Introduction

Chakrabarty, Arnab; Mannan, Sam; Çağın, Tahir

doi:10.1016/b978-0-12-396975-0.00001-2

Cited by 2 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The strategy for reducing GIS audit complexity is centered on interpretation of elements that represent GIS as a system [30]. Essential elements and their relationships are understood qualitatively by applying partition as the main reduction strategy of abstracting audit elements [9].…”

Section: Reduction In Gismentioning

confidence: 99%

Developing a GIS Audit Framework in the Context of Information Technology though a Reductive Model Approach

Njiru¹,

Siriba²,

Karanja³

2023

JGIS

View full text Add to dashboard Cite

A GIS audit framework is necessary considering the diverse nature of GIS with regard to components, applications and industry. In practice, checklists are generated during the audit process based on specific objectives. There is no standardized list of items that can be used as a reference. The purpose of this study was to develop a GIS audit framework as a foundation for GIS audits. The framework provides that comprehensive approach to various GIS aspects during the audit process. The design builds on a developed conceptual framework where most significant categories of GIS audit parameters namely data quality, software utilization, GIS competency and procedures (work flows) were identified. The study adopted a reductive model approach to simplify the complexity associated with each category of GIS audit parameter. The resultant audit elements for each category are organized in a matrix that forms an integral part of the framework. The columns comprise audit goal, audit questions and audit subjects as indicators which are qualitatively measured.The rows comprise the parameters (data quality, software utilization, personnel competency and procedure (workflows)). To use the framework, an auditor only needs to create an audit checklist that consists of particular parameters and indicators from the framework depending on audit objective. As part of an on-going research, the next step will involve validating the framework through a mock testing process.

show abstract

Section: Reduction In Gismentioning

confidence: 99%

Developing a GIS Audit Framework in the Context of Information Technology though a Reductive Model Approach

Njiru¹,

Siriba²,

Karanja³

2023

JGIS

View full text Add to dashboard Cite

show abstract

“…Additionally, with industrial growth present in many developing countries, efficient process monitoring is essential for newer and more complex processes. Monitoring of these processes is required in order to ensure process safety, maintain product quality, increase economic benefits, and also to ensure that the process adheres to strict environmental regulation standards [2].…”

Section: Introductionmentioning

confidence: 99%

Fault Detection of Single and Interval Valued Data Using Statistical Process Monitoring Techniques

Sheriff¹,

Basha²,

Karim³

et al. 2020

Fault Detection, Diagnosis and Prognosis

View full text Add to dashboard Cite

Principal component analysis (PCA) is a linear data analysis technique widely used for fault detection and isolation, data modeling, and noise filtration. PCA may be combined with statistical hypothesis testing methods, such as the generalized likelihood ratio (GLR) technique in order to detect faults. GLR functions by using the concept of maximum likelihood estimation (MLE) in order to maximize the detection rate for a fixed false alarm rate. The benchmark Tennessee Eastman Process (TEP) is used to examine the performance of the different techniques, and the results show that for processes that experience both shifts in the mean and/or variance, the best performance is achieved by independently monitoring the mean and variance using two separate GLR charts, rather than simultaneously monitoring them using a single chart. Moreover, single-valued data can be aggregated into interval form in order to provide a more robust model with improved fault detection performance using PCA and GLR. The TEP example is used once more in order to demonstrate the effectiveness of using of interval-valued data over single-valued data.

show abstract

Introduction

Cited by 2 publications

References 0 publications

Developing a GIS Audit Framework in the Context of Information Technology though a Reductive Model Approach

Developing a GIS Audit Framework in the Context of Information Technology though a Reductive Model Approach

Fault Detection of Single and Interval Valued Data Using Statistical Process Monitoring Techniques

Contact Info

Product

Resources

About