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Full-Text Articles in Entire DC Network
Z-Sky: An Efficient Skyline Query Processing Framework Based On Z-Order, Ken C. K. Lee, Wang-Chien Lee, Baihua Zheng, Huajing Li, Yuan Tian
Z-Sky: An Efficient Skyline Query Processing Framework Based On Z-Order, Ken C. K. Lee, Wang-Chien Lee, Baihua Zheng, Huajing Li, Yuan Tian
Research Collection School Of Computing and Information Systems
Given a set of data points in a multidimensional space, a skyline query retrieves those data points that are not dominated by any other point in the same dataset. Observing that the properties of Z-order space filling curves (or Z-order curves) perfectly match with the dominance relationships among data points in a geometrical data space, we, in this paper, develop and present a novel and efficient processing framework to evaluate skyline queries and their variants, and to support skyline result updates based on Z-order curves. This framework consists of ZBtree, i.e., an index structure to organize a source dataset and …
Efficient Mutual Nearest Neighbor Query Processing For Moving Object Trajectories, Yunjun Gao, Baihua Zheng, Gencai Chen, Qing Li, Chun Chen, Gang Chen
Efficient Mutual Nearest Neighbor Query Processing For Moving Object Trajectories, Yunjun Gao, Baihua Zheng, Gencai Chen, Qing Li, Chun Chen, Gang Chen
Research Collection School Of Computing and Information Systems
Given a set D of trajectories, a query object q, and a query time extent Γ, a mutual (i.e., symmetric) nearest neighbor (MNN) query over trajectories finds from D, the set of trajectories that are among the k1 nearest neighbors (NNs) of q within Γ, and meanwhile, have q as one of their k2 NNs. This type of queries is useful in many applications such as decision making, data mining, and pattern recognition, as it considers both the proximity of the trajectories to q and the proximity of q to the trajectories. In this paper, we first formalize MNN search …
Satrap: Data And Network Heterogeneity Aware P2p Data-Mining, Hock Kee Ang, Vivekanand Gopalkrishnan, Anwitaman Datta, Wee Keong Ng, Steven C. H. Hoi
Satrap: Data And Network Heterogeneity Aware P2p Data-Mining, Hock Kee Ang, Vivekanand Gopalkrishnan, Anwitaman Datta, Wee Keong Ng, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
Distributed classification aims to build an accurate classifier by learning from distributed data while reducing computation and communication cost A P2P network where numerous users come together to share resources like data content, bandwidth, storage space and CPU resources is an excellent platform for distributed classification However, two important aspects of the learning environment have often been overlooked by other works, viz., 1) location of the peers which results in variable communication cost and 2) heterogeneity of the peers' data which can help reduce redundant communication In this paper, we examine the properties of network and data heterogeneity and propose …
Designing The Dynamics Of Service Innovations, Arcot Desai Narasimhalu
Designing The Dynamics Of Service Innovations, Arcot Desai Narasimhalu
Research Collection School Of Computing and Information Systems
There is no serious tool available to design service innovations even as it is gaining in important attention from the academic and industrial worlds. This paper presents a method that is specifically developed to help service innovators plan and design their innovations. The method recognizes the dependencies that exist across a service provider, customers and suppliers and help identify potential inconsistencies in the design of service innovations.
Using Hadoop And Cassandra For Taxi Data Analytics: A Feasibility Study, Alvin Jun Yong Koh, Xuan Khoa Nguyen, C. Jason Woodard
Using Hadoop And Cassandra For Taxi Data Analytics: A Feasibility Study, Alvin Jun Yong Koh, Xuan Khoa Nguyen, C. Jason Woodard
Research Collection School Of Computing and Information Systems
This paper reports on a preliminary study to assess the feasibility of using the Open Cirrus Cloud Computing Research testbed to provide offline and online analytical support for taxi fleet operations. In the study, we benchmarked the performance gains from distributing the offline analysis of GPS location traces over multiple virtual machines using the Apache Hadoop implementation of the MapReduce paradigm. We also explored the use of the Apache Cassandra distributed database system for online retrieval of vehicle trace data. While configuring the testbed infrastructure was straightforward, we encountered severe I/O bottlenecks in running the benchmarks due to the lack …
Income, Endogenous Market Structure And Innovation, Mei Lin, Shaojin Li, Andrew B. Whinston
Income, Endogenous Market Structure And Innovation, Mei Lin, Shaojin Li, Andrew B. Whinston
Research Collection School Of Computing and Information Systems
We investigate the effect of income distribution on R&D in a dynamic framework. Our model captures both the infinite R&D race among heterogeneous innovators and a market where successful innovators generate revenues. The market structure of successful innovations is endogenous – firms produce vertically differentiated substitute goods and compete in price. Based on firms' equilibrium market revenues, we derive numerical solutions of the Markov perfect equilibrium innovation rate of the dynamic problem. A key insight in our results is that explicitly modeling price competition and the market structure plays an important role in evaluating the impact of rising income inequality …
Semantic Context Modeling With Maximal Margin Conditional Random Fields For Automatic Image Annotation, Yu Xiang, Xiangdong Zhou, Zuotao Liu, Tat-Seng Chua, Chong-Wah Ngo
Semantic Context Modeling With Maximal Margin Conditional Random Fields For Automatic Image Annotation, Yu Xiang, Xiangdong Zhou, Zuotao Liu, Tat-Seng Chua, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
Context modeling for Vision Recognition and Automatic Image Annotation (AIA) has attracted increasing attentions in recent years. For various contextual information and resources, semantic context has been exploited in AIA and brings promising results. However, previous works either casted the problem into structural classification or adopted multi-layer modeling, which suffer from the problems of scalability or model efficiency. In this paper, we propose a novel discriminative Conditional Random Field (CRF) model for semantic context modeling in AIA, which is built over semantic concepts and treats an image as a whole observation without segmentation. Our model captures the interactions between semantic …
A Social Transitivity-Based Data Dissemination Scheme For Opportunistic Networks, Jaesung Ku, Yangwoo Ko, Jisun An, Dongman Lee
A Social Transitivity-Based Data Dissemination Scheme For Opportunistic Networks, Jaesung Ku, Yangwoo Ko, Jisun An, Dongman Lee
Research Collection School Of Computing and Information Systems
A social-based routing protocol for opportunistic networks considers the direct delivery as forwarding metrics. By ignoring the indirect delivery through intermediate nodes, it misses chances to find paths that are better in terms of delivery ratio and time. To overcome this limitation, we propose to incorporate transitivity, which considers the indirect delivery through intermediate nodes, as one of the forwarding metrics. We also found that some message forwards do not improve the delivery performance. To reduce the number of these useless forwards, the proposed scheme forwards messages to an encountered node when the increase of total utility value is greater …
Mining Antagonistic Communities From Social Networks, Kuan Zhang, David Lo, Ee Peng Lim
Mining Antagonistic Communities From Social Networks, Kuan Zhang, David Lo, Ee Peng Lim
Research Collection School Of Computing and Information Systems
During social interactions in a community, there are often sub-communities that behave in opposite manner. These antagonistic sub-communities could represent groups of people with opposite tastes, factions within a community distrusting one another, etc. Taking as input a set of interactions within a community, we develop a novel pattern mining approach that extracts for a set of antagonistic sub-communities. In particular, based on a set of user specified thresholds, we extract a set of pairs of sub-communities that behave in opposite ways with one another. To prevent a blow up in these set of pairs, we focus on extracting a …
Weakly-Supervised Hashing In Kernel Space, Yadong Mu, Jialie Shen, Shuicheng Yan
Weakly-Supervised Hashing In Kernel Space, Yadong Mu, Jialie Shen, Shuicheng Yan
Research Collection School Of Computing and Information Systems
The explosive growth of the vision data motivates the recent studies on efficient data indexing methods such as locality-sensitive hashing (LSH). Most existing approaches perform hashing in an unsupervised way. In this paper we move one step forward and propose a supervised hashing method, i.e., the LAbel-regularized Max-margin Partition (LAMP) algorithm. The proposed method generates hash functions in weakly-supervised setting, where a small portion of sample pairs are manually labeled to be “similar” or “dissimilar”. We formulate the task as a Constrained Convex-Concave Procedure (CCCP), which can be relaxed into a series of convex sub-problems solvable with efficient Quadratic-Program (QP). …
Do Wikipedians Follow Domain Experts? A Domain-Specific Study On Wikipedia Contribution, Yi Zhang, Aixin Sun, Anwitaman Datta, Kuiyu Chang, Ee Peng Lim
Do Wikipedians Follow Domain Experts? A Domain-Specific Study On Wikipedia Contribution, Yi Zhang, Aixin Sun, Anwitaman Datta, Kuiyu Chang, Ee Peng Lim
Research Collection School Of Computing and Information Systems
Wikipedia is one of the most successful online knowledge bases, attracting millions of visits daily. Not surprisingly, its huge success has in turn led to immense research interest for a better understanding of the collaborative knowledge building process. In this paper, we performed a (terrorism) domain-specific case study, comparing and contrasting the knowledge evolution in Wikipedia with a knowledge base created by domain experts. Specifically, we used the Terrorism Knowledge Base (TKB) developed by experts at MIPT. We identified 409 Wikipedia articles matching TKB records, and went ahead to study them from three aspects: creation, revision, and link evolution. We …
A Hybrid Method To Detect Deflation Fraud In Cost-Per-Action Online Advertising, Xuhua Ding
A Hybrid Method To Detect Deflation Fraud In Cost-Per-Action Online Advertising, Xuhua Ding
Research Collection School Of Computing and Information Systems
Web advertisers prefer the cost-per-action (CPA) advertisement model whereby an advertiser pays a web publisher according to the actual amount of transactions, rather than the volume of advertisement clicks. The main obstacle for a wide deployment of this model is the deflation fraud. Namely, a dishonest advertiser under-reports the transaction count in order to discharge less. In this paper, we present a mechanism to detect such a fraud using a hybrid of cryptography and probability tools. With the assistance from a small number of users, the publisher can detect deflation fraud with a success probability growing exponentially with the fraud …
On Trustworthiness Of Cpu Usage Metering And Accounting, Mei Liu, Xuhua Ding
On Trustworthiness Of Cpu Usage Metering And Accounting, Mei Liu, Xuhua Ding
Research Collection School Of Computing and Information Systems
In the envisaged utility computing paradigm, a user taps a service provider’s computing resources to accomplish her tasks, without deploying the needed hardware and software in her own IT infrastructure. To make the service profitable, the service provider charges the user based on the resources consumed. A commonly billed resource is CPU usage. A key factor to ensure the success of such a business model is the trustworthiness of the resource metering scheme. In this paper, we provide a systematic study on the trustworthiness of CPU usage metering. Our results show that the metering schemes in commodity operating systems should …
Revisiting Unpredictability-Based Rfid Privacy Models, Junzuo Lai, Robert Huijie Deng, Yingjiu Li
Revisiting Unpredictability-Based Rfid Privacy Models, Junzuo Lai, Robert Huijie Deng, Yingjiu Li
Research Collection School Of Computing and Information Systems
Recently, there have been several attempts in establishing formal RFID privacy models in the literature. These models mainly fall into two categories: one based on the notion of indistinguishability of two RFID tags, denoted as ind-privacy, and the other based on the unpredictability of the output of an RFID protocol, denoted as unp-privacy. Very recently, at CCS’09, Ma et al. proposed a modified unp-privacy model, referred to as unp ′-privacy. In this paper, we first revisit the existing RFID privacy models and point out their limitations. We then propose a new RFID privacy model, denoted as …
Otl: A Framework Of Online Transfer Learning, Peilin Zhao, Steven C. H. Hoi
Otl: A Framework Of Online Transfer Learning, Peilin Zhao, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
In this paper, we investigate a new machine learning framework called Online Transfer Learning (OTL) that aims to transfer knowledge from some source domain to an online learning task on a target domain. We do not assume the target data follows the same class or generative distribution as the source data, and our key motivation is to improve a supervised online learning task in a target domain by exploiting the knowledge that had been learned from large amount of training data in source domains. OTL is in general challenging since data in both domains not only can be different in …
Player Performance Prediction In Massively Multiplayer Online Role-Playing Games (Mmorpgs), Kyong Jin Shim, Richa Sharan, Jaideep Srivastava
Player Performance Prediction In Massively Multiplayer Online Role-Playing Games (Mmorpgs), Kyong Jin Shim, Richa Sharan, Jaideep Srivastava
Research Collection School Of Computing and Information Systems
In this study, we propose a comprehensive performance management tool for measuring and reporting operational activities of game players. This study uses performance data of game players in EverQuest II, a popular MMORPG developed by Sony Online Entertainment, to build performance prediction models forgame players. The prediction models provide a projection of player’s future performance based on his past performance, which is expected to be a useful addition to existing player performance monitoring tools. First, we show that variations of PECOTA [2] and MARCEL [3], two most popular baseball home run prediction methods, can be used for game player performance …
Prediction Of Protein Subcellular Localization: A Machine Learning Approach, Kyong Jin Shim
Prediction Of Protein Subcellular Localization: A Machine Learning Approach, Kyong Jin Shim
Research Collection School Of Computing and Information Systems
Subcellular localization is a key functional characteristic of proteins. Optimally combining available information is one of the key challenges in today's knowledge-based subcellular localization prediction approaches. This study explores machine learning approaches for the prediction of protein subcellular localization that use resources concerning Gene Ontology and secondary structures. Using the spectrum kernel for feature representation of amino acid sequences and secondary structures, we explore an SVM-based learning method that classifies six subcellular localization sites: endoplasmic reticulum, extracellular, Golgi, membrane, mitochondria, and nucleus.
Visualizing And Exploring Evolving Information Networks In Wikipedia, Ee Peng Lim, Agus Trisnajaya Kwee, Nelman Lubis Ibrahim, Aixin Sun, Anwitaman Datta, Kuiyu Chang, Maureen Maureen
Visualizing And Exploring Evolving Information Networks In Wikipedia, Ee Peng Lim, Agus Trisnajaya Kwee, Nelman Lubis Ibrahim, Aixin Sun, Anwitaman Datta, Kuiyu Chang, Maureen Maureen
Research Collection School Of Computing and Information Systems
Information networks in Wikipedia evolve as users collaboratively edit articles that embed the networks. These information networks represent both the structure and content of community’s knowledge and the networks evolve as the knowledge gets updated. By observing the networks evolve and finding their evolving patterns, one can gain higher order knowledge about the networks and conduct longitudinal network analysis to detect events and summarize trends. In this paper, we present SSNetViz+, a visual analytic tool to support visualization and exploration of Wikipedia’s information networks. SSNetViz+ supports time-based network browsing, content browsing and search. Using a terrorism information network as an …
Stevent: Spatio-Temporal Event Model For Social Network Discovery, Hady W. Lauw, Ee Peng Lim, Hwee Hwa Pang, Teck-Tim Tan
Stevent: Spatio-Temporal Event Model For Social Network Discovery, Hady W. Lauw, Ee Peng Lim, Hwee Hwa Pang, Teck-Tim Tan
Research Collection School Of Computing and Information Systems
Spatio-temporal data concerning the movement of individuals over space and time contains latent information on the associations among these individuals. Sources of spatio-temporal data include usage logs of mobile and Internet technologies. This article defines a spatio-temporal event by the co-occurrences among individuals that indicate potential associations among them. Each spatio-temporal event is assigned a weight based on the precision and uniqueness of the event. By aggregating the weights of events relating two individuals, we can determine the strength of association between them. We conduct extensive experimentation to investigate both the efficacy of the proposed model as well as the …
Efficient Processing Of Exact Top-K Queries Over Disk-Resident Sorted Lists, Hwee Hwa Pang, Xuhua Ding, Baihua Zheng
Efficient Processing Of Exact Top-K Queries Over Disk-Resident Sorted Lists, Hwee Hwa Pang, Xuhua Ding, Baihua Zheng
Research Collection School Of Computing and Information Systems
The top-k query is employed in a wide range of applications to generate a ranked list of data that have the highest aggregate scores over certain attributes. As the pool of attributes for selection by individual queries may be large, the data are indexed with per-attribute sorted lists, and a threshold algorithm (TA) is applied on the lists involved in each query. The TA executes in two phases--find a cut-off threshold for the top-k result scores, then evaluate all the records that could score above the threshold. In this paper, we focus on exact top-k queries that involve monotonic linear …
Anytime Planning For Decentralized Pomdps Using Expectation Maximization, Akshat Kumar, Shlomo Zilberstein
Anytime Planning For Decentralized Pomdps Using Expectation Maximization, Akshat Kumar, Shlomo Zilberstein
Research Collection School Of Computing and Information Systems
Decentralized POMDPs provide an expressive framework for multi-agent sequential decision making. While finite-horizon DECPOMDPs have enjoyed signifcant success, progress remains slow for the infinite-horizon case mainly due to the inherent complexity of optimizing stochastic controllers representing agent policies. We present a promising new class of algorithms for the infinite-horizon case, which recasts the optimization problem as inference in a mixture of DBNs. An attractive feature of this approach is the straightforward adoption of existing inference techniques in DBNs for solving DEC-POMDPs and supporting richer representations such as factored or continuous states and actions. We also derive the Expectation Maximization (EM) …
Bridging Lightweight And Heavyweight Task Organization: The Role Of Tags In Adopting New Task Categories, Christoph Treude, Margaret-Anne Storey
Bridging Lightweight And Heavyweight Task Organization: The Role Of Tags In Adopting New Task Categories, Christoph Treude, Margaret-Anne Storey
Research Collection School Of Computing and Information Systems
In collaborative software development projects, tasks are often used as a mechanism to coordinate and track shared development work. Modern development environments provide explicit support for task management where tasks are typically organized and managed through predefined categories. Although there have been many studies that analyze data available from task management systems, there has been relatively little work on the design of task management tools. In this paper we explore how tagging with freely assigned keywords provides developers with a lightweight mechanism to further categorize and annotate development tasks. We investigate how tags that are frequently used over a long …
A Comparative Exploration Of Freebsd Bug Lifetimes, Gargi Bougie, Christoph Treude, Daniel M. Germán, Margaret-Anne Storey
A Comparative Exploration Of Freebsd Bug Lifetimes, Gargi Bougie, Christoph Treude, Daniel M. Germán, Margaret-Anne Storey
Research Collection School Of Computing and Information Systems
In this paper, we explore the viability of mining the basic data provided in bug repositories to predict bug lifetimes. We follow the method of Lucas D. Panjer as described in his paper, Predicting Eclipse Bug Lifetimes. However, in place of Eclipse data, the FreeBSD bug repository is used. We compare the predictive accuracy of five different classification algorithms applied to the two data sets. In addition, we propose future work on whether there is a more informative way of classifying bugs than is considered by current bug tracking systems.
Point-Based Backup For Decentralized Pompds: Complexity And New Algorithms, Akshat Kumar, Shlomo Zilberstein
Point-Based Backup For Decentralized Pompds: Complexity And New Algorithms, Akshat Kumar, Shlomo Zilberstein
Research Collection School Of Computing and Information Systems
Decentralized POMDPs provide an expressive framework for sequential multi-agent decision making. Despite their high complexity, there has been significant progress in scaling up existing algorithms, largely due to the use of point-based methods. Performing point-based backup is a fundamental operation in state-of-the-art algorithms. We show that even a single backup step in the multi-agent setting is NP-Complete. Despite this negative worst-case result, we present an efficient and scalable optimal algorithm as well as a principled approximation scheme. The optimal algorithm exploits recent advances in the weighted CSP literature to overcome the complexity of the backup operation. The polytime approximation scheme …
Exclusive Lasso For Multi-Task Feature Selection, Yang Zhou, Rong Jin, Steven C. H. Hoi
Exclusive Lasso For Multi-Task Feature Selection, Yang Zhou, Rong Jin, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
We propose a novel group regularization which we call exclusive lasso. Unlike the group lasso regularizer that assumes co-varying variables in groups, the proposed exclusive lasso regularizer models the scenario when variables in the same group compete with each other. Analysis is presented to illustrate the properties of the proposed regularizer. We present a framework of kernel-based multi-task feature selection algorithm based on the proposed exclusive lasso regularizer. An efficient algorithm is derived to solve the related optimization problem. Experiments with document categorization show that our approach outperforms state-of-the-art algorithms for multi-task feature selection.
Learning User Profiles For Personalized Information Dissemination, Ah-Hwee Tan, Christine Teo
Learning User Profiles For Personalized Information Dissemination, Ah-Hwee Tan, Christine Teo
Research Collection School Of Computing and Information Systems
Personalized information systems represent the recent effort of delivering information to users more effectively in the modern electronic age. This paper illustrates how a supervised Adaptive Resonance Theory (ART) system, known as fuzzy ARAM, can be used to learn user profiles for personalized information dissemination. ARAM learning is on-line, fast, and incremental. Acquisition of new knowledge does not require re-training on previously learned cases. ARAM integrates both user-defined and system-learned knowledge in a single framework. Therefore inconsistency between the two knowledge sources will not arise. ARAM has been used to develop a personalized news system known as PIN. Preliminary experiments …
A Discriminative Model Approach For Accurate Duplicate Bug Report Retrieval, Chengnian Sun, David Lo, Xiaoyin Wang, Siau-Cheng Khoo
A Discriminative Model Approach For Accurate Duplicate Bug Report Retrieval, Chengnian Sun, David Lo, Xiaoyin Wang, Siau-Cheng Khoo
Research Collection School Of Computing and Information Systems
Bug repositories are usually maintained in software projects. Testers or users submit bug reports to identify various issues with systems. Sometimes two or more bug reports correspond to the same defect. To address the problem with duplicate bug reports, a person called a triager needs to manually label these bug reports as duplicates, and link them to their "master" reports for subsequent maintenance work. However, in practice there are considerable duplicate bug reports sent daily; requesting triagers to manually label these bugs could be highly time consuming. To address this issue, recently, several techniques have be proposed using various similarity …
Understanding The Values Of Mobile Technology In Education: A Value-Focused Thinking Approach, Hong Sheng, Keng Siau, Fiona Nah
Understanding The Values Of Mobile Technology In Education: A Value-Focused Thinking Approach, Hong Sheng, Keng Siau, Fiona Nah
Research Collection School Of Computing and Information Systems
Mobile technology has mobilized the human interaction in all dimensions by supporting mobile collaboration. As collaboration is key to learning in today's educational environment, mobile technology has tremendous potential in supporting and improving education and its delivery. Given that mobile technology for education is a new phenomenon that is gaining popularity, the values of using mobile technology to support education need to be further researched and better understood. In this research, we used the Value-Focused Thinking approach to interview students and instructors to identify the values of education that are enabled by mobile technology. These values are represented in the …
Time-Bound Hierarchical Key Assignment: An Overview, Wen Tao Zhu, Robert H. Deng, Jianying Zhou, Feng Bao
Time-Bound Hierarchical Key Assignment: An Overview, Wen Tao Zhu, Robert H. Deng, Jianying Zhou, Feng Bao
Research Collection School Of Computing and Information Systems
The access privileges in distributed systems can be effectively organized as a partial-order hierarchy that consists of distinct security classes, and the access rights are often designated with certain temporal restrictions. The time-bound hierarchical key assignment problem is to assign distinct cryptographic keys to distinct security classes according to their privileges so that users from a higher class can use their class key to derive the keys of lower classes, and these keys are time-variant with respect to sequentially allocated temporal units called time slots. In this paper, we present the involved principle, survey the state of the art, and …
The Role Of Emergent Knowledge Structures In Collaborative Software Development, Christoph Treude
The Role Of Emergent Knowledge Structures In Collaborative Software Development, Christoph Treude
Research Collection School Of Computing and Information Systems
Many collaboration features in software development tools draw on lightweight technologies such as tagging and wikis. We propose to study the role of emergent knowledge structures created through these features. Using a mixed-methods approach, we investigate which processes emergent knowledge structures support and how tool support can leverage them.