Pandemics like COVID-19 often cause dramatic losses of human lives and societal impacts, urging efficient and effective contact tracing, especially in indoor venues where the risk of infection is higher. In this work, we formulate a novel query called Indoor Contact Query (ICQ) over raw, uncertain indoor positioning data that digitalizes people's indoor mobility. Given a query object$o$, e.g., a virus-carrying person, an ICQ analyzes uncertain indoor positioning data to find objects that most likely had close contact with$o$for a long period of time. To process ICQ, we propose a set of techniques. First, we design an enhanced indoor graph model to organize different types of data necessary for ICQ. Second, for indoor moving objects, we devise methods to determine uncertain regions and to derive positioning samples missing in the raw data. Third, we propose a query processing framework with a close contact determination method, a search algorithm, and multiple acceleration strategies. We conduct extensive experiments on synthetic and real datasets, which verify the efficiency and effectiveness of our proposals.

Similarity computation is the core building block for GPS trajectory analyses. Nevertheless, due to the inherent limitations of GPS technology and devices, similar trajectories may have noises and low sampling rates, resulting in being inaccurately considered dissimilar. To fortify the robustness of trajectory similarity computation, we propose a novel contrastive representation learning framework (CLEAR). We adaptively combine spatial information with sequential information to model essential properties of trajectory data. Subsequently, we rank multiple positive instances (i.e., different variations of an anchor trajectory) based on their similarities to the anchor instance. We propose a specialized loss function that strategically harnesses these positive instances, iteratively associating harder positive instances with higher rank values. Moreover, we propose a multiple augmentation strategy to generate and utilize multiple positive instances. We conduct extensive experiments on two real-world trajectory datasets. The results validate the superiority of CLEAR over state-of-the-art models in terms of robust trajectory similarity computation against noises and low sampling rates.

The recent years have witnessed the growing popularity of indoor location-based services (LBS) in practice and research. Among others, indoor shortest path query (ISPQ) is of fundamental importance for indoor LBS. However, existing works on ISPQ ignore indoor temporal variations, e.g., the open and close times associated with entities like doors and rooms. In this paper, we define a new type of query called Indoor Temporal-variation aware Shortest Path Query (ITSPQ). It returns the valid shortest path based on the up-to-date indoor topology at the query time. A set of techniques is designed to answer ITSPQ efficiently. We design a graph structure (IT-Graph) that captures indoor temporal variations. To process ITSPQ using IT-Graph, we design two algorithms that check a doors accessibility synchronously and asynchronously. Furthermore, we propose a novel index structure (IT-Index) that extends the state-of-the-art index significantly by storing dynamic door-to-door distances in a compact distance cube associated with tree nodes. When processing ITSPQ using IT-Index, we make use of the distance cube to avoid time-consuming indoor distance computation on-the-fly. We evaluate the proposed techniques using extensive experiments on synthetic and real data. The results show that our IT-Index based method is the most efficient for processing ITSPQ at a modest cost of index memory consumption.

Pandemics often cause dramatic losses of human lives and impact our societies in many aspects such as public health, tourism, and economy. To contain the spread of an epidemic like COVID-19, efficient and effective contact tracing is important, especially in indoor venues where the risk of infection is higher. In this work, we formulate and study a novel query called Indoor Contact Query (ICQ) over raw, uncertain indoor positioning data that digitalizes people's movements indoors. Given a query object$o$, e.g., a person confirmed to be a virus carrier, anICQanalyzes uncertain indoor positioning data to find objects that most likely had close contact with$o$for a long period of time. To processICQ, we propose a set of techniques. First, we design an enhanced indoor graph model to organize different types of data necessary forICQ. Second, for indoor moving objects, we devise methods to determine uncertain regions and to derive positioning samples missing in the raw data. Third, we propose a query processing framework with a close contact determination method, a search algorithm, and the acceleration strategies. We conduct extensive experiments on synthetic and real datasets to evaluate our proposals. The results demonstrate the efficiency and effectiveness of our proposals.

As people spend large parts of their lives in indoor venues like shopping malls, airports, and office buildings, there are increasing demands of indoor keyword-aware routing, i.e., finding an indoor path that covers interesting keywords. In this work, we demonstrate an Indoor Keyword-Aware Routing System (IKAROS) which efficiently answers the indoor top-$k$keyword-aware routing query (IKRQ). Given two indoor points$s$and$t$, an IKRQ returns$k\ s$-to-$t$routes that do not exceed a given distance constraint but have optimal ranking scores integrating keyword relevance and spatial distance. To enable cross-platform IKRQ services for end-users, IKAROS adopts the Browser/Server system architecture. The browser provides the interface for users to specify queries and view the results, while the server manages the indoor spatial and textual information and processes queries. Specifically, IKAROS implements two search algorithms with different routing expansions through a tailored indoor space model. Our demonstration covers the major system components such as configuring a query's parameters, choosing a query search algorithm, and visualizing the returned route.

Indoor Location-based Services (LBS) facilitate people in indoor scenarios such as airports, train stations, shopping malls, and office buildings. Indoor spatial queries are the foundation to support indoor LBSs. However, the existing techniques for indoor spatial queries are limited to support more advanced queries that consider semantic information, temporal variations, and crowd influence. This work studies indoor spatial queries for indoor LBSs. Some typical proposals for indoor spatial queries are compared theoretically and experimentally. Then, it studies three advanced indoor spatial queries, a) Indoor Keyword-aware Routing Query. b) Indoor Temporal-variation aware Routing Query. c) Indoor Crowd-aware Routing Query. A series of techniques are proposed to solve these problems.

To support indoor spatial queries and indoor location-based services (LBS), multiple techniques including model/indexes and search algorithms have been proposed. In this work, we conduct an extensive experimental study on existing proposals for indoor spatial queries. We survey five model/indexes, compare their algorithmic characteristics, and analyze their space and time complexities. We also design an in-depth benchmark with real and synthetic datasets, evaluation tasks and performance metrics. Enabled by the benchmark, we obtain and report the performance results of all model/indexes under investigation. By analyzing the results, we summarize the pros and cons of all techniques and suggest the best choice for typical scenarios.

With the increasingly available indoor positioning technologies, indoor location-based services (LBS) are becoming popular. Among indoor LBS applications, indoor routing is particularly in demand. In the literature, there are several existing studies on indoor keyword-aware routing queries, each considering different criteria when finding an optimal route. However, none of these studies explicitly constraint the time budget for the route. In this paper, we propose a new problem formulation TIKRQ that considers the time needed for a user to complete the route, in addition to other criteria such as static cost and textual relevance. A set-based search algorithm and effective pruning strategies are proposed for TIKRQ. We conduct extensive experiments to verify the efficiency of our proposals.

Indoor venues accommodate many people who collectively form crowds. Such crowds in turn influence people's routing choices, e.g., people may prefer to avoid crowded rooms when walking from A to B. This paper studies two types of crowd-aware indoor path planning queries. The Indoor Crowd-Aware Fastest Path Query (FPQ) finds a path with the shortest travel time in the presence of crowds, whereas the Indoor Least Crowded Path Query (LCPQ) finds a path encountering the least objects en route. To process the queries, we design a unified framework with three major components. First, an indoor crowd model organizes indoor topology and captures object flows between rooms. Second, a time-evolving population estimator derives room populations for a future timestamp to support crowd-aware routing cost computations in query processing. Third, two exact and two approximate query processing algorithms process each type of query. All algorithms are based on graph traversal over the indoor crowd model and use the same search framework with different strategies of updating the populations during the search process. All proposals are evaluated experimentally on synthetic and real data. The experimental results demonstrate the efficiency and scalability of our framework and query processing algorithms.

People have many activities indoors and there is an increasing demand of keyword-aware route planning for indoor venues. In this paper, we study the indoor top-k keyword-aware routing query (IKRQ). Given two indoor points s and t, an IKRQ returns k s-to-t routes that do not exceed a given distance constraint but have optimal ranking scores integrating keyword relevance and spatial distance. It is challenging to efficiently compute the ranking scores and find the best yet diverse routes in a large indoor space with complex topology. We propose prime routes to diversify top-k routes, devise mapping structures to organize indoor keywords and compute route keyword relevances, and derive pruning rules to reduce search space in routing. With these techniques, we design two search algorithms with different routing expansions. Experiments on synthetic and real data demonstrate the efficiency of our proposals.

Indoor shortest path query (ISPQ) is of fundamental importance for indoor location-based services (LBS). However, existing ISPQs ignore indoor temporal variations, e.g., the open and close times associated with entities like doors and rooms. In this paper, we define a new type of query called Indoor Temporal-variation aware Shortest Path Query (ITSPQ). It returns the valid shortest path based on the up-to-date indoor topology at the query time. A set of techniques is designed to answer ITSPQ efficiently. We design a graph structure (IT-Graph) that captures indoor temporal variations. To process ITSPQ using IT-Graph, we design two algorithms that check a door's accessibility synchronously and asynchronously, respectively. We experimentally evaluate the proposed techniques using synthetic data. The results show that our methods are efficient.