On Nearby-Fit Spatial Keyword Queries (Extended Abstract)

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 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.

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-k spatial keyword (MkSK) queries over spatial keyword data. State-of-the-art solutions for moving queries employ safe zones that guarantee the validity of reported results as long as the user remains within a zone. 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 computation on the server 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. Empirical studies with real data suggest that our proposals are efficient.

Level-aware collective spatial keyword queries

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 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.

Geo-textual data are ubiquitous these days. Recent study on spatial keyword search focused on the processing of queries which retrieve objects that match certain keywords within a spatial region. To ensure effective data retrieval, various extensions were done including the tolerance of errors in keyword matching and the search-as-you-type feature using prefix matching. We present MESA, a map application to support different variants of spatial keyword query. In this demonstration, we adopt the autocompletion paradigm that generates the initial query as a prefix matching query. If there are few matching results, other variants are performed as a form of relaxation that reuses the processing done in earlier phases. The types of relaxation allowed include spatial region expansion and exact/approximate prefix/substring matching. MESA adopts the client-server architecture. It provides fuzzy type-ahead search over geo-textual data. The core of MESA is to adopt a unifying search strategy, which incrementally applies the relaxation in an appropriate order to maximize the efficiency of query processing. In addition, MESA equips a user-friendly interface to interact with users and visualize results. MESA also provides customized search to meet the needs of different users.

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.

As Geographic Information Systems (GIS) technologies have evolved, more and more GIS applications and geospatial data are available on the web. Spatial objects in a given query range can be retrieved using spatial range query ? one of the most widely used query types in GIS and spatial databases. However, it can be challenging to retrieve these data from various web applications where access to the data is only possible through restrictive web interfaces that support certain types of queries. A typical scenario is the existence of numerous business web sites that provide their branch locations through a limited "nearest location" web interface. For example, a chain restaurant's web site such as McDonalds can be queried to find some of the closest locations of its branches to the user's home address. However, even though the site has the location data of all restaurants in, for example, the state of California, it is difficult to retrieve the entire data set efficiently due to its restrictive web interface. Considering that k-Nearest Neighbor (k-NN) search is one of the most popular web interfaces in accessing spatial data on the web, this paper investigates the problem of retrieving geospatial data from the web for a given spatial range query using only k-NN searches. Based on the classification of k-NN interfaces on the web, we propose a set of range query algorithms to completely cover the rectangular shape of the query range (completeness) while minimizing the number of k-NN searches as possible (efficiency). We evaluated the efficiency of the proposed algorithms through statistical analysis and empirical experiments using both synthetic and real data sets.

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.

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.

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.

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.

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.