Set Of Query Keywords Research Articles

Keyword-Covered Group Enlargement Community Search

The goal of community search across attributed graphs is to locate the community that takes both attribute cohensiveness and constrained structure into account. The keyword closeness of subgraph is usually measured by similarity distance. However, existing works focus on how to find a community that has most relevant to the keywords of the query vertex through the similarity score, whereas we pay more attention to a community that can jointly cover keywords and find subgraph with the maximum core. To address this problem, we propose a novel query keyword-covered group enlargement community search [Formula: see text]. Given an initial subgraph and a set of query keywords, the [Formula: see text] search aims to find the community which satisfies the following conditions: (1) it jointly covers all query keywords; (2) it is a subgraph with the maximum core; (3) it is added the minimum vertex set that meets the conditions (1) and (2). We design a baseline enumerateKGEC algorithm ([Formula: see text]), which enumerates all the vertex combinations that cover the remaining query keywords. To further accelerate the search speed, we propose two heuristic algorithms candidate set-based algorithm [Formula: see text] and candidate set and keyword combination algorithm [Formula: see text], which can effectively speed up the search and find a feasible solution. Finally, we evaluate the performance of our algorithms on two real datasets and show effectiveness and efficiency of our algorithms for [Formula: see text] problem.

Graph threshold algorithm

Recently more and more information sources are connected together and become a sort of complex graphs that can be exploited not only as a structured and semi-structured data such as rdb or xml, RDF or NoSQL, but also as many kinds of unstructured data such as web, bioinformatics, genometrics, patents, social media, knowlege graphs, IoT, hidden graph from deep learning results. State of the art studies have suggested methods of presenting data as hyper-graphs in search queries and finding search results in subgraphs or graph embeddings rather than a list of individual node results. We study the problem of retrieving top-k graph results with the query relevances; that is, given a set of query keywords Q on a graph G, we aim to find a subgraph g of G such that g is highly related to Q and closely linked under g. In order to consider the relevant graph results and the connectivity simultaneously, we present an effective algorithm graph threshold algorithm (GTA) based on a threshold algorithm (TA) which works efficiently in non-graph structure. We show that GTA does not need unnecessary searches under given objective functions, and prove the existence of an upper bound of the size of subgraph for top-k results, called $${\textit{hop}}_{{\textit{max}}}$$ , which makes it efficient to find the combined results. Finally, we conduct the performance studies on real and synthetic graphs, which demonstrate that our algorithm significantly outperforms conventional approaches with respect to time complexity and cost consumption.

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 $$m\hbox {CK}$$.

Compact group discovery in attributed graphs and social networks

Social networks and many other graphs are attributed, meaning that their nodes are labelled with textual information such as personal data, expertise or interests. In attributed graphs, a common data analysis task is to find subgraphs whose nodes contain a given set of keywords. In many applications, the size of the subgraph should be limited (i.e., a subgraph with thousands of nodes is not desired). In this work, we introduce the problem of compact attributed group (AG) discovery. Given a set of query keywords and a desired solution size, the task is to find subgraphs with the desired number of nodes, such that the nodes are closely connected and each node contains as many query keywords as possible. We prove that finding an optimal solution is NP-hard and we propose approximation algorithms with a guaranteed ratio of two. Since the number of qualifying AGs may be large, we also show how to find approximate top-k AGs with polynomial delay. Finally, we experimentally verify the effectiveness and efficiency of our techniques on real-world graphs.

Searching activity trajectory with keywords

Driven by the advances in location positioning techniques and the popularity of location sharing services, semantic enriched trajectory data has become unprecedentedly available. While finding relevant Point-of-Interests (PoIs) based on users’ locations and query keywords has been extensively studied in the past years, it is, however, largely untouched to explore the keyword queries in the context of activity trajectory database. In this paper, we study the problem of searching activity trajectories by keywords. Given a set of query keywords, a keyword-oriented query for activity trajectory (KOAT) returns k trajectories that contain the most relevant keywords to the query and yield the least travel effort in the meantime. The main difference between KOAT and conventional spatial keyword queries is that there is no query location in KOAT, which means the search area cannot be localized. To capture the travel effort in the context of query keywords, a novel score function, called spatio-textual ranking function, is first defined. Then we develop a hybrid index structure called GiKi to organize the trajectories hierarchically, which enables pruning the search space by spatial and textual similarity simultaneously. Finally an efficient search algorithm and fast evaluation of the value of spatio-textual ranking function are proposed. In addition, we extend the proposed techniques of KOAT to support range-based query and order sensitive query, which can be applied for more practical applications. The results of our empirical studies based on real check-in datasets demonstrate that our proposed index and algorithms can achieve good scalability.

ENCONTRANDO OS LOCAIS DE INTERESSE COM MAIOR POPULARIDADE A PARTIR DO CRITÉRIO ESPACIAL E TEXTUAL

Spatial data is increasingly present in our daily lives. We use various applications that use this data, such as Google Maps and Uber. There are a huge number of interesting questions that can be performed on these data, for example, a user is interested in staying at the hotel that has the largest number of restaurants in their neighborhood, in this query their current location (latitude and longitude), radius (e.g. 100 m) and the keyword “restaurant”. This project proposes a new query type that can select the best spatial objects taking into account the number of relevant spatio-textual objects, for a given set of query keywords, in its neighborhood. We present algorithms to process this query efficiently and evaluate the algorithms proposed in real datasets.

Towards Efficient Framework for Time-Aware Spatial Keyword Queries on Road Networks

The spatial keyword query takes as inputs a query location and a set of query keywords and returns the answer objects by considering both their spatial distances to the query location and textual similarity with the query keywords. However, temporal information plays an important role in the spatial keyword query (where there is, to our knowledge, no prior work considering temporal information of the objects), since objects are not always valid. For instance, visitors may plan their trips according to the opening hours of attractions. Moreover, in real-life applications, objects are located on a predefined road network, and the spatial proximity of two objects is measured by the shortest path distance or travelling time between them. In this article, we study the problem of time-aware spatial keyword (TSK) query , which assumes that objects are located on the road network, and finds the k objects satisfying users’ spatio-temporal description and textual constraint. We first present the pruning strategy and algorithm based on an existing index. Then, we design an efficient index structure called TG index and propose several algorithms using the TG index that can prune the search space with both spatio-temporal and textual information simultaneously. Further, we show that the TG index technique can also be applied to improve the performance of time-travel text search and spatial keyword query. Extensive experiments using both real and synthetic datasets demonstrate the effectiveness and efficiency of the presented index and algorithms.

Inverted Linear Quadtree: Efficient Top K Spatial Keyword Search

With advances in geo-positioning technologies and geo-location services, there are a rapidly growing amount of spatio-textual objects collected in many applications such as location based services and social networks, in which an object is described by its spatial location and a set of keywords (terms). Consequently, the study of spatial keyword search which explores both location and textual description of the objects has attracted great attention from the commercial organizations and research communities. In the paper, we study two fundamental problems in the spatial keyword queries: top $k$ spatial keyword search (TOPK-SK), and batch top $k$ spatial keyword search (BTOPK-SK). Given a set of spatio-textual objects, a query location and a set of query keywords, the TOPK-SK retrieves the closest $k$ objects each of which contains all keywords in the query. BTOPK-SK is the batch processing of sets of TOPK-SK queries. Based on the inverted index and the linear quadtree, we propose a novel index structure, called inverted linear quadtree (IL-Quadtree), which is carefully designed to exploit both spatial and keyword based pruning techniques to effectively reduce the search space. An efficient algorithm is then developed to tackle top $k$ spatial keyword search. To further enhance the filtering capability of the signature of linear quadtree, we propose a partition based method. In addition, to deal with BTOPK-SK, we design a new computing paradigm which partition the queries into groups based on both spatial proximity and the textual relevance between queries. We show that the IL-Quadtree technique can also efficiently support BTOPK-SK. Comprehensive experiments on real and synthetic data clearly demonstrate the efficiency of our methods.

Selectivity estimation on streaming spatio-textual data using local correlations

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.

Efficient Duplication Free and Minimal Keyword Search in Graphs

Keyword search over a graph searches for a subgraph that contains a set of query keywords. A problem with most existing keyword search methods is that they may produce duplicate answers that contain the same set of content nodes (i.e., nodes containing a query keyword) although these nodes may be connected differently in different answers. Thus, users may be presented with many similar answers with trivial differences. In addition, some of the nodes in an answer may contain query keywords that are all covered by other nodes in the answer. Removing these nodes does not change the coverage of the answer but can make the answer more compact. The answers in which each content node contains at least one unique query keyword are called minimal answers in this paper. We define the problem of finding duplication-free and minimal answers, and propose algorithms for finding such answers efficiently. Extensive performance studies using two large real data sets confirm the efficiency and effectiveness of the proposed methods.

Playing Nice

Given a set of query locations and a set of query keywords, a Trajectory Cover (CT) query over a repository of mobile trajectories returns a minimal set of trajectories that maximally covers the query keywords and are also spatially close to the query locations. Processing CT queries over mobile trajectories requires substantially different algorithms than those for location range queries. The main contributions of this work are three-fold. First, we introduce a notion of Trajectory Cover that enables mobile users to get most relevant trajectories of their interest to plan their route of travel. Second, we show that CT search is an NP-hard problem and we present a greedy algorithm that combines spatial proximity and keyword proximity with an efficient filter. We use the promising value filter to select better candidate trajectories for inclusion in the CT and prune out non-promising trajectories in the Greedy search process. We also develop a lower bound and upper bound mechanism to efficiently compute the promising value of each trajectory, allowing our promising value based greedy algorithm to scale to large trajectory databases. Finally, we conduct a performance study to demonstrate that our greedy algorithm is efficient in evaluating trajectory cover queries.