ORCID Profile
0000-0002-0794-0404
Current Organisation
University of South Australia
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Research Topics
In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Top 5 Research Topics
ANZSRC Field of Research (FoR)
Data Format | Data Structures | Data Storage Representations | Database Management | Conceptual Modelling | Machine learning not elsewhere classified | Control Systems, Robotics and Automation | Artificial Intelligence and Image Processing | Electrical and Electronic Engineering | Information Systems | Data mining and knowledge discovery | Pattern Recognition and Data Mining | Decision Support and Group Support Systems | Information Storage, Retrieval And Management | Information Systems Management | Mineral Processing/Beneficiation | Data management and data science
ANZSRC Socio-Economic Objective (SEO)
Information processing services | Computer Software and Services not elsewhere classified | Beneficiation or Dressing of Iron Ores | Mining and Extraction of Precious (Noble) Metal Ores | Mining and Extraction of Copper Ores |
Related Links
Publications
Mobile cloud security: An adversary model for lightweight browser security
Publisher: Elsevier BV
Date: 2017
Unifying Spatial, Temporal and Semantic Features for an Effective GPS Trajectory-Based Location Recommendation
Publisher: Springer International Publishing
Date: 2015
Top-k Similarity Matching in Large Graphs with Attributes
Publisher: Springer International Publishing
Date: 2014
On discovery of functional dependencies from data
Publisher: Elsevier BV
Date: 07-2013
Preserving Functional Dependency in XML Data Transformation
Publisher: Springer Berlin Heidelberg
Date: 2008
What is the Most Effective Intervention to Increase Job Retention for this Disabled Worker?
Publisher: ACM
Date: 14-08-2022
Updating typical XML views
Publisher: Springer Berlin Heidelberg
Date: 2012
Implementation and performance analysis of incremental equations for nested relations
Publisher: IEEE Comput. Soc
Date: 2000
Checking Satisfactions of XML Referential Integrity Constraints
Publisher: Springer Berlin Heidelberg
Date: 2009
Ancestral Instrument Method for Causal Inference without Complete Knowledge
Publisher: International Joint Conferences on Artificial Intelligence Organization
Date: 07-2022
Abstract: Unobserved confounding is the main obstacle to causal effect estimation from observational data. Instrumental variables (IVs) are widely used for causal effect estimation when there exist latent confounders. With the standard IV method, when a given IV is valid, unbiased estimation can be obtained, but the validity requirement on a standard IV is strict and untestable. Conditional IVs have been proposed to relax the requirement of standard IVs by conditioning on a set of observed variables (known as a conditioning set for a conditional IV). However, the criterion for finding a conditioning set for a conditional IV needs a directed acyclic graph (DAG) representing the causal relationships of both observed and unobserved variables. This makes it challenging to discover a conditioning set directly from data. In this paper, by leveraging maximal ancestral graphs (MAGs) for causal inference with latent variables, we study the graphical properties of ancestral IVs, a type of conditional IVs using MAGs, and develop the theory to support data-driven discovery of the conditioning set for a given ancestral IV in data under the pretreatment variable assumption. Based on the theory, we develop an algorithm for unbiased causal effect estimation with a given ancestral IV and observational data. Extensive experiments on synthetic and real-world datasets demonstrate the performance of the algorithm in comparison with existing IV methods.
Cloning for privacy protection in multiple independent data publications
Publisher: ACM
Date: 24-10-2011
A general framework for privacy preserving data publishing
Publisher: Elsevier BV
Date: 12-2013
Recommending Personalized Interventions to Increase Employability of Disabled Jobseekers
Publisher: Springer International Publishing
Date: 2022
Mining Approximate Keys based on Reasoning from XML Data
Publisher: Natural Sciences Publishing
Date: 07-2014
DOI: 10.12785/AMIS/080459
Applications of Online UV‐Vis Spectrophotometer for Drinking Water Quality Monitoring and Process Control: A Review
Publisher: MDPI AG
Date: 13-04-2022
DOI: 10.3390/S22082987
Abstract: Water quality monitoring is an essential component of water quality management for water utilities for managing the drinking water supply. Online UV-Vis spectrophotometers are becoming popular choices for online water quality monitoring and process control, as they are reagent free, do not require s le pre-treatments and can provide continuous measurements. The advantages of the online UV-Vis sensors are that they can capture events and allow quicker responses to water quality changes compared to conventional water quality monitoring. This review summarizes the applications of online UV-Vis spectrophotometers for drinking water quality management in the last two decades. Water quality measurements can be performed directly using the built-in generic algorithms of the online UV-Vis instruments, including absorbance at 254 nm (UV254), colour, dissolved organic carbon (DOC), total organic carbon (TOC), turbidity and nitrate. To enhance the usability of this technique by providing a higher level of operations intelligence, the UV-Vis spectra combined with chemometrics approach offers simplicity, flexibility and applicability. The use of anomaly detection and an early warning was also discussed for drinking water quality monitoring at the source or in the distribution system. As most of the online UV-Vis instruments studies in the drinking water field were conducted at the laboratory- and pilot-scale, future work is needed for industrial-scale evaluation with ab appropriate validation methodology. Issues and potential solutions associated with online instruments for water quality monitoring have been provided. Current technique development outcomes indicate that future research and development work is needed for the integration of early warnings and real-time water treatment process control systems using the online UV-Vis spectrophotometers as part of the water quality management system.
Radial Projection: A Feature Extraction Method for Topographical Shapes
Publisher: Springer Berlin Heidelberg
Date: 2005
DOI: 10.1007/11581772_51
Transition of Keys in XML Data Transformation
Publisher: IEEE
Date: 10-2008
DOI: 10.1109/CSA.2008.35
Mining Causal Association Rules
Publisher: IEEE
Date: 12-2013
On Defining Referential Integrity for XML
Publisher: IEEE
Date: 10-2008
DOI: 10.1109/CSA.2008.36
Building Diversified Multiple Trees for classification in high dimensional noisy biomedical data
Publisher: Springer Science and Business Media LLC
Date: 10-10-2017
A revised result on chasing tree patterns under schema graphs
Publisher: Elsevier BV
Date: 03-2017
A typology of bullying behaviours: The experiences of Australian nurses
Publisher: Wiley
Date: 14-07-2010
DOI: 10.1111/J.1365-2702.2009.03160.X
Abstract: Aim and objective. This study sought to explore the nature of bullying in the Australian nursing workplace. Background. While there is widespread concern about the extent and consequences of bullying among nurses, to date, there have been no published reports cataloguing the types of behaviours that constitute bullying. Design. Reported here are findings from the first stage of a three‐stage sequential mixed methods study. Methods. The first, qualitative stage of this study employed in‐depth, semi structured interviews with 26 nurses who had experienced bullying from two Australian area health services. Content analysis of the verbatim interview transcripts was performed using the nvivo 7 software program. Results. The analysis identified six major categories and constituent sub‐categories. The typology of bullying behaviours reported here is one of these major categories. Conclusion. The typology of behaviours developed from the study provides detailed insights into the complexity of bullying experienced by nurses. The behaviours were labelled: personal attack, erosion of professional competence and reputation, and attack through work roles and tasks. These themes provide insight into the construct of bullying by providing a detailed catalogue of bullying behaviours that show that bullying is frequently masked in work tasks or work processes and focused on damaging the reputation and status of targets. Relevance to clinical practice. The detailed catalogue of bullying behaviours draws attention to the breadth of the bullying experience. It is anticipated the typology will be of use to nurses, managers and other professionals who are interested in responding to the problem of bullying in nursing.
Personalized Interventions to Increase the Employment Success of People With Disability
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Towards the preservation of functional dependency in XML data transformation
Publisher: Inderscience Publishers
Date: 2010
A Framework for Mining Functional Dependencies from Large Distributed Databases
Publisher: IEEE
Date: 10-2010
On mining association rules with semantic constraints in XML
Publisher: IEEE
Date: 09-2011
Discovering statistically non-redundant subgroups
Publisher: Elsevier BV
Date: 09-2014
How do the existing fairness metrics and unfairness mitigation algorithms contribute to ethical learning analytics?
Publisher: Wiley
Date: 12-04-2022
DOI: 10.1111/BJET.13217
Abstract: With the widespread use of learning analytics (LA), ethical concerns about fairness have been raised. Research shows that LA models may be biased against students of certain demographic subgroups. Although fairness has gained significant attention in the broader machine learning (ML) community in the last decade, it is only recently that attention has been paid to fairness in LA. Furthermore, the decision on which unfairness mitigation algorithm or metric to use in a particular context remains largely unknown. On this premise, we performed a comparative evaluation of some selected unfairness mitigation algorithms regarded in the fair ML community to have shown promising results. Using a 3‐year program dropout data from an Australian university, we comparatively evaluated how the unfairness mitigation algorithms contribute to ethical LA by testing for some hypotheses across fairness and performance metrics. Interestingly, our results show how data bias does not always necessarily result in predictive bias. Perhaps not surprisingly, our test for fairness‐utility tradeoff shows how ensuring fairness does not always lead to drop in utility. Indeed, our results show that ensuring fairness might lead to enhanced utility under specific circumstances. Our findings may to some extent, guide fairness algorithm and metric selection for a given context. What is already known about this topic LA is increasingly being used to leverage actionable insights about students and drive student success. LA models have been found to make discriminatory decisions against certain student demographic subgroups—therefore, raising ethical concerns. Fairness in education is nascent. Only a few works have examined fairness in LA and consequently followed up with ensuring fair LA models. What this paper adds A juxtaposition of unfairness mitigation algorithms across the entire LA pipeline showing how they compare and how each of them contributes to fair LA. Ensuring ethical LA does not always lead to a dip in performance. Sometimes, it actually improves performance as well. Fairness in LA has only focused on some form of outcome equality, however equality of outcome may be possible only when the playing field is levelled. Implications for practice and/or policy Based on desired notion of fairness and which segment of the LA pipeline is accessible, a fairness‐minded decision maker may be able to decide which algorithm to use in order to achieve their ethical goals. LA practitioners can carefully aim for more ethical LA models without trading significant utility by selecting algorithms that find the right balance between the two objectives. Fairness enhancing technologies should be cautiously used as guides—not final decision makers. Human domain experts must be kept in the loop to handle the dynamics of transcending fair LA beyond equality to equitable LA.
A framework for reputation bootstrapping based on reputation utility and game theories
Publisher: IEEE
Date: 11-2011
SensorTree: Bursty Propagation Trees as Sensors for Protest Event Detection
Publisher: Springer International Publishing
Date: 2018
Data privacy against composition attack
Publisher: Springer Berlin Heidelberg
Date: 2012
On the Expressive Power of Logics on Constraint Databases with Complex Objects
Publisher: Springer Science and Business Media LLC
Date: 07-2019
The influence of rainfall intensity and duration on sediment pathways and subsequent clogging in permeable pavements
Publisher: Elsevier BV
Date: 09-2019
DOI: 10.1016/J.JENVMAN.2019.05.151
Abstract: The trapping of sediments within permeable pavements during infiltration is an important process that contributes to their water quality treatment performance. However, this process also leads to clogging, which decreases the infiltration capacity of the pavement. With different rainfall intensities and durations, this study investigates the amount and size of sediment passing through a porous paver, as well as through the gravel-filled gaps that separate adjacent pavers. One of the major challenges in this study was to design an experiment where the characteristics of the sediment particles that are trapped while passing through these two different infiltration pathways are assessed. This was overcome by developing a new type of rainfall application device in combination with a two-tiered sediment capturing system. A better understanding of the infiltration pathways of sediment and the associated clogging processes should help designers improve the effective life of permeable pavements. Overall, it was found that while the porosity of porous pavers serves a useful function in terms of removing excess surface water during and after a rainfall event, it serves little purpose in removing sediment from stormwater.
An Effective Spatio-Temporal Approach for Predicting Future Semantic Locations
Publisher: Springer International Publishing
Date: 2016
Local Search for Efficient Causal Effect Estimation
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2023
Derivation of incremental equations for nested relations
Publisher: IEEE Comput. Soc
Date: 2001
On Defining Functional Dependency for XML
Publisher: IEEE
Date: 09-2009
DOI: 10.1109/ICSC.2009.20
Discovering Ancestral Instrumental Variables for Causal Inference From Observational Data
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
An architecture for data warehouse systems
Publisher: IEEE
Date: 1998
Recommending the Most Effective Intervention to Improve Employment for Job Seekers with Disability
Publisher: ACM
Date: 14-08-2021
Assessing the Causal Impact of Online Instruction due to COVID-19 on Students’ Grades and its aftermath on Grade Prediction Models
Publisher: ACM
Date: 07-09-2022
Strong functional dependencies and their application to normal forms in XML
Publisher: Association for Computing Machinery (ACM)
Date: 09-2004
Abstract: In this article, we address the problem of how to extend the definition of functional dependencies (FDs) in incomplete relations to XML documents (called XFDs) using the well-known strong satisfaction approach.We propose a syntactic definition of strong XFD satisfaction in an XML document and then justify it by showing that, similar to the case in relational databases, for the case of simple paths, keys in XML are a special case of XFDs. We also propose a normal form for XML documents based on our definition of XFDs and provide a formal justification for it by proving that it is a necessary and sufficient condition for the elimination of redundancy in an XML document.
Logics for Representing Data Mining Tasks in Inductive Databases
Publisher: Springer International Publishing
Date: 2014
Utility Aware Clustering for Publishing Transactional Data
Publisher: Springer International Publishing
Date: 2017
A Method for Describing Knowledge Work Processes
Publisher: IEEE
Date: 04-2008
Toward Unique and Unbiased Causal Effect Estimation From Data With Hidden Variables
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2023
Accurate data-driven prediction does not mean high reproducibility
Publisher: Springer Science and Business Media LLC
Date: 17-01-2020
STMM: Semantic and Temporal-Aware Markov Chain Model for Mobility Prediction
Publisher: Springer International Publishing
Date: 2015
Detecting signals of detrimental prescribing cascades from social media
Publisher: Elsevier BV
Date: 07-2016
DOI: 10.1016/J.ARTMED.2016.06.002
Abstract: Prescribing cascade (PC) occurs when an adverse drug reaction (ADR) is misinterpreted as a new medical condition, leading to further prescriptions for treatment. Additional prescriptions, however, may worsen the existing condition or introduce additional adverse effects (AEs). Timely detection and prevention of detrimental PCs is essential as drug AEs are among the leading causes of hospitalization and deaths. Identifying detrimental PCs would enable warnings and contraindications to be disseminated and assist the detection of unknown drug AEs. Nonetheless, the detection is difficult and has been limited to case reports or case assessment using administrative health claims data. Social media is a promising source for detecting signals of detrimental PCs due to the public availability of many discussions regarding treatments and drug AEs. In this paper, we investigate the feasibility of detecting detrimental PCs from social media. The detection, however, is challenging due to the data uncertainty and data rarity in social media. We propose a framework to mine sequences of drugs and AEs that signal detrimental PCs, taking into account the data uncertainty and data rarity. We conduct experiments on two real-world datasets collected from Twitter and Patient health forum. Our framework achieves encouraging results in the validation against known detrimental PCs (F1=78% for Twitter and 68% for Patient) and the detection of unknown potential detrimental PCs (Precision@50=72% and NDCG@50=95% for Twitter, Precision@50=86% and NDCG@50=98% for Patient). In addition, the framework is efficient and scalable to large datasets. Our study demonstrates the feasibility of generating hypotheses of detrimental PCs from social media to reduce pharmacists' guesswork.
Semantic Constraint-Based XML Updating
Publisher: Springer Berlin Heidelberg
Date: 2010
Transformation of Data with Constraints for Integration: An Information System Approach
Publisher: Springer Berlin Heidelberg
Date: 2009
Performance analysis and improvement for transformation operators in XML data integration
Publisher: Springer Berlin Heidelberg
Date: 2008
Querying relational databases through XSLT
Publisher: Elsevier BV
Date: 2004
The implication problem for ‘closest node’ functional dependencies in complete XML documents
Publisher: Elsevier BV
Date: 07-2012
Methods to Mitigate Risk of Composition Attack in Independent Data Publications
Publisher: Springer International Publishing
Date: 2015
Causal Decision Trees
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2017
On the equivalence between FDs in XML and FDs in relations
Publisher: Springer Science and Business Media LLC
Date: 15-06-2007
Constraint preserving transformation from relational schema to XML schema
Publisher: Springer Science and Business Media LLC
Date: 10-10-2006
Alternative particle compensation techniques for online water quality monitoring using UV–Vis spectrophotometer
Publisher: Elsevier BV
Date: 09-2020
A hybrid approach to prevent composition attacks for independent data releases
Publisher: Elsevier BV
Date: 11-2016
Assessing the Fairness of Course Success Prediction Models in the Face of (Un)equal Demographic Group Distribution
Publisher: ACM
Date: 20-07-2023
A two-layer multi-dimensional trustworthiness metric for web service composition
Publisher: Springer Berlin Heidelberg
Date: 2013
Preserving Referential Integrity Constraints in XML Data Transformation
Publisher: Springer Berlin Heidelberg
Date: 2009
An improvement of symbolic aggregate approximation distance measure for time series
Publisher: Elsevier BV
Date: 08-2014
Sufficient dimension reduction for average causal effect estimation
Publisher: Springer Science and Business Media LLC
Date: 20-04-2022
DOI: 10.1007/S10618-022-00832-5
Abstract: A large number of covariates can have a negative impact on the quality of causal effect estimation since confounding adjustment becomes unreliable when the number of covariates is large relative to the number of s les. Propensity score is a common way to deal with a large covariate set, but the accuracy of propensity score estimation (normally done by logistic regression) is also challenged by the large number of covariates. In this paper, we prove that a large covariate set can be reduced to a lower dimensional representation which captures the complete information for adjustment in causal effect estimation. The theoretical result enables effective data-driven algorithms for causal effect estimation. Supported by the result, we develop an algorithm that employs a supervised kernel dimension reduction method to learn a lower dimensional representation from the original covariate space, and then utilises nearest neighbour matching in the reduced covariate space to impute the counterfactual outcomes to avoid the large sized covariate set problem. The proposed algorithm is evaluated on two semisynthetic and three real-world datasets and the results show the effectiveness of the proposed algorithm.
Discover dependencies from data - A review
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2012
Effective Pruning for the Discovery of Conditional Functional Dependencies
Publisher: Oxford University Press (OUP)
Date: 24-06-2012
Distributed Anonymization for Multiple Data Providers in a Cloud System
Publisher: Springer Berlin Heidelberg
Date: 2013
FastOPM—A practical method for partial match of time series
Publisher: Elsevier BV
Date: 10-2022
Solar energy harvesting clear glass for building-integrated photovoltaics
Publisher: IEEE
Date: 12-2014
Privacy Protection for Genomic Data: Current Techniques and Challenges
Publisher: Springer Berlin Heidelberg
Date: 2010
Efficient Mining of Non-derivable Emerging Patterns
Publisher: Springer International Publishing
Date: 2015
DATM: A Novel Data Agnostic Topic Modeling Technique With Improved Effectiveness for Both Short and Long Text
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Should Learning Analytics Models Include Sensitive Attributes? Explaining the Why
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2023
Investigating the Impact of Using IR Bands on Early Fire Smoke Detection from Landsat Imagery with a Lightweight CNN Model
Publisher: MDPI AG
Date: 25-06-2022
DOI: 10.3390/RS14133047
Abstract: Smoke plumes are the first things seen from space when wildfires occur. Thus, fire smoke detection is important for early fire detection. Deep Learning (DL) models have been used to detect fire smoke in satellite imagery for fire detection. However, previous DL-based research only considered lower spatial resolution sensors (e.g., Moderate-Resolution Imaging Spectroradiometer (MODIS)) and only used the visible (i.e., red, green, blue (RGB)) bands. To contribute towards solutions for early fire smoke detection, we constructed a six-band imagery dataset from Landsat 5 Thematic Mapper (TM) and Landsat 8 Operational Land Imager (OLI) with a 30-metre spatial resolution. The dataset consists of 1836 images in three classes, namely “Smoke”, “Clear”, and “Other_aerosol”. To prepare for potential on-board-of-small-satellite detection, we designed a lightweight Convolutional Neural Network (CNN) model named “Variant Input Bands for Smoke Detection (VIB_SD)”, which achieved competitive accuracy with the state-of-the-art model SAFA, with less than 2% of its number of parameters. We further investigated the impact of using additional Infra-Red (IR) bands on the accuracy of fire smoke detection with VIB_SD by training it with five different band combinations. The results demonstrated that adding the Near-Infra-Red (NIR) band improved prediction accuracy compared with only using the visible bands. Adding both Short-Wave Infra-Red (SWIR) bands can further improve the model performance compared with adding only one SWIR band. The case study showed that the model trained with multispectral bands could effectively detect fire smoke mixed with cloud over small geographic extents.
Using causal discovery for feature selection in multivariate numerical time series
Publisher: Springer Science and Business Media LLC
Date: 09-07-2014
Query translation from XSLT to SQL
Publisher: IEEE Comput. Soc
Date: 2003
Technical Correspondence
Publisher: Association for Computing Machinery (ACM)
Date: 30-06-2015
DOI: 10.1145/2757214
Abstract: To address the frequently occurring situation where data is inexact or imprecise, a number of extensions to the classical notion of a functional dependency (FD) integrity constraint have been proposed in recent years. One of these extensions is the notion of a differential dependency (DD), introduced in the recent article “Differential Dependencies: Reasoning and Discovery” by Song and Chen in the March 2011 edition of this journal. A DD generalises the notion of an FD by requiring only that the values of the attribute from the RHS of the DD satisfy a distance constraint whenever the values of attributes from the LHS of the DD satisfy a distance constraint. In contrast, an FD requires that the values from the attributes in the RHS of an FD be equal whenever the values of the attributes from the LHS of the FD are equal. The article “Differential Dependencies: Reasoning and Discovery” investigated a number of aspects of DDs, the most important of which, since they form the basis for the other topics investigated, were the consistency problem (determining whether there exists a relation instance that satisfies a set of DDs) and the implication problem (determining whether a set of DDs logically implies another DD). Concerning these problems, a number of results were claimed in “Differential Dependencies: Reasoning and Discovery”. In this article we conduct a detailed analysis of the correctness of these results. The outcomes of our analysis are that, for almost every claimed result, we show there are either fundamental errors in the proof or the result is false. For some of the claimed results we are able to provide corrected proofs, but for other results their correctness remains open.
Stabilising Job Survival Analysis for Disability Employment Services in Unseen Environments
Publisher: ACM
Date: 04-08-2023
Causal heterogeneity discovery by bottom-up pattern search for personalised decision making
Publisher: Springer Science and Business Media LLC
Date: 02-08-2023
DOI: 10.1007/S10489-022-03860-2
Abstract: In personalised decision making, evidence is required to determine whether an action (treatment) is suitable for an in idual. Such evidence can be obtained by modelling treatment effect heterogeneity in subgroups. The existing interpretable modelling methods take a top-down approach to search for subgroups with heterogeneous treatment effects and they may miss the most specific and relevant context for an in idual. In this paper, we design a Treatment effect pattern (TEP) to represent treatment effect heterogeneity in data. To achieve an interpretable presentation of TEPs, we use a local causal structure around the outcome to explicitly show how those important variables are used in modelling. We also derive a formula for unbiasedly estimating the Conditional Average Causal Effect (CATE) using the local structure in our problem setting. In the discovery process, we aim at minimising heterogeneity within each subgroup represented by a pattern. We propose a bottom-up search algorithm to discover the most specific patterns fitting in idual circumstances the best for personalised decision making. Experiments show that the proposed method models treatment effect heterogeneity better than three other existing tree based methods in synthetic and real world data sets.
Divide and Conquer: Targeted Adversary Detection using Proximity and Dependency
Publisher: IEEE
Date: 12-2021
Information based data anonymization for classification utility
Publisher: Elsevier BV
Date: 12-2011
A probabilistic approach to mitigate composition attacks on privacy in non-coordinated environments
Publisher: Elsevier BV
Date: 09-2014
Can Android VoIP voice conversations be decoded? I can eavesdrop on your Android VoIP communication
Publisher: Wiley
Date: 27-05-2016
DOI: 10.1002/CPE.3845
Preserving key in XML data transformation
Publisher: Springer Science and Business Media LLC
Date: 12-08-2009
From Observational Studies to Causal Rule Mining
Publisher: Association for Computing Machinery (ACM)
Date: 24-11-2015
DOI: 10.1145/2746410
Abstract: Randomised controlled trials (RCTs) are the most effective approach to causal discovery, but in many circumstances it is impossible to conduct RCTs. Therefore, observational studies based on passively observed data are widely accepted as an alternative to RCTs. However, in observational studies, prior knowledge is required to generate the hypotheses about the cause-effect relationships to be tested, and hence they can only be applied to problems with available domain knowledge and a handful of variables. In practice, many datasets are of high dimensionality, which leaves observational studies out of the opportunities for causal discovery from such a wealth of data sources. In another direction, many efficient data mining methods have been developed to identify associations among variables in large datasets. The problem is that causal relationships imply associations, but the reverse is not always true. However, we can see the synergy between the two paradigms here. Specifically, association rule mining can be used to deal with the high-dimensionality problem, whereas observational studies can be utilised to eliminate noncausal associations. In this article, we propose the concept of causal rules (CRs) and develop an algorithm for mining CRs in large datasets. We use the idea of retrospective cohort studies to detect CRs based on the results of association rule mining. Experiments with both synthetic and real-world datasets have demonstrated the effectiveness and efficiency of CR mining. In comparison with the commonly used causal discovery methods, the proposed approach generally is faster and has better or competitive performance in finding correct or sensible causes. It is also capable of finding a cause consisting of multiple variables—a feature that other causal discovery methods do not possess.
Privacy Protection for Genomic Data: Current Techniques and Challenges
Publisher: Springer Berlin Heidelberg
Date: 2010
Containment and disjointedness in partitioned normal form relations
Publisher: Springer Science and Business Media LLC
Date: 03-2002
Maintaining views in object-relational databases
Publisher: ACM
Date: 06-11-2000
On the Performances of Checking XML Key and Functional Dependency Satisfactions
Publisher: Springer Berlin Heidelberg
Date: 2009
Assessing Classifier Fairness with Collider Bias
Publisher: Springer International Publishing
Date: 2022
Irrelevant updates and self-maintainability in transitive closure database views
Publisher: Elsevier BV
Date: 2004
Exploring Groups from Heterogeneous Data via Sparse Learning
Publisher: Springer Berlin Heidelberg
Date: 2013
Training Neural Networks with Random Noise Images for Adversarial Robustness
Publisher: ACM
Date: 26-10-2021
On Defining Keys for XML
Publisher: IEEE
Date: 07-2008
LoPAD: A Local Prediction Approach to Anomaly Detection
Publisher: Springer International Publishing
Date: 2020
Related Organisations
Related Funding Activities
Discovery Projects - Grant ID: DP230101122
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Amount: $420,000.00
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View Funded ActivityDiscovery Projects - Grant ID: DP0667206
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View Funded ActivityDiscovery Projects - Grant ID: DP200101210
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Amount: $360,000.00
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View Funded ActivityDiscovery Projects - Grant ID: DP0559202
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End Date: 02-2010
Amount: $253,000.00
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View Funded ActivityDiscovery Projects - Grant ID: DP0878405
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View Funded Activity