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Big Data, Big Theory: Moving Beyond New Empiricism To Generate Powerful Explanations, Sarah Katherine Howard, Karl A. Maton, Ellie Rennie, Jun Ma, Jie Yang, Julian Thomas, Matthew Ciao, Rangan Srikhanta
Big Data, Big Theory: Moving Beyond New Empiricism To Generate Powerful Explanations, Sarah Katherine Howard, Karl A. Maton, Ellie Rennie, Jun Ma, Jie Yang, Julian Thomas, Matthew Ciao, Rangan Srikhanta
SMART Infrastructure Facility - Papers
Abstract presented at the 3rd ISA Forum of Sociology, 10-14 July 2016, Vienna, Austria
Predictive Inference For Big, Spatial, Non-Gaussian Data: Modis Cloud Data And Its Change-Of-Support, Aritra Sengupta, Noel A. Cressie, Brian H. Kahn, Richard Frey
Predictive Inference For Big, Spatial, Non-Gaussian Data: Modis Cloud Data And Its Change-Of-Support, Aritra Sengupta, Noel A. Cressie, Brian H. Kahn, Richard Frey
Faculty of Engineering and Information Sciences - Papers: Part A
Remote sensing of the earth with satellites yields datasets that can be massive in size, nonstationary in space, and non-Gaussian in distribution. To overcome computational challenges, we use the reduced-rank spatial random effects (SRE) model in a statistical analysis of cloud-mask data from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on board NASA's Terra satellite. Parameterisations of cloud processes are the biggest source of uncertainty and sensitivity in different climate models' future projections of Earth's climate. An accurate quantification of the spatial distribution of clouds, as well as a rigorously estimated pixel-scale clear-sky-probability process, is needed to establish reliable estimates …
Public Cloud Data Auditing With Practical Key Update And Zero Knowledge Privacy, Yong Yu, Yannan Li, Man Ho Au, Willy Susilo, Kim-Kwang Raymond Choo, Xinpeng Zhang
Public Cloud Data Auditing With Practical Key Update And Zero Knowledge Privacy, Yong Yu, Yannan Li, Man Ho Au, Willy Susilo, Kim-Kwang Raymond Choo, Xinpeng Zhang
Faculty of Engineering and Information Sciences - Papers: Part A
Data integrity is extremely important for cloud based storage services, where cloud users no longer have physical possession of their outsourced files. A number of data auditing mechanisms have been proposed to solve this problem. However, how to update a cloud user's private auditing key (as well as the authenticators those keys are associated with) without the user's re-possession of the data remains an open problem. In this paper, we propose a key-updating and authenticator-evolving mechanism with zero-knowledge privacy of the stored files for secure cloud data auditing, which incorporates zero knowledge proof systems, proxy re-signatures and homomorphic linear authenticators. …
Two-Factor Data Security Protection Mechanism For Cloud Storage System, Joseph K. Liu, Kaitai Liang, Willy Susilo, Jianghua Liu, Yang Xiang
Two-Factor Data Security Protection Mechanism For Cloud Storage System, Joseph K. Liu, Kaitai Liang, Willy Susilo, Jianghua Liu, Yang Xiang
Faculty of Engineering and Information Sciences - Papers: Part A
In this paper, we propose a two-factor data security protection mechanism with factor revocability for cloud storage system. Our system allows a sender to send an encrypted message to a receiver through a cloud storage server. The sender only needs to know the identity of the receiver but no other information (such as its public key or its certificate). The receiver needs to possess two things in order to decrypt the ciphertext. The first thing is his/her secret key stored in the computer. The second thing is a unique personal security device which connects to the computer. It is impossible …
A Data-Driven Predictive Model For Residential Mobility In Australia - A Generalised Linear Mixed Model For Repeated Measured Binary Data, Mohammad-Reza Namazi-Rad, Payam Mokhtarian, Nagesh Shukla, Albert Munoz
A Data-Driven Predictive Model For Residential Mobility In Australia - A Generalised Linear Mixed Model For Repeated Measured Binary Data, Mohammad-Reza Namazi-Rad, Payam Mokhtarian, Nagesh Shukla, Albert Munoz
Faculty of Engineering and Information Sciences - Papers: Part A
Household relocation modelling is an integral part of the Government planning process as residential movements influence the demand for community facilities and services. This study will address the problem of modelling residential relocation choice by estimating a logit-link class model. The proposed model estimates the probability of an event which triggers household relocation. The attributes considered in this study are: requirement for bedrooms, employment status, income status, household characteristics, and tenure (i.e. duration living at the current location). Accurate prediction of household relocations for population units should rely on real world observations. In this study, a longitudinal survey data gathered …
Towards Data Analytics Of Pathogen-Host Protein-Protein Interaction: A Survey, Huaming Chen, Jun Shen, Lei Wang, Jiangning Song
Towards Data Analytics Of Pathogen-Host Protein-Protein Interaction: A Survey, Huaming Chen, Jun Shen, Lei Wang, Jiangning Song
Faculty of Engineering and Information Sciences - Papers: Part A
"Big Data" is immersed in many disciplines, including computer vision, economics, online resources, bioinformatics and so on. Increasing researches are conducted on data mining and machine learning for uncovering and predicting related domain knowledge. Protein-protein interaction is one of the main areas in bioinformatics as it is the basis of the biological functions. However, most pathogen-host protein-protein interactions, which would be able to reveal much more infectious mechanisms between pathogen and host, are still up for further investigation. Considering a decent feature representation of pathogen-host protein-protein interactions (PHPPI), currently there is not a well structured database for research purposes, not …
A Bottom-Up Data Collection Methodology For Characterising The Residential Building Stock In Australia, Clayton Mcdowell, Georgios Kokogiannakis, Paul Cooper, Michael P. Tibbs
A Bottom-Up Data Collection Methodology For Characterising The Residential Building Stock In Australia, Clayton Mcdowell, Georgios Kokogiannakis, Paul Cooper, Michael P. Tibbs
Faculty of Engineering and Information Sciences - Papers: Part B
In Australia the majority of the current residential building stock has been constructed with little regard to energy consumption or thermal comfort. With only 1-2 % of Australia's building stock being replaced each year retrofitting solutions are necessary if residential energy consumption is to be reduced. Australia's records of the characteristics of its current building stock are minimal and outdated and thus these need to be renewed to enable the evaluation of retrofit upgrade strategies. Thus this paper presents a methodology and results of a bottom-up data collection tool that captured building and occupant characteristics from 200 elderly low income …