Dependable Real-Time Data Mining

Thuraisingham, B.; Khan, Latifur; Clifton, Chris; Maurer, John A.; Ceruti, Marion G.

doi:10.1109/isorc.2005.24

Cited by 14 publications

(8 citation statements)

References 12 publications

(4 reference statements)

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“…Thus, it is important for the training model to be retrained and to be scaled to the dimensions of the increasing data. Thus, scalability of the training model to higher dimensions, cost or efficiency of the model scaling and the robustness of the models' inferences and predictive accuracy in the higher dimensions are important considerations (Thuraisingham et al 2005). The scalability of the training model, the robustness of the models' inferences and predictive accuracy were considered earlier for all three models, so here we focus on the efficiency of the model scaling.…”

Section: Real Time Credit Scoringmentioning

confidence: 99%

Credit Risk Scoring with Bayesian Network Models

Leong

2015

Comput Econ

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This paper proposes a Bayesian network model to address censoring, class imbalance and real-time implementation issues in credit risk scoring. It shows that the Bayesian network model performs well against competing models (logistic regression model and neural network model) along several dimensions such as accuracy, sensitivity, precision and the receiver characteristic curve. Better performance of the Bayesian network model is particularly salient with class imbalance, higher dimensions and a rejection sample. Furthermore, the Bayesian network model can be scaled efficiently when implemented onto a larger dataset, thus making it amenable for real-time implementation.

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Section: Real Time Credit Scoringmentioning

confidence: 99%

Credit Risk Scoring with Bayesian Network Models

Leong

2015

Comput Econ

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“…It is noted that internal users will require real-time PPDM, and specific algorithms for real-time data mining will be required to provide dependable results [21].…”

Section: ) Privacy-preserving Data Mining (Ppdm)mentioning

confidence: 99%

On Management of the Health Content Lifecycle

Samuel

Zaı̈ane

2013

PHF

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Abstract-TheInternet is an ideal tool for promoting public health goals of prolonging life, health, and improving the quality of life. There are many websites with health-related information where one can go to as an information source, for health advice, or selfdiagnosis. However, these health websites require a more acute awareness of ethical issues due to potential life threatening risks from misuse of information. Providing disclaimers and accreditation logos only goes so far in covering potential legal conflicts, but fulfilling ethical obligations for non-maleficence requires more action on our part. As such, the content lifecycle of these websites requires greater emphasis on privacy, security, and trustworthiness. We propose and give a high-level description of a Health Content Management System (HCMS) that addresses both the managerial, as well as the ethical issues with health content. Surveys of existing health websites and content management systems demonstrate the need for the proposed system. Moreover, the novelty of the proposed HCMS is appraised and asserted in comparison with similar health framework concepts. Our contributions include survey results of more than 50 health websites, taxonomy of health websites' characteristics, discussion about legal versus ethical obligations, and a blueprint for typical and novel features for health websites. Moreover, this study presents a new approach to analysing health content via lifecycles.

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“…Sensor data should be managed in real time [5] as the results of sensor outputs and are be quite dynamic. Solutions for real-time data mining also can be applied to data fusion.…”

Section: Figure 1 Data and Pedigree Metadata Automationmentioning

confidence: 99%

“…Solutions for real-time data mining also can be applied to data fusion. For example, one approach is to focus on data streams over certain intervals [5] and data-fusion algorithms most appropriate to data combinations available during that interval.…”

Section: Figure 1 Data and Pedigree Metadata Automationmentioning

confidence: 99%

Pedigree Information for Enhanced Situation and Threat Assessment

Ceruti¹,

Das

Ashenfelter³

et al. 2006

2006 9th International Conference on Information Fusion

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This paper describes how pedigree is used to support and enhance situation and threat assessment. It is based on the findings of the technology group of the Data Fusion Levels Two and Three Workshop sponsored by the Office of Naval Research held in Arlington, VA from 15-18 Nov. 2005. It identifies areas that need improvement in situation assessment and threat assessment, such as interoperability, automation, pedigree management, system usability, reliability, and uncertainty. The concept of pedigree must include "standard" metadata, lineage, plus a computational model of the quality of the information. The system must automatically propagate changes and update to derived products when source information or sourcepedigree information changes. Several other processes must be automated: generate pedigree, identify and auto fill gaps, fuse pedigree, update pedigree, display of information quality and confidence. The paper concludes with suggestions for future research and development.

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Dependable Real-Time Data Mining

Cited by 14 publications

References 12 publications

Credit Risk Scoring with Bayesian Network Models

Credit Risk Scoring with Bayesian Network Models

On Management of the Health Content Lifecycle

Pedigree Information for Enhanced Situation and Threat Assessment

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