Dynamic monitoring of optimal locations in road network databases

Optimal location (OL) queries are a type of spatial queries that are particularly useful for the strategic planning of resources. Given a set of existing facilities and a set of clients, an OL query asks for a location to build a new facility that optimizes a certain cost metric (defined based on the distances between the clients and the facilities). Several techniques have been proposed to address OL queries, assuming that all clients and facilities reside in an \(L_p\) space. In practice, however, movements between spatial locations are usually confined by the underlying road network, and hence, the actual distance between two locations can differ significantly from their \(L_p\) distance. Motivated by the deficiency of the existing techniques, this paper presents a comprehensive study on OL queries in road networks. We propose a unified framework that addresses three variants of OL queries that find important applications in practice, and we instantiate the framework with several novel query processing algorithms. We further extend our framework to efficiently monitor the OLs when locations for facilities and/or clients have been updated. Our dynamic update methods lead to efficient answering of continuous optimal location queries. We demonstrate the efficiency of our solutions through extensive experiments with large real data.     

Efficient trip scheduling algorithms for groups

Given the source and destination locations of $n$ group members and a set of required point of interest (POI) types such as restaurants and shopping centers, a Group Trip Scheduling (GTS) query schedules $n$ individual trips such that each POI type is included in exactly one trip and an aggregate trip overhead distance for visiting the required POI types is minimized. Each trip starts at a member’s source location, goes through some POIs, and ends at the member’s destination location. The trip distance of a group member is the distance from her source to destination via the POIs that the group member visits, and the trip overhead distance of the group member is measured by subtracting the distance between her source and destination locations (without visiting any POI type) from her trip distance. The aggregate trip overhead distance is either the summation or the maximum of the trip overhead distances of the group members for visiting the POIs. A GTS query enables a group to schedule independent trips for its members in order to perform a set of tasks with the minimum travel cost. For example, family members normally have many outdoor tasks to perform within a short time for the proper management of home. The members may need to go to a bank to withdraw or deposit money, a pharmacy to buy medicine, or a supermarket to buy groceries. Similarly, organizers of an event may need to visit different POI types to perform many tasks. These scenarios motivate us to introduce a GTS query, a novel query type in spatial databases. We develop an efficient approach to process GTS queries and variants for the Euclidean space and road networks. By exploiting geometric properties, we refine the POI search space and prune POIs, which in turn reduce the query processing overhead significantly. In addition, we propose a dynamic programming technique to eliminate the trip combinations that cannot be part of the optimal query answer. We show that processing a GTS query is NP-hard and propose an approximation algorithm to further reduce the query processing overhead. We perform extensive experiments using real and synthetic datasets and show that our approach outperforms a straightforward approach with a large margin.     

Representation and processing of information related to real world events

In this paper, we discuss a formal system for representing and analyzing real world events. The event representation discussed in this paper accounts for three important event attributes, namely, time, space, and label. We introduce the notion of sequence templates that appears natural for capturing event related semantics; and in semantically analyzing user queries. To harness this potential, we present a formal structure to represent the queries related to real world events as well as an approach to semantically analyze a user query, and collate event related information to be dispatched to the user. Finally, we discuss the design and implementation of the Query-Event Analysis System (QEAS), which is an integrated system to (a) identify a best-matching sequence template(s) given a user query; (b) derive the meta-events based on the selected sequence templates; and (c) and use the meta-event information to answer the user query.     

Efficient data dissemination using locale covers

Location-dependent data are central to many emerging applications, ranging from traffic information services to sensor networks. The standard pull- and push-based data dissemination models become unworkable since the data volumes and number of clients are high.We address this problem using locale covers, a subset of the original set of locations of interest, chosen to include at least one location in a suitably defined neighborhood of any client. Since location-dependent values are highly correlated with location, a query can be answered using a location close to the query point. Typical closeness measures might be Euclidean distance, or a $k$-nearest neighbor criterion.We show that location-dependent queries may be answered satisfactorily using locale covers. Our approach is independent of locations and speeds of clients, and is applicable to mobile clients.We also introduce a nested locale cover scheme that ensures fair access latencies, and allows clients to refine the accuracy of their information over time. We also prove two important results: one regarding the greedy algorithm for sensor covers and the other pertaining to randomized locale covers for $k$-nearest neighbor queries.     

Spatial-aware interest group queries in location-based social networks

With the recent advances in positioning and smartphone technologies, a number of social networks such as Twitter, Foursquare and Facebook are acquiring the dimension of location, thus bridging the gap between the physical world and online social networking services. Most of the location-based social networks released check-in services that allow users to share their visiting locations with their friends. In this paper, users' interests are modeled by check-in actions. We propose a new type of Spatial-aware Interest Group (SIG) query that retrieves a user group of size k where each user is interested in the query keywords and they are close to each other in the Euclidean space. We prove that the SIG query problem is NP-complete. A family of efficient algorithms based on the IR-tree is thus proposed for the processing of SIG queries. Experiments on two real datasets show that our proposed algorithms achieve orders of magnitude improvement over the baseline algorithm.     

Blind evaluation of location based queries using space transformation to preserve location privacy

In this paper we propose a fundamental approach to perform the class of Range and Nearest Neighbor (NN) queries, the core class of spatial queries used in location-based services, without revealing any location information about the query in order to preserve users’ private location information. The idea behind our approach is to utilize the power of one-way transformations to map the space of all objects and queries to another space and resolve spatial queries blindly in the transformed space. Traditional encryption based techniques, solutions based on the theory of private information retrieval, or the recently proposed anonymity and cloaking based approaches cannot provide stringent privacy guarantees without incurring costly computation and/or communication overhead. In contrast, we propose efficient algorithms to evaluate KNN and range queries privately in the Hilbert transformed space. We also propose a dual curve query resolution technique which further reduces the costs of performing range and KNN queries using a single Hilbert curve. We experimentally evaluate the performance of our proposed range and KNN query processing techniques and verify the strong level of privacy achieved with acceptable computation and communication overhead.     

Spatial inverse query processing

Traditional spatial queries return, for a given query object q, all database objects that satisfy a given predicate, such as epsilon range and k-nearest neighbors. This paper defines and studies inverse spatial queries, which, given a subset of database objects Q and a query predicate, return all objects which, if used as query objects with the predicate, contain Q in their result. We first show a straightforward solution for answering inverse spatial queries for any query predicate. Then, we propose a filter-and-refinement framework that can be used to improve efficiency. We show how to apply this framework on a variety of inverse queries, using appropriate space pruning strategies. In particular, we propose solutions for inverse epsilon range queries, inverse k-nearest neighbor queries, and inverse skyline queries. Furthermore, we show how to relax the definition of inverse queries in order to ensure non-empty result sets. Our experiments show that our framework is significantly more efficient than naive approaches.     

Location privacy: going beyond K-anonymity,cloaking and anonymizers

With many location-based services, it is implicitly assumed that the location server receives actual users locations to respond to their spatial queries. Consequently, information customized to their locations, such as nearest points of interest can be provided. However, there is a major privacy concern over sharing such sensitive information with potentially malicious servers, jeopardizing users’ private information. The anonymity- and cloaking-based approaches proposed to address this problem cannot provide stringent privacy guarantees without incurring costly computation and communication overhead. Furthermore, they require a trusted intermediate anonymizer to protect user locations during query processing. This paper proposes a fundamental approach based on private information retrieval to process range and K-nearest neighbor queries, the prevalent queries used in many location-based services, with stronger privacy guarantees compared to those of the cloaking and anonymity approaches. We performed extensive experiments on both real-world and synthetic datasets to confirm the effectiveness of our approaches.     

Rule-based composite event queries: the language XChangeEQ and its semantics

Web systems, Web services, and Web-based publish/subscribe systems communicate events as XML messages and in many cases, require composite event detection: it is not sufficient to react to single event messages, but events have to be considered in relation to other events that are received over time. This entails a need for expressive, high-level languages for querying composite events. Emphasizing language design and formal semantics, we describe the rule-based composite event query language XChange^EQ. XChange^EQ is designed to completely cover and integrate the four complementary querying dimensions: event data, event composition, temporal relationships, and event accumulation. Semantics are provided as a model theory with accompanying fixpoint theory, an approach that is established for rule languages but has not been applied to event queries so far. Because they are highly declarative, thus easy to understand and well suited for query optimization, such semantics are desirable for event queries.     

Continuous monitoring of range spatial keyword query over moving objects

In this paper, we propose an efficient solution for processing continuous range spatial keyword queries over moving spatio-textual objects (namely, CRSK-mo queries). Major challenges in efficient processing of CRSK-mo queries are as follows: (i) the query range is determined based on both spatial proximity and textual similarity; thus a straightforward spatial proximity based pruning of the search space is not applicable as any object far from a query location with a high textual similarity score can still be the answer (and vice versa), (ii) frequent location updates may invalidate a query result, and thus require frequent re-computing of the result set for any object updates. To address these challenges, the key idea of our approach is to exploit the spatial and textual upper bounds between queries and objects to form safe zones (at the client-side) and buffer regions (at the server-side), and then use these bounds to quickly prune objects and queries through smart in-memory data structures. We conduct extensive experiments with a synthetic dataset that verify the effectiveness and efficiency of our proposed algorithm.     

Countering overlapping rectangle privacy attack for moving kNN queries

An important class of LBSs is supported by the moving k nearest neighbor (MkNN) query, which continuously returns the k nearest data objects for a moving user. For example, a tourist may want to observe the five nearest restaurants continuously while exploring a city so that she can drop in to one of them anytime. Using this kind of services requires the user to disclose her location continuously and therefore may cause privacy leaks derived from the user's locations. A common approach to protecting a user's location privacy is the use of imprecise locations (e.g., regions) instead of exact positions when requesting LBSs. However, simply updating a user's imprecise location to a location-based service provider (LSP) cannot ensure a user's privacy for an MkNN query: continuous disclosure of regions enable LSPs to refine more precise location of the user. We formulate this type of attack to a user's location privacy that arises from overlapping consecutive regions, and provide the first solution to counter this attack. Specifically, we develop algorithms which can process an MkNN query while protecting the user's privacy from the above attack. Extensive experiments validate the effectiveness of our privacy protection technique and the efficiency of our algorithm.     

Weighted path queries on semistructured databases

Path queries have been extensively used to query semistructured data, such as the Web and XML documents. In this paper we introduce weighted path queries, an extension of path queries enabling several classes of optimization problems (such as the computation of shortest paths) to be easily expressed. Weighted path queries are based on the notion of weighted regular expression, i.e., a regular expression whose symbols are associated to a weight. We characterize the problem of answering weighted path queries and provide an algorithm for computing their answer. We also show how weighted path queries can be effectively embedded into query languages for XML data to express in a simple and compact form several meaningful research problems.     

Probabilistic location-dependent queries at different location granularities

Approaches for the processing of location-dependent queries usually assume that the location data are expressed precisely, usually using GPS locations. However, this is unrealistic because positioning methods do not have a perfect accuracy (e.g., the positioning approach used in cellular networks handles only the cell where mobile users are located). Besides, users may need to express queries based on concepts of locations other than traditional GPS locations, which we call location granules.In this paper, we focus on location granule-based query processing (i.e., processing of queries with location granules) in situations where the location data available is imprecise, which we have called probabilistic location-dependent queries. For that purpose, we exploit the concept of uncertainty location granule, which represents the location uncertainty of an object. In particular, we tackle the problem of processing probabilistic inside (range) constraints. We analyze in detail how those constraints can be processed, taking into account both the existence of location uncertainty affecting the relevant objects and the location granularity specified. An extensive experimental evaluation shows the feasibility of the proposed probabilistic query processing approach and analyzes the advantages of using index structures to speed up the query processing.     

A comparative study of two approaches for supporting optimal network location queries

Given a set S of sites and a set O of weighted objects, an optimal location query finds the location(s) where introducing a new site maximizes the total weight of the objects that are closer to the new site than to any other site. With such a query, for instance, a franchise corporation (e.g., McDonald’s) can find a location to open a new store such that the number of potential store customers (i.e., people living close to the store) is maximized. Optimal location queries are computationally complex to compute and require efficient solutions that scale with large datasets. Previously, two specific approaches have been proposed for efficient computation of optimal location queries. However, they both assume p-norm distance (namely, L₁ and L₂/Euclidean); hence, they are not applicable where sites and objects are located on spatial networks. In this article, we focus on optimal network location (ONL) queries, i.e., optimal location queries in which objects and sites reside on a spatial network. We introduce two complementary approaches, namely EONL (short for Expansion-based ONL) and BONL (short for Bound-based ONL), which enable efficient computation of ONL queries with datasets of uniform and skewed distributions, respectively. Moreover, with an extensive experimental study we verify and compare the efficiency of our proposed approaches with real world datasets, and we demonstrate the importance of considering network distance (rather than p-norm distance) with ONL queries.     

The Farthest Spatial Skyline Queries

Pareto-optimal objects are favored as each of such objects has at least one competitive edge against all other objects, or “not dominated”. Recently, in the database literature, skyline queries have gained attention as an effective way to identify such pareto-optimal objects. In particular, this paper studies the pareto-optimal objects in perspective of facility or business locations. More specifically, given data points P and query points Q in two-dimensional space, our goal is to retrieve data points that are farther from at least one query point than all the other data points. Such queries are helpful in identifying spatial locations far away from undesirable locations, e.g., unpleasant facilities or business competitors. To solve this problem, we first study a baseline Algorithm TFSS and propose an efficient progressive Algorithm BBFS, which significantly outperforms TFSS by exploiting spatial locality. We also develop an efficient approximation algorithm to trade accuracy for efficiency. We validate our proposed algorithms using extensive evaluations over synthetic and real datasets.     

Scalable processing of continuous K-nearest neighbor queries with uncertain velocity

Continuous K-nearest neighbor (CKNN) query is an important type of spatio-temporal queries. Given a time interval [t_s, t_e] and a moving query object q, a CKNN query is to find the K-nearest neighbors (KNNs) of q at each time instant within [t_s, t_e]. In this paper, we focus on the issue of scalable processing of CKNN queries over moving objects with uncertain velocity. Due to the large amount of CKNN queries that need to be evaluated concurrently, efficiently processing such queries inevitably becomes more complicated. We propose an index structure, namely the CI-tree, to predetermine and organize the candidates for each query issued by the user from anywhere and anytime. When the CKNN queries are evaluated, their corresponding candidates can be rapidly retrieved by traversing the CI-tree so that the processing time is greatly reduced. A comprehensive set of experiments is performed to demonstrate the effectiveness and the efficiency of the CI-tree.     

Spatio-temporal querying recurrent multimedia databases using a semantic sequence state graph

We present an indexing method for spatiotemporal data: semantic sequence state graph (S³G). S³G maintains objects with their locations as states and events as transitions. The spatial information is maintained in states while the semantic events result in temporal ordering between the states. If the objects visit the same locations repeatedly, we call such databases as recurrent databases. Our querying interface supports queries based on spatio-temporal logic that includes operators such as ??next?? and ??eventually??. The interactive querying interface enables the user to build the query interactively and see the intermediate results of the query.     

Efficient Processing of Continual Range Queries for Location-Aware Mobile Services

Efficient processing of continual range queries is important in providing location-aware mobile services. In this paper, we study a new main memory-based approach to indexing continual range queries to support location-aware mobile services. The query index is used to quickly answer the following question continually: “Which moving objects are currently located inside the boundaries of individual queries?” We present a covering tile-based (COVET) query index. A set of virtual tiles are predefined, each with a unique ID. One or more of the virtual tiles are used to strictly cover the region defined by an individual range query. The query ID is inserted into the ID lists associated with the covering tiles. These covering tiles touch each other only at the edges. A COVET index maintains a mapping between a covering tile and all the queries that contain that tile. For any object position, search is conducted indirectly via the covering tiles. More importantly, a COVET-based query index allows query evaluation to take advantage of incremental changes in object locations. Computation can be saved for those objects that have not moved outside the boundaries of covering tiles. Simulations are conducted to evaluate the effectiveness of the COVET index and compare virtual tiles of different shapes and sizes.     

Querying Incomplete Information in Semistructured Data

Semistructured data occur in situations where information lacks a homogeneous structure and is incomplete. Yet, up to now the incompleteness of information has not been reflected by special features of query languages. Our goal is to investigate the principles of queries that allow for incomplete answers. We do not present, however, a concrete query language. Queries over classical structured data models contain a number of variables and constraints on these variables. An answer is a binding of the variables by elements of the database such that the constraints are satisfied. In the present paper, we loosen this concept in so far as we allow also answers that are partial; that is, not all variables in the query are bound by such an answer. Partial answers make it necessary to refine the model of query evaluation. The first modification relates to the satisfaction of constraints: in some circumstances we consider constraints involving unbound variables as satisfied. Second, in order to prevent a proliferation of answers, we only accept answers that are maximal in the sense that there are no assignments that bind more variables and satisfy the constraints of the query. Our model of query evaluation consists of two phases, a search phase and a filter phase. Semistructured databases are essentially labeled directed graphs. In the search phase, we use a query graph containing variables to match a maximal portion of the database graph. We investigate three different semantics for query graphs, which give rise to three variants of matching. For each variant, we provide algorithms and complexity results. In the filter phase, the maximal matchings resulting from the search phase are subjected to constraints, which may be weak or strong. Strong constraints require all their variables to be bound, while weak constraints do not. We describe a polynomial algorithm for evaluating a special type of queries with filter constraints, and assess the complexity of evaluating other queries for several kinds of constraints. In the final part, we investigate the containment problem for queries consisting only of search constraints under the different semantics.     

DART+: Direction-aware bichromatic reverse k nearest neighbor query processing in spatial databases

This article presents a novel type of queries in spatial databases, called the direction-aware bichromatic reverse k nearest neighbor(DBRkNN) queries, which extend the bichromatic reverse nearest neighbor queries. Given two disjoint sets, P and S, of spatial objects, and a query object q in S, the DBRkNN query returns a subset P′ of P such that k nearest neighbors of each object in P′ include q and each object in P′ has a direction toward q within a pre-defined distance. We formally define the DBRkNN query, and then propose an efficient algorithm, called DART, for processing the DBRkNN query. Our method utilizes a grid-based index to cluster the spatial objects, and the B⁺-tree to index the direction angle. We adopt a filter-refinement framework that is widely used in many algorithms for reverse nearest neighbor queries. In the filtering step, DART eliminates all the objects that are away from the query object more than a pre-defined distance, or have an invalid direction angle. In the refinement step, remaining objects are verified whether the query object is actually one of the k nearest neighbors of them. As a major extension of DART, we also present an improved algorithm, called DART+, for DBRkNN queries. From extensive experiments with several datasets, we show that DART outperforms an R-tree-based naive algorithm in both indexing time and query processing time. In addition, our extension algorithm, DART+, also shows significantly better performance than DART.