to construct a spatial-textual hybrid index for subscribed queries and matching the incoming objects utilizing the filtering capabilities of the index. For CkQST, the minimal spatial search range covering kNN objects changes frequently with the arrival and expiration of qualified objects, and the cost of updating the index is prohibitively high. To efficiently evaluate CkQST, we extend Quad-tree with an inverted index, and exploit it with three techniques, i.e. a memory-based cost model, a block-based ordered inverted index and an adaptive insertion strategy. The experiments on comprehensive datasets demonstrate the effectiveness and efficiency of our proposed techniques.