With the proliferation of location-based services, geo-textual data is becoming ubiquitous. Objects involved in geo-textual data include geospatial locations, textual descriptions or keywords, and often numerical attributes (e.g., expenses and users' ratings for points of interest). One popular spatial keyword queries on geo-textual data is the Collective Spatial Keyword Query (CoSKQ), which is to find, for a query consisting of a query location and some query keywords, a set of multiple objects such that the objects in the set collectively cover all the query keywords, and the object set is of good quality according to some criterion. In this work, we demonstrate a Collective Spatial Keyword Queries System (CSKQS) that supports both the conventional CoSKQ, and a variant called Cost-Aware and Distance-Constrained CoSKQ. CSKQS adopts the client/server system architecture, enabling users to access it through a web browser. The client side interface allows users to specify their queries and view the results, while the server side handles query processing and stores the data. CSKQS also offers an interactive map visualization, providing an intuitive view of the spatial relationship between the result object set and query location.

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.

Co-location patterns, which capture the phenomenon that objects with certain labels are often located in close geographic proximity, are defined based on a support measure which quantifies the prevalence of a pattern candidate in the form of a label set. Existing support measures share the idea of counting the number of instances of a given label set$C$as its support, where an instance of$C$is an object set whose objects collectively carry all labels in$C$and are located close to one another. However, they suffer from various weaknesses, e.g., fail to capture all possible instances, or overlook the cases when multiple instances overlap. In this paper, we propose a new measure called Fraction-Score which counts instancesfractionallyif they overlap. Fraction-Score captures all possible instances, and handles the cases where instances overlap appropriately (so that the supports defined are more meaningful and anti-monotonic). We develop efficient algorithms to solve the co-location pattern mining problem defined with Fraction-Score. Furthermore, to obtain representative patterns, we develop an efficient algorithm for mining the maximal co-location patterns, which are those patterns without proper superset patterns. We conduct extensive experiments using real and synthetic datasets, which verified the superiority of our proposals.

Indoor location-based services rely on the availability of sufficiently accurate positioning in indoor spaces. A popular approach to positioning relies on so-called radio maps that contain pairs of a vector of Wi-Fi signal strength indicator values (RSSIs), called a fingerprint, and a location label, called a reference point (RP), in which the fingerprint was observed. The positioning accuracy depends on the quality of the radio maps and their fingerprints. Radio maps are often sparse, with many pairs containing vectors missing many RSSIs as well as RPs. Aiming to improve positioning accuracy, we present a complete set of techniques to impute such missing values in radio maps. We differentiate two types of missing RSSIs: missing not at random (MNAR) and missing at random (MAR). Specifically, we design a framework encompassing a missing RSSI differentiator followed by a data imputer for missing values. The differentiator identifies MARs and MNARs via clustering-based fingerprint analysis. Missing RSSIs and RPs are then imputed jointly by means of a novel encoder-decoder architecture that leverages temporal dependencies in data collection as well as correlations among fingerprints and RPs. A time-lag mechanism is used to consider the aging of data, and a sparsity-friendly attention mechanism is used to focus attention score calculation on observed data. Extensive experiments with real data from two buildings show that our proposal outperforms the alternatives with significant advantages in terms of imputation accuracy and indoor positioning accuracy.

With the proliferation of location-based services, geo-textual data is becoming ubiquitous. Objects involved in geo-textual data include geospatial locations, textual descriptions or keywords, and various attributes (e.g., a point-of-interest has its expenses and users' ratings). One prominent type of spatial keyword queries is to find, for a query consisting of a location and keywords, a set of objects that covers all the keywords and is of good quality according to some criteria. Existing studies define the criteria either based on the geospatial information of the objects solely or simply treat the geospatial and attribute information of the objects together without differentiation. As a result, they cannot provide users flexibility to express finer grained preferences on the objects. In this paper, we propose a new criterion which is to find a set of objects where the distance (defined based on the geospatial information) is at most a threshold specified by users and the cost (defined based on the attribute information) is optimized. We develop a suite of three algorithms including an exact algorithm and two approximation algorithms with provable guarantees for the problem. We conducted extensive experiments on real datasets which verified the efficiency and effectiveness of proposed algorithms.

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.

With the proliferation of location-based services, geo-textual data is becoming ubiquitous. Objects involved in geo-textual data include geospatial locations, textual descriptions or keywords, and various attributes (e.g., a point-of-interest has its expenses and users' ratings). Many types of spatial keyword queries have been proposed on geo-textual data. Among them, one prominent type is to find, for a query consisting of a query location and some query keywords, a set of multiple objects such that the objects in the set collectively cover all the query keywords, and the object set is of good quality according to some criterion. Existing studies define the criterion either based on the geospatial information of the objects solely, or simply treat the geospatial information and the attribute information of the objects together without differentiation though they may have different semantics and scales. As a result, they cannot provide users flexibility to express finer grained preferences on the objects. In this paper, we propose a new criterion which is to find a set of objects where the distance (defined based on the geospatial information) is at most a threshold specified by users and the cost (defined based on the attribute information) is optimized. We develop a suite of two algorithms including an exact algorithm and an approximation algorithm with provable guarantees for the problem. We conducted extensive experiments on both real and synthetic datasets, which verified the efficiency and effectiveness of proposed algorithms.

The COVID-19 pandemic has caused over 6 million deaths since 2020. To contain the spread of the virus, social distancing is one of the most simple yet effective approaches. Motivated by this, in this paper we study the problem of continuous social distance monitoring (SDM) in indoor space, in which we can monitor and predict the pairwise distances between moving objects (people) in a building in real time. SDM can also serve as the fundamental service for downstream applications, e.g., a mobile alert application that prevents its users from potential close contact with others. To facilitate the monitoring process, we propose a framework that takes the current and future uncertain locations of the objects into account, and finds the object pairs that are close to each other in a near future. We develop efficient algorithms to update the result when object locations update. We carry out experiments on both real and synthetic datasets. The results verify the efficiency and effectiveness of our proposed framework and algorithms.

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.

With the proliferation of spatial-textual data such as location-based services and geo-tagged websites, spatial keyword queries are ubiquitous in real life. One example of spatial-keyword query is the so-called collective spatial keyword query (CoSKQ) which is to find, for a given query consisting a query location and several query keywords, a set of objects which covers the query keywords collectively and has the smallest cost wrt the query location. Quite a few cost functions have been proposed for CoSKQ and correspondingly, different approaches have been developed. However, given these cost functions in different forms and approaches in different structures, one could hardly compare existing cost functions systematically and needs to implement all approaches in order to tackle the CoSKQ problem with different cost functions, which is effort-consuming. In this paper, we design a unified cost function which generalizes the majority of existing cost functions for CoSKQ and develop a unified approach which works as well as (and sometimes better than) best-known approaches based on different cost functions. Experiments were conducted on both real and synthetic datasets which verified our proposed approach.

Co-location patterns are well-established on spatial objects with categorical labels, which capture the phenomenon that objects with certain labels are often located in close geographic proximity. Similar to frequent itemsets, co-location patterns are defined based on a support measure which quantifies the popularity (or prevalence) of a pattern candidate (a label set). Quite a few support measures exist for defining co-location patterns and they share an idea of counting the number of instances of a given label set C as its support, where an instance of C is an object set whose objects carry all the labels in C and are located close to one another. Unfortunately, these measures suffer from various weaknesses, e.g., some fail to capture all possible instances while some others overlook the cases when multiple instances overlap. In this paper, we propose a new measure called Fraction-Score whose idea is to count instances fractionally if they overlap. Compared to existing measures, Fraction-Score not only captures all possible instances, but also handles the cases where instances overlap appropriately (so that the supports defined are more meaningful and consistent with the desirable anti-monotonicity property). To solve the co-location pattern mining problem based on Fraction-Score, we develop efficient algorithms which are significantly faster than a baseline that adapts the state-of-the-art. We conduct extensive experiments using both real and synthetic datasets, which verified the superiority of Fraction-Score and also the efficiency of our developed algorithms.

With the proliferation of spatial-textual data such as location-based services and geo-tagged websites, spatial keyword queries are ubiquitous in real life. One example of spatial-keyword query is the so-called collective spatial keyword query (CoSKQ) which is to find for a given query consisting a query location and several query keywords a set of objects which covers the query keywords collectively and has the smallest costwrt the query location. In the literature, many different functions were proposed for defining the cost and correspondingly, many different approaches were developed for the CoSKQ problem. In this paper, we study the CoSKQ problem systematically by proposing a unified cost function and a unified approach for the CoSKQ problem (with the unified cost function). The unified cost function includes all existing cost functions as special cases and the unified approach solves the CoSKQ problem with the unified cost function in a unified way. Experiments were conducted on both real and synthetic datasets which verified our proposed approach.