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Research Collection School Of Computing and Information Systems

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Full-Text Articles in Databases and Information Systems

Discovering Causal Dependencies In Mobile Context-Aware Recommenders, Ghim-Eng Yap, Ah-Hwee Tan, Hwee Hwa Pang May 2006

Discovering Causal Dependencies In Mobile Context-Aware Recommenders, Ghim-Eng Yap, Ah-Hwee Tan, Hwee Hwa Pang

Research Collection School Of Computing and Information Systems

Mobile context-aware recommender systems face unique challenges in acquiring context. Resource limitations make minimizing context acquisition a practical need, while the uncertainty inherent to the mobile environment makes missing context values a major concern. This paper introduces a scalable mechanism based on Bayesian network learning in a tiered context model to overcome both of these challenges. Extensive experiments on a restaurant recommender system showed that our mechanism can accurately discover causal dependencies among context, thereby enabling the effective identification of the minimal set of important context for a specific user and task, as well as providing highly accurate recommendations even …

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Time-Dependent Semantic Similarity Measure Of Queries Using Historical Click-Through Data, Qiankun Zhao, Steven C. H. Hoi, Tie-Yan Liu, Sourav S. Bhowmick, Michael R. Lyu, Wei-Ying Ma May 2006

Time-Dependent Semantic Similarity Measure Of Queries Using Historical Click-Through Data, Qiankun Zhao, Steven C. H. Hoi, Tie-Yan Liu, Sourav S. Bhowmick, Michael R. Lyu, Wei-Ying Ma

Research Collection School Of Computing and Information Systems

It has become a promising direction to measure similarity of Web search queries by mining the increasing amount of click-through data logged by Web search engines, which record the interactions between users and the search engines. Most existing approaches employ the click-through data for similarity measure of queries with little consideration of the temporal factor, while the click-through data is often dynamic and contains rich temporal information. In this paper we present a new framework of time-dependent query semantic similarity model on exploiting the temporal characteristics of historical click-through data. The intuition is that more accurate semantic similarity values between …

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Large-Scale Text Categorization By Batch Mode Active Learning, Steven C. H. Hoi, Rong Jin, Michael R. Lyu May 2006

Large-Scale Text Categorization By Batch Mode Active Learning, Steven C. H. Hoi, Rong Jin, Michael R. Lyu

Research Collection School Of Computing and Information Systems

Large-scale text categorization is an important research topic for Web data mining. One of the challenges in large-scale text categorization is how to reduce the human efforts in labeling text documents for building reliable classification models. In the past, there have been many studies on applying active learning methods to automatic text categorization, which try to select the most informative documents for labeling manually. Most of these studies focused on selecting a single unlabeled document in each iteration. As a result, the text categorization model has to be retrained after each labeled document is solicited. In this paper, we present …

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Real-Time Non-Rigid Shape Recovery Via Active Appearance Models For Augmented Reality, Jianke Zhu, Steven C. H. Hoi, Michael R. Lyu May 2006

Real-Time Non-Rigid Shape Recovery Via Active Appearance Models For Augmented Reality, Jianke Zhu, Steven C. H. Hoi, Michael R. Lyu

Research Collection School Of Computing and Information Systems

One main challenge in Augmented Reality (AR) applications is to keep track of video objects with their movement, orientation, size, and position accurately. This poses a challenging task to recover nonrigid shape and global pose in real-time AR applications. This paper proposes a novel two-stage scheme for online non-rigid shape recovery toward AR applications using Active Appearance Models (AAMs). First, we construct 3D shape models from AAMs offline, which do not involve processing of the 3D scan data. Based on the computed 3D shape models, we propose an efficient online algorithm to estimate both 3D pose and non-rigid shape parameters …

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Clip-Based Similarity Measure For Query-Dependent Clip Retrieval And Video Summarization, Yuxin Peng, Chong-Wah Ngo May 2006

Clip-Based Similarity Measure For Query-Dependent Clip Retrieval And Video Summarization, Yuxin Peng, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

This paper proposes a new approach and algorithm for the similarity measure of video clips. The similarity is mainly based on two bipartite graph matching algorithms: maximum matching (MM) and optimal matching (OM). MM is able to rapidly filter irrelevant video clips, while OM is capable of ranking the similarity of clips according to visual and granularity factors. We apply the similarity measure for two tasks: retrieval and summarization. In video retrieval, a hierarchical retrieval framework is constructed based on MM and OM. The validity of the framework is theoretically proved and empirically verified on a video database of 21 …

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On In-Network Synopsis Join Processing For Sensor Networks, Hai Yu, Ee Peng Lim, Jun Zhang May 2006

On In-Network Synopsis Join Processing For Sensor Networks, Hai Yu, Ee Peng Lim, Jun Zhang

Research Collection School Of Computing and Information Systems

The emergence of sensor networks enables applications that deploy sensors to collaboratively monitor environment and process data collected. In some scenarios, we are interested in using join queries to correlate data stored in different regions of a sensor network, where the data volume is large, making it prohibitive to transmit all data to a central server for joining. In this paper, we present an in-network synopsis join strategy for evaluating join queries in sensor networks with communication efficiency. In this strategy, we prune data that do not contribute to the join results in the early stage of the join processing, …

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Sgpm: Static Group Pattern Mining Using Apriori-Like Sliding Window, John Goh, David Taniar, Ee Peng Lim Apr 2006

Sgpm: Static Group Pattern Mining Using Apriori-Like Sliding Window, John Goh, David Taniar, Ee Peng Lim

Research Collection School Of Computing and Information Systems

Mobile user data mining is a field that focuses on extracting interesting pattern and knowledge out from data generated by mobile users. Group pattern is a type of mobile user data mining method. In group pattern mining, group patterns from a given user movement database is found based on spatio-temporal distances. In this paper, we propose an improvement of efficiency using area method for locating mobile users and using sliding window for static group pattern mining. This reduces the complexity of valid group pattern mining problem. We support the use of static method, which uses areas and sliding windows instead …

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A Unified Log-Based Relevance Feedback Scheme For Image Retrieval, Steven Hoi, Michael R. Lyu, Rong Jin Apr 2006

A Unified Log-Based Relevance Feedback Scheme For Image Retrieval, Steven Hoi, Michael R. Lyu, Rong Jin

Research Collection School Of Computing and Information Systems

Relevance feedback has emerged as a powerful tool to boost the retrieval performance in content-based image retrieval (CBIR). In the past, most research efforts in this field have focused on designing effective algorithms for traditional relevance feedback. Given that a CBIR system can collect and store users' relevance feedback information in a history log, an image retrieval system should be able to take advantage of the log data of users' feedback to enhance its retrieval performance. In this paper, we propose a unified framework for log-based relevance feedback that integrates the log of feedback data into the traditional relevance feedback …

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Enterprise Agility And The Enabling Role Of Information Technology, Eric Overby, Anandhi S. Bharadwaj, V. Sambamurthy Apr 2006

Enterprise Agility And The Enabling Role Of Information Technology, Eric Overby, Anandhi S. Bharadwaj, V. Sambamurthy

Research Collection School Of Computing and Information Systems

In turbulent environments, enterprise agility, that is, the ability of firms to sense environmental change and respond readily, is an important determinant of firm success. We define and deconstruct enterprise agility, delineate enterprise agility from similar concepts in the business research literature, explore the underlying capabilities that support enterprise agility, explicate the enabling role of information technology (IT) and digital options, and propose a method for measuring enterprise agility. The concepts in this paper are offered as foundational building blocks for the overall research program on enterprise agility and the enabling role of IT.

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Fisa: Feature-Based Instance Selection For Imbalanced Text Classification, Aixin Sun, Ee Peng Lim, Boualem Benatallah, Mahbub Hassan Apr 2006

Fisa: Feature-Based Instance Selection For Imbalanced Text Classification, Aixin Sun, Ee Peng Lim, Boualem Benatallah, Mahbub Hassan

Research Collection School Of Computing and Information Systems

Support Vector Machines (SVM) classifiers are widely used in text classification tasks and these tasks often involve imbalanced training. In this paper, we specifically address the cases where negative training documents significantly outnumber the positive ones. A generic algorithm known as FISA (Feature-based Instance Selection Algorithm), is proposed to select only a subset of negative training documents for training a SVM classifier. With a smaller carefully selected training set, a SVM classifier can be more efficiently trained while delivering comparable or better classification accuracy. In our experiments on the 20-Newsgroups dataset, using only 35% negative training examples and 60% learning …

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Searching Substructures With Superimposed Distance, Xifeng Yan, Feida Zhu, Jiawei Han, Philip S. Yu Apr 2006

Searching Substructures With Superimposed Distance, Xifeng Yan, Feida Zhu, Jiawei Han, Philip S. Yu

Research Collection School Of Computing and Information Systems

Efficient indexing techniques have been developed for the exact and approximate substructure search in large scale graph databases. Unfortunately, the retrieval problem of structures with categorical or geometric distance constraints is not solved yet. In this paper, we develop a method called PIS (Partition-based Graph Index and Search) to support similarity search on substructures with superimposed distance constraints. PIS selects discriminative fragments in a query graph and uses an index to prune the graphs that violate the distance constraints. We identify a criterion to distinguish the selectivity of fragments in multiple graphs and develop a partition method to obtain a …

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In-Network Processing Of Nearest Neigbor Queries For Wireless Sensor Networks, Yuxia Yao, Xueyan Tang, Ee Peng Lim Apr 2006

In-Network Processing Of Nearest Neigbor Queries For 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. The sensor nodes in the network have the abilities to sense, store, compute and communicate. To enable object tracking applications, spatial queries such as nearest neighbor queries are to be supported in these networks. The queries can be injected by the user at any sensor node. Due to the limited power supply for sensor nodes, energy efficiency is the major concern in query processing. Centralized data storage and query processing schemes do not favor energy efficiency. In this paper, we propose …

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Tacit Knowledge, Nonaka And Takeuchi Seci Model And Informal Knowledge Processes, Siu Loon Hoe Mar 2006

Tacit Knowledge, Nonaka And Takeuchi Seci Model And Informal Knowledge Processes, Siu Loon Hoe

Research Collection School Of Computing and Information Systems

The organizational behavior and knowledge management literature has devoted a lot attention on how structural knowledge processes enhance learning. There has been little emphasis on the informal knowledge processes and the construct remains undefined. The purpose of this paper is to highlight the importance of informal knowledge processes, propose a definition for these processes and link them to the socialization and internalization processes suggested by Nonaka and Takeuchi in the SECI model. The paper offers a fresh perspective on how informal knowledge processes in organizations help to enhance the organization’s learning capability. It will enable scholars and managers to have …

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Efficient Mining Of Group Patterns From User Movement Data, Yida Wang, Ee Peng Lim, San-Yih Hwang Jan 2006

Efficient Mining Of Group Patterns From User Movement Data, Yida Wang, Ee Peng Lim, San-Yih Hwang

Research Collection School Of Computing and Information Systems

In this paper, we present a new approach to derive groupings of mobile users based on their movement data. We assume that the user movement data are collected by logging location data emitted from mobile devices tracking users. We formally define group pattern as a group of users that are within a distance threshold from one another for at least a minimum duration. To mine group patterns, we first propose two algorithms, namely AGP and VG-growth. In our first set of experiments, it is shown when both the number of users and logging duration are large, AGP and VG-growth are …

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Advances In Data Modeling Research, G. Allen, A. Bajaj, V. Khatri, S. Ram, Keng Siau Jan 2006

Advances In Data Modeling Research, G. Allen, A. Bajaj, V. Khatri, S. Ram, Keng Siau

Research Collection School Of Computing and Information Systems

In this paper, we summarize the discussions of the panel on "Advances in Data Modeling Research," held at the Americas Conference on Information Systems (AMCIS) in 2005. We focus on four primary areas where data modeling research offers rich opportunities: spatio-temporal semantics, genome research, ontological analysis and empirical evaluation of existing models. We highlight past work in each area and also discuss open questions, with a view to promoting future research in the overall data modeling area.

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Using Social Development Lenses To Understand E-Government Development, Keng Siau, Y. Long Jan 2006

Using Social Development Lenses To Understand E-Government Development, Keng Siau, Y. Long

Research Collection School Of Computing and Information Systems

As governments at different levels and all around the world are increasingly using the Web to enhance and improve their services, understanding e-government development and exploring factors that affect e-government development have become important research topics. The purpose of this research is to investigate factors explaining e-government development in terms of social development lenses. Based on growth and regional development theories, the paper hypothesizes that income level, development status, and region are three factors that differentiate e- government development in countries. Group comparison tests are conducted using secondary data from the United Nations and the United Nations Development Programme. The …

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In-Network Join Processing For Sensor Networks, Hai Yu, Ee Peng Lim, Jun Zhang Jan 2006

In-Network Join Processing For Sensor Networks, Hai Yu, Ee Peng Lim, Jun Zhang

Research Collection School Of Computing and Information Systems

Recent advances in hardware and wireless technologies have led to sensor networks consisting of large number of sensors capable of gathering and processing data collectively. Query processing on these sensor networks has to consider various inherent constraints. While simple queries such as select and aggregate queries in wireless sensor networks have been addressed in the literature, the processing of join queries in sensor networks remains to be investigated. In this paper, we present a synopsis join strategy for evaluating join queries in sensor networks with communication efficiency. In this strategy, instead of directly joining two relations distributed in a sensor …

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Using Cognitive Mapping Techniques To Supplement Uml And Up In Information Requirements Determination, Keng Siau, Tan Xin Jan 2006

Using Cognitive Mapping Techniques To Supplement Uml And Up In Information Requirements Determination, Keng Siau, Tan Xin

Research Collection School Of Computing and Information Systems

Information requirements determination is a critical task in system development projects. Many human issues may hinder the efforts to accurately capture and clearly understand users' information requirements. In this paper, we review the cognitive underpinnings of some human issues, which are discussed in existing literature and relevant to information requirements determination. We then propose the use of cognitive mapping techniques in the process of information requirements determination. Three widely used cognitive mapping techniques - causal mapping, semantic mapping, and concept mapping – are briefly introduced in this article. A case is used to illustrate how to use these cognitive mapping …

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E-Healthcare In Abc County Health Department (Abcchd): Trade-Offs Analysis And Evaluation, Keng Siau, Hwee-Joo Kam Jan 2006

E-Healthcare In Abc County Health Department (Abcchd): Trade-Offs Analysis And Evaluation, Keng Siau, Hwee-Joo Kam

Research Collection School Of Computing and Information Systems

The issue of privacy stirred a tumultuous uproar when the ABC County Health Department (ABCCHD) was planning for an e-Healthcare system that utilized information technology to streamline the administration process of patients. ABCCHD had hired a software vendor, Info-Health, a company that specialized in information system development for the healthcare industry to help in the project. The privacy of patients with Sexually Transmitted Diseases/Human Immunity System was a thorny issue in the implementation of the e-Healthcare system. A trade-off between privacy and cost was discussed and debated. Three alternatives, with varying degrees of privacy and cost, were considered.

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Systems Analysis & Design: An Essential Part Of Is Education, Albert L. Harris, Michael Lang, Briony J. Oates, Keng Siau Jan 2006

Systems Analysis & Design: An Essential Part Of Is Education, Albert L. Harris, Michael Lang, Briony J. Oates, Keng Siau

Research Collection School Of Computing and Information Systems

Systems analysis and design has been as critical building block in Information Systems (IS) education since the inception of the IS major. Whether it is taught using the traditional or "structured approach or the object-oriented approach, it exposes students to the different methods, tools, and techniques used in developing new systems, develops students analytical and problem-solving skills, teaches fact-finding and data gathering techniques, and provides teamwork skills. All of these are valuable skills for systems analysts. This paper introduces the reader of this special issue on systems analysis and design education to the issue, discusses the two approaches to teaching …

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The Roles Of Digital Libraries In Teaching And Learning Geography, Chew-Hung Chang, John Hedberg, Tiong-Sa Teh, Ee Peng Lim Jan 2006

The Roles Of Digital Libraries In Teaching And Learning Geography, Chew-Hung Chang, John Hedberg, Tiong-Sa Teh, Ee Peng Lim

Research Collection School Of Computing and Information Systems

Adopting a problem-centred approach helps students to learn Geography more effectively as they are able to identify and generalize about where different resources or activities are spatially located and they learn to associate certain patterns and processes with geographical changes. In an era where web-based student-centred inquiry is gaining popularity as a mode of learning Geography, the role of digital libraries as delivery trucks (in Clark’s terminology, 1983) needs to be better understood. An obvious affordance of the digital library is that it organizes information around themes for problems to be solved. This paper describes a developmental project to build …

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Grid-Partition Index: A Hybrid Approach To Nearest-Neighbor Queries In Wireless Location-Based Services, Baihua Zheng, Jianliang Xu, Wang-Chien Lee, Dik Lun Lee Jan 2006

Grid-Partition Index: A Hybrid Approach To Nearest-Neighbor Queries In Wireless Location-Based Services, Baihua Zheng, Jianliang Xu, Wang-Chien Lee, Dik Lun Lee

Research Collection School Of Computing and Information Systems

Traditional nearest-neighbor (NN) search is based on two basic indexing approaches: object-based indexing and solution-based indexing. The former is constructed based on the locations of data objects: using some distance heuristics on object locations. The latter is built on a precomputed solution space. Thus, NN queries can be reduced to and processed as simple point queries in this solution space. Both approaches exhibit some disadvantages, especially when employed for wireless data broadcast in mobile computing environments. In this paper, we introduce a new index method, called the grid-partition index, to support NN search in both ondemand access and periodic broadcast …

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Webarc: Website Archival Using A Structured Approach, Ee Peng Lim, Maria Marissa Dec 2005

Webarc: Website Archival Using A Structured Approach, Ee Peng Lim, Maria Marissa

Research Collection School Of Computing and Information Systems

Website archival refers to the task of monitoring and storing snapshots of website(s) for future retrieval and analysis. This task is particularly important for websites that have content changing over time with older information constantly overwritten by newer one. In this paper, we propose WEBARC as a set of software tools to allow users to construct a logical structure for a website to be archived. Classifiers are trained to. determine relevant web pages and their categories, and subsequently used in website downloading. The archival schedule can be specified and executed by a scheduler. A website viewer is also developed to …

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Improving The Quality Of Conceptual Modeling Using Cognitive Mapping Techniques, Keng Siau, X. Tan Dec 2005

Improving The Quality Of Conceptual Modeling Using Cognitive Mapping Techniques, Keng Siau, X. Tan

Research Collection School Of Computing and Information Systems

Conceptual modeling involves the understanding and communication between system analysts and end-users. Many factors may affect the quality of conceptual modeling processes as well as the models per se. Human cognition plays a pivotal role in understanding these factors and cognitive mapping techniques are effective tools to elicit and represent human cognition. In this paper, we look at the use of cognitive mapping techniques to improve the quality of conceptual modeling. We review frameworks on quality in conceptual modeling and examine the role of human cognition in conceptual modeling. The paper also discusses how human cognition is related to quality …

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Authenticating Query Results In Data Publishing, Di Ma, Robert H. Deng, Hwee Hwa Pang, Jianying Zhou Dec 2005

Authenticating Query Results In Data Publishing, Di Ma, Robert H. Deng, Hwee Hwa Pang, Jianying Zhou

Research Collection School Of Computing and Information Systems

We propose a communication-efficient authentication scheme to authenticate query results disseminated by untrusted data publishing servers. In our scheme, signatures of multiple tuples in the result set are aggregated into one and thus the communication overhead incurred by the signature keeps constant. Next attr-MHTs (tuple based Merkle Hash Tree) are built to further reduce the communication overhead incurred by auxiliary authentication information (AAI). Besides the property of communication-efficiency, our scheme also supports dynamic SET operations (UNION, INTERSECTION) and dynamic JOIN with immunity to reordering attack.

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A Framework To Learn Bayesian Network From Changing, Multiple-Source Biomedical Data, Li G., Tze-Yun Leong Dec 2005

A Framework To Learn Bayesian Network From Changing, Multiple-Source Biomedical Data, Li G., Tze-Yun Leong

Research Collection School Of Computing and Information Systems

Structure learning in Bayesian network is a big issue. Many efforts have tried to solve this problem and quite a few algorithms have been proposed. However, when we attempt to apply the existing methods to microarray data, there are three main challenges: 1) there are many variables in the data set, 2) the sample size is small, and 3) microarray data are changing from experiment to experiment and new data are available quickly. To address these three problems, we assume that the major functions of a kind of cells do not change too much in different experiments, and propose a …

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A Threshold-Based Algorithm For Continuous Monitoring Of K Nearest Neighbors, Kyriakos Mouratidis, Dimitris Papadias, Spiridon Bakiras, Yufei Tao Nov 2005

A Threshold-Based Algorithm For Continuous Monitoring Of K Nearest Neighbors, Kyriakos Mouratidis, Dimitris Papadias, Spiridon Bakiras, Yufei Tao

Research Collection School Of Computing and Information Systems

Assume a set of moving objects and a central server that monitors their positions over time, while processing continuous nearest neighbor queries from geographically distributed clients. In order to always report up-to-date results, the server could constantly obtain the most recent position of all objects. However, this naïve solution requires the transmission of a large number of rapid data streams corresponding to location updates. Intuitively, current information is necessary only for objects that may influence some query result (i.e., they may be included in the nearest neighbor set of some client). Motivated by this observation, we present a threshold-based algorithm …

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Accurately Extracting Coherent Relevant Passages Using Hidden Markov Models, Jing Jiang, Chengxiang Zhai Nov 2005

Accurately Extracting Coherent Relevant Passages Using Hidden Markov Models, Jing Jiang, Chengxiang Zhai

Research Collection School Of Computing and Information Systems

In this paper, we present a principled method for accurately extracting coherent relevant passages of variable lengths using HMMs. We show that with appropriate parameter estimation, the HMM method outperforms a number of strong baseline methods on two data sets.

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Query Processing In Spatial Databases Containing Obstacles, Jun Zhang, Dimitris Papadias, Kyriakos Mouratidis, Manli Zhu Nov 2005

Query Processing In Spatial Databases Containing Obstacles, Jun Zhang, Dimitris Papadias, Kyriakos Mouratidis, Manli Zhu

Research Collection School Of Computing and Information Systems

Despite the existence of obstacles in many database applications, traditional spatial query processing assumes that points in space are directly reachable and utilizes the Euclidean distance metric. In this paper, we study spatial queries in the presence of obstacles, where the obstructed distance between two points is defined as the length of the shortest path that connects them without crossing any obstacles. We propose efficient algorithms for the most important query types, namely, range search, nearest neighbours, e-distance joins, closest pairs and distance semi-joins, assuming that both data objects and obstacles are indexed by R-trees. The effectiveness of the proposed …

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Mining Ontological Knowledge From Domain-Specific Text Documents, Xing Jiang, Ah-Hwee Tan Nov 2005

Mining Ontological Knowledge From Domain-Specific Text Documents, Xing Jiang, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Traditional text mining systems employ shallow parsing techniques and focus on concept extraction and taxonomic relation extraction. This paper presents a novel system called CRCTOL for mining rich semantic knowledge in the form of ontology from domain-specific text documents. By using a full text parsing technique and incorporating both statistical and lexico-syntactic methods, the knowledge extracted by our system is more concise and contains a richer semantics compared with alternative systems. We conduct a case study wherein CRCTOL extracts ontological knowledge, specifically key concepts and semantic relations, from a terrorism domain text collection. Quantitative evaluation, by comparing with a state-of-the-art …

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