Continuous Visible Nearest Neighbor Query Processing In Spatial Databases,
2011
Zhejiang University
Continuous Visible Nearest Neighbor Query Processing In Spatial Databases, Yunjun Gao, Baihua Zheng, Gencai Chen, Qing Li, Xiaofa Guo
Research Collection School Of Computing and Information Systems
In this paper, we identify and solve a new type of spatial queries, called continuous visible nearest neighbor (CVNN) search. Given a data set P, an obstacle set O, and a query line segment q in a two-dimensional space, a CVNN query returns a set of $${\langle p, R\rangle}$$ tuples such that $${p \in P}$$ is the nearest neighbor to every point r along the interval $${R \subseteq q}$$ as well as pis visible to r. Note that p may be NULL, meaning that all points in P are invisible to all points in R due to the obstruction of …
Supervisory Evolutionary Optimization Strategy For Adaptive Maintenance Schedules,
2011
Singapore Management University
Supervisory Evolutionary Optimization Strategy For Adaptive Maintenance Schedules, Zhaoxia Wang, C. S. Chang
Research Collection School Of Computing and Information Systems
No abstract provided.
Topical Keyphrase Extraction From Twitter,
2011
Peking University
Topical Keyphrase Extraction From Twitter, Xin Zhao, Jing Jiang, Jing He, Yang Song, Palakorn Achananuparp, Ee Peng Lim, Xiaoming Li
Research Collection School Of Computing and Information Systems
Summarizing and analyzing Twitter content is an important and challenging task. In this paper, we propose to extract topical keyphrases as one way to summarize Twitter. We propose a context-sensitive topical PageRank method for keyword ranking and a probabilistic scoring function that considers both relevance and interestingness of keyphrases for keyphrase ranking. We evaluate our proposed methods on a large Twitter data set. Experiments show that these methods are very effective for topical keyphrase extraction.
Using Service Responsibility Tables To Supplement Uml In Analyzing E-Service Systems,
2011
Singapore Management University
Using Service Responsibility Tables To Supplement Uml In Analyzing E-Service Systems, Xin Tan, Steven Alter, Keng Siau
Research Collection School Of Computing and Information Systems
This paper proposes using Service Responsibility Tables (SRTs) as a tool in analyzing e-service systems. First it discusses difficulties and deficiencies of using formal modeling languages such as UML in analyzing e-service systems. It proposes using SRTs as an informal language and lightweight analytical tool to be used by business professionals in analyzing e-service systems. SRTs are based on a service value chain framework but do not rely on abstract concepts and constructs, and therefore can be used by business professionals to supplement UML. We suggest a set of heuristics for transforming SRTs into two key UML diagrams, thereby illustrating …
Online Fault Detection Of Induction Motors Using Frequency Domain Independent Components Analysis,
2011
Singapore Management University
Online Fault Detection Of Induction Motors Using Frequency Domain Independent Components Analysis, Zhaoxia Wang, C. S. Chang
Research Collection School Of Computing and Information Systems
This paper proposes an online fault detection method for induction motors using frequency-domain independent component analysis. Frequency-domain results, which are obtained by applying Fast Fourier Transform (FFT) to measured stator current time-domain waveforms, are analyzed with the aim of extracting frequency signatures of healthy and faulty motors with broken rotor-bar or bearing problem. Independent components analysis (ICA) is applied for such an aim to the FFT results. The obtained independent components as well as the FFT results are then used to obtain the combined fault signatures. The proposed method overcomes problems occurring in many existing FFT-based methods. Results using laboratory-collected …
Link Type Based Pre-Cluster Pair Model For Coreference Resolution,
2011
Singapore Management University
Link Type Based Pre-Cluster Pair Model For Coreference Resolution, Yang Song, Houfeng Wang, Jing Jiang
Research Collection School Of Computing and Information Systems
This paper presents our participation in the CoNLL-2011 shared task, Modeling Unrestricted Coreference in OntoNotes. Coreference resolution, as a difficult and challenging problem in NLP, has attracted a lot of attention in the research community for a long time. Its objective is to determine whether two mentions in a piece of text refer to the same entity. In our system, we implement mention detection and coreference resolution seperately. For mention detection, a simple classification based method combined with several effective features is developed. For coreference resolution, we propose a link type based pre-cluster pair model. In this model, pre-clustering of …
Parallelizing Scale Invariant Feature Transform On A Distributed Memory Cluster,
2011
University of Arkansas, Fayetteville
Parallelizing Scale Invariant Feature Transform On A Distributed Memory Cluster, Stanislav Bobovych
Computer Science and Computer Engineering Undergraduate Honors Theses
Scale Invariant Feature Transform (SIFT) is a computer vision algorithm that is widely-used to extract features from images. We explored accelerating an existing implementation of this algorithm with message passing in order to analyze large data sets. We successfully tested two approaches to data decomposition in order to parallelize SIFT on a distributed memory cluster.
Methods For Multilevel Parallelism On Gpu Clusters: Application To A Multigrid Accelerated Navier-Stokes Solver,
2011
Boise State University
Methods For Multilevel Parallelism On Gpu Clusters: Application To A Multigrid Accelerated Navier-Stokes Solver, Dana A. Jacobsen
Boise State University Theses and Dissertations
Computational Fluid Dynamics (CFD) is an important field in high performance computing with numerous applications. Solving problems in thermal and fluid sciences demands enormous computing resources and has been one of the primary applications used on supercomputers and large clusters. Modern graphics processing units (GPUs) with many-core architectures have emerged as general-purpose parallel computing platforms that can accelerate simulation science applications substantially. While significant speedups have been obtained with single and multiple GPUs on a single workstation, large problems require more resources. Conventional clusters of central processing units (CPUs) are now being augmented with GPUs in each compute-node to tackle …
Fragile Online Relationship: A First Look At Unfollow Dynamics In Twitter,
2011
Singapore Management University
Fragile Online Relationship: A First Look At Unfollow Dynamics In Twitter, Haewoon Kwak, Hyunwoo Chun, Sue. Moon
Research Collection School Of Computing and Information Systems
We analyze the dynamics of the behavior known as 'unfollow' in Twitter. We collected daily snapshots of the online relationships of 1.2 million Korean-speaking users for 51 days as well as all of their tweets. We found that Twitter users frequently unfollow. We then discover the major factors, including the reciprocity of the relationships, the duration of a relationship, the followees' informativeness, and the overlap of the relationships, which affect the decision to unfollow. We conduct interview with 22 Korean respondents to supplement the quantitative results.They unfollowed those who left many tweets within a short time, created tweets about uninteresting …
Continuous Nearest Neighbor Search In The Presence Of Obstacles,
2011
Singapore Management University
Continuous Nearest Neighbor Search In The Presence Of Obstacles, Yunjun Gao, Baihua Zheng, Gang Chen, Chun Chen, Qing Li
Research Collection School Of Computing and Information Systems
Despite the ubiquity of physical obstacles (e.g., buildings, hills, and blindages, etc.) in the real world, most of spatial queries ignore the obstacles. In this article, we study a novel form of continuous nearest-neighbor queries in the presence of obstacles, namely continuous obstructed nearest-neighbor (CONN) search, which considers the impact of obstacles on the distance between objects. Given a data setP, an obstacle set O, and a query line segment q, in a two-dimensional space, a CONN query retrieves the nearest neighbor p ∈ P of each point p′ on q according to the obstructed distance, the shortest path between …
A Probabilistic Analysis Of Misparking In Reservation Based Parking Garages,
2011
Old Dominion University
A Probabilistic Analysis Of Misparking In Reservation Based Parking Garages, Vikas G. Ashok
Computer Science Theses & Dissertations
Parking in major cities is an expensive and annoying affair, the reason ascribed to the limited availability of parking space. Modern parking garages provide parking reservation facility, thereby ensuring availability to prospective customers. Misparking in such reservation based parking garages creates confusion and aggravates driver frustration. The general conception about misparking is that it tends to completely cripple the normal functioning of the system leading to chaos and confusion. A single mispark tends to have a ripple effect and therefore spawns a chain of misparks. The chain terminates when the last mispark occurs at the parking slot reserved by the …
Comparing Twitter And Traditional Media Using Topic Models,
2011
Peking University
Comparing Twitter And Traditional Media Using Topic Models, Wayne Xin Zhao, Jing Jiang, Jianshu Weng, Jing He, Ee Peng Lim, Hongfei Yan, Xiaoming Li
Research Collection School Of Computing and Information Systems
Twitter as a new form of social media can potentially contain much useful information, but content analysis on Twitter has not been well studied. In particular, it is not clear whether as an information source Twitter can be simply regarded as a faster news feed that covers mostly the same information as traditional news media. In This paper we empirically compare the content of Twitter with a traditional news medium, New York Times, using unsupervised topic modeling. We use a Twitter-LDA model to discover topics from a representative sample of the entire Twitter. We then use text mining techniques to …
Predicting Item Adoption Using Social Correlation,
2011
Singapore Management University
Predicting Item Adoption Using Social Correlation, Freddy Chong-Tat Chua, Hady W. Lauw, Ee Peng Lim
Research Collection School Of Computing and Information Systems
Users face a dazzling array of choices on the Web when it comes to choosing which product to buy, which video to watch, etc. The trend of social information processing means users increasingly rely not only on their own preferences, but also on friends when making various adoption decisions. In this paper, we investigate the effects of social correlation on users’ adoption of items. Given a user-user social graph and an item-user adoption graph, we seek to answer the following questions: 1) whether the items adopted by a user correlate to items adopted by her friends, and 2) how to …
Efficient Topological Olap On Information Networks,
2011
Peking University
Efficient Topological Olap On Information Networks, Qiang Qu, Feida Zhu, Xifeng Yan, Jiawei Han, Philip Yu, Hongyan Li
Research Collection School Of Computing and Information Systems
We propose a framework for efficient OLAP on information networks with a focus on the most interesting kind, the topological OLAP (called “T-OLAP”), which incurs topological changes in the underlying networks. T-OLAP operations generate new networks from the original ones by rolling up a subset of nodes chosen by certain constraint criteria. The key challenge is to efficiently compute measures for the newly generated networks and handle user queries with varied constraints. Two effective computational techniques, T-Distributiveness and T-Monotonicity are proposed to achieve efficient query processing and cube materialization. We also provide a T-OLAP query processing framework into which these …
Mkboost: A Framework Of Multiple Kernel Boosting,
2011
Singapore Management University
Mkboost: A Framework Of Multiple Kernel Boosting, Hao Xia, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
Multiple kernel learning (MKL) has been shown as a promising machine learning technique for data mining tasks by integrating with multiple diverse kernel functions. Traditional MKL methods often formulate the problem as an optimization task of learning both optimal combination of kernels and classifiers, and attempt to resolve the challenging optimization task by various techniques. Unlike the existing MKL methods, in this paper, we investigate a boosting framework of exploring multiple kernel learning for classification tasks. In particular, we present a novel framework of Multiple Kernel Boosting (MKBoost), which applies boosting techniques for learning kernel-based classifiers with multiple kernels. Based …
Multi-Objective Zone Mapping In Large-Scale Distributed Virtual Environments,
2011
Singapore Management University
Multi-Objective Zone Mapping In Large-Scale Distributed Virtual Environments, Nguyen Binh Duong Ta, Suiping Zhou, Wentong Cai, Xueyan Tang, Rassul Avani
Research Collection School Of Computing and Information Systems
In large-scale distributed virtual environments (DVEs), the NP-hard zone mapping problem concerns how to assign distinct zones of the virtual world to a number of distributed servers to improve overall interactivity. Previously, this problem has been formulated as a single-objective optimization problem, in which the objective is to minimize the total number of clients that are without QoS. This approach may cause considerable network traffic and processing overhead, as a large number of zones may need to be migrated across servers. In this paper, we introduce a multi-objective approach to the zone mapping problem, in which both the total number …
Mining Social Images With Distance Metric Learning For Automated Image Tagging,
2011
Nanyang Technological University
Mining Social Images With Distance Metric Learning For Automated Image Tagging, Pengcheng Wu, Steven C. H. Hoi, Peilin Zhao, Ying He
Research Collection School Of Computing and Information Systems
With the popularity of various social media applications, massive social images associated with high quality tags have been made available in many social media web sites nowadays. Mining social images on the web has become an emerging important research topic in web search and data mining. In this paper, we propose a machine learning framework for mining social images and investigate its application to automated image tagging. To effectively discover knowledge from social images that are often associated with multimodal contents (including visual images and textual tags), we propose a novel Unified Distance Metric Learning (UDML) scheme, which not only …
Interdisciplinary Computational Projects Utilizing The Hpc Cluster,
2011
Dyson College of Arts and Sciences, Pace University
Interdisciplinary Computational Projects Utilizing The Hpc Cluster, Demos Athanasopoulos
Cornerstone 3 Reports : Interdisciplinary Informatics
No abstract provided.
A Parallel Graph Sampling Algorithm For Analyzing Gene Correlation Networks,
2011
University of Nebraska at Omaha
A Parallel Graph Sampling Algorithm For Analyzing Gene Correlation Networks, Kathryn Dempsey Cooper, Kanimathi Duraisamy, Hesham Ali, Sanjukta Bhowmick
Interdisciplinary Informatics Faculty Publications
Effcient analysis of complex networks is often a challenging task due to its large size and the noise inherent in the system. One popular method of overcoming this problem is through graph sampling, that is extracting a representative subgraph from the larger network. The accuracy of the sample is validated by comparing the combinatorial properties of the subgraph and the original network. However, there has been little study in comparing networks based on the applications that they represent. Furthermore, sampling methods are generally applied agnostically, without mapping to the requirements of the underlying analysis. In this paper,we introduce a parallel …
In-Degree Dynamics Of Large-Scale P2p Systems,
2011
University of Dayton
In-Degree Dynamics Of Large-Scale P2p Systems, Zhongmei Yao, Daren B. H. Cline, Dmitri Loguinov
Computer Science Faculty Publications
This paper builds a complete modeling framework for understanding user churn and in-degree dynamics in unstructured P2P systems in which each user can be viewed as a stationary alternating renewal process. While the classical Poisson result on the superposition of n stationary renewal processes for n→∞ requires that each point process become sparser as n increases, it is often difficult to rigorously show this condition in practice. In this paper, we first prove that despite user heterogeneity and non-Poisson arrival dynamics, a superposition of edge-arrival processes to a live user under uniform selection converges to a Poisson process when …
