Top-k Spatial Keyword Queries Research Articles

FogLBS: Utilizing fog computing for providing mobile Location-Based Services to mobile customers

The growth of Location-Based Services (LBSs) has been made possible by the widespread use of GPS-enabled devices. Some important LBSs require the ability to quickly process moving spatial-keyword queries over moving objects, such as when a moving customer is looking for a nearby mobile fuel delivery service. While there have been solutions proposed for scenarios where either the queries or the objects being queried are moving, there is still a need for solutions that can handle scenarios where both are in motion. This research focuses on the application of fog computing to provide real-time processing of moving spatial-keyword queries for LBSs. Specifically, the research proposes a new model, FogLBS, designed to efficiently process moving continuous top-k spatial-keyword queries over moving objects in a directed streets network, with a particular emphasis on the use case of a mobile service provider. FogLBS computes queries’ answer sets for time intervals and incrementally updates them using novel optimization techniques and indexing structures. By implementing FogLBS in a fog computing architecture, the model is able to meet the real-time requirements of the service provider application and other similar LBSs. The results of extensive experiments demonstrate the effectiveness of the proposed model in terms of efficiency, scalability, and accuracy, making it a valuable contribution to the field of fog computing in LBSs.

Efficient processing of top-k frequent spatial keyword queries.

The rapid popularization of high-speed mobile communication technology and the continuous development of mobile network devices have given spatial textual big data (STBD) new dimensions due to their ability to record geographical objects from multiple sources and with complex attributes. Data mining from spatial textual datasets has become a meaningful study. As a popular topic for STBD, the top-k spatial keyword query has been developed in various forms to deal with different retrievals requirements. However, previous research focused mainly on indexing locational attributes and retrievals of few target attributes, and these correlations between large numbers of the textual attributes have not been fully studied and demonstrated. To further explore interrelated-knowledge in the textual attributes, this paper defines the top-k frequent spatial keyword query (tfSKQ) and proposes a novel hybrid index structure, named RCL-tree, based on the concept lattice theory. We also develop the tfSKQ algorithms to retrieve the most frequent and nearest spatial objects in STBD. One existing method and two baseline algorithms are implemented, and a series of experiments are carried out using real datasets to evaluate its performance. Results demonstrated the effectiveness and efficiency of the proposed RCL-tree in tfSKQ with the complex spatial multi keyword query conditions.

Privacy-Preserving Top-k Spatial Keyword Queries in Fog-Based Cloud Computing

With the popularity of location based services, spatial keyword query has become an important application. In order to save the storage and computational costs, most data owners will outsource the data to the cloud server, but this will lead to two problems, such as privacy leakage and heavy network bandwidth burden. To solve above problems, we propose a Privacy-preserving top-k Spatial Keyword queries based on Fog computing, namly PSKF. To further improve the search efficiency, we use the IR-tree to build the index and store it in the cloud server. Each fog server also saves a different subtree of the IR-tree, so that we can decide which fog server to participate in the query by pruning. Formal security analysis shows that our proposed PSKF achieves Indistinguishability under Known-Plaintext Attacks (IND-KPA), and extensive experiments demonstrate that our proposed scheme is efficient and feasible in practical applications.

A Parallel Processing Method for Moving Top-K Spatial Keyword Query

We propose an influential set based moving k keyword query processing model, which avoids the shortcoming of safe region-based approaches that the update cost and update frequency cannot be optimized simultaneously. Based on the model, we design a parallel query processing method and a parallel validation method for multicore processing platforms. The time complexity of the algorithms is O((log|D|+p.k)/p.k) and O(log p.k), respectively, which are all O(1/k) times the time complexity of the state-of-the-art method. The experiment result confirms the superiority of our algorithms over the state-of-the-art method.

Efficient Processing of Moving Top-k Spatial Keyword Queries in Directed and Dynamic Road Networks

A top-k spatial keyword (TkSk) query ranks objects based on the distance to the query location and textual relevance to the query keywords. Several solutions have been proposed for top-k spatial keyword queries. However, most of the studies focus on Euclidean space or only investigate the snapshot queries where both the query and data object are static. A few algorithms study TkSk queries in undirected road networks where each edge is undirected and the distance between two points is the length of the shortest path connecting them. However, TkSk queries have not been thoroughly investigated in directed and dynamic spatial networks where each edge has a particular orientation and its weight changes according to the traffic conditions. Therefore, in this study, we address this problem by presenting a new method, called COSK, for processing continuous top-k spatial keyword queries for moving queries in directed and dynamic road networks. We first propose an efficient framework to process snapshot TkSK queries. Furthermore, we propose a safe-exit-based approach to monitor the validity of the results for moving TkSK queries. Our experimental results demonstrate that COSK significantly outperforms existing techniques in terms of query processing time and communication cost.

A single quadtree-based algorithm for top-[formula omitted] spatial keyword query

Recent years have witnessed the generation of a massive amount of spatial–textual data. In view of this, a new type of query coined spatial keyword query has been proposed to deal with the location-based services with additional keyword constraint. This paper studies one of the most popular spatial keyword queries called Top-k Spatial Keyword Query(TkSKQ). Explicitly speaking, given a set of objects, a TkSKQ finds the k objects that are closest to the querier with each of these k objects satisfying all the keywords specified by the query. This kind of query is of paramount importance in a variety of application domains such as location-based recommendation and advertisement.The state-of-art algorithm for processing a TkSKQ is highly sensitive to the number of query keywords specified in the query such that its performance degrades significantly with an increase in the number of keywords. To remedy this drawback, this paper proposes a novel mechanism that utilizes an additional keyword list to enhance the efficiency of the existing solution. Based on this indexing technique, our algorithm needs only traverse a single quadtree when processing a TkSKQ. Moreover, we study how to prioritize the keywords in the vocabulary so as to optimize the performance of our technique. Furthermore, we deal with a generalized version of the TkSKQ problem, called HkSKQ. A similar technique can also be useful for solving HkSKQ. Experimental results on both synthetic and real data reveal the superiority of our proposed scheme.

Popularity-aware spatial keyword search on activity trajectories

The proliferation of GPS-enabled smart mobile devices enables us to collect a large-scale trajectories of moving objects with GPS tags. While the raw trajectories that only consists of positional information have been studied extensively, many recent works have been focusing on enriching the raw trajectories with semantic knowledge. The resulting data, called activity trajectories, embed the information about behaviors of the moving objects and support a variety of applications for better quality of services. In this paper, we propose a Top-k Spatial Keyword (TkSK) query for activity trajectories, with the objective to find a set of trajectories that are not only close geographically but also meet the requirements of the query semantically. Such kind of query can deliver more informative results than existing spatial keyword queries for static objects, since activity trajectories are able to reflect the popularity of user activities and reveal preferable combinations of facilities. However, it is a challenging task to answer this query efficiently due to the inherent difficulties in indexing trajectories as well as the new complexity introduced by the textual dimension. In this work, we provide a comprehensive solution, including the novel similarity function, hybrid indexing structure, efficient search algorithm and further optimizations. Extensive empirical studies on real trajectory set have demonstrated the scalability of our proposed solution.

Authentication of Moving Top-k Spatial Keyword Queries

A moving top- <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> spatial keyword (M <inline-formula> <tex-math notation="LaTeX">$k$</tex-math> </inline-formula> SK) query, which takes into account a continuously moving query location, enables a mobile client to be continuously aware of the top- <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> spatial web objects that best match a query with respect to location and text relevance. The increasing mobile use of the web and the proliferation of geo-positioning render it of interest to consider a scenario where spatial keyword search is outsourced to a separate service provider capable at handling the voluminous spatial web objects available from various sources. A key challenge is that the service provider may return inaccurate or incorrect query results (intentionally or not), e.g., due to cost considerations or invasion of hackers. Therefore, it is attractive to be able to authenticate the query results at the client side. Existing authentication techniques are either inefficient or inapplicable for the kind of query we consider. We propose new authentication data structures, the MIR-tree and MIR <inline-formula> <tex-math notation="LaTeX">$^*$</tex-math></inline-formula> -tree, that enable the authentication of MkSK queries at low computation and communication costs. We design a verification object for authenticating MkSK queries, and we provide algorithms for constructing verification objects and using these for verifying query results. A thorough experimental study on real data shows that the proposed techniques are capable of outperforming two baseline algorithms by orders of magnitude.

Privacy-Preserving Top-k Spatial Keyword Queries in Untrusted Cloud Environments

With the rapid development of location-based services in mobile Internet, spatial keyword queries have been widely employed in various real-life applications in recent years. To realize the great flexibility and cost savings, more and more data owners are motivated to outsource their spatio-textual data services to the cloud. However, directly outsourcing such services to the untrusted cloud may arise serious privacy concerns. In this paper, we study the privacy-preserving top- $k$ spatial keyword query problem in untrusted cloud environments. Existing studies primarily focus on the design of privacy-preserving schemes for either spatial or keyword queries, and they cannot be applied to solve the privacy-preserving spatial keyword query problem. To address this problem, we present a novel privacy-preserving top- $k$ spatial keyword query scheme. In particular, we build an encrypted tree index to facilitate privacy-preserving top- $k$ spatial keyword queries, where spatial and textual data are encrypted in a unified way. To search with the encrypted tree index, we propose two effective techniques for the similarity computations between queries and tree nodes under encryption. To improve query performance on large-scale spatio-textual data, we further propose a keyword-based secure pruning method. Thorough analysis shows the validity and security of our scheme. Extensive experimental results on real datasets demonstrate our scheme achieves high efficiency and good scalability.

G-Index Model: A generic model of index schemes for top-k spatial-keyword queries

A top-k spatial-keyword query returns the k best spatio-textual objects ranked based on their proximity to the query location and relevance to the query keywords. Various index schemes have been proposed for top-k spatial-keyword queries; however, a unified framework covering all these schemes has not been proposed. In this paper, we present a generic model of index schemes for top-k spatial-keyword queries, which we call G-Index Model. First, G-Index Model is a unified framework that exhaustively investigates all the possible index schemes for top-k spatial-keyword queries. For this, we conjecture that data clustering is the key element in composing various index schemes and generate index schemes as combinations of clustering. The result shows that all the existing methods map to those generated by G-Index Model. Using G-Index Model, we also discover two new methods that have not been reported before. Second, we show that G-Index Model is generic, i.e., it can generate index schemes for a class of queries integrating arbitrary multiple data types. For this, we show that G-Index Model can enumerate index schemes for two classes of queries: the spatial-keyword query (without the top-k constraint) and the top-k spatial-keyword-relational query, which adds the relational data type to the top-k spatial-keyword query. Third, we propose a cost model of the generated methods for the top-k spatial-keyword query. Consequently, the cost model allows us to do physical database design so as to find an optimal index scheme for a given usage pattern (i.e., a set of query loads and frequencies). We validate the cost model through extensive experiments.

Efficient continuous top-k spatial keyword queries on road networks

With the development of GPS-enabled mobile devices, more and more pieces of information on the web are geotagged. Spatial keyword queries, which consider both spatial locations and textual descriptions to find objects of interest, adapt well to this trend. Therefore, a considerable number of studies have focused on the interesting problem of efficiently processing spatial keyword queries. However, most of them assume Euclidean space or examine a single snapshot query only. This paper investigates a novel problem, namely, continuous top-k spatial keyword queries on road networks, for the first time. We propose two methods that can monitor such moving queries in an incremental manner and reduce repetitive traversing of network edges for better performance. Experimental evaluation using large real datasets demonstrates that the proposed methods both outperform baseline methods significantly. Discussion about the parameters affecting the efficiency of the two methods is also presented to reveal their relative advantages.

Moving spatial keyword queries

Web users and content are increasingly being geo-positioned. This development gives prominence to spatial keyword queries, which involve both the locations and textual descriptions of content. We study the efficient processing of continuously moving top-kspatial keyword (MkSK) queries over spatial text data. State-of-the-art solutions for moving queries employsafe zonesthat guarantee the validity of reported results as long as the user remains within the safe zone associated with a result. However, existing safe-zone methods focus solely on spatial locations and ignore text relevancy.We propose two algorithms for computing safe zones that guarantee correct results at any time and that aim to optimize the server-side computation as well as the communication between the server and the client. We exploit tight and conservative approximations of safe zones and aggressive computational space pruning. We present techniques that aim to compute the next safe zone efficiently, and we present two types of conservative safe zones that aim to reduce the communication cost. Empirical studies with real data suggest that the proposals are efficient.To understand the effectiveness of the proposed safe zones, we study analytically the expected area of a safe zone, which indicates on average for how long a safe zone remains valid, and we study the expected number of influence objects needed to define a safe zone, which gives an estimate of the average communication cost. The analytical modeling is validated through empirical studies.

Joint Top-K Spatial Keyword Query Processing

Web users and content are increasingly being geopositioned, and increased focus is being given to serving local content in response to web queries. This development calls for spatial keyword queries that take into account both the locations and textual descriptions of content. We study the efficient, joint processing of multiple top-k spatial keyword queries. Such joint processing is attractive during high query loads and also occurs when multiple queries are used to obfuscate a user's true query. We propose a novel algorithm and index structure for the joint processing of top-k spatial keyword queries. Empirical studies show that the proposed solution is efficient on real data sets. We also offer analytical studies on synthetic data sets to demonstrate the efficiency of the proposed solution.

RASIM: a rank-aware separate index method for answering top-k spatial keyword queries

A top-k spatial keyword query returns k objects having the highest (or lowest) scores with regard to spatial proximity as well as text relevancy. Approaches for answering top-k spatial keyword queries can be classified into two categories: the separate index approach and the hybrid index approach. The separate index approach maintains the spatial index and the text index independently and can accommodate new data types. However, it is difficult to support top-k pruning and merging efficiently at the same time since it requires two different orders for clustering the objects: the first based on scores for top-k pruning and the second based on object IDs for efficient merging. In this paper, we propose a new separate index method called Rank-Aware Separate Index Method (RASIM) for top-k spatial keyword queries. RASIM supports both top-k pruning and efficient merging at the same time by clustering each separate index in two different orders through the partitioning technique. Specifically, RASIM partitions the set of objects in each index into rank-aware (RA) groups that contain the objects with similar scores and applies the first order to these groups according to their scores and the second order to the objects within each group according to their object IDs. Based on the RA groups, we propose two query processing algorithms: (i) External Threshold Algorithm (External TA) that supports top-k pruning in the unit of RA groups and (ii) Generalized External TA that enhances the performance of External TA by exploiting special properties of the RA groups. RASIM is the first research work that supports top-k pruning based on the separate index approach. Naturally, it keeps the advantages of the separate index approach. In addition, in terms of storage and query processing time, RASIM is more efficient than the IR-tree method, which is the prevailing method to support top-k pruning to date and is based on the hybrid index approach. Experimental results show that, compared with the IR-tree method, the index size of RASIM is reduced by up to 1.85 times, and the query performance is improved by up to 3.22 times.