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.     

Efficient processing of label-constraint reachability queries in large graphs

In this paper, we study a variant of reachability queries, called label-constraint reachability (LCR) queries. Specifically, given a label set S and two vertices u₁ and u₂ in a large directed graph G, we check the existence of a directed path from u₁ to u₂, where edge labels along the path are a subset of S. We propose the path-label transitive closure method to answer LCR queries. Specifically, we t4ransform an edge-labeled directed graph into an augmented DAG by replacing the maximal strongly connected components as bipartite graphs. We also propose a Dijkstra-like algorithm to compute path-label transitive closure by re-defining the “distance” of a path. Comparing with the existing solutions, we prove that our method is optimal in terms of the search space. Furthermore, we propose a simple yet effective partition-based framework (local path-label transitive closure+online traversal) to answer LCR queries in large graphs. We prove that finding the optimal graph partition to minimize query processing cost is a NP-hard problem. Therefore, we propose a sampling-based solution to find the sub-optimal partition. Moreover, we address the index maintenance issues to answer LCR queries over the dynamic graphs. Extensive experiments confirm the superiority of our method.     

Top-<Emphasis Type="Italic">k</Emphasis> coupled keyword recommendation for relational keyword queries

Providing top-k typical relevant keyword queries would benefit the users who cannot formulate appropriate queries to express their imprecise query intentions. By extracting the semantic relationships both between keywords and keyword queries, this paper proposes a new keyword query suggestion approach which can provide typical and semantically related queries to the given query. Firstly, a keyword coupling relationship measure, which considers both intra- and inter-couplings between each pair of keywords, is proposed. Then, the semantic similarity of different keyword queries can be measured by using a semantic matrix, in which the coupling relationships between keywords in queries are reserved. Based on the query semantic similarities, we next propose an approximation algorithm to find the most typical queries from query history by using the probability density estimation method. Lastly, a threshold-based top-k query selection method is proposed to expeditiously evaluate the top-k typical relevant queries. We demonstrate that our keyword coupling relationship and query semantic similarity measures can capture the coupling relationships between keywords and semantic similarities between keyword queries accurately. The efficiency of query typicality analysis and top-k query selection algorithm is also demonstrated.     

Fast Smallest Lowest Common Ancestor Computation Based on Stable Match

In this paper, we focus on efficient processing of XML keyword queries based on smallest lowest common ancestor (SLCA) semantics. For a given query Q with m Key words we propose to use stable matches as the basis for SLCA computation, where each stable match M consists of m nodes that belong to the m distinct keyword inverted lists of Q. M satisfies that no other lowest common ancestor (LCA) node of Q can be found to be located after the first node of M and be a descendant of the LCA of M, based on which the operation of locating a stable match can skip more useless nodes. We propose two stable match based algorithms for SLCA computation, i.e., BSLCA and HSLCA. BSLCA processes two keyword inverted lists each time from the shortest to the longest, while HSLCA processes all keyword inverted lists in a holistic way to avoid the problem of redundant computation invoked by BSLCA. Our extensive experimental results verify the performance advantages of our methods according to various evaluation metrics.     

Detecting proximity events in sensor networks

Sensor networks are often used to perform monitoring tasks, such as animal and vehicle tracking, or the surveillance of enemy forces in military applications. In this paper we introduce the concept of proximity queries, which allow us to report interesting events, observed by nodes in the network that lie within a certain distance from each other. An event is triggered when a user-programmable predicate is satisfied on a sensor node. We study the problem of computing proximity queries in sensor networks and propose several alternative techniques that differ in the number of messages exchanged by the nodes and the quality of the returned answers. Our solutions utilize a distributed routing index, maintained by the nodes in the network, that is dynamically updated as new observations are obtained by the nodes. This distributed index allows us to efficiently process multiple proximity queries involving several different event types within a fraction of the cost that a straightforward evaluation requires. We present an extensive experimental study to show the benefits of our techniques under different scenarios using both synthetic and real data sets. Our results demonstrate that our algorithms scale better and require significantly fewer messages compared to a straightforward execution of the queries.     

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.     

A graph-theoretic approach to optimize keyword queries in relational databases

Keyword search can provide users an easy method to query large and complex databases without any knowledge of structured query languages or underlying database schema. Most of the existing studies have focused on generating candidate structured queries relevant to keywords. Due to the large size of generated queries, the execution costs may be prohibitive. However, existing studies lack the idea of a generalized method to optimize the plan of the large set of generated queries. In this paper, we introduce a graph-theoretic optimization approach. We propose a general graph model, Weighted Operator Graph, to address the costs of keyword query evaluation plans. The proposed model is flexible to integrate all of the cost-based plans in a uniform way. We define a Keyword Query Optimization Problem based on a theoretical cost model as a graph-theoretic problem and show it to be a NP-hard problem. We propose a greedy heuristic Maximum Propagation that reduces the size of the intermediate result as early as possible. The proposed algorithm allows us to achieve efficiency in terms of query evaluation costs. The experimental studies on both synthetic and real data set results show that our work outperforms the existing work.     

Keyword proximity search in XML trees

Recent works have shown the benefits of keyword proximity search in querying XML documents in addition to text documents. For example, given query keywords over Shakespeare's plays in XML, the user might be interested in knowing how the keywords cooccur. In this paper, we focus on XML trees and define XML keyword, proximity queries to return the (possibly heterogeneous) set of minimum connecting trees (MCTs) of the matches to the individual keywords in the query. We consider efficiently executing keyword proximity queries on labeled trees (XML) in various settings: 1) when the XML database has been preprocessed and 2) when no indices are available on the XML database. We perform a detailed experimental evaluation to study the benefits of our approach and show that our algorithms considerably outperform prior algorithms and other applicable approaches.     

No-but-semantic-match: computing semantically matched xml keyword search results

Users are rarely familiar with the content of a data source they are querying, and therefore cannot avoid using keywords that do not exist in the data source. Traditional systems may respond with an empty result, causing dissatisfaction, while the data source in effect holds semantically related content. In this paper we study this no-but-semantic-match problem on XML keyword search and propose a solution which enables us to present the top-k semantically related results to the user. Our solution involves two steps: (a) extracting semantically related candidate queries from the original query and (b) processing candidate queries and retrieving the top-k semantically related results. Candidate queries are generated by replacement of non-mapped keywords with candidate keywords obtained from an ontological knowledge base. Candidate results are scored using their cohesiveness and their similarity to the original query. Since the number of queries to process can be large, with each result having to be analyzed, we propose pruning techniques to retrieve the top-k results efficiently. We develop two query processing algorithms based on our pruning techniques. Further, we exploit a property of the candidate queries to propose a technique for processing multiple queries in batch, which improves the performance substantially. Extensive experiments on two real datasets verify the effectiveness and efficiency of the proposed approaches.