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A Social Transitivity-Based Data Dissemination Scheme For Opportunistic Networks, Jaesung KU, Yangwoo KO, Jisun AN, Dongman LEE 2010 Singapore Management University

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 …


Efficient Mutual Nearest Neighbor Query Processing For Moving Object Trajectories, Yunjun GAO, Baihua ZHENG, Gencai CHEN, Qing LI, Chun CHEN, Gang CHEN 2010 Zhejiang University

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 2010 Nanyang Technological University

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 …


Otl: A Framework Of Online Transfer Learning, Peilin ZHAO, Steven C. H. HOI 2010 Nanyang Technological University

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 …


Using Hadoop And Cassandra For Taxi Data Analytics: A Feasibility Study, Alvin Jun Yong KOH, Xuan Khoa NGUYEN, C. Jason WOODARD 2010 Singapore Management University

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 …


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 2010 Pennsylvania State University

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 …


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 2010 Singapore Management University

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 …


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 2010 Singapore Management University

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 …


Do Wikipedians Follow Domain Experts? A Domain-Specific Study On Wikipedia Contribution, Yi ZHANG, Aixin SUN, Anwitaman DATTA, Kuiyu CHANG, Ee Peng LIM 2010 Nanyang Technological University

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 …


Variance Reduction Techniques For Estimating Quantiles And Value-At-Risk, Fang Chu 2010 New Jersey Institute of Technology

Variance Reduction Techniques For Estimating Quantiles And Value-At-Risk, Fang Chu

Dissertations

Quantiles, as a performance measure, arise in many practical contexts. In finance, quantiles are called values-at-risk (VARs), and they are widely used in the financial industry to measure portfolio risk. When the cumulative distribution function is unknown, the quantile can not be computed exactly and must be estimated. In addition to computing a point estimate for the quantile, it is important to also provide a confidence interval for the quantile as a way of indicating the error in the estimate. A problem with crude Monte Carlo is that the resulting confidence interval may be large, which is often the case …


Iris : Digital Scholarship, Publishing, And Preservation At Northeastern University, Hillary Corbett 2010 Northeastern University

Iris : Digital Scholarship, Publishing, And Preservation At Northeastern University, Hillary Corbett

Hillary Corbett

No abstract provided.


Sense Of Place In Virtual World Learning Environments: A Conceptual Exploration, Vipin Arora, Deepak Khazanchi 2010 University of Nebraska at Omaha

Sense Of Place In Virtual World Learning Environments: A Conceptual Exploration, Vipin Arora, Deepak Khazanchi

Information Systems and Quantitative Analysis Faculty Proceedings & Presentations

In this paper we conceptually explore the notion of sense of place and its potential use in the design of a ‗place for learning‘ in 3D immersive environments such as virtual worlds. We draw from earlier research in the fields of environmental psychology, social psychology and Human Computer Interaction. Our goal in this paper is to summarize the conceptual foundations that will form the basis for further empirical research aimed to inform institutions aspiring to create learning spaces in 3D virtual worlds.


Capacity-Driven Pricing Mechanism In Special Service Industries, Lijian Chen, Suraj M. Alexander 2010 University of Dayton

Capacity-Driven Pricing Mechanism In Special Service Industries, Lijian Chen, Suraj M. Alexander

MIS/OM/DS Faculty Publications

We propose a capacity driven pricing mechanism for several service industries in which the customer behavior, the price demand relationship, and the competition are significantly distinct from other industries. According our observation, we found that the price demand relationship in these industries cannot be modeled by fitted curves; the customers would neither plan in advance nor purchase the service strategically; and the competition would be largely local. We analyze both risk neutral and risk aversion pricing models and conclude the proposed capacity driven model would be the optimal solution under mild assumptions. The resulting pricing mechanism has been implemented at …


Linked Sensor Data, Harshal Kamlesh Patni, Cory Andrew Henson, Amit P. Sheth 2010 Wright State University - Main Campus

Linked Sensor Data, Harshal Kamlesh Patni, Cory Andrew Henson, Amit P. Sheth

Kno.e.sis Publications

A number of government, corporate, and academic organizations are collecting enormous amounts of data provided by environmental sensors. However, this data is too often locked within organizations and underutilized by the greater community. In this paper, we present a framework to make this sensor data openly accessible by publishing it on the Linked Open Data (LOD) Cloud. This is accomplished by converting raw sensor observations to RDF and linking with other datasets on LOD. With such a framework, organizations can make large amounts of sensor data openly accessible, thus allowing greater opportunity for utilization and analysis.


Some Trust Issues In Social Networks And Sensor Networks, Krishnaprasad Thirunarayan, Pramod Anantharam, Cory Andrew Henson, Amit P. Sheth 2010 Wright State University - Main Campus

Some Trust Issues In Social Networks And Sensor Networks, Krishnaprasad Thirunarayan, Pramod Anantharam, Cory Andrew Henson, Amit P. Sheth

Kno.e.sis Publications

Trust and reputation are becoming increasingly important in diverse areas such as search, e-commerce, social media, semantic sensor networks, etc. We review past work and explore future research issues relevant to trust in social/sensor networks and interactions. We advocate a balanced, iterative approach to trust that marries both theory and practice. On the theoretical side, we investigate models of trust to analyze and specify the nature of trust and trust computation. On the practical side, we propose to uncover aspects that provide a basis for trust formation and techniques to extract trust information from concrete social/sensor networks and interactions. We …


Personalization By Website Transformation: Theory And Practice, Saverio Perugini 2010 University of Dayton

Personalization By Website Transformation: Theory And Practice, Saverio Perugini

Computer Science Faculty Publications

We present an analysis of a progressive series of out-of-turn transformations on a hierarchical website to personalize a user’s interaction with the site. We formalize the transformation in graph-theoretic terms and describe a toolkit we built that enumerates all of the traversals enabled by every possible complete series of these transformations in any site and computes a variety of metrics while simulating each traversal therein to qualify the relationship between a site’s structure and the cumulative effect of support for the transformation in a site. We employed this toolkit in two websites. The results indicate that the transformation enables users …


Distance-Based Measures Of Inconsistency And Incoherency For Description Logics, Yue Ma, Pascal Hitzler 2010 Wright State University - Main Campus

Distance-Based Measures Of Inconsistency And Incoherency For Description Logics, Yue Ma, Pascal Hitzler

Computer Science and Engineering Faculty Publications

Inconsistency and incoherency are two sorts of erroneous information in a DL ontology which have been widely discussed in ontology-based applications. For example, they have been used to detect modeling errors during ontology construction. To provide more informative metrics which can tell the differences between inconsistent ontologies and between incoherent terminologies, there has been some work on measuring inconsistency of an ontology and on measuring incoherency of a terminology. However, most of them merely focus either on measuring inconsistency or on measuring incoherency and no clear ideas of how to extend them to allow for the other. In this paper, …


Exploiting Query Logs For Cross-Lingual Query Suggestions., Wei GAO, Cheng NIU, Jian-Yun NIE, Ming ZHOU, Kam-Fai WONG, Hsiao-Wuen HON 2010 Singapore Management University

Exploiting Query Logs For Cross-Lingual Query Suggestions., Wei Gao, Cheng Niu, Jian-Yun Nie, Ming Zhou, Kam-Fai Wong, Hsiao-Wuen Hon

Research Collection School Of Computing and Information Systems

Query suggestion aims to suggest relevant queries for a given query, which helps users better specify their information needs. Previous work on query suggestion has been limited to the same language. In this article, we extend it to cross-lingual query suggestion (CLQS): for a query in one language, we suggest similar or relevant queries in other languages. This is very important to the scenarios of cross-language information retrieval (CLIR) and other related cross-lingual applications. Instead of relying on existing query translation technologies for CLQS, we present an effective means to map the input query of one language to queries of …


A Comparative Study On Text Categorization, Aditya Chainulu Karamcheti 2010 University of Nevada Las Vegas

A Comparative Study On Text Categorization, Aditya Chainulu Karamcheti

UNLV Theses, Dissertations, Professional Papers, and Capstones

Automated text categorization is a supervised learning task, defined as assigning category labels to new documents based on likelihood suggested by a training set of labeled documents. Two examples of methodology for text categorizations are Naive Bayes and K-Nearest Neighbor.

In this thesis, we implement two categorization engines based on Naive Bayes and K-Nearest Neighbor methodology. We then compare the effectiveness of these two engines by calculating standard precision and recall for a collection of documents. We will further report on time efficiency of these two engines.


Learning User Profiles For Personalized Information Dissemination, Ah-hwee TAN, Christine TEO 2010 Singapore Management University

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 …


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