22,808 research outputs found
Diversified spatial keyword search on road networks
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
TopCom: Index for Shortest Distance Query in Directed Graph
Finding shortest distance between two vertices in a graph is an important
problem due to its numerous applications in diverse domains, including
geo-spatial databases, social network analysis, and information retrieval.
Classical algorithms (such as, Dijkstra) solve this problem in polynomial time,
but these algorithms cannot provide real-time response for a large number of
bursty queries on a large graph. So, indexing based solutions that pre-process
the graph for efficiently answering (exactly or approximately) a large number
of distance queries in real-time is becoming increasingly popular. Existing
solutions have varying performance in terms of index size, index building time,
query time, and accuracy. In this work, we propose T OP C OM , a novel
indexing-based solution for exactly answering distance queries. Our experiments
with two of the existing state-of-the-art methods (IS-Label and TreeMap) show
the superiority of T OP C OM over these two methods considering scalability and
query time. Besides, indexing of T OP C OM exploits the DAG (directed acyclic
graph) structure in the graph, which makes it significantly faster than the
existing methods if the SCCs (strongly connected component) of the input graph
are relatively small
Shortest Path and Distance Queries on Road Networks: An Experimental Evaluation
Computing the shortest path between two given locations in a road network is
an important problem that finds applications in various map services and
commercial navigation products. The state-of-the-art solutions for the problem
can be divided into two categories: spatial-coherence-based methods and
vertex-importance-based approaches. The two categories of techniques, however,
have not been compared systematically under the same experimental framework, as
they were developed from two independent lines of research that do not refer to
each other. This renders it difficult for a practitioner to decide which
technique should be adopted for a specific application. Furthermore, the
experimental evaluation of the existing techniques, as presented in previous
work, falls short in several aspects. Some methods were tested only on small
road networks with up to one hundred thousand vertices; some approaches were
evaluated using distance queries (instead of shortest path queries), namely,
queries that ask only for the length of the shortest path; a state-of-the-art
technique was examined based on a faulty implementation that led to incorrect
query results. To address the above issues, this paper presents a comprehensive
comparison of the most advanced spatial-coherence-based and
vertex-importance-based approaches. Using a variety of real road networks with
up to twenty million vertices, we evaluated each technique in terms of its
preprocessing time, space consumption, and query efficiency (for both shortest
path and distance queries). Our experimental results reveal the characteristics
of different techniques, based on which we provide guidelines on selecting
appropriate methods for various scenarios.Comment: VLDB201
An empirical study of inter-concept similarities in multimedia ontologies
Generic concept detection has been a widely studied topic in recent research on multimedia analysis and retrieval, but the issue of how to exploit the structure of a multimedia ontology as well as different inter-concept relations, has not received similar attention. In this paper, we present results from our empirical analysis of different types of similarity among semantic concepts in two multimedia ontologies, LSCOM-Lite and CDVP-206. The results show promise that the proposed methods may be helpful in providing insight into the existing inter-concept relations within an ontology and selecting the most facilitating set of concepts and hierarchical relations. Such an analysis as this can be utilized in various tasks such as building more reliable concept detectors and designing large-scale ontologies
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