Qing Li

The University of Hong Kong, Hong Kong, Hong Kong

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Publications (286)127.14 Total impact

  • [Show abstract] [Hide abstract]
    ABSTRACT: The reverse skyline query is very useful in many decision making applications. Given a multi-dimensional dataset P and a query point q, the reverse skyline query returns all the points in P whose dynamic skyline contains q. Although the reverse skyline retrieval has been well-studied in the literature, there is, to the best of our knowledge, no prior work on one of the most intuitive and practical types of reverse skyline queries, namely, group-by reverse skyline (GRS) query, which retrieves the reverse skyline for each group in a specified dataset. We formalize the GRS query including monochromatic and bichromatic versions, and identify its properties, and then propose a set of efficient algorithms for computing the group-by reverse skyline. Extensive experimental evaluation using both real and synthetic datasets demonstrates the performance of our proposed algorithms in terms of effectiveness and efficiency under a variety of experimental settings.
    World Wide Web 10/2015; DOI:10.1007/s11280-015-0372-y · 1.47 Impact Factor
  • Yi Zhuang · Nan Jiang · Qing Li · Lei Chen · Chunhua Ju ·
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    ABSTRACT: This article addresses a multi-query optimization problem for distributed medical image retrieval in mobile wireless networks by exploiting the dependencies in the derivation of a retrieval evaluation plan. To the best of our knowledge, this is the first work investigating batch medical image retrieval (BMIR) processing in a mobile wireless network environment. Four steps are incorporated in our BMIR algorithm. First, when a number of retrieval requests (i.e., m retrieval images and m radii) are simultaneously submitted by users, then a cost-based dynamic retrieval (CDRS) scheduling procedure is invoked to efficiently and effectively identify the correlation among the retrieval spheres (requests) based on a cost model. Next, an index-based image set reduction (ISR) is performed at the execution-node level in parallel. Then, a refinement processing of the candidate images is conducted to get the answers. Finally, at the transmissionnode level, the corresponding image fragment (IF) replicas are chosen based on an adaptive multi-resolution (AMR)-based IF replicas selection scheme, and transmitted to the user-node level by a priority-based IF replicas transmission (PIFT) scheme. The experimental results validate the efficiency and effectiveness of the algorithm in minimizing the response time and increasing the parallelism of I/O and CPU.
    ACM Transactions on Internet Technology 08/2015; 15(3):1-27. DOI:10.1145/2783437 · 0.77 Impact Factor
  • Yunjun Gao · Qing Liu · Lu Chen · Gang Chen · Qing Li ·
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    ABSTRACT: The skyline query is a powerful tool for multi-criteria decision making. However, it may return too many skyline objects to offer any meaningful insight. In this paper, we introduce a new operator, namely, the most desirable skyline object (MDSO) query, to identify manageable size of truly interesting skyline objects. Given a multi-dimensional object set and an integer k, a MDSO query returns the most preferable k skyline objects, based on the newly defined ranking criterion that considers, for each skyline object s, the number of the objects dominated by s and their accumulated (potential) weights. We devise the ranking criterion, formalize the MDSO query, and propose three algorithms for processing MDSO queries. In addition, we extend our methods to tackle the constrained MDSO (CMDSO) query. Extensive experimental results on both real and synthetic datasets show that our presented ranking criterion is significant, and our proposed algorithms are efficient and scalable.
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    ABSTRACT: A location-aware news feed system enables mobile users to share geo-tagged user-generated messages, e.g., a user can receive nearby messages that are the most relevant to her. In this paper, we present MobiFeed that is a framework designed for scheduling news feeds for mobile users. MobiFeed consists of three key functions, location prediction, relevance measure, and news feed scheduler. The location prediction function is designed to estimate a mobile user’s locations based on a path prediction algorithm. The relevance measure function is implemented by combining the vector space model with non-spatial and spatial factors to determine the relevance of a message to a user. The news feed scheduler works with the other two functions to generate news feeds for a mobile user at her current and predicted locations with the best overall quality. We propose a heuristic algorithm as well as an optimal algorithm for the location-aware news feed scheduler. The performance of MobiFeed is evaluated through extensive experiments using a real road map and a real social network data set. The scalability of MobiFeed is also investigated using a synthetic data set. Experimental results show that MobiFeed obtains a relevance score two times higher than the state-of-the-art approach, and it can scale up to a large number of geo-tagged messages.
    GeoInformatica 07/2015; 19(3). DOI:10.1007/s10707-014-0223-5 · 0.75 Impact Factor
  • Yunjun Gao · Qing Liu · Baihua Zheng · Li Mou · Gang Chen · Qing Li ·
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    ABSTRACT: In this paper, for the first time, we identify and solve the problem of efficient reverse k-skyband (RkSB) query processing. Given a set P of multi-dimensional points and a query point q, an RkSB query returns all the points in P whose dynamic k-skyband contains q. We formalize RkSB retrieval, and then propose five algorithms for computing the RkSB of an arbitrary query point efficiently. Our methods utilize a conventional data-partitioning index (e.g., R-tree) on the dataset, and employ pre-computation, reuse and pruning techniques to boost the query efficiency. In addition, we extend our solutions to tackle an interesting variant of reverse skyline queries, namely, ranked reverse skyline (RRS) query where, given a data set P, a parameter K, and a preference function f, the goal is to find the K reverse skyline points that have the minimal score according to the user-specified function f. Extensive experiments using both real and synthetic data sets demonstrate the effectiveness of our proposed pruning heuristics and the performance of our proposed algorithms under a variety of experimental settings.
    Information Sciences 02/2015; 293:11–34. DOI:10.1016/j.ins.2014.08.052 · 4.04 Impact Factor
  • Xiao Wei · Xiangfeng Luo · Qing Li · Jun Zhang · Zheng Xu ·
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    ABSTRACT: Online comment has become a popular and efficient way for sellers to acquire feedback from customers and improve their service quality. However, some key issues need to be solved about evaluating and improving the hotel service quality based on online comments automatically, such as how to use the less trustworthy online comments, how to discover the quality defects from online comments, and how to recommend more feasible or economical evaluation indexes to improve the service quality based on online comments. To solve the above problems, this paper first improves fuzzy comprehensive evaluation (FCE) by importing trustworthy degree to it and proposes an automatic hotel service quality assessment method using the improved FCE, which can automatically get more trustworthy evaluation from a large amount of less trustworthy online comments. Then, the causal relations among evaluation indexes are mined from online comments to build the fuzzy cognitive map for the hotel service quality, which is useful to unfold the problematic areas of hotel service quality, and recommend more economical solutions to improving the service quality. Finally, both case studies and experiments are conducted to demonstrate that the proposed methods are effective in evaluating and improving the hotel service quality using online comments.
    IEEE Transactions on Fuzzy Systems 02/2015; 23(1):72-84. DOI:10.1109/TFUZZ.2015.2390226 · 8.75 Impact Factor
  • Yi Zhuang · Nan Jiang · Qing Li · Dickson K. W. Chiu · Hua Hu ·
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    ABSTRACT: This paper presents a Personalized large Social Image Transmission method in mobile wireless network(MWN) environment, called the P sit. The whole transmission process of the P sit works as follows: first, when a social image I S is prepared to transmit from a sender node to user U R , a preprocessing step is then conducted to obtain the optimal image fragment(IF) replica based on the users’ preference model and the network bandwidth at the sender node. After that, the candidate IFs are transferred to the receiver node from the slave one according to the transmission priorities. Finally, the IFs can be recovered and displayed at the receiver node level. The proposed method includes five enabling techniques: 1) neighborhood-based tag enrichment processing, 2) user attention degree(UAD) derivation of the regions of interest(ROI), 3) an adaptive multi-resolution-based IF replica selection method, 4) a UAD-based IF replica placement method, and 5) a priority-based robust IF transmission scheme. The experimental results show that the performance of our approach is both efficient and effective, minimizing the response time by decreasing the network transmission cost while increasing the parallelism of I/O and CPU.
    Multimedia Tools and Applications 01/2015; DOI:10.1007/s11042-014-2413-4 · 1.35 Impact Factor
  • Source
    Tao Jiang · Bin Zhang · Dan Lin · Yunjun Gao · Qing Li ·
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    ABSTRACT: In this paper, we define a novel type of skyline query, namely top-k combinatorial metric skyline (kCMS) query. The kCMS query aims to find k combinations of data points according to a monotonic preference function such that each combination has the query object in its metric skyline. The kCMS query will enable a new set of location-based applications that the traditional skyline queries cannot offer. To answer the kCMS query, we propose two efficient query algorithms, which leverage a suite of techniques including the sorting and threshold mechanisms, reusing technique, and heuristics pruning to incrementally and quickly generate combinations of possible query results. We have conducted extensive experimental studies, and the results demonstrate both effectiveness and efficiency of our proposed algorithms.

  • IEEE Transactions on Computers 01/2015; DOI:10.1109/TC.2015.2485215 · 1.66 Impact Factor
  • Xiangfeng Luo · Jun Zhang · Qing Li · Xiao Wei · Lei Lu ·
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    ABSTRACT: This paper advocates for a novel approach to recommend texts at various levels of difficulties based on a proposed method, the algebraic complexity of texts (ACT). Different from traditional complexity measures that mainly focus on surface features like the numbers of syllables per word, characters per word, or words per sentence, ACT draws from the perspective of human concept learning, which can reflect the complex semantic relations inside texts. To cope with the high cost of measuring ACT, the Degree-2 Hypothesis of ACT is proposed to reduce the measurement from unrestricted dimensions to three dimensions. Based on the principle of “mental anchor,” an extension of ACT and its general edition [denoted as extension of text algebraic complexity (EACT) and general extension of text algebraic complexity (GEACT)] are developed, which take keywords’ and association rules’ complexities into account. Finally, using the scores given by humans as a benchmark, we compare our proposed methods with linguistic models. The experimental results show the order GEACT>EACT>ACT> Linguistic models, which means GEACT performs the best, while linguistic models perform the worst. Additionally, GEACT with lower convex functions has the best ability in measuring the algebraic complexities of text understanding. It may also indicate that the human complexity curve tends to be a curve like lower convex function rather than linear functions.
    IEEE Transactions on Human-Machine Systems 10/2014; 44(5):638-649. DOI:10.1109/THMS.2014.2329874 · 1.98 Impact Factor
  • Yunjun Gao · Xiaoye Miao · Huiyong Cui · Gang Chen · Qing Li ·
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    ABSTRACT: The skyline operator has been extensively explored in the literature, and most of the existing approaches assume that all dimensions are available for all data items. However, many practical applications such as sensor networks, decision making, and location-based services, may involve incomplete data items, i.e., some dimensional values are missing, due to the device failure or the privacy preservation. This paper is the first, to our knowledge, study of k-skyband (kSB) query processing on incomplete data, where multi-dimensional data items are missing some values of their dimensions. We formalize the problem, and then present two efficient algorithms for processing it. Our methods introduce some novel concepts including expired skyline, shadow skyline, and thickness warehouse, in order to boost the search performance. As a second step, we extend our techniques to tackle constrained skyline (CS) and group-by skyline (GBS) queries over incomplete data. Extensive experiments with both real and synthetic data sets demonstrate the effectiveness and efficiency of our proposed algorithms under various experimental settings.
    Expert Systems with Applications 08/2014; 41(10):4959–4974. DOI:10.1016/j.eswa.2014.02.033 · 2.24 Impact Factor
  • Hao Huang · Kevin Chiew · Yunjun Gao · Qinming He · Qing Li ·
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    ABSTRACT: Rare category discovery aims at identifying unlabeled data examples of rare categories in a given data set. The existing approaches to rare category discovery often need a certain number of labeled data examples as the training set, which are usually difficult and expensive to acquire in practice. To save the cost however, if these methods only use a small training set, their accuracy may not be satisfactory for real applications. In this paper, for the first time, we propose the concept of rare category exploration, aiming to discover all data examples of a rare category from a seed (which is a labeled data example of this rare category) instead of from a training set. To this end, we present an approach known as the FRANK algorithm which transforms rare category exploration to local community detection from a seed in a kNN (k-nearest neighbors) graph with an automatically selected k value. Extensive experimental results on real data sets verify the effectiveness and efficiency of our FRANK algorithm.
    Expert Systems with Applications 07/2014; 41(9):4197–4210. DOI:10.1016/j.eswa.2013.12.039 · 2.24 Impact Factor
  • Jun Zhang · Qing Li · Xiangfeng Luo · Xiao Wei ·
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    ABSTRACT: One of the most fundamental works for providing better Web services is the discovery of inter-word relations. However, the state of the art is either to acquire specific relations (e.g., causality) by involving much human efforts, or incapable of specifying relations in detail when no human effort is needed. In this paper, we propose a novel mechanism based on linguistics and cognitive psychology to automatically learn and specify association relations between words. The proposed mechanism, termed as ALSAR, includes two major processes: the first is to learn association relations from the perspective of verb valency grammar in linguistics, and the second is to further lable/specify the association relations with the help of related verbs. The resultant mechanism (i.e., ALSAR) is able to provide semantic descriptors which make inter-word relations more explicit without involving any human labeling. Furthermore, ALSAR incurs a very low complexity, and experimental evaluations on Chinese news articles crawled from Baidu News demonstrate good performance of ALSAR.
    Web-Age Information Management, 06/2014: pages 578-589;
  • Source
    Yi Cai · Qing Li · Haoran Xie · Huaqin Min ·
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    ABSTRACT: With the increase in resource-sharing websites such as YouTube and Flickr, many shared resources have arisen on the Web. Personalized searches have become more important and challenging since users demand higher retrieval quality. To achieve this goal, personalized searches need to take users' personalized profiles and information needs into consideration. Collaborative tagging (also known as folksonomy) systems allow users to annotate resources with their own tags, which provides a simple but powerful way for organizing, retrieving and sharing different types of social resources. In this article, we examine the limitations of previous tag-based personalized searches. To handle these limitations, we propose a new method to model user profiles and resource profiles in collaborative tagging systems. We use a normalized term frequency to indicate the preference degree of a user on a tag. A novel search method using such profiles of users and resources is proposed to facilitate the desired personalization in resource searches. In our framework, instead of the keyword matching or similarity measurement used in previous works, the relevance measurement between a resource and a user query (termed the query relevance) is treated as a fuzzy satisfaction problem of a user's query requirements. We implement a prototype system called the Folksonomy-based Multimedia Retrieval System (FMRS). Experiments using the FMRS data set and the MovieLens data set show that our proposed method outperforms baseline methods.
    Neural networks: the official journal of the International Neural Network Society 06/2014; 58. DOI:10.1016/j.neunet.2014.05.017 · 2.71 Impact Factor
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    ABSTRACT: One of the most challenging problems in aspect-based opinion mining is aspect extraction, which aims to identify expressions that describe aspects of products (called aspect expressions) and categorize domain-specific synonymous expressions. Although a number of methods of aspect extraction have been proposed before, very few of them are designed to improve the interpretability of generated aspects. Existing methods either generate multiple fine-grained aspects without proper categorization or categorize semantically unrelated product aspects (e.g., by unsupervised topic modeling). In this paper, we first examine previous studies on product aspect extraction. To overcome the limitations of existing methods, two novel semi-supervised models for product aspect extraction are then proposed. More specifically, the proposed methodology first extracts seeding aspects and related terms from detailed product descriptions readily available on E-commerce websites. Next, product reviews are regrouped according to these seeding aspects so that more effective textual contexts for topic modeling are built. Finally, two novel semi-supervised topic models are developed to extract human-comprehensible product aspects. For the first proposed topic model, the Fine-grained Labeled LDA (FL-LDA), seeding aspects are applied to guide the model to discover words that are related to these seeding aspects. For the second model, the Unified Fine-grained Labeled LDA (UFL-LDA), we incorporate unlabeled documents to extend the FL-LDA model so that words related to the seeding aspects or other high-frequency words in customer reviews are extracted. Our experimental results demonstrate that the proposed methods outperform state-of-the-art methods.
    Knowledge-Based Systems 06/2014; 71. DOI:10.1016/j.knosys.2014.05.018 · 2.95 Impact Factor
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    ABSTRACT: This paper presents an efficient and robust content-based large medical image retrieval method in mobile Cloud computing environment, called the Mirc. The whole query process of the Mirc is composed of three steps. First, when a clinical user submits a query image Iq, a parallel image set reduction process is conducted at a master node. Then the candidate images are transferred to the slave nodes for a refinement process to obtain the answer set. The answer set is finally transferred to the query node. The proposed method including an priority-based robust image block transmission scheme is specifically designed for solving the instability and the heterogeneity of the mobile cloud environment, and an index-support image set reduction algorithm is introduced for reducing the data transfer cost involved. We also propose a content-aware and bandwidth-conscious multi-resolution-based image data replica selection method and a correlated data caching algorithm to further improve the query performance. The experimental results show that the performance of our approach is both efficient and effective, minimizing the response time by decreasing the network transfer cost while increasing the parallelism of I/O and CPU.
    Information Sciences 04/2014; 263:60–86. DOI:10.1016/j.ins.2013.10.013 · 4.04 Impact Factor
  • Source
    Tao Jiang · Yunjun Gao · Bin Zhang · Dan Lin · Qing Li ·
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    ABSTRACT: In this paper, we study a new skyline operator, namely, mutual skyline query (MSQ), which retrieves all the data objects that are contained in the dynamic skyline and meanwhile the reverse skyline of a specified query object q. MSQ has many applications such as marketing analysis, task allocation, and personalized matching. Motivated by this, we first formalize MSQ in both monochromatic and bichromatic cases, and then propose several algorithms for processing MSQ. Our methods utilize a conventional data-partitioning index on the dataset, employ the advantage of reusing technique, and exploit effective pruning heuristics to improve the query processing. Extensive experiments using both real and synthetic datasets demonstrate the effectiveness and efficiency of our proposed algorithms under various experimental settings.
    Expert Systems with Applications 03/2014; 41(4):1885-1900. DOI:10.1016/j.eswa.2013.08.085 · 2.24 Impact Factor

  • Computer Science and Information Systems 01/2014; 11(1):IX-XI. · 0.48 Impact Factor

  • ICWL Workshops; 01/2014
  • Xiao Wei · Qing Li · Feiyue Ye · Jun Zhang · Rongfang Bie ·
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    ABSTRACT: In the Internet of Things, wireless sensor networks (WSN) is in charge of gathering and transferring environment data. It is an essential work to mine data semantic in WSN in the data derived from sensors to improve the WSN. This paper proposes the Data Association Network of sensors (DAN) to organize the mined association semantic relations among sensors into an effective form. Because DAN holds the rich data semantic of WSN, it can improve WSN in some aspects, such as detecting the abnormal sensors, simulating the data of faulty sensors, or optimizing the topology of WSN. Experimental results show that the proposed method can mine the associated relations among sensor nodes effectively, and the DAN is helpful in solving some problems of WSN.
    Automatika 12/2013; 54(4). DOI:10.7305/automatika.54-4.419 · 0.31 Impact Factor

Publication Stats

2k Citations
127.14 Total Impact Points


  • 2000-2015
    • The University of Hong Kong
      • • Department of Computer Science
      • • Department of Information Technology & Engineering
      Hong Kong, Hong Kong
  • 1970-2015
    • City University of Hong Kong
      • Department of Computer Science
      Chiu-lung, Kowloon City, Hong Kong
  • 2014
    • Lands Department of The Government of the Hong Kong Special Administrative Region
      Hong Kong, Hong Kong
  • 2007-2012
    • USTC-CityU Joint Advanced Research Center
      Hong Kong, Hong Kong
  • 2010
    • Shanghai University
      • School of Computer Engineering and Sciences
      Shanghai, Shanghai Shi, China
  • 2007-2009
    • Zhejiang Normal University
      Jinhua, Zhejiang Sheng, China
    • Arizona State University
      Phoenix, Arizona, United States
  • 2008
    • Southwestern University of Finance and Economics
      Hua-yang, Sichuan, China
  • 2002-2004
    • The Hong Kong Institute of Education
      Hong Kong, Hong Kong
  • 1998
    • The Hong Kong Polytechnic University
      • Department of Computing
      Hong Kong, Hong Kong
  • 1997
    • University of New South Wales
      • School of Computer Science and Engineering
      Kensington, New South Wales, Australia
  • 1994-1996
    • The Hong Kong University of Science and Technology
      • Department of Computer Science and Engineering
      Chiu-lung, Kowloon City, Hong Kong