Efficient continuously moving top-k spatial keyword query processing

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- <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> 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- <inline-formula><tex-math notation="LaTeX">$k$ </tex-math></inline-formula> spatial keyword query scheme. In particular, we build an encrypted tree index to facilitate privacy-preserving top- <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> 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.

The proliferation of geo-textual data gives prominence to spatial keyword search. The basic top-k spatial keyword query, returns k geo-textual objects that rank the highest according to their textual relevance and spatial proximity to query keywords and a query location. We define, study, and provide means of computing the reverse top-k keyword-based location query. This new type of query takes a set of keywords, a query object q, and a number k as arguments, and it returns a spatial region such that any top-k spatial keyword query with the query keywords and a location in this region would contain object q in its result. This query targets applications in market analysis, geographical planning, and location optimization, and it may support applications related to safe zones and influence zones that are used widely in location-based services. We show that computing an exact query result requires evaluating and merging a set of weighted Voronoi cells, which is expensive. We therefore devise effective algorithms that approximate result regions with quality guarantees. We develop novel pruning techniques on top of an index, and we offer a series of optimization techniques that aim to further accelerate query processing. Empirical studies suggest that the proposed query processing is efficient and scalable.

The fast development of GPS equipped devices has aroused widespread use of spatial keyword querying in location based services nowadays. Existing spatial keyword indexing and querying methodologies mainly focus on the spatial and textual similarities, while leaving the semantic understanding of keywords in spatial web objects and queries to be ignored. To address this issue, this paper studies the problem of semantic based spatial keyword querying. It seeks to return the k objects most similar to the query, subject to not only their spatial and textual properties, but also the coherence of their semantic meanings. To achieve that, we propose a novel indexing structure called NIQ-tree, which integrates spatial, textual and semantic information in a hierarchical manner, so as to prune the search space effectively in query processing. Extensive experiments are carried out to evaluate and compare it with other two baseline algorithms.

Owing to the widespread use of location-Aware devices and the increased popularity of micro-blogging applications, we are witnessing a rapid proliferation of geo-Textual data. In this demonstration, we present iZone, an efficient system for determining influence zones over geo-Textual data. Specifically, iZone allows users to browse geo-Textual objects, evaluate the influence zones of specified geo-Textual objects, and obtain explanations of the evaluation results. The iZone system adopts a browser-server model. The server side integrates two types of spatial keyword search, namely top-k spatial keyword query and reverse top-k keyword-based location query, to support the functionality of the system. A variety of spatial indexes are employed to enhance the efficiency of the system. The browser side provides a map-based GUI interface, which enables convenient and user-friendly interaction with the system. Using a real hotel dataset from Hong Kong, iZone offers hands-on experience with influence zone evaluation in real-life applications.

With the proliferation of geo-textual objects on the web, extensive efforts have been devoted to improving the efficiency of top- $k$ k spatial keyword queries in different settings. However, comparatively much less work has been reported on enhancing the quality and usability of such queries. In this context, we propose means of enhancing the usability of a top- $k$ k group spatial keyword query, where a group of users aim to find $k$ k objects that contain given query keywords and are nearest to the users. Specifically, when users receive the result of such a query, they may find that one or more objects that they expect to be in the result are in fact missing, and they may wonder why. To address this situation, we develop a so-called why-not query that is able to minimally modify the original query into a query that returns the expected, but missing, objects, in addition to other objects. Specifically, we formalize the why-not query in relation to the top- $k$ k group spatial keyword query, called the W hy-not G roup S patial K eyword Query ( $\mathsf{WGSK}$ WGSK ) that is able to provide a group of users with a more satisfactory query result. We propose a three-phase framework for efficiently computing the $\mathsf{WGSK}$ WGSK . The first phase substantially reduces the search space for the subsequent phases by retrieving a set of objects that may affect the ranking of the user-expected objects. The second phase provides an incremental sampling algorithm that generates candidate weightings of more promising queries. The third phase determines the penalty of each refined query and returns the query with minimal penalty, i.e., the minimally modified query. Extensive experiments with real and synthetic data offer evidence that the proposed solution excels over baselines with respect to both effectiveness and efficiency.

The fast development of GPS equipped devices has aroused widespread use of spatial keyword querying in location based services nowadays. Existing spatial keyword query methodologies mainly focus on the spatial and textual similarities, while leaving the semantic understanding of keywords in spatial Web objects and queries to be ignored. To address this issue, this paper studies the problem of semantic based spatial keyword querying. It seeks to return the k objects most similar to the query, subject to not only their spatial and textual properties, but also the coherence of their semantic meanings. To achieve that, we propose novel indexing structures, which integrate spatial, textual and semantic information in a hierarchical manner, so as to prune the search space effectively in query processing. Extensive experiments are carried out to evaluate and compare them with other baseline algorithms.

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.

With the increasing pervasiveness of the geo-positioning technologies, there is an enormous amount of spatio-textual objects available in many applications such as location based services and social networks. Consequently, various types of spatial keyword searches which explore both locations and textual descriptions of the objects have been intensively studied by the research communities and commercial organizations. In many important applications (e.g., location based services), the closeness of two spatial objects is measured by the road network distance. Moreover, the result diversification is becoming a common practice to enhance the quality of the search results. Motived by the above facts, in this paper we study the problem of diversified spatial keyword search on road networks which considers both the relevance and the spatial diversity of the results. An efficient signature-based inverted indexing technique is proposed to facilitate the spatial keyword query processing on road networks. Then we develop an efficient diversified spatial keyword search algorithm by taking advantage of spatial keyword pruning and diversity pruning techniques. Comprehensive experiments on real and synthetic data clearly demonstrate the efficiency of our methods.

The conventional works on spatial keyword queries for a knowledge base focus on finding a subtree to cover all the query keywords. The retrieved subtree is rooted at a place vertex, spatially close to a query location and compact in terms of the query keywords. However, user requirements may not be satisfied by a single subtree in some application scenarios. A group of subtrees should be combined together to collectively cover the query keywords. In this paper, we propose and study a novel way of searching on a spatial knowledge, namely collective spatial keyword query on a knowledge base (CoSKQ-KB). We formalize the problem of CoSKQ-KB and design a baseline method for CoSKQ-KB (BCK). To further speed up the query processing, an improved scalable method for CoSKQ-KB (iSCK) is proposed based on a set of efficient pruning and early termination techniques. In addition, we conduct empirical experiments on two real-world datasets to show the efficiency and effectiveness of our proposed algorithms.

Given the prevalence and volume of local search queries, today's search engines are required to find results by both spatial proximity and textual relevance at high query throughput. Existing techniques to answer such spatial keyword queries employ a keyword aggregation strategy that suffers from certain drawbacks when applied to road networks. Instead, we propose the K-SPIN framework, which uses an alternative keyword separation strategy that is more suitable on road networks. While this strategy was previously thought to entail prohibitive pre-processing costs, we further propose novel techniques to make our framework viable and even light-weight. Thorough experimentation shows that K-SPIN outperforms the state-of-the-art by up to two orders of magnitude on a wide range of settings and real-world datasets.

In this paper, we investigate the selectivity estimation problem for streaming spatio-textual data, which arises in many social network and geo-location applications. Specifically, given a set of continuously and rapidly arriving spatio-textual objects, each of which is described by a geo-location and a short text, we aim to accurately estimate the cardinality of a spatial keyword query on objects seen so far, where a spatial keyword query consists of a search region and a set of query keywords. To the best of our knowledge, this is the first work to address this important problem. We first extend two existing techniques to solve this problem, and show their limitations. Inspired by two key observations on the "locality" of the correlations among query keywords, we propose a local correlation based method by utilizing an augmented adaptive space partition tree ( A 2 SP -tree for short) to approximately learn a local Bayesian network on-the-fly for a given query and estimate its selectivity. A novel local boosting approach is presented to further enhance the learning accuracy of local Bayesian networks. Our comprehensive experiments on real-life datasets demonstrate the superior performance of the local correlation based algorithm in terms of estimation accuracy compared to other competitors.

Level-aware collective spatial keyword queries

Conventional spatial keyword queries confront the difficulty of returning desired objects that are synonyms but morphologically different to query keywords. To overcome this flaw, this paper investigates the interactive spatial keyword querying with semantics. It aims to enhance the conventional queries by not only making sense of the query keywords, but also refining the understanding of query semantics through interactions. On top of the probabilistic topic model, a novel interactive strategy is proposed to precisely infer the latent query semantics by learning from user feedbacks. In each interaction, the returned objects are carefully selected to ensure effective inference of user intended query semantics. Query processing is carried out on a small candidate object set at each round of interaction, and the whole querying process terminates when the latent query semantics learned from user feedback becomes explicit enough. The experimental results on real check-in dataset demonstrates that the quality of results has been significantly improved through limited number of interactions.

Spatial objects often come with textual information, such as Points of Interest (POIs) with their descriptions, which are referred to as geo-textual data. To retrieve such data, spatial keyword queries that take into account both spatial proximity and textual relevance have been extensively studied. Existing indexes designed for spatial keyword queries are mostly built based on the geo-textual data without considering the distribution of queries already received. However, previous studies have shown that utilizing the known query distribution can improve the index structure for future query processing. In this paper, we propose WISK, a learned index for spatial keyword queries, which self-adapts for optimizing querying costs given a query workload. One key challenge is how to utilize both structured spatial attributes and unstructured textual information during learning the index. We first divide the data objects into partitions, aiming to minimize the processing costs of the given query workload. We prove the NP-hardness of the partitioning problem and propose a machine learning model to find the optimal partitions. Then, to achieve more pruning power, we build a hierarchical structure based on the generated partitions in a bottom-up manner with a reinforcement learning-based approach. We conduct extensive experiments on real-world datasets and query workloads with various distributions, and the results show that WISK outperforms all competitors, achieving up to 8× speedup in querying time with comparable storage overhead.

With the increasing popularity of geo-positioning technologies and mobile Internet, spatial keyword data services have attracted growing interest from both the industrial and academic communities in recent years. Meanwhile, a massive amount of data is increasingly being outsourced to cloud in the encrypted form for enjoying the advantages of cloud computing while without compromising data privacy. Most existing works primarily focus on the privacy-preserving schemes for either spatial or keyword queries, and they cannot be directly applied to solve the spatial keyword query problem over encrypted data. In this paper, we study the challenging problem of Privacy-preserving Boolean Range Query (PBRQ) over encrypted spatial databases. In particular, we propose two novel PBRQ schemes. Firstly, we present a scheme with linear search complexity based on the space-filling curve code and Symmetric-key Hidden Vector Encryption (SHVE). Then, we use tree structures to achieve faster-than-linear search complexity. Thorough security analysis shows that data security and query privacy can be guaranteed during the query process. Experimental results using real-world datasets show that the proposed schemes are efficient and feasible for practical applications, which is at least ×70 faster than existing techniques in the literature.