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Research Collection School Of Computing and Information Systems

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Articles 2911 - 2940 of 3436

Full-Text Articles in Databases and Information Systems

Tube (Text-Cube) For Discovering Documentary Evidence Of Associations Among Entities, Hady Lauw, Ee Peng Lim, Hwee Hwa Pang Mar 2007

Tube (Text-Cube) For Discovering Documentary Evidence Of Associations Among Entities, Hady Lauw, Ee Peng Lim, Hwee Hwa Pang

Research Collection School Of Computing and Information Systems

User-driven discovery of associations among entities, and documents that provide evidence for these associations, is an important search task conducted by researchers and do-main information specialists. Entities here refer to real or abstract objects such as people, organizations, ideologies, etc. Associations are the inter-relationships among entities. Most current works in query-driven document retrieval and finding representative subgraphs are ill-suited for the task as they lack an awareness of entity types as well as an intuitive representation of associations. We propose the TUBE model, a text cube approach for discovering associations and documentary evidence of these associations. The model consists of …

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Mining Generalized Associations Of Semantic Relations From Textual Web Content, Tao Jiang, Ah-Hwee Tan, We Wang Feb 2007

Mining Generalized Associations Of Semantic Relations From Textual Web Content, Tao Jiang, Ah-Hwee Tan, We Wang

Research Collection School Of Computing and Information Systems

Traditional text mining techniques transform free text into flat bags of words representation, which does not preserve sufficient semantics for the purpose of knowledge discovery. In this paper, we present a two-step procedure to mine generalized associations of semantic relations conveyed by the textual content of Web documents. First, RDF (resource description framework) metadata representing semantic relations are extracted from raw text using a myriad of natural language processing techniques. The relation extraction process also creates a term taxonomy in the form of a sense hierarchy inferred from WordNet. Then, a novel generalized association pattern mining algorithm (GP-Close) is applied …

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Clustering And Combinatorial Optimization In Recursive Supervised Learning, Kiruthika Ramanathan, Sheng Uei Guan Feb 2007

Clustering And Combinatorial Optimization In Recursive Supervised Learning, Kiruthika Ramanathan, Sheng Uei Guan

Research Collection School Of Computing and Information Systems

The use of combinations of weak learners to learn a dataset has been shown to be better than the use of a single strong learner. In fact, the idea is so successful that boosting, an algorithm combining several weak learners for supervised learning, has been considered to be the best off the shelf classifier. However, some problems still exist, including determining the optimal number of weak learners and the over fitting of data. In an earlier work, we developed the RPHP algorithm which solves both these problems by using a combination of global search, weak learning and pattern distribution. In …

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Searching And Tagging: Two Sides Of The Same Coin?, Qiaozhu Mei, Jing Jiang, Hang Su, Chengxiang Zhai Jan 2007

Searching And Tagging: Two Sides Of The Same Coin?, Qiaozhu Mei, Jing Jiang, Hang Su, Chengxiang Zhai

Research Collection School Of Computing and Information Systems

This paper presents the duality hypothesis of search and tagging, two important behaviors of web users. The hypothesis states that if a user views a document D in the search results for query Q, the user would tend to assign document $D$ a tag identical to or similar to Q; similarly, if a user tags a document D with a tag T, the user would tend to view document D if it is in the search results obtained using T as a query. We formalize this hypothesis with a unified probabilistic model for search and tagging, and show that empirical …

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Anticipatory Event Detection Via Classification, He Qi, Kuiyu Chang, Ee Peng Lim Jan 2007

Anticipatory Event Detection Via Classification, He Qi, Kuiyu Chang, Ee Peng Lim

Research Collection School Of Computing and Information Systems

The idea of event detection is to identify interesting patterns from a constant stream of incoming news documents. Previous research in event detection has largely focused on identifying the first event or tracking subsequent events belonging to a set of pre-assigned topics such as earthquakes, airline disasters, etc. In this paper, we describe a new problem, called anticipatory event detection (AED), which aims to detect if a user-specified event has transpired. AED can be viewed as a personalized combination of event tracking and new event detection. We propose using sentence-level and document-level classification approaches to solve the AED problem for …

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National Culture And Its Effects On Knowledge Communication In Online Virtual Communities, Keng Siau, Fiona Fui-Hoon Nah, Min Ling Jan 2007

National Culture And Its Effects On Knowledge Communication In Online Virtual Communities, Keng Siau, Fiona Fui-Hoon Nah, Min Ling

Research Collection School Of Computing and Information Systems

Online virtual communities provide a powerful means of knowledge sharing. Despite the prevalence of online virtual communities, there is a paucity of research to investigate the effect of national culture differences on knowledge sharing in online virtual communities. Are there differences between online virtual communities from different national cultures? This research studies the differences in knowledge-sharing activities between US-based and China-based online virtual communities. Hofstede's dimensions of national culture serve as the theoretical foundation for this research.

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Mining Multiple Visual Appearances Of Semantics For Image Annotation, Hung-Khoon Tan, Chong-Wah Ngo Jan 2007

Mining Multiple Visual Appearances Of Semantics For Image Annotation, Hung-Khoon Tan, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

This paper investigates the problem of learning the visual semantics of keyword categories for automatic image annotation. Supervised learning algorithms which learn only a single concept point of a category are limited in their effectiveness for image annotation. We propose to use data mining techniques to mine multiple concepts, where each concept may consist of one or more visual parts, to capture the diverse visual appearances of a single keyword category. For training, we use the Apriori principle to efficiently mine a set of frequent blobsets to capture the semantics of a rich and diverse visual category. Each concept is …

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Direct Code Access In Self-Organizing Neural Networks For Reinforcement Learning, Ah-Hwee Tan Jan 2007

Direct Code Access In Self-Organizing Neural Networks For Reinforcement Learning, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

TD-FALCON is a self-organizing neural network that incorporates Temporal Difference (TD) methods for reinforcement learning. Despite the advantages of fast and stable learning, TD-FALCON still relies on an iterative process to evaluate each available action in a decision cycle. To remove this deficiency, this paper presents a direct code access procedure whereby TD-FALCON conducts instantaneous searches for cognitive nodes that match with the current states and at the same time provide maximal reward values. Our comparative experiments show that TD-FALCON with direct code access produces comparable performance with the original TD-FALCON while improving significantly in computation efficiency and network complexity.

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Integrating Semantic Templates With Decision Tree For Image Semantic Learning, Ying Liu, Dengsheng Zhang, Guojun Lu, Ah-Hwee Tan Jan 2007

Integrating Semantic Templates With Decision Tree For Image Semantic Learning, Ying Liu, Dengsheng Zhang, Guojun Lu, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Decision tree (DT) has great potential in image semantic learning due to its simplicity in implementation and its robustness to incomplete and noisy data. Decision tree learning naturally requires the input attributes to be nominal (discrete). However, proper discretization of continuous-valued image features is a difficult task. In this paper, we present a decision tree based image semantic learning method, which avoids the difficult image feature discretization problem by making use of semantic template (ST) defined for each concept in our database. A ST is the representative feature of a concept, generated from the low-level features of a collection of …

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On The Lower Bound Of Local Optimums In K-Means Algorithms, Zhenjie Zhang, Bing Tian Dai, Anthony K.H. Tung Dec 2006

On The Lower Bound Of Local Optimums In K-Means Algorithms, Zhenjie Zhang, Bing Tian Dai, Anthony K.H. Tung

Research Collection School Of Computing and Information Systems

No abstract provided.

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Rapid Identification Of Column Heterogeneity, Bing Tian Dai, Nick Koudas, Beng Chin Ooi, Divesh Srivastava, Suresh Venkatasubramanian Dec 2006

Rapid Identification Of Column Heterogeneity, Bing Tian Dai, Nick Koudas, Beng Chin Ooi, Divesh Srivastava, Suresh Venkatasubramanian

Research Collection School Of Computing and Information Systems

No abstract provided.

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Clique Percolation For Finding Naturally Cohesive And Overlapping Document Clusters, Wei Gao, Kam-Fai Wong, Yunqing Xia, Ruifeng Xu Dec 2006

Clique Percolation For Finding Naturally Cohesive And Overlapping Document Clusters, Wei Gao, Kam-Fai Wong, Yunqing Xia, Ruifeng Xu

Research Collection School Of Computing and Information Systems

Techniques for find document clusters mostly depend on models that impose strong explicit and/or implicit priori assumptions. As a consequence, the clustering effects tend to be unnatural and stray away from the intrinsic grouping natures of a document collection. We apply a novel graph-theoretic technique called Clique Percolation Method (CPM) for document clustering. In this method, a process of enumerating highly cohesive maximal document cliques is performed in a random graph, where those strongly adjacent cliques are mingled to form naturally overlapping clusters. Our clustering results can unveil the inherent structural connections of the underlying data. Experiments show that CPM …

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Measuring Qualities Of Articles Contributed By Online Communities, Ee Peng Lim, Ba-Quy Vuong, Hady W. Lauw, Aixin Sun Dec 2006

Measuring Qualities Of Articles Contributed By Online Communities, Ee Peng Lim, Ba-Quy Vuong, Hady W. Lauw, Aixin Sun

Research Collection School Of Computing and Information Systems

Using open source Web editing software (e.g., wiki), online community users can now easily edit, review and publish articles collaboratively. While much useful knowledge can be derived from these articles, content users and critics are often concerned about their qualities. In this paper, we develop two models, namely basic model and peer review model, for measuring the qualities of these articles and the authorities of their contributors. We represent collaboratively edited articles and their contributors in a bipartite graph. While the basic model measures an article's quality using both the authorities of contributors and the amount of contribution from each …

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Query-Based Watermarking For Xml Data, Xuan Zhou, Hwee Hwa Pang, Kian-Lee Tan Dec 2006

Query-Based Watermarking For Xml Data, Xuan Zhou, Hwee Hwa Pang, Kian-Lee Tan

Research Collection School Of Computing and Information Systems

As increasing amount of XML data is exchanged over the internet, copyright protection of this type of data is becoming an important requirement for many applications. In this paper, we introduce a rights protection scheme for XML data based on digital watermarking. One of the main challenges for watermarking XML data is that the data could be easily reorganized by an adversary in an attempt to destroy any embedded watermark. To overcome it, we propose a query-based watermarking scheme, which creates queries to identify available watermarking capacity, such that watermarks could be recovered from reorganized data through query rewriting. The …

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Designing Web Sites For Customer Loyalty Across Business Domains: A Multilevel Analysis, S. Mithas, Narayanasamy Ramasubbu, M. S. Krishnan, C. Fornell Dec 2006

Designing Web Sites For Customer Loyalty Across Business Domains: A Multilevel Analysis, S. Mithas, Narayanasamy Ramasubbu, M. S. Krishnan, C. Fornell

Research Collection School Of Computing and Information Systems

Web Sites are important components of Internet strategy for organizations. This paper develops a theoretical model for understanding the effect of Web site design elements on customer loyalty to a Web site. We show the relevance of the business domain of a Web site to gain a contextual understanding of relative importance of Web site design elements. We use a hierarchical linear modeling approach to model multilevel and cross-level interactions that have not been explicitly considered in previous research. By analyzing, data on more than 12,000 online customer surveys for 43 Web sites in several business domains, we find that …

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Towards Effective Content-Based Music Retrieval With Multiple Acoustic Feature Combination, Jialie Shen, John Shepherd, Ann H. H. Ngu Dec 2006

Towards Effective Content-Based Music Retrieval With Multiple Acoustic Feature Combination, Jialie Shen, John Shepherd, Ann H. H. Ngu

Research Collection School Of Computing and Information Systems

In this paper, we present a new approach to constructing music descriptors to support efficient content-based music retrieval and classification. The system applies multiple musical properties combined with a hybrid architecture based on principal component analysis (PCA) and a multilayer perceptron neural network. This architecture enables straightforward incorporation of multiple musical feature vectors, based on properties such as timbral texture, pitch, and rhythm structure, into a single low-dimensioned vector that is more effective for classification than the larger individual feature vectors. The use of supervised training enables incorporation of human musical perception that further enhances the classification process. We compare …

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Towards Effective Content-Based Music Retrieval With Multiple Acoustic Feature Composition, Jialie Shen, John Shepherd, Ngu Ahh Dec 2006

Towards Effective Content-Based Music Retrieval With Multiple Acoustic Feature Composition, Jialie Shen, John Shepherd, Ngu Ahh

Research Collection School Of Computing and Information Systems

In this paper, we present a new approach to constructing music descriptors to support efficient content-based music retrieval and classification. The system applies multiple musical properties combined with a hybrid architecture based on principal component analysis (PCA) and a multilayer perceptron neural network. This architecture enables straightforward incorporation of multiple musical feature vectors, based on properties such as timbral texture, pitch, and rhythm structure, into a single low-dimensioned vector that is more effective for classification than the larger individual feature vectors. The use of supervised training enables incorporation of human musical perception that further enhances the classification process. We compare …

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An Experimental Study On U-Commerce Adoption: Impact Of Personalization And Privacy Concerns, H. Sheng, Fiona Fui-Hoon Nah, Keng Siau Dec 2006

An Experimental Study On U-Commerce Adoption: Impact Of Personalization And Privacy Concerns, H. Sheng, Fiona Fui-Hoon Nah, Keng Siau

Research Collection School Of Computing and Information Systems

Ubiquitous commerce (u-commerce) represents "anytime, anywhere" commerce. U-commerce can provide a high level of personalization, which can bring significant benefits to customers. However, privacy is a major concern to customers and an obstacle to the adoption of u-commerce. This research examines how personalization and context can impact customers' privacy concerns as well as intention to adopt u-commerce applications. As u-commerce is new and emerging, we used the scenario-based approach to operationalize personalization and context in an experimental study. The experimental results show that the effects of personalization on customers' privacy concerns and adoption intention are situation dependent.

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Identifying Difficulties In Learning Uml, Keng Siau, Poi-Peng Loo Dec 2006

Identifying Difficulties In Learning Uml, Keng Siau, Poi-Peng Loo

Research Collection School Of Computing and Information Systems

Despite its recognition as a standard object-oriented modeling language, Unified Modeling Language (UML) has been criticized for such deficiencies as semantic inconsistencies, vagueness, and conflicting notations. the relationship between these deficiencies and the difficulties in the learning process is the focus of this study. A concept mapping technique is used to unveil the learning difficulties and suggestions for alleviating them are provided.

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Continuous Monitoring Of Knn Queries In Wireless Sensor Networks, Yuxia Yao, Xueyan Tang, Ee Peng Lim Dec 2006

Continuous Monitoring Of Knn Queries In Wireless Sensor Networks, Yuxia Yao, Xueyan Tang, Ee Peng Lim

Research Collection School Of Computing and Information Systems

Wireless sensor networks have been widely used for civilian and military applications, such as environmental monitoring and vehicle tracking. In these applications, continuous query processing is often required and their efficient evaluation is a critical requirement to be met. Due to the limited power supply for sensor nodes, energy efficiency is a major performance measure in such query evaluation. In this paper, we focus on continuous kNN query processing. We observe that the centralized data storage and monitoring schemes do not favor energy efficiency. We therefore propose a localized scheme to monitor long running nearest neighbor queries in sensor networks. …

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A Model For Anticipatory Event Detection, Qi He, Kuiyu Chang, Ee Peng Lim Nov 2006

A Model For Anticipatory Event Detection, Qi He, Kuiyu Chang, Ee Peng Lim

Research Collection School Of Computing and Information Systems

Event detection is a very important area of research that discovers new events reported in a stream of text documents. Previous research in event detection has largely focused on finding the first story and tracking the events of a specific topic. A topic is simply a set of related events defined by user supplied keywords with no associated semantics and little domain knowledge. We therefore introduce the Anticipatory Event Detection (AED) problem: given some user preferred event transition in a topic, detect the occurence of the transition for the stream of news covering the topic. We confine the events to …

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Understanding User Perceptions On Usefulness And Usability Of An Integrated Wiki-G-Portal, Yin-Leng Theng, Yuanyuan Li, Ee Peng Lim, Zhe Wang, Dion Hoe-Lian Goh, Chew-Hung Chang, Kalyani Chatterjea, Jun Zhang Nov 2006

Understanding User Perceptions On Usefulness And Usability Of An Integrated Wiki-G-Portal, Yin-Leng Theng, Yuanyuan Li, Ee Peng Lim, Zhe Wang, Dion Hoe-Lian Goh, Chew-Hung Chang, Kalyani Chatterjea, Jun Zhang

Research Collection School Of Computing and Information Systems

This paper describes a pilot study on Wiki-G-Portal, a project integrating Wikipedia, an online encyclopedia, into G-Portal, a Web-based digital library, of geography resources. Initial findings from the pilot study seemed to suggest positive perceptions on usefulness and usability of Wiki-G-Portal, as well as subjects' attitude and intention to use.

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Integration Of Wikipedia And A Geography Digital Library, Ee Peng Lim, Zhe Wang, Darwin Sadeli, Yuanyuan Li, Chew-Hung Chang, Kalyani Chatterjea, Dion Hoe-Lian Goh, Yin-Leng Theng, Jun Zhang, Aixin Sun Nov 2006

Integration Of Wikipedia And A Geography Digital Library, Ee Peng Lim, Zhe Wang, Darwin Sadeli, Yuanyuan Li, Chew-Hung Chang, Kalyani Chatterjea, Dion Hoe-Lian Goh, Yin-Leng Theng, Jun Zhang, Aixin Sun

Research Collection School Of Computing and Information Systems

In this paper, we address the problem of integrating Wikipedia, an online encyclopedia, and G-Portal, a web-based digital library, in the geography domain. The integration facilitates the sharing of data and services between the two web applications that are of great value in learning. We first present an overall system architecture for supporting such an integration and address the metadata extraction problem associated with it. In metadata extraction, we focus on extracting and constructing metadata for geo-political regions namely cities and countries. Some empirical performance results will be presented. The paper will also describe the adaptations of G-Portal and Wikipedia …

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Extracting Link Chains Of Relationship Instances From A Website, Myo-Myo Naing, Ee Peng Lim, Roger Hsiang-Li Chiang Oct 2006

Extracting Link Chains Of Relationship Instances From A Website, Myo-Myo Naing, Ee Peng Lim, Roger Hsiang-Li Chiang

Research Collection School Of Computing and Information Systems

Web pages from a Web site can often be associated with concepts in an ontology, and pairs of Web pages also can be associated with relationships between concepts. With such associations, the Web site can be searched, browsed, or even reorganized based on the concept and relationship labels of its Web pages. In this article, we study the link chain extraction problem that is critical to the extraction of Web pages that are related. A link chain is an ordered list of anchor elements linking two Web pages related by some semantic relationship. We propose a link chain extraction method …

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Critical Success Factors For Erp Implementation And Upgrade, Fiona Fui-Hoon Nah, Santiago Delgado Oct 2006

Critical Success Factors For Erp Implementation And Upgrade, Fiona Fui-Hoon Nah, Santiago Delgado

Research Collection School Of Computing and Information Systems

Seven categories of critical success factors were identified from the ERP literature: (1) business plan and vision; (2) change management, (3) communication; (4) ERP team composition, skills and compensation; (5) management support and championship; (6) project management, (7) system analysis, selection and technical implementation. We conducted a case study of two organizations that had implemented and upgraded ERP systems. We adopted Markus and Tanis' four-phase model and compared the importance of these critical success factors across the phases of ERP implementation and upgrade. The importance of these factors across the phases of ERP implementation and upgrade is very similar. 'Business …

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Service Pattern Discovery Of Web Service Mining In Web Service Registry-Repository, Qianhui Althea Liang, Jen-Yao Chung, Steven M. Miller, Yang Ouyang Oct 2006

Service Pattern Discovery Of Web Service Mining In Web Service Registry-Repository, Qianhui Althea Liang, Jen-Yao Chung, Steven M. Miller, Yang Ouyang

Research Collection School Of Computing and Information Systems

This paper presents and elaborates the concept of Web service usage patterns and pattern discovery through service mining. We define three different levels of service usage data: i) user request level, ii) template level and iii) instance level. At each level, we investigate patterns of service usage data and the discovery of these patterns. An algorithm for service pattern discovery at the template level is presented. We show the system architecture of a service-mining enabled service registry repository. Web service patterns, pattern discovery and pattern mining supports the discovery and composition of complex services, which in turn supports the application …

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Fast Tracking Of Near-Duplicate Keyframes In Broadcast Domain With Transitivity Propagation, Chong-Wah Ngo, Wan-Lei Zhao, Yu-Gang Jiang Oct 2006

Fast Tracking Of Near-Duplicate Keyframes In Broadcast Domain With Transitivity Propagation, Chong-Wah Ngo, Wan-Lei Zhao, Yu-Gang Jiang

Research Collection School Of Computing and Information Systems

The identification of near-duplicate keyframe (NDK) pairs is a useful task for a variety of applications such as news story threading and content-based video search. In this paper, we propose a novel approach for the discovery and tracking of NDK pairs and threads in the broadcast domain. The detection of NDKs in a large data set is a challenging task due to the fact that when the data set increases linearly, the computational cost increases in a quadratic speed, and so does the number of false alarms. This paper explores the symmetric and transitive nature of near-duplicate for the effective …

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Audio Similarity Measure By Graph Modeling And Matching, Yuxin Peng, Chong-Wah Ngo, Cuihua Fang, Xiaoou Chen, Jianguo Xiao Oct 2006

Audio Similarity Measure By Graph Modeling And Matching, Yuxin Peng, Chong-Wah Ngo, Cuihua Fang, Xiaoou Chen, Jianguo Xiao

Research Collection School Of Computing and Information Systems

This paper proposes a new approach for the similarity measure and ranking of audio clips by graph modeling and matching. Instead of using frame-based or salient-based features to measure the acoustical similarity of audio clips, segment-based similarity is proposed. The novelty of our approach lies in two aspects: segment-based representation, and the similarity measure and ranking based on four kinds of similarity factors. In segmentbased representation, segments not only capture the change property of audio clip, but also keep and present the change relation and temporal order of audio features. In the similarity measure and ranking, four kinds of similarity …

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Natural Document Clustering By Clique Percolation In Random Graphs, Wei Gao, Kam-Fai Wong Oct 2006

Natural Document Clustering By Clique Percolation In Random Graphs, Wei Gao, Kam-Fai Wong

Research Collection School Of Computing and Information Systems

Document clustering techniques mostly depend on models that impose explicit and/or implicit priori assumptions as to the number, size, disjunction characteristics of clusters, and/or the probability distribution of clustered data. As a result, the clustering effects tend to be unnatural and stray away more or less from the intrinsic grouping nature among the documents in a corpus. We propose a novel graph-theoretic technique called Clique Percolation Clustering (CPC). It models clustering as a process of enumerating adjacent maximal cliques in a random graph that unveils inherent structure of the underlying data, in which we unleash the commonly practiced constraints in …

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Continuous Nearest Neighbor Monitoring In Road Networks, Kyriakos Mouratidis, Man Lung Yiu, Dimitris Papadias, Nikos Mamoulis Sep 2006

Continuous Nearest Neighbor Monitoring In Road Networks, Kyriakos Mouratidis, Man Lung Yiu, Dimitris Papadias, Nikos Mamoulis

Research Collection School Of Computing and Information Systems

Recent research has focused on continuous monitoring of nearest neighbors (NN) in highly dynamic scenarios, where the queries and the data objects move frequently and arbitrarily. All existing methods, however, assume the Euclidean distance metric. In this paper we study k-NN monitoring in road networks, where the distance between a query and a data object is determined by the length of the shortest path connecting them. We propose two methods that can handle arbitrary object and query moving patterns, as well as °uctuations of edge weights. The ¯rst one maintains the query results by processing only updates that may invalidate …

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