Towards Why-Not Spatial Keyword Top-k Queries: A Direction-Aware Approach - 2018


With the continued proliferation of location-based mostly services, a growing number of internet-accessible information objects are geo-tagged and have text descriptions. An important question over such web objects is that the direction-aware spatial keyword question that aims to retrieve the prime-k objects that best match query parameters in terms of spatial distance and textual similarity in a very given query direction. In some cases, it can be tough for users to specify appropriate question parameters. When obtaining a question result, users might realize some desired objects are unexpectedly missing and might thus querythe complete result. Enabling why-not questions in this setting could aid users to retrieve better results, therefore improving the general utility of the question functionality. This Project studies the directionaware why-not spatial keyword top-k question downside. We tend to propose economical question refinement techniques to revive missing objects by minimally modifying users' direction-aware queries. We have a tendency to prove that the best refined query directions lie in a finite solution area for a special case and cut back the look for the optimal refinement to a linear programming drawback for the general case. In depth experimental studies demonstrate that the proposed techniques outperform a baseline method by two orders of magnitude and are sturdy in an exceedingly broad range of settings.

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