Path-Based Continuous Spatial Keyword Queries

Fangshu Chen, Pengfei Zhang, Chengcheng Yu, Huaizhong Lin, Shan Tang, Xiaoming Hu

Abstract: In this paper, we study the path based continuous spatial keyword queries, which find the answer set continuously when the query point moves on a given path. Under this setting, we explore two primitive spatial keyword queries, namely <svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-0.2063999pt" id="M1" height="9.49473pt" version="1.1" viewBox="-0.0498162 -9.28833 6.66314 9.49473" width="6.66314pt"><g transform="matrix(.013,0,0,-0.013,0,0)"><path id="g113-108" d="M480 416C480 431 465 448 438 448C388 448 312 383 252 330C217 299 188 273 155 237H153L257 680C262 700 263 712 253 712C240 712 183 684 97 674L92 648L126 647C166 646 172 645 163 606L23 -6L29 -12C51 -5 77 2 107 8C115 62 130 128 142 180C153 193 179 220 204 241C231 170 259 106 288 54C317 0 336 -12 358 -12C381 -12 423 2 477 80L460 100C434 74 408 54 398 54C385 54 374 65 351 107C326 154 282 241 263 299C296 332 351 377 403 377C424 377 436 372 445 368C449 366 456 368 462 375C472 386 480 402 480 416Z"/></g></svg> nearest neighbor query and range query. The technical challenges lie in that: (1) retrieving qualified vertices in large road networks efficiently, and (2) issuing the query continuously for points on the path, which turns out to be inapplicable. To overcome the above challenges, we first propose a backbone road network index structure (BNI), which supports the distance computation efficiently and offers a global insights of the whole road network. Motivated by the safe zone technique, we then transform our queries to the issue of finding event points, which capture the changes of answer set. By this transformation, our queries are to be simple and feasible. To answer the queries, we propose a Two-Phase Progressive (TPP) computing framework, which first computes the answer sets for some crucial vertices on the path, and then identifies the event points by the retrieved answer sets. Extensive experiments on both real and synthetic data sets are conducted to evaluate the performance of our proposed algorithms, and the results show that our algorithms outperform competitors by several orders of magnitude.