Numerical Analysis and Scientific Computing Commons^™

A Domain Specific Model For Generating Etl Workflows From Business Intents, Wesley Deneke

Graduate Theses and Dissertations

Extract-Transform-Load (ETL) tools have provided organizations with the ability to build and maintain workflows (consisting of graphs of data transformation tasks) that can process the flood of digital data. Currently, however, the specification of ETL workflows is largely manual, human time intensive, and error prone. As these workflows become increasingly complex, the users that build and maintain them must retain an increasing amount of knowledge specific to how to produce solutions to business objectives using their domain's ETL workflow system. A program that can reduce the human time and expertise required to define such workflows, producing accurate ETL solutions with …

Boosting Multi-Kernel Locality-Sensitive Hashing For Scalable Image Retrieval, Hao Xia, Steven C. H. Hoi, Pengcheng Wu, Rong Jin

Research Collection School Of Computing and Information Systems

Similarity search is a key challenge for multimedia retrieval applications where data are usually represented in high-dimensional space. Among various algorithms proposed for similarity search in high-dimensional space, Locality-Sensitive Hashing (LSH) is the most popular one, which recently has been extended to Kernelized Locality-Sensitive Hashing (KLSH) by exploiting kernel similarity for better retrieval efficacy. Typically, KLSH works only with a single kernel, which is often limited in real-world multimedia applications, where data may originate from multiple resources or can be represented in several different forms. For example, in content-based multimedia retrieval, a variety of features can be extracted to represent …

Online Feature Selection For Mining Big Data, Steven C. H. Hoi, Jialei Wang, Peilin Zhao, Rong Jin

Research Collection School Of Computing and Information Systems

Most studies of online learning require accessing all the attributes/features of training instances. Such a classical setting is not always appropriate for real-world applications when data instances are of high dimensionality or the access to it is expensive to acquire the full set of attributes/features. To address this limitation, we investigate the problem of Online Feature Selection (OFS) in which the online learner is only allowed to maintain a classifier involved a small and fixed number of features. The key challenge of Online Feature Selection is how to make accurate prediction using a small and fixed number of active features. …

On The K-Mer Frequency Spectra Of Organism Genome And Proteome Sequences With A Preliminary Machine Learning Assessment Of Prime Predictability, Nathan O. Schmidt

Boise State University Theses and Dissertations

A regular expression and region-specific filtering system for biological records at the National Center for Biotechnology database is integrated into an object oriented sequence counting application, and a statistical software suite is designed and deployed to interpret the resulting k-mer frequencies|with a priority focus on nullomers. The proteome k-mer frequency spectra of ten model organisms and the genome k-mer frequency spectra of two bacteria and virus strains for the coding and non-coding regions are comparatively scrutinized. We observe that the naturally-evolved (NCBI/organism) and the artificially-biased (randomly-generated) sequences exhibit a clear deviation from the artificially-unbiased (randomly-generated) histogram distributions. …

Shortest Path Computation With No Information Leakage, Kyriakos Mouratidis, Man Lung Yiu

Research Collection School Of Computing and Information Systems

Shortest path computation is one of the most common queries in location-based services (LBSs). Although particularly useful, such queries raise serious privacy concerns. Exposing to a (potentially untrusted) LBS the client’s position and her destination may reveal personal information, such as social habits, health condition, shopping preferences, lifestyle choices, etc. The only existing method for privacy-preserving shortest path computation follows the obfuscation paradigm; it prevents the LBS from inferring the source and destination of the query with a probability higher than a threshold. This implies, however, that the LBS still deduces some information (albeit not exact) about the client’s location …

Modeling Concept Dynamics For Large Scale Music Search, Jialie Shen, Hwee Hwa Pang, Meng Wang, Shuicheng Yan

Research Collection School Of Computing and Information Systems

Continuing advances in data storage and communication technologies have led to an explosive growth in digital music collections. To cope with their increasing scale, we need effective Music Information Retrieval (MIR) capabilities like tagging, concept search and clustering. Integral to MIR is a framework for modelling music documents and generating discriminative signatures for them. In this paper, we introduce a multimodal, layered learning framework called DMCM. Distinguished from the existing approaches that encode music as an ensemble of order-less feature vectors, our framework extracts from each music document a variety of acoustic features, and translates them into low-level encodings over …

Topic Discovery From Tweet Replies, Bingtian Dai, Ee Peng Lim, Philips Kokoh Prasetyo

Research Collection School Of Computing and Information Systems

Twitter is a popular online social information network service which allows people to read and post messages up to 140 characters, known as “tweets”. In this paper, we focus on the tweets between pairs of individuals, i.e., the tweet replies, and propose a generative model to discover topics among groups of twitter users. Our model has then been evaluated with a tweet dataset to show its effectiveness.

Joint Learning For Coreference Resolution With Markov Logic, Yang Song, Jing Jiang, Xin Zhao, Sujian Li, Houfeng Wang

Research Collection School Of Computing and Information Systems

Pairwise coreference resolution models must merge pairwise coreference decisions to generate final outputs. Traditional merging methods adopt different strategies such as the best first method and enforcing the transitivity constraint, but most of these methods are used independently of the pairwise learning methods as an isolated inference procedure at the end. We propose a joint learning model which combines pairwise classification and mention clustering with Markov logic. Experimental results show that our joint learning system outperforms independent learning systems. Our system gives a better performance than all the learning-based systems from the CoNLL-2011 shared task on the same dataset. Compared …

Identifying Event-Related Bursts Via Social Media Activities, Xin Zhao, Baihan Shu, Jing Jiang, Yang Song, Hongfei Yan, Xiaoming Li

Research Collection School Of Computing and Information Systems

Activities on social media increase at a dramatic rate. When an external event happens, there is a surge in the degree of activities related to the event. These activities may be temporally correlated with one another, but they may also capture different aspects of an event and therefore exhibit different bursty patterns. In this paper, we propose to identify event-related bursts via social media activities. We study how to correlate multiple types of activities to derive a global bursty pattern. To model smoothness of one state sequence, we propose a novel function which can capture the state context. The experiments …

Enhancing Access Privacy Of Range Retrievals Over B+Trees, Hwee Hwa Pang, Jilian Zhang, Kyriakos Mouratidis

Research Collection School Of Computing and Information Systems

Users of databases that are hosted on shared servers cannot take for granted that their queries will not be disclosed to unauthorized parties. Even if the database is encrypted, an adversary who is monitoring the I/O activity on the server may still be able to infer some information about a user query. For the particular case of a B+-tree that has its nodes encrypted, we identify properties that enable the ordering among the leaf nodes to be deduced. These properties allow us to construct adversarial algorithms to recover the B+-tree structure from the I/O traces generated by range queries. Combining …

Finding Bursty Topics From Microblogs, Qiming Diao, Jing Jiang, Feida Zhu, Ee Peng Lim

Research Collection School Of Computing and Information Systems

Microblogs such as Twitter reflect the general public’s reactions to major events. Bursty topics from microblogs reveal what events have attracted the most online attention. Although bursty event detection from text streams has been studied before, previous work may not be suitable for microblogs because compared with other text streams such as news articles and scientific publications, microblog posts are particularly diverse and noisy. To find topics that have bursty patterns on microblogs, we propose a topic model that simultaneousy captures two observations: (1) posts published around the same time are more likely to have the same topic, and (2) …

Logistics Orchestration Modeling And Evaluation For Humanitarian Relief, Hoong Chuin Lau, Zhengping Li, Xin Du, Heng Jiang, Robert De Souza

Research Collection School Of Computing and Information Systems

This paper proposes an orchestration model for post-disaster response that is aimed at automating the coordination of scarce resources that minimizes the loss of human lives. In our setting, different teams are treated as agents and their activities are "orchestrated" to optimize rescue performance. Results from simulation are analysed to evaluate the performance of the optimization model.

The R Journal (June 2012) 4(1): Complete Issue, The R Foundation

The R Journal

Contributed Research Articles

Analysing Seasonal Data, Adrian G. Barnett, Peter Baker, and Annette J. Dobson

MARSS: Multivariate Autoregressive State-space Models for Analyzing Time-series Data, Elizabeth E. Holmes, Eric J. Ward, and Kellie Wills

openair: Data Analysis Tools for the Air Quality Community, Karl Ropkins and David C. Carslaw

Foreign Library Interface, Daniel Adler

Vdgraph: A Package for Creating Variance Dispersion Graphs, John Lawson

xgrid and R: Parallel Distributed Processing Using Heterogeneous Groups of Apple Computers, Sarah C. Anoke, Yuting Zhao, Rafael Jaeger, and Nicholas J. Horton

maxent: An R Package for Low-memory Multinomial Logistic Regression with Support for Semi-automated Text …

Who Did What? The Roles Of R Package Authors And How To Refer To Them, Kurt Hornik, Duncan Murdoch, Achim Zeileis

The R Journal

Computational infrastructure for rep resenting persons and citations has been avail able in R for several years, but has been restructured through enhanced classes "person" and "bibentry" in recent versions of R. The new features include support for the specification of the roles of package authors (e.g. maintainer, author, contributor, translator, etc.) and more flexible formatting/printing tools among various other improvements. Here, we introduce the new classes and their methods and indicate how this functionality is employed in the management of R packages. Specifically, we show how the authors of R packages can be specified along with their roles in …

Xgrid And R: Parallel Distributed Processing Using Heterogeneous Groups Of Apple Computers, Sarah C. Anoke, Yuting Zhao, Rafael Jaeger, Nicholas J. Horton

The R Journal

The Apple Xgrid system provides access to groups (or grids) of computers that can be used to facilitate parallel processing. We describe the xgrid package which facilitates access to this system to undertake independent simulations or other long-running jobs that can be divided into replicate runs within R. Detailed examples are provided to demonstrate the interface, along with results from a simulation study of the performance gains using a variety of grids. Use of the grid for “embarassingly parallel” independent jobs has the potential for major speedups in time to completion. Appendices provide guidance on setting up the workflow, utilizing …

Openair: Data Analysis Tools For The Air Quality Community, Karl Ropkins, David C. Carslaw

The R Journal

The openair package contains data analysis tools for the air quality community. This paper provides an overview of data importers, main functions, and selected utilities and workhorse functions within the package and the function output class, as of package version 0.4-14. It is intended as an explanation of the rationale for the package and a technical description for those wishing to work more inter actively with the main functions or develop additional functions to support ‘higher level’ use of openair and R.

Maxent: An R Package For Low-Memory Multinomial Logistic Regression With Support For Semi-Automated Text Classification, Timothy P. Jurka

The R Journal

maxent is a package with tools for data classification using multinomial logistic regression, also known as maximum entropy. The focus of this maximum entropy classifier is to minimize memory consumption on very large datasets, particularly sparse document-term matrices represented by the tm text mining pack age.

Marss: Multivariate Autoregressive State-Space Models For Analyzing Time-Series Data, Elizabeth E. Holmes, Eric J. Ward, Kellie Wills

The R Journal

MARSS is a package for fitting multivariate autoregressive state-space models to time-series data. The MARSS package implements state-space models in a maximum likelihood framework. The core functionality of MARSSis based on likelihood maximization using the Kalman filter/smoother, combined with an EM algorithm. To make comparisons with other packages available, parameter estimation is also permitted via direct search routines avail able in ’optim’. The MARSS package allows data to contain missing values and allows a wide variety of model structures and constraints to be specified (such as fixed or shared parameters). In addition to model-fitting, the package provides bootstrap routines for …

Vdgraph: A Package For Creating Variance Dispersion Graphs, John Lawson

The R Journal

This article introduces the package Vdgraph that is used form a king variance dispersion graphs of response surface designs.The package includes functions that make the variance dispersion graph of one design or compare variance dispersion graphs of two designs, which are stored in data frames or matrices.The package also contains several minimum run response surface designs (stored as matrices) that are not available in other R packages.

Analysing Seasonal Data, Adrian G. Barnett, Peter Baker, Annette J. Dobson

The R Journal

Many common diseases, such as the flu and cardiovascular disease, increase markedly in winter and dip in summer. These seasonal patterns have been part of life for millennia and were first noted in ancient Greece by both Hippocrates and Herodotus. Recent interest has focused on climate change, and the concern that seasons will become more extreme with harsher winter and summer weather. We describe a set of R functions designed to model seasonal pat terns in disease. We illustrate some simple descriptive and graphical methods, a more complex method that is able to model non-stationary patterns, and the case-crossover to …