论文标题
在大时空数据库中进行接触跟踪查询的有效索引
An Efficient Index for Contact Tracing Query in a Large Spatio-Temporal Database
论文作者
论文摘要
在本文中，我们研究了一个新颖的联系人跟踪查询（CTQ），该查询找到了与查询用户$ direct $ $ contact $的用户，或$ $ $ $ contact $ $ $ $ $ $ $ the $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $在随后的时间戳则来自一个大型时空数据库。在新的Covid-19-19大流行世界时代，CTQ对于寻找潜在的COVID-19暴露患者的列表至关重要。回答CTQ的一种直接方法是使用传统的时空索引。但是，这些指数无法达到目的，因为每个用户都涵盖了潜在疾病扩散的时间跨度，因此几乎无法使用有效的修剪技术。我们提出了一个多级索引，即QR-Tree，该索引同时考虑空间覆盖范围和为组用户的共同访问模式，以便将可能与查询用户符合的用户分组在一起。更具体地说，我们使用Quadtree将用户移动轨迹划分W.R.T.空间和时间，然后使用R-Tree利用这些用户跟踪的这些时空映射到组用户。 QR-Tree促进了有效的修剪，并允许仅访问可以成为CTQ的候选答案的潜在用户集。使用真实数据集的实验显示了我们方法的有效性。
In this paper, we study a novel contact tracing query (CTQ) that finds users who have been in $direct$ $contact$ with the query user or $in$ $contact$ $with$ $the$ $already$ $contacted$ $users$ in subsequent timestamps from a large spatio-temporal database. The CTQ is of paramount importance in the era of new COVID-19 pandemic world for finding possible list of potential COVID-19 exposed patients. A straightforward way to answer the CTQ is using traditional spatio-temporal indexes. However, these indexes cannot serve the purpose as each user covers a large area within the time-span of potential disease spreading and thus they can hardly use efficient pruning techniques. We propose a multi-level index, namely QR-tree, that consider both space coverage and the co-visiting patterns to group users so that users who are likely to meet the query user are grouped together. More specifically, we use a quadtree to partition user movement traces w.r.t. space and time, and then exploit these space-time mapping of user traces to group users using an R-tree. The QR-tree facilitates efficient pruning and enables accessing only potential sets of user who can be the candidate answers for the CTQ. Experiments with real datasets show the effectiveness of our approach.
