关键词感知的最优路径搜索
Keyword-aware Optimal Route Search
摘要：
识别优选路线是在地图服务中找到应用的重要问题。当用户计划在城市内旅行时，用户可能想要找到“最受欢迎的路线”，使得它经过购物中心、餐馆和酒吧，并且往返于他的酒店的旅行时间在4小时内。“然而，现有的路线规划工作中没有一个算法可以用来回答这些问题。出于这一动机，我们定义的问题，关键字感知的最佳路线查询，表示为KOR，这是找到一个最佳的路线，使它涵盖了一组用户指定的关键字，一个指定的预算约束得到满足，和一个客观的分数的路线是最佳的。回答KOR查询的问题是NP难的。我们设计了一个近似算法OSSscaling与可证明的近似界。在此基础上，提出了一种更有效的近似算法BucketBound。我们还设计了一个贪婪近似算法。实验结果表明，所有算法都能有效地回答KOR查询，而BucketBound和贪心算法运行速度更快.实证研究还提供了深入了解所提出的算法的准确性。
英文摘要：
Identifying a preferable route is an important problem that finds applications in map services. When a user plans a trip within a city, the user may want to find "a most popular route such that it passes by shopping mall, restaurant, and pub, and the travel time to and from his hotel is within 4 hours." However, none of the algorithms in the existing work on route planning can be used to answer such queries. Motivated by this, we define the problem of keyword-aware optimal route query, denoted by KOR, which is to find an optimal route such that it covers a set of user-specified keywords, a specified budget constraint is satisfied, and an objective score of the route is optimal. The problem of answering KOR queries is NP-hard. We devise an approximation algorithm OSScaling with provable approximation bounds. Based on this algorithm, another more efficient approximation algorithm BucketBound is proposed. We also design a greedy approximation algorithm. Results of empirical studies show that all the proposed algorithms are capable of answering KOR queries efficiently, while the BucketBound and Greedy algorithms run faster. The empirical studies also offer insight into the accuracy of the proposed algorithms.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
