Towards Why-Not Spatial Keyword Top-k Queries: A Direction-Aware Approach - 2018 PROJECT TITLE :Towards Why-Not Spatial Keyword Top-k Queries: A Direction-Aware Approach - 2018ABSTRACT: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. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Structure Based User Identification across Social Networks - 2018 Ultra High-Dimensional Nonlinear Feature Selection for Big Biological Data - 2018
