A method for improving graph queries processing using positional inverted index (P.I.I) idea in search engines and parallelization techniques

The idea of positional inverted index is exploited for indexing of graph database. The main idea is the use of hashing tables in order to prune a considerable portion of graph database that cannot contain the answer set. These tables are implemented using column-based techniques and are used to store graphs of database, frequent sub-graphs and the neighborhood of nodes. In order to exact checking of remaining graphs, the vertex invariant is used for isomorphism test which can be parallel implemented. The results of evaluation indicate that proposed method outperforms existing methods.

Approximate aggregate nearest neighbor search on moving objects trajectories

Aggregate nearest neighbor(ANN) search retrieves for two spatial datasets T and Q, segment(s) of one or more trajectories from the set T having minimum aggregate distance to points in Q. When interacting with large amounts of trajectories, this process would be very time-consuming due to consecutive page loads. An approximate method for finding segments with minimum aggregate distance is proposed which can improve the response time. In order to index large volumes of trajectories, scalable and efficient trajectory index(SETI) structure is used. But some refinements are provided to temporal index of SETI to improve the performance of proposed method. The experiments were performed with different number of query points and percentages of dataset. It is shown that proposed method besides having an acceptable precision, can reduce the computation time significantly. It is also shown that the main fraction of search time among load time, ANN and computing convex and centroid, is related to ANN.

Efficient keyword search over graph-structured data based on minimal covered r-cliques

Keyword search is an alternative for structured languages in querying graph-structured data.A result to a keyword query is a connected structure covering all or part of the queried keywords.The textual coverage and structural compactness have been known as the two main properties of a relevant result to a keyword query.Many previous works examined these properties after retrieving all of the candidate results using a ranking function in a comparative manner.However,this needs a time-consuming search process,which is not appropriate for an interactive system in which the user expects results in the least possible time.This problem has been addressed in recent works by confining the shape of results to examine their coverage and compactness during the search.However,these methods still suffer from the existence of redundant nodes in the retrieved results.In this paper,we introduce the semantic of minimal covered r-clique(MCCr)for the results of a keyword query as an extended model of existing definitions.We propose some efficient algorithms to detect the MCCrs of a given query.These algorithms can retrieve a comprehensive set of non-duplicate MCCrs in response to a keyword query.In addition,these algorithms can be executed in a distributive manner,which makes them outstanding in the field of keyword search.We also propose the approximate versions of these algorithms to retrieve the top-k approximate MCCrs in a polynomial delay.It is proved that the approximate algorithms can retrieve results in two-approximation.Extensive experiments on two real-world datasets confirm the efficiency and effectiveness of the proposed algorithms.

Personalized microblog recommendations based on trustpropagation and implicit microblog similarity

1 Introduction and main contributions Everyone knows about the effectiveness of microblog social recommender systems,such as Twitter,in sharing information[1–3]One of the challenges posed by social recommender systems is the offering of relevant recommendations to the target user in spite of the heterogeneity of the links in the social network.Therefore,the mere consideration of links cannot offer proper recommendations to the target user.