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Articles 2131 - 2160 of 3560

Full-Text Articles in Databases and Information Systems

A Hassle-Free Unsupervised Domain Adaptation Method Using Instance Similarity Features, Jianfei Yu, Jing Jiang Jul 2015

A Hassle-Free Unsupervised Domain Adaptation Method Using Instance Similarity Features, Jianfei Yu, Jing Jiang

Research Collection School Of Computing and Information Systems

We present a simple yet effective unsupervised domain adaptation method that can be generally applied for different NLP tasks. Our method uses unlabeled target domain instances to induce a set of instance similarity features. These features are then combined with the original features to represent labeled source domain instances. Using three NLP tasks, we show that our method consistently out-performs a few baselines, including SCL, an existing general unsupervised domain adaptation method widely used in NLP. More importantly, our method is very easy to implement and incurs much less computational cost than SCL.

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Landmark Classification With Hierarchical Multi-Modal Exemplar Feature, Lei Zhu, Jialie Shen, Hai Jin, Liang Xie, Ran Zheng Jul 2015

Landmark Classification With Hierarchical Multi-Modal Exemplar Feature, Lei Zhu, Jialie Shen, Hai Jin, Liang Xie, Ran Zheng

Research Collection School Of Computing and Information Systems

Landmark image classification attracts increasing research attention due to its great importance in real applications, ranging from travel guide recommendation to 3-D modelling and visualization of geolocation. While large amount of efforts have been invested, it still remains unsolved by academia and industry. One of the key reasons is the large intra-class variance rooted from the diverse visual appearance of landmark images. Distinguished from most existing methods based on scalable image search, we approach the problem from a new perspective and model landmark classification as multi-modal categorization, which enjoys advantages of low storage overhead and high classification efficiency. Toward this …

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Qcri: Answer Selection For Community Question Answering - Experiment For Arabic And English, Massimo Nicosia, Simone Filice, Alberto Barron-Cedeno, Iman Saleh, Hamdy Mubarak, Wei Gao, Preslav Nakov, Giovanni Da San Martino, Alessandro Moschitti, Kareem Darwish, Lluis Marquz Marquz, Shafiq Joty, Walid Magdy Magdy Jun 2015

Qcri: Answer Selection For Community Question Answering - Experiment For Arabic And English, Massimo Nicosia, Simone Filice, Alberto Barron-Cedeno, Iman Saleh, Hamdy Mubarak, Wei Gao, Preslav Nakov, Giovanni Da San Martino, Alessandro Moschitti, Kareem Darwish, Lluis Marquz Marquz, Shafiq Joty, Walid Magdy Magdy

Research Collection School Of Computing and Information Systems

This paper describes QCRI’s participation in SemEval-2015 Task 3 “Answer Selection in Community Question Answering”, which targeted real-life Web forums, and was offered in both Arabic and English. We apply a supervised machine learning approach considering a manifold of features including among others word n-grams, text similarity, sentiment analysis, the presence of specific words, and the context of a comment. Our approach was the best performing one in the Arabic subtask and the third best in the two English subtasks

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Semi-Supervised Domain Adaptation With Subspace Learning For Visual Recognition, Ting Yao, Yingwei Pan, Chong-Wah Ngo, Houqiang Li, Tao Mei Jun 2015

Semi-Supervised Domain Adaptation With Subspace Learning For Visual Recognition, Ting Yao, Yingwei Pan, Chong-Wah Ngo, Houqiang Li, Tao Mei

Research Collection School Of Computing and Information Systems

In many real-world applications, we are often facing the problem of cross domain learning, i.e., to borrow the labeled data or transfer the already learnt knowledge from a source domain to a target domain. However, simply applying existing source data or knowledge may even hurt the performance, especially when the data distribution in the source and target domain is quite different, or there are very few labeled data available in the target domain. This paper proposes a novel domain adaptation framework, named Semi-supervised Domain Adaptation with Subspace Learning (SDASL), which jointly explores invariant lowdimensional structures across domains to correct data …

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Multimodal Learning With Deep Boltzmann Machine For Emotion Prediction In User Generated Videos, Lei Pang, Chong-Wah Ngo Jun 2015

Multimodal Learning With Deep Boltzmann Machine For Emotion Prediction In User Generated Videos, Lei Pang, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

Detecting emotions from user-generated videos, such as“anger” and “sadness”, has attracted widespread interest recently. The problem is challenging as effectively representing video data with multi-view information (e.g., audio, video or text) is not trivial. In contrast to the existing works that extract features from each modality (view) separately followed by early or late fusion, we propose to learn a joint density model over the space of multi-modal inputs (including visual, auditory and textual modalities) with Deep Boltzmann Machine (DBM). The model is trained directly on the user-generated Web videos without any labeling effort. More importantly, the deep architecture enlightens the …

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Face Video Retrieval With Image Query Via Hashing Across Euclidean Space And Riemannian Manifold, Y. Li, R. Wang, Zhiwu Huang, S. Shan, X. Chen Jun 2015

Face Video Retrieval With Image Query Via Hashing Across Euclidean Space And Riemannian Manifold, Y. Li, R. Wang, Zhiwu Huang, S. Shan, X. Chen

Research Collection School Of Computing and Information Systems

Retrieving videos of a specific person given his/her face image as query becomes more and more appealing for applications like smart movie fast-forwards and suspect searching. It also forms an interesting but challenging computer vision task, as the visual data to match, i.e., still image and video clip are usually represented quite differently. Typically, face image is represented as point (i.e., vector) in Euclidean space, while video clip is seemingly modeled as a point (e.g., covariance matrix) on some particular Riemannian manifold in the light of its recent promising success. It thus incurs a new hashing-based retrieval problem of matching …

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Projection Metric Learning On Grassmann Manifold With Application To Video Based Face Recognition, Zhiwu Huang, R. Wang, S. Shan, X. Chen Jun 2015

Projection Metric Learning On Grassmann Manifold With Application To Video Based Face Recognition, Zhiwu Huang, R. Wang, S. Shan, X. Chen

Research Collection School Of Computing and Information Systems

In video based face recognition, great success has been made by representing videos as linear subspaces, which typically lie in a special type of non-Euclidean space known as Grassmann manifold. To leverage the kernel-based methods developed for Euclidean space, several recent methods have been proposed to embed the Grassmann manifold into a high dimensional Hilbert space by exploiting the well established Project Metric, which can approximate the Riemannian geometry of Grassmann manifold. Nevertheless, they inevitably introduce the drawbacks from traditional kernel-based methods such as implicit map and high computational cost to the Grassmann manifold. To overcome such limitations, we propose …

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Online Multimodal Co-Indexing And Retrieval Of Weakly Labeled Web Image Collections, Lei Meng, Ah-Hwee Tan, Cyril Leung, Liqiang Nie, Tan-Seng Chua, Chunyan Miao Jun 2015

Online Multimodal Co-Indexing And Retrieval Of Weakly Labeled Web Image Collections, Lei Meng, Ah-Hwee Tan, Cyril Leung, Liqiang Nie, Tan-Seng Chua, Chunyan Miao

Research Collection School Of Computing and Information Systems

Weak supervisory information of web images, such as captions, tags, and descriptions, make it possible to better understand images at the semantic level. In this paper, we propose a novel online multimodal co-indexing algorithm based on Adaptive Resonance Theory, named OMC-ART, for the automatic co-indexing and retrieval of images using their multimodal information. Compared with existing studies, OMC-ART has several distinct characteristics. First, OMCART is able to perform online learning of sequential data. Second, OMC-ART builds a two-layer indexing structure, in which the first layer co-indexes the images by the key visual and textual features based on the generalized distributions …

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Emif: Towards A Scalable And Effective Indexing Framework For Large Scale Music Retrieval, Jialie Shen, Tao Mei, Dacheng Tao, Xuelong Li, Yong Rui Jun 2015

Emif: Towards A Scalable And Effective Indexing Framework For Large Scale Music Retrieval, Jialie Shen, Tao Mei, Dacheng Tao, Xuelong Li, Yong Rui

Research Collection School Of Computing and Information Systems

This article presents a novel indexing framework called EMIF (Effective Music Indexing Framework) to facilitate scalable and accurate content based music retrieval. EMIF system architecture is designed based on a "classification-and-indexing" principle and consists of two main functionality layers: 1) a novel semantic-sensitive classification to identify input music's category and 2) multiple indexing structures - one local indexing structure corresponds to one semantic category. EMIF's layered architecture not only enables superior search accuracy but also reduces query response time significantly. To evaluate the system, a set of comprehensive experimental studies have been carried out using large test collection and EMIF …

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Method For Matching Probabilistic Encrypted Data, Hwee Hwa Pang, Xuhua Ding Jun 2015

Method For Matching Probabilistic Encrypted Data, Hwee Hwa Pang, Xuhua Ding

Research Collection School Of Computing and Information Systems

Determining if a first encrypted data of a first data value is equal to a second encrypted data of a second data value. Comprising: a first cyclic group; a second cyclic group including a first element. Applying an operation to the first cyclic group to map its elements to an element in the second cyclic group. Randomly selecting a second element from the first cyclic group; producing the first encrypted data by mapping the second element and the first data value into one or more elements of the first cyclic group. Randomly selecting a third element from the first cyclic …

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Should We Use The Sample? Analyzing Datasets Sampled From Twitter's Stream Api, Yazhe Wang, Jamie Callan, Baihua Zheng Jun 2015

Should We Use The Sample? Analyzing Datasets Sampled From Twitter's Stream Api, Yazhe Wang, Jamie Callan, Baihua Zheng

Research Collection School Of Computing and Information Systems

Researchers have begun studying content obtained from microblogging services such as Twitter to address a variety of technological, social, and commercial research questions. The large number of Twitter users and even larger volume of tweets often make it impractical to collect and maintain a complete record of activity; therefore, most research and some commercial software applications rely on samples, often relatively small samples, of Twitter data. For the most part, sample sizes have been based on availability and practical considerations. Relatively little attention has been paid to how well these samples represent the underlying stream of Twitter data. To fill …

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Reliable Patch Trackers: Robust Visual Tracking By Exploiting Reliable Patches, Yang Li, Jianke Zhu, Steven C. H. Hoi Jun 2015

Reliable Patch Trackers: Robust Visual Tracking By Exploiting Reliable Patches, Yang Li, Jianke Zhu, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

Most modern trackers typically employ a bounding box given in the first frame to track visual objects, where their tracking results are often sensitive to the initialization. In this paper, we propose a new tracking method, Reliable Patch Trackers (RPT), which attempts to identify and exploit the reliable patches that can be tracked effectively through the whole tracking process. Specifically, we present a tracking reliability metric to measure how reliably a patch can be tracked, where a probability model is proposed to estimate the distribution of reliable patches under a sequential Monte Carlo framework. As the reliable patches distributed over …

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The Role Of Intermediary In Sustainable Lending: An Economic Analysis Of Crowdfunding Platform, Ling Ge, Zhiling Guo Jun 2015

The Role Of Intermediary In Sustainable Lending: An Economic Analysis Of Crowdfunding Platform, Ling Ge, Zhiling Guo

Research Collection School Of Computing and Information Systems

Is the interest-free crowdfunding platform a promising alternative to the non-zero interest platform? This study investigates the lenders and borrowers’ incentives and choices between an indirect, non-zero interest rate platform intermediated by a field partner and a direct-lending, interest-free platform. We model the field partner as a profit maximizer that filters qualified borrowers to enable the lenders’ capital to be better utilized on the crowdfunding platform. We show that, under certain conditions, both the borrowers and lenders are better off from the existence of the field partner. The existence of field partner is necessary to effectively segment the market and …

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Assessing The Opportunities And Challenges With Big Data In The Mobile Payments Ecosystem, Jun Liu, Robert John Kauffman, Dan Ma Jun 2015

Assessing The Opportunities And Challenges With Big Data In The Mobile Payments Ecosystem, Jun Liu, Robert John Kauffman, Dan Ma

Research Collection School Of Computing and Information Systems

Information and communication technology (ICT) is an important driver of mobile payments in the financial services industry. Mobile payments (m-payments) technologies enable new channels for consumer payments for goods and services purchases, and other forms of economic exchange. The m-payments ecosystem involves multiple distinct stakeholders, and a high level of consumer data-sharing. In this paper, we will assess the current m-payments ecosystem, and discuss the challenges and opportunities with big data captured from mpayments transactions. We will also propose new directions to encourage research that will shed the light on how stakeholders can facilitate the successful adoption and realize the …

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Dynamic Redeployment To Counter Congestion Or Starvation In Vehicle Sharing Systems, Supriyo Ghosh, Pradeep Varakantham, Yossiri Adulyasak, Patrick Jaillet Jun 2015

Dynamic Redeployment To Counter Congestion Or Starvation In Vehicle Sharing Systems, Supriyo Ghosh, Pradeep Varakantham, Yossiri Adulyasak, Patrick Jaillet

Research Collection School Of Computing and Information Systems

Extensive usage of private vehicles has led to increased traffic congestion, carbon emissions, and usage of non-renewable resources. These concerns have led to the wide adoption of vehicle sharing (ex: bike sharing, car sharing) systems in many cities of the world. In vehicle-sharing systems, base stations (ex: docking stations for bikes) are strategically placed throughout a city and each of the base stations contain a pre-determined number of vehicles at the beginning of each day. Due to the stochastic and individualistic movement of customers,there is typically either congestion (more than required)or starvation (fewer than required) of vehicles at certain base …

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Author Topic Model-Based Collaborative Filtering For Personalized Poi Recommendations, Shuhui Jiang, Xueming Qian, Jialie Shen, Yun Fu, Tao Mei Jun 2015

Author Topic Model-Based Collaborative Filtering For Personalized Poi Recommendations, Shuhui Jiang, Xueming Qian, Jialie Shen, Yun Fu, Tao Mei

Research Collection School Of Computing and Information Systems

From social media has emerged continuous needs for automatic travel recommendations. Collaborative filtering (CF) is the most well-known approach. However, existing approaches generally suffer from various weaknesses. For example, sparsity can significantly degrade the performance of traditional CF. If a user only visits very few locations, accurate similar user identification becomes very challenging due to lack of sufficient information for effective inference. Moreover, existing recommendation approaches often ignore rich user information like textual descriptions of photos which can reflect users' travel preferences. The topic model (TM) method is an effective way to solve the "sparsity problem," but is still far …

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Keeping Pace With Criminals: Designing Patrol Allocation Against Adaptive Opportunistic Criminals, Chao Zhang, Arunesh Sinha, Milind Tambe May 2015

Keeping Pace With Criminals: Designing Patrol Allocation Against Adaptive Opportunistic Criminals, Chao Zhang, Arunesh Sinha, Milind Tambe

Research Collection School Of Computing and Information Systems

Police patrols are used ubiquitously to deter crimes in urban areas. A distinctive feature of urban crimes is that criminals react opportunistically to patrol officers' assignments. Compared to strategic attackers (such as terrorists) with a well-laid out plan, opportunistic criminals are less strategic in planning attacks and more flexible in executing them. In this paper, our goal is to recommend optimal police patrolling strategy against such opportunistic criminals. We first build a game-theoretic model that captures the interaction between officers and opportunistic criminals. However, while different models of adversary behavior have been proposed, their exact form remains uncertain. Rather than …

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Report On The Fg 2015 Video Person Recognition Evaluation, J.R. Beveridge, H. Zhang, B.A. Draper, P.J. Flynn, Z. Feng, P. Huber, J. Kittler, Zhiwu Huang, Li S., Li Y., M. Kan, R. Wang, S. Shan, X. Chen, Li H., G. Hua, V. Struc, J. Krizaj, C. Ding, D. Tao May 2015

Report On The Fg 2015 Video Person Recognition Evaluation, J.R. Beveridge, H. Zhang, B.A. Draper, P.J. Flynn, Z. Feng, P. Huber, J. Kittler, Zhiwu Huang, Li S., Li Y., M. Kan, R. Wang, S. Shan, X. Chen, Li H., G. Hua, V. Struc, J. Krizaj, C. Ding, D. Tao

Research Collection School Of Computing and Information Systems

This report presents results from the Video Person Recognition Evaluation held in conjunction with the 11th IEEE International Conference on Automatic Face and Gesture Recognition. Two experiments required algorithms to recognize people in videos from the Point-and-Shoot Face Recognition Challenge Problem (PaSC). The first consisted of videos from a tripod mounted high quality video camera. The second contained videos acquired from 5 different handheld video cameras. There were 1401 videos in each experiment of 265 subjects. The subjects, the scenes, and the actions carried out by the people are the same in both experiments. Five groups from around the world …

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Beyond Traits: Social Context Based Personality Model, Jaroslaw Kochanowicz, Ah-Hwee Tan, Daniel Thalmann May 2015

Beyond Traits: Social Context Based Personality Model, Jaroslaw Kochanowicz, Ah-Hwee Tan, Daniel Thalmann

Research Collection School Of Computing and Information Systems

The relation between individual’s personality and environmental context is a key issue in psychology, recently also in character simulations. This paper contributes to both domains by proposing a socio-cognitive, contextual personality model - a new voice in a century old problem of personality, but also an approach to simulating groups of more humanlike agents. After analyzing the influence of popularity of ‘trait personality models’ on psychology and computer simulation, we propose Social Context based Personality model - a continuation and specification of the Cognitive-Affective Personality System theory. The discussion, model and implementation are provided, followed by an example application in …

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Mining Patterns Of Unsatisfiable Constraints To Detect Infeasible Paths, Sun Ding, Hee Beng Kuan Tan, Lwin Khin Shar May 2015

Mining Patterns Of Unsatisfiable Constraints To Detect Infeasible Paths, Sun Ding, Hee Beng Kuan Tan, Lwin Khin Shar

Research Collection School Of Computing and Information Systems

Detection of infeasible paths is required in many areas including test coverage analysis, test case generation, security vulnerability analysis, etc. Existing approaches typically use static analysis coupled with symbolic evaluation, heuristics, or path-pattern analysis. This paper is related to these approaches but with a different objective. It is to analyze code of real systems to build patterns of unsatisfiable constraints in infeasible paths. The resulting patterns can be used to detect infeasible paths without the use of constraint solver and evaluation of function calls involved, thus improving scalability. The patterns can be built gradually. Evaluation of the proposed approach shows …

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Self-Organizing Neural Networks Integrating Domain Knowledge And Reinforcement Learning, Teck-Hou Teng, Ah-Hwee Tan, Jacek M. Zurada May 2015

Self-Organizing Neural Networks Integrating Domain Knowledge And Reinforcement Learning, Teck-Hou Teng, Ah-Hwee Tan, Jacek M. Zurada

Research Collection School Of Computing and Information Systems

The use of domain knowledge in learning systems is expected to improve learning efficiency and reduce model complexity. However, due to the incompatibility with knowledge structure of the learning systems and real-time exploratory nature of reinforcement learning (RL), domain knowledge cannot be inserted directly. In this paper, we show how self-organizing neural networks designed for online and incremental adaptation can integrate domain knowledge and RL. Specifically, symbol-based domain knowledge is translated into numeric patterns before inserting into the self-organizing neural networks. To ensure effective use of domain knowledge, we present an analysis of how the inserted knowledge is used by …

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A Nested Recursive Logit Model For Route Choice Analysis, Tien Mai, Mogens Fosgerau, Emma Frejinger May 2015

A Nested Recursive Logit Model For Route Choice Analysis, Tien Mai, Mogens Fosgerau, Emma Frejinger

Research Collection School Of Computing and Information Systems

We propose a route choice model that relaxes the independence from irrelevant alternatives property of the logit model by allowing scale parameters to be link specific. Similar to the recursive logit (RL) model proposed by Fosgerau et al. (2013), the choice of path is modeled as a sequence of link choices and the model does not require any sampling of choice sets. Furthermore, the model can be consistently estimated and efficiently used for prediction.A key challenge lies in the computation of the value functions, i.e. the expected maximum utility from any position in the network to a destination. The value …

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Breaking The News: First Impressions Matter On Online News, Julio Reis, Fabr´Icio Benevenuto, Pedro Olmo, Raquel Prates, Haewoon Kwak, Jisun An May 2015

Breaking The News: First Impressions Matter On Online News, Julio Reis, Fabr´Icio Benevenuto, Pedro Olmo, Raquel Prates, Haewoon Kwak, Jisun An

Research Collection School Of Computing and Information Systems

A growing number of people are changing the way they consume news, replacing the traditional physical newspapers and magazines by their virtual online versions or/and weblogs. The interactivity and immediacy present in online news are changing the way news are being produced and exposed by media corporations. News websites have to create effective strategies to catch people’s attention and attract their clicks. In this paper we investigate possible strategies used by online news corporations in the design of their news headlines. We analyze the content of 69,907 headlines produced by four major global media corporations during a minimum of eight …

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Map: A Computational Model For Adaptive Persuasion, Yilin Kang, Ah-Hwee Tan May 2015

Map: A Computational Model For Adaptive Persuasion, Yilin Kang, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

While a variety of persuasion agents have been created and applied in different domains such as marketing, military training and health industry, there is a lack of a model which provides a unified framework for different persuasion strategies. Specifically, persuasion is not adaptable to the individuals’ personal states in different situations. Grounded in the Elaboration Likelihood Model (ELM), this paper presents a computational model called Model for Adaptive Persuasion (MAP) for virtual agents. MAP is a semi-connected network model which enables an agent to adapt its persuasion strategies through feedback. We have implemented and evaluated a MAP-based virtual nurse agent …

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Discovering The Rise And Fall Of Software Engineering Ideas From Scholarly Publication Data, Subhajit Datta, Santonu Sarkar, Sajeev A. S. M., Nishant Kumar May 2015

Discovering The Rise And Fall Of Software Engineering Ideas From Scholarly Publication Data, Subhajit Datta, Santonu Sarkar, Sajeev A. S. M., Nishant Kumar

Research Collection School Of Computing and Information Systems

For researchers and practitioners of a relatively young discipline like software engineering, an enduring concern is to identify the acorns that will grow into oaks -- ideas remaining most current in the long run. Additionally, it is interesting to know how the ideas have risen in importance, and fallen, perhaps to rise again. We analyzed a corpus of 19,000+ papers written by 21,000+ authors across 16 software engineering publication venues from 1975 to 2010, to empirically determine the half-life of software engineering research topics. We adapted existing measures of half-life as well as defined a specific measure based on publication …

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Moving Average Reversion Strategy For On-Line Portfolio Selection, Bin Li, Steven C. H. Hoi, Doyen Sahoo, Zhi-Yong Liu May 2015

Moving Average Reversion Strategy For On-Line Portfolio Selection, Bin Li, Steven C. H. Hoi, Doyen Sahoo, Zhi-Yong Liu

Research Collection School Of Computing and Information Systems

On-line portfolio selection, a fundamental problem in computational finance, has attracted increasing interest from artificial intelligence and machine learning communities in recent years. Empirical evidence shows that stock's high and low prices are temporary and stock prices are likely to follow the mean reversion phenomenon. While existing mean reversion strategies are shown to achieve good empirical performance on many real datasets, they often make the single-period mean reversion assumption, which is not always satisfied, leading to poor performance in certain real datasets. To overcome this limitation, this article proposes a multiple-period mean reversion, or so-called "Moving Average Reversion" (MAR), and …

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Characterizing Silent Users In Social Media Communities, Wei Gong, Ee-Peng Lim, Feida Zhu May 2015

Characterizing Silent Users In Social Media Communities, Wei Gong, Ee-Peng Lim, Feida Zhu

Research Collection School Of Computing and Information Systems

Silent users often constitute a significant proportion of an online user-generated content system. In the context of social media such as Twitter, users can opt to be silent all or most of the time. They are often called the invisible participants or lurkers. As lurkers contribute little to the online content, existing analysis often overlooks their presence and voices. However, we argue that understanding lurkers is important in many applications such as recommender systems, targeted advertising, and social sensing. This research therefore seeks to characterize lurkers in social media and propose methods to profile them. We examine 18 weeks of …

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Advances In Knowledge Discovery And Data Mining Part Ii, Tru Cao, Ee Peng Lim, Zhi-Hua Zhou, Tu-Bao Ho, David Wai-Lok Cheung, Hiroshi Motoda May 2015

Advances In Knowledge Discovery And Data Mining Part Ii, Tru Cao, Ee Peng Lim, Zhi-Hua Zhou, Tu-Bao Ho, David Wai-Lok Cheung, Hiroshi Motoda

Research Collection School Of Computing and Information Systems

No abstract provided.

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Advances In Knowledge Discovery And Data Mining: 19th Pacific-Asia Conference, Pakdd 2015, Ho Chi Minh City, Vietnam, May 19-22, 2015, Proceedings, Part I, Tru Cao, Ee-Peng Lim, Zhi-Hua Zhou, Tu-Bao Ho, David Wai-Lok Cheung, Hiroshi Motoda May 2015

Advances In Knowledge Discovery And Data Mining: 19th Pacific-Asia Conference, Pakdd 2015, Ho Chi Minh City, Vietnam, May 19-22, 2015, Proceedings, Part I, Tru Cao, Ee-Peng Lim, Zhi-Hua Zhou, Tu-Bao Ho, David Wai-Lok Cheung, Hiroshi Motoda

Research Collection School Of Computing and Information Systems

No abstract provided.

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Efficient Reverse Top-K Boolean Spatial Keyword Queries On Road Networks, Yunjun Gao, Xu Qin, Baihua Zheng, Gang Chen May 2015

Efficient Reverse Top-K Boolean Spatial Keyword Queries On Road Networks, Yunjun Gao, Xu Qin, Baihua Zheng, Gang Chen

Research Collection School Of Computing and Information Systems

Reverse k nearest neighbor (RkNN) queries have a broad application base such as decision support, profile-based marketing, and resource allocation. Previous work on RkNN search does not take textual information into consideration or limits to the Euclidean space. In the real world, however, most spatial objects are associated with textual information and lie on road networks. In this paper, we introduce a new type of queries, namely, reverse top-k Boolean spatial keyword (RkBSK) retrieval, which assumes objects are on the road network and considers both spatial and textual information. Given a data set P on a road network and a …

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