ORCID Profile
0000-0002-3519-7013
Current Organisation
University of New South Wales
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Publisher: ACM
Date: 03-11-2019
Publisher: Elsevier BV
Date: 02-2017
Publisher: ACM
Date: 27-05-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2022
Publisher: Elsevier BV
Date: 2020
Publisher: Springer International Publishing
Date: 2019
Publisher: IEEE
Date: 04-2012
DOI: 10.1109/ICDE.2012.44
Publisher: Association for Computing Machinery (ACM)
Date: 08-2012
Abstract: Spatial web objects that possess both a geographical location and a textual description are gaining in prevalence. This gives prominence to spatial keyword queries that exploit both location and textual arguments. Such queries are used in many web services such as yellow pages and maps services. We present SWORS, the Spatial Web Object Retrieval System, that is capable of efficiently retrieving spatial web objects that satisfy spatial keyword queries. Specifically, SWORS supports two types of queries: a) the location-aware top- k text retrieval (L k T) query that retrieves k in idual spatial web objects taking into account query location proximity and text relevancy b) the spatial keyword group (SKG) query that retrieves a group of objects that cover the query keywords and are nearest to the query location and have the shortest inter-object distances. SWORS provides browser-based interfaces for desktop and laptop computers and provides a client application for mobile devices. The interfaces and the client enable users to formulate queries and view the query results on a map. The server side stores the data and processes the queries. We use three real-life data sets to demonstrate the functionality and performance of SWORS.
Publisher: Association for Computing Machinery (ACM)
Date: 30-06-2015
DOI: 10.1145/2772600
Abstract: With the proliferation of geo-positioning and geo-tagging techniques, spatio-textual objects that possess both a geographical location and a textual description are gaining in prevalence, and spatial keyword queries that exploit both location and textual description are gaining in prominence. However, the queries studied so far generally focus on finding in idual objects that each satisfy a query rather than finding groups of objects where the objects in a group together satisfy a query. We define the problem of retrieving a group of spatio-textual objects such that the group's keywords cover the query's keywords and such that the objects are nearest to the query location and have the smallest inter-object distances. Specifically, we study three instantiations of this problem, all of which are NP-hard. We devise exact solutions as well as approximate solutions with provable approximation bounds to the problems. In addition, we solve the problems of retrieving top- k groups of three instantiations, and study a weighted version of the problem that incorporates object weights. We present empirical studies that offer insight into the efficiency of the solutions, as well as the accuracy of the approximate solutions.
Publisher: ACM
Date: 26-04-2010
Publisher: ACM
Date: 02-11-2009
Publisher: No publisher found
Date: 2018
Publisher: No publisher found
Date: 2012
Publisher: IEEE
Date: 05-2016
Publisher: Association for Computing Machinery (ACM)
Date: 09-2010
Abstract: The location-aware keyword query returns ranked objects that are near a query location and that have textual descriptions that match query keywords. This query occurs inherently in many types of mobile and traditional web services and applications, e.g., Yellow Pages and Maps services. Previous work considers the potential results of such a query as being independent when ranking them. However, a relevant result object with nearby objects that are also relevant to the query is likely to be preferable over a relevant object without relevant nearby objects. The paper proposes the concept of prestige-based relevance to capture both the textual relevance of an object to a query and the effects of nearby objects. Based on this, a new type of query, the Location-aware top- k Prestige-based Text retrieval (L k PT) query, is proposed that retrieves the top- k spatial web objects ranked according to both prestige-based relevance and location proximity. We propose two algorithms that compute L k PT queries. Empirical studies with real-world spatial data demonstrate that L k PT queries are more effective in retrieving web objects than a previous approach that does not consider the effects of nearby objects and they show that the proposed algorithms are scalable and outperform a baseline approach significantly.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: IEEE
Date: 04-2018
Publisher: ACM
Date: 06-07-2022
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: IEEE
Date: 05-2022
Publisher: Springer Science and Business Media LLC
Date: 27-04-2019
Publisher: Association for Computing Machinery (ACM)
Date: 05-2014
Abstract: We consider an application scenario where points of interest (PoIs) each have a web presence and where a web user wants to identify a region that contains relevant PoIs that are relevant to a set of keywords, e.g., in preparation for deciding where to go to conveniently explore the PoIs. Motivated by this, we propose the length-constrained maximum-sum region (LCMSR) query that returns a spatial-network region that is located within a general region of interest, that does not exceed a given size constraint, and that best matches query keywords. Such a query maximizes the total weight of the PoIs in it w.r.t. the query keywords. We show that it is NP-hard to answer this query. We develop an approximation algorithm with a (5 + ε) approximation ratio utilizing a technique that scales node weights into integers. We also propose a more efficient heuristic algorithm and a greedy algorithm. Empirical studies on real data offer detailed insight into the accuracy of the proposed algorithms and show that the proposed algorithms are capable of computing results efficiently and effectively.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: IEEE
Date: 05-2022
Publisher: ACM
Date: 04-08-2023
Publisher: IEEE
Date: 04-2021
Publisher: Elsevier BV
Date: 11-2021
Publisher: No publisher found
Date: 2019
Publisher: ACM
Date: 12-06-2011
Publisher: Springer International Publishing
Date: 2018
Publisher: ACM
Date: 22-06-2013
Publisher: Association for Computing Machinery (ACM)
Date: 13-06-2023
DOI: 10.1145/3589332
Abstract: Spatial objects often come with textual information, such as Points of Interest (POIs) with their descriptions, which are referred to as geo-textual data. To retrieve such data, spatial keyword queries that take into account both spatial proximity and textual relevance have been extensively studied. Existing indexes designed for spatial keyword queries are mostly built based on the geo-textual data without considering the distribution of queries already received. However, previous studies have shown that utilizing the known query distribution can improve the index structure for future query processing. In this paper, we propose WISK, a learned index for spatial keyword queries, which self-adapts for optimizing querying costs given a query workload. One key challenge is how to utilize both structured spatial attributes and unstructured textual information during learning the index. We first ide the data objects into partitions, aiming to minimize the processing costs of the given query workload. We prove the NP-hardness of the partitioning problem and propose a machine learning model to find the optimal partitions. Then, to achieve more pruning power, we build a hierarchical structure based on the generated partitions in a bottom-up manner with a reinforcement learning-based approach. We conduct extensive experiments on real-world datasets and query workloads with various distributions, and the results show that WISK outperforms all competitors, achieving up to 8× speedup in querying time with comparable storage overhead.
Publisher: International Joint Conferences on Artificial Intelligence Organization
Date: 07-2020
Abstract: Predicting the link between two nodes is a fundamental problem for graph data analytics. In attributed graphs, both the structure and attribute information can be utilized for link prediction. Most existing studies focus on transductive link prediction where both nodes are already in the graph. However, many real-world applications require inductive prediction for new nodes having only attribute information. It is more challenging since the new nodes do not have structure information and cannot be seen during the model training. To solve this problem, we propose a model called DEAL, which consists of three components: two node embedding encoders and one alignment mechanism. The two encoders aim to output the attribute-oriented node embedding and the structure-oriented node embedding, and the alignment mechanism aligns the two types of embeddings to build the connections between the attributes and links. Our model DEAL is versatile in the sense that it works for both inductive and transductive link prediction. Extensive experiments on several benchmark datasets show that our proposed model significantly outperforms existing inductive link prediction methods, and also outperforms the state-of-the-art methods on transductive link prediction.
Publisher: Association for Computing Machinery (ACM)
Date: 07-2012
Abstract: Identifying a preferable route is an important problem that finds applications in map services. When a user plans a trip within a city, the user may want to find "a most popular route such that it passes by shopping mall, restaurant , and pub , and the travel time to and from his hotel is within 4 hours." However, none of the algorithms in the existing work on route planning can be used to answer such queries. Motivated by this, we define the problem of keyword-aware optimal route query, denoted by KOR, which is to find an optimal route such that it covers a set of user-specified keywords, a specified budget constraint is satisfied, and an objective score of the route is optimal. The problem of answering KOR queries is NP-hard. We devise an approximation algorithm OSScaling with provable approximation bounds. Based on this algorithm, another more efficient approximation algorithm BucketBound is proposed. We also design a greedy approximation algorithm. Results of empirical studies show that all the proposed algorithms are capable of answering KOR queries efficiently, while the BucketBound and Greedy algorithms run faster. The empirical studies also offer insight into the accuracy of the proposed algorithms.
Publisher: International Joint Conferences on Artificial Intelligence Organization
Date: 07-2020
Abstract: Region search is an important problem in location-based services due to its wide applications. In this paper, we study the problem of optimal region search with submodular maximization (ORS-SM). This problem considers a region as a connected subgraph. We compute an objective value over the locations in the region using a submodular function and a budget value by summing up the costs of edges in the region, and aim to search the region with the largest objective score under a budget value constraint. ORS-SM supports many applications such as the most ersified region search. We prove that the problem is NP-hard and develop two approximation algorithms with guaranteed error bounds. We conduct experiments on two applications using three real-world datasets. The results demonstrate that our algorithms can achieve high-quality solutions and are faster than a state-of-the-art method by orders of magnitude.
Publisher: ACM
Date: 06-11-2017
Publisher: ACM
Date: 26-10-2021
Publisher: ACM
Date: 09-05-2017
Publisher: Springer Science and Business Media LLC
Date: 12-10-2020
Publisher: IEEE
Date: 04-2018
Publisher: IEEE
Date: 04-2015
Publisher: IEEE
Date: 04-2020
Publisher: IEEE
Date: 04-2019
Publisher: IEEE
Date: 04-2013
Publisher: ACM
Date: 26-10-2021
Publisher: Association for Computing Machinery (ACM)
Date: 2016
Abstract: The problem of maximizing bichromatic reverse k nearest neighbor queries (BR k NN) has been extensively studied in spatial databases. In this work, we present a related query for spatial-textual databases that finds an optimal location, and a set of keywords that maximizes the size of bichromatic reverse spatial textual k nearest neighbors (MaxBRST k NN). Such a query has many practical applications including social media advertisements where a limited number of relevant advertisements are displayed to each user. The problem is to find the location and the text contents to include in an advertisement so that it will be displayed to the maximum number of users. The increasing availability of spatial-textual collections allows us to answer these queries for both spatial proximity and textual similarity. This paper is the first to consider the MaxBRST k NN query. We show that the problem is NP-hard and present both approximate and exact solutions.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Association for Computing Machinery (ACM)
Date: 05-2012
Abstract: Community Question Answering (CQA) is a popular type of service where users ask questions and where answers are obtained from other users or from historical question-answer pairs. CQA archives contain large volumes of questions organized into a hierarchy of categories. As an essential function of CQA services, question retrieval in a CQA archive aims to retrieve historical question-answer pairs that are relevant to a query question. This article presents several new approaches to exploiting the category information of questions for improving the performance of question retrieval, and it applies these approaches to existing question retrieval models, including a state-of-the-art question retrieval model. Experiments conducted on real CQA data demonstrate that the proposed techniques are effective and efficient and are capable of outperforming a variety of baseline methods significantly.
Publisher: International Joint Conferences on Artificial Intelligence Organization
Date: 07-2020
Abstract: Given a graph G and a query vertex q, the topic of community search (CS), aiming to retrieve a dense subgraph of G containing q, has gained much attention. Most existing works focus on undirected graphs which overlooks the rich information carried by the edge directions. Recently, the problem of community search over directed graphs (or CSD problem) has been studied [Fang et al., 2019b] it finds a connected subgraph containing q, where the in-degree and out-degree of each vertex within the subgraph are at least k and l, respectively. However, existing solutions are inefficient, especially on large graphs. To tackle this issue, in this paper we propose a novel index called D-Forest, which allows a CSD query to be completed within the optimal time cost. We further propose efficient index construction methods. Extensive experiments on six real large graphs show that our index-based query algorithm is up to two orders of magnitude faster than existing solutions.
Publisher: Association for Computing Machinery (ACM)
Date: 09-2010
Abstract: With the increasing deployment and use of GPS-enabled devices, massive amounts of GPS data are becoming available. We propose a general framework for the mining of semantically meaningful, significant locations, e.g., shopping malls and restaurants, from such data. We present techniques capable of extracting semantic locations from GPS data. We capture the relationships between locations and between locations and users with a graph. Significance is then assigned to locations using random walks over the graph that propagates significance among the locations. In doing so, mutual reinforcement between location significance and user authority is exploited for determining significance, as are aspects such as the number of visits to a location, the durations of the visits, and the distances users travel to reach locations. Studies using up to 100 million GPS records from a confined spatio-temporal region demonstrate that the proposal is effective and is capable of outperforming baseline methods and an extension of an existing proposal.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Springer Science and Business Media LLC
Date: 21-02-2018
Publisher: Association for Computing Machinery (ACM)
Date: 08-01-2022
DOI: 10.1145/3494558
Abstract: Given a heterogeneous information network (HIN) H, a head node h , a meta-path P, and a tail node t , the meta-path prediction aims at predicting whether h can be linked to t by an instance of P. Most existing solutions either require predefined meta-paths, which limits their scalability to schema-rich HINs and long meta-paths, or do not aim at predicting the existence of an instance of P. To address these issues, in this article, we propose a novel prediction model, called ABLE, by exploiting the A ttention mechanism and B i L STM for E mbedding. Particularly, we present a concatenation node embedding method by considering the node types and a dynamic meta-path embedding method that carefully considers the importance and positions of edge types in the meta-paths by the Attention mechanism and BiLSTM model, respectively. A triplet embedding is then derived to complete the prediction. We conduct extensive experiments on four real datasets. The empirical results show that ABLE outperforms the state-of-the-art methods by up to 20% and 22% of improvement of AUC and AP scores, respectively.
Publisher: International Joint Conferences on Artificial Intelligence Organization
Date: 07-2018
Abstract: We present a novel, simple and systematic convergence analysis of gradient descent for eigenvector computation. As a popular, practical, and provable approach to numerous machine learning problems, gradient descent has found successful applications to eigenvector computation as well. However, surprisingly, it lacks a thorough theoretical analysis for the underlying geodesically non-convex problem. In this work, the convergence of the gradient descent solver for the leading eigenvector computation is shown to be at a global rate O(min{ (lambda_1/Delta_p)^2 log(1/epsilon), 1/epsilon }), where Delta_p=lambda_p-lambda_p+1 represents the generalized positive eigengap and always exists without loss of generality with lambda_i being the i-th largest eigenvalue of the given real symmetric matrix and p being the multiplicity of lambda_1. The rate is linear at (lambda_1/Delta_p)^2 log(1/epsilon) if (lambda_1/Delta_p)^2=O(1), otherwise sub-linear at O(1/epsilon). We also show that the convergence only logarithmically instead of quadratically depends on the initial iterate. Particularly, this is the first time the linear convergence for the case that the conventionally considered eigengap Delta_1= lambda_1 - lambda_2=0 but the generalized eigengap Delta_p satisfies (lambda_1/Delta_p)^2=O(1), as well as the logarithmic dependence on the initial iterate are established for the gradient descent solver. We are also the first to leverage for analysis the log principal angle between the iterate and the space of globally optimal solutions. Theoretical properties are verified in experiments.
Publisher: Association for Computing Machinery (ACM)
Date: 02-2020
Abstract: Recently, the topic of community search (CS) has gained plenty of attention. Given a query vertex, CS looks for a dense subgraph that contains it. Existing studies mainly focus on homogeneous graphs in which vertices are of the same type, and cannot be directly applied to heterogeneous information networks (HINs) that consist of multi-typed, interconnected objects, such as the bibliographic networks and knowledge graphs. In this paper, we study the problem of community search over large HINs that is, given a query vertex q , find a community from an HIN containing q , in which all the vertices are with the same type of q and have close relationships. To model the relationship between two vertices of the same type, we adopt the well-known concept of meta-path , which is a sequence of relations defined between different types of vertices. We then measure the cohesiveness of the community by extending the classic minimum degree metric with a meta-path. We further propose efficient query algorithms for finding communities using these cohesiveness metrics. We have performed extensive experiments on five real large HINs, and the results show that the proposed solutions are effective for searching communities. Moreover, they are much faster than the baseline solutions.
Publisher: ACM
Date: 31-05-2020
Publisher: Association for Computing Machinery (ACM)
Date: 08-2014
Abstract: Massive amount of data that are geo-tagged and associated with text information are being generated at an unprecedented scale. These geo-textual data cover a wide range of topics. Users are interested in receiving up-to-date geo-textual objects (e.g., geo-tagged Tweets) such that their locations meet users' need and their texts are interesting to users. For ex le, a user may want to be updated with tweets near her home on the topic "dengue fever headache". AB@In this demonstration, we present SOPS, the u S /u atial-Keyw u o /u rd u P /u ublish/ u S /u ubscribe System, that is capable of efficiently processing spatial keyword continuous queries. SOPS supports two types of queries: (1) u B /u oolean u R /u ange u C /u ontinuous (BRC) query that can be used to subscribe the geo-textual objects satisfying a boolean keyword expression and falling in a specified spatial region (2) u T /u empor u a /u l u S /u atial- u K /u eyword Top- k Continuous (TaSK) query that continuously maintains up-to-date top- k most relevant results over a stream of geo-textual objects. SOPS enables users to formulate their queries and view the real-time results over a stream of geo-textual objects by browser-based user interfaces. On the server side, we propose solutions to efficiently processing a large number of BRC queries (tens of millions) and TaSK queries over a stream of geo-textual objects.
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Xin Cao.