Collective Spatial Keyword Query Research Articles

Time-aware approximate collective keyword search in traffic networks

The collective spatial keyword query (CoSKQ), an important variant of spatial keyword query, aims to find a set of objects collectively covering the user’s query keywords, that are close to the query location and are close to each other. However, existing works only focus on the CoSKQ problem of exact keyword matching and cannot handle spelling errors and conventional spelling differences (for example, colour vs. colour), that are common in real applications. Moreover, query time information is not considered. To this end, this paper takes the lead in studying the problem of Time-aware Approximate Collective spatial Keyword query processing in traffic networks (TACoSKQ), where the objects are located on a predefined traffic network. We first prove that the TACoSKQ problem is NP-complete, and design a hybrid index called TDAG-tree to support query-object distance pruning, inter-object distance pruning, approximate keyword pruning, and temporal pruning simultaneously. Then, we present two approximate algorithms with provable approximation bounds to efficiently support TACoSKQ query processing on traffic networks. Finally, extensive experiments using three real datasets demonstrate the efficiency and accuracy of our proposed algorithms.

Efficient index-independent approaches for the collective spatial keyword queries

In abundant location-based service applications, it is necessary to process continuous spatial keyword queries over geo-textual data streaming. As an important spatial keyword query, the collective spatial keyword (CSK) query aims to find a set of objects such that it covers all the given keywords collectively, the objects within the set are nearest to the query point, and it has the minimum distance between different objects. The existing approaches for the CSK query are mostly index-based algorithms. Although these approaches gain superior performance, their applicability is significantly limited by the necessity to create an index to organize the dataset.Therefore, these index-based approaches cannot be utilized to process data streaming that prevalently exists in most location-based service applications. In addition, the existing algorithms have much room for improvement as the distances between different objects are overlooked when generating feasible candidate sets. Moreover, the results returned by the proposed algorithms could be further refined to offer better decision support for users. In this paper, a greedy algorithm and an approximate algorithm with a provable approximate bound are proposed for the CSK query. Our approaches are appropriate to the CSK queries where the datasets are not suitable to be organized by indexes and can get better query results with less objects and smaller function scores. To boost the query performance, new pruning strategies and heuristic rules are developed. The experimental results demonstrate scalability, efficiency, and effectiveness of the proposed algorithms.

Time-aware collective spatial keyword query

The collective spatial keyword query is a hot research topic in the database community in recent years, which considers both the positional relevance to the query location and textual relevance to the query keywords. However, in real life, the temporal information of object is not always valid. Based on this, we define a new query, namely time-aware collective spatial keyword query (TCoSKQ), which considers the positional relevance, textual relevance, and temporal relevance between objects and query at the same time. Two evaluation functions are defined to meet different needs of users, for each of which we propose an algorithm. Effective pruning strategies are proposed to improve query efficiency based on the two algorithms. Finally, the experimental results show that the proposed algorithms are efficient and scalable.

Cost-Aware and Distance-Constrained Collective Spatial Keyword Query

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.

On spatial keyword covering

This article introduces and solves a spatial keyword cover problem (SK-Cover for short), which aims to identify the group of spatio-textual objects covering all the keywords in a query and minimizing a distance cost function that leads to fewer objects in the answer set. In a broad sense, SK-Cover has been actively studied in the literature of spatial keyword search, such as the m-closest keywords query and the collective spatial keyword query. However, these existing works focus on minimizing only the largest pairwise distance even though the actual spatial cost is highly influenced by the number of objects in the answer group. Motivated by this, the present article further generalizes the problem definition in such a way that the total cost takes the cardinality of the group as well as the spatial distance. We prove that SK-Cover is not only NP-hard but also does not allow an approximation better than $$O(\log {|T|})$$ in polynomial time, where T is the set of query keywords. We first establish an $$O(\log {|T|})$$-approximation algorithm, which is asymptotically optimal in terms of the approximability of SK-Cover, together with effective accessing strategies and pruning rules to improve the overall efficiency and scalability. Despite the NP-hardness of SK-Cover, this article also develops exact solutions that find the optimal group of objects in a reasonably fast manner in practice, especially when it is required to cover a relatively small number of query keywords. In addition to our algorithmic results, we empirically show that our approximation algorithm always achieves the best accuracy and the efficiency comparable to that of a state-of-the-art algorithm intended for $$m\hbox {CK}$$, a problem similar to yet theoretically easier than SK-Cover, and also demonstrate that our exact algorithm using the proposed approximation scheme runs much faster than the baseline algorithm adapted from the existing solution for $$m\hbox {CK}$$.

Collective Keyword Query on a Spatial Knowledge Base

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.

Efficient processing of moving collective spatial keyword queries

As a major type of continuous spatial queries, the moving spatial keyword queries have been studied extensively. Most existing studies focus on retrieving single objects, each of which is close to the query object and relevant to the query keywords. Nevertheless, a single object may not satisfy all the needs of a user, e.g., a user who is driving may want to withdraw money, wash her car, and buy some medicine, which could only be satisfied by multiple objects. We thereby formulate a new type of queries named the moving collective spatial keyword query (MCSKQ). This type of queries continuously reports a set of objects that collectively cover the query keywords as the query moves. Meanwhile, the returned objects must also be close to the query object and close to each other. Computing the exact result set is an NP-hard problem. To reduce the query processing costs, we propose algorithms, based on safe region techniques, to maintain the exact result set while the query object is moving. We further propose two approximate algorithms to obtain even higher query efficiency with precision bounds. All the proposed algorithms are also applicable to MCSKQ with weighted objects and MCSKQ in the domain of road networks. We verify the effectiveness and efficiency of the proposed algorithms both theoretically and empirically, and the results confirm the superiority of the proposed algorithms over the baseline algorithms.

도로망 환경에서 G-트리 구조를 이용한 리버스 콜렉티브 공간 키워드 질의 처리

도로망 환경에서 G-트리 구조를 이용한 리버스 콜렉티브 공간 키워드 질의 처리

Collective spatial keyword search on activity trajectories

Collective spatial keyword query (CSKQ) is one of the most useful spatial queries in location-based service systems. Although the availability of large-scale activity trajectories has given us useful knowledge of users’ behavior, existing activity trajectory search methods are unable to support CSKQ queries reasonably. This paper studies effective and efficient CSKQ processing on activity trajectories to cover the gap. Specifically, we first formalize the problem by a trajectory based model that considers the spatial, activity and popularity issues, enabling more rational CSKQ results to be returned. To avoid high I/O cost, a novel hybrid index structure is further proposed to seamlessly integrate multi-domain information, so that inferior trajectories can be pruned during query processing. A novel candidate sub-trajectory search algorithm is also presented to reduce computation overhead by a linear scan on the trajectory. The experimental results on real check-in datasets demonstrate the efficiency and scalability of our proposed solution.

Reverse collective spatial keyword query processing on road networks with G-tree index structure

Reverse collective spatial keyword query processing on road networks with G-tree index structure

Top-K Collective Spatial Keyword Queries

With the advent of a large number of spatial-textual data, collective spatial keyword queries have been widely studied in recent years. However, the collective spatial keyword query studied so far usually looks for only a set of objects. In addition, the existing collective spatial keyword query algorithms are all based on index structure, which requires excessive additional memory overhead. In this paper, we study the Top- $k$ collective spatial keyword queries( $\text{T}k$ CoSKQ), which aims at retrieving a set $G$ including $k$ sets of objects. Each group of object set can cover all the query keywords, and the objects in the set are close to the query position and have the minimum inter-object distance. We prove that the $\text{T}k$ CoSKQ problem is NP-hard, and then propose two index-independent algorithms based on the spatial-textual similarity constraint, containing an exact algorithm and a heuristic algorithm. In addition, a variety of effective pruning strategies are presented to minimize the search scope. A large number of experiments on real datasets demonstrate the effectiveness and scalability of the proposed algorithms.

On Generalizing Collective Spatial Keyword Queries

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. 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.

Level-aware collective spatial keyword queries

The collective spatial keyword query (CoSKQ), which takes a location and a set of keywords as arguments, finds a group of objects that collectively satisfy the query and achieve the smallest cost. However, few studies concern the keyword level (e.g., the level of hotels), which is of critical importance for decision support. Motivated by this, we study a novel query paradigm, namely Level-aware Collective Spatial Keyword (LCSK) query. The LCSK query asks for a group of objects that cover the query keywords collectively with a threshold constraint and minimize the cost function, which takes into account both the cost of objects and the spatial distance. In our settings, each keyword that appears in the textual description of objects is associated with a level for capturing the feature of keyword.We prove the LCSK query is NP-hard, and devise exact algorithm as well as approximate algorithm with provable approximation bound to this problem. The proposed exact algorithm, namely MergeList, explores the candidate space progressively with several pruning strategies, which is based on the keyword hash table index structure. Unfortunately, this approach is not scalable to large datasets. We thus develop an approximate algorithm called MaxMargin. It finds the answer by traversing the proposed LIR-tree in the best-first fashion. Moreover, two optimizing strategies are used to improve the query performance. The experiments on real and synthetic datasets verify that the proposed approximate algorithm runs much faster than the competitor with desired accuracy.

Efficient Collective Spatial Keyword Query Processing on Road Networks

The collective spatial keyword query (CSKQ), an important variant of spatial keyword queries, aims to find a set of the objects that collectively cover users' queried keywords, and those objects are close to the query location and have small inter-object distances. Existing works only focus on the CSKQ problem in the Euclidean space, although we observe that, in many real-life applications, the closeness of two spatial objects is measured by their road network distance. Thus, existing methods cannot solve the problem of network-based CSKQ efficiently. In this paper, we study the problem of collective spatial keyword query processing on road networks , where the objects are located on a predefined road network. We first prove that this problem is NP-complete , and then we propose two approximate algorithms with provable approximation bounds and one exact algorithm, for supporting CSKQ on road networks efficiently. Extensive experiments using real datasets demonstrate the efficiency and accuracy of our presented algorithms.