InProceedings ofthe 20th ACMSIGSPATIALInternational Conference on Advances inGeographic Information Systems, Redondo Beach, CA,USA,November 2012. Multiresolution Select-Distinct Queries on Large Geographic Point Sets

Many spatial applications require the ability to display locations of data entries on an online map. For example, an online photosharingservicemaywishtodisplayphotosaccordingtowherethey weretaken.Sincemanyphotos canoccupy thesameareaandoverlap each other within a display window, less popular or older images (based on a given measure of importance) can be discarded so that these more popular or newer photos become more distinct. A straightforward solution to this problem is (i) to use a window query to retrieve data entries within a given display window; (ii) to discard data entries in proximity of a more important one. This method works well in a high spatial selectivity setting, e.g., when thewindowqueryreturnsasmallnumberofentries,buttheperformance drastically degrades as the spatial selectivity decreases. We considerthisproblemasselectingdistinctdataentriesfromagiven dataset, where the “distinctiveness ” of a data entry depends on its relative importance in comparison to that of other data entries in proximity. In this paper, we propose a new query type called the multi-resolution select-distinct (MRSD) query. The main novelty of our query processing method is a voting system built upon an ensemble of interrelatedindexes, whichallowsus toefficientlydetermine the degree of distinctiveness of all points within a query window.Usingarealdatasetofover9millionlocations,ourexperimental results show that our proposed method is capable of consistently producing subsecond response times, while the window query-based method takes more than 10 seconds on average in a low spatial selectivitysetting. Categories andSubject Descriptor