Assessing data quality issues in the Emergency Department through data and process mapping

Data and information quality is a well-established research topic and gradually appears on the decision-makers' top concern lists. Many studies have been conducted on how to investigate the generic data/information quality issues and factors by providing a high-level abstract framework or model. Based on these previous studies, this study tries to discuss the actual data quality issues with the operation-level and middle-level managers emerged during the emergency department data collection and reporting processes. By conduct data quality issues and business processes mapping, possible data quality issues are summarised under the well-known TOP model and the recommendations of data quality improvement are suggested.)

Blind alleys, blinding and blinded aliens : mapping the outer limits of the technical image

Marcia Jane's installation work is situated along a historic line of critical enquiry traceable back to Goethe. her energy field series re-tools for digital use the formal erasures of sturcturalist film, where the relationship between the signifier and the signified are inverted.

Evaluation of four supervised learning methods for benthic habitat mapping using backscatter from multi-beam sonar

An understanding of the distribution and extent of marine habitats is essential for the implementation of ecosystem-based management strategies. Historically this had been difficult in marine environments until the advancement of acoustic sensors. This study demonstrates the applicability of supervised learning techniques for benthic habitat characterization using angular backscatter response data. With the advancement of multibeam echo-sounder (MBES) technology, full coverage datasets of physical structure over vast regions of the seafloor are now achievable. Supervised learning methods typically applied to terrestrial remote sensing provide a cost-effective approach for habitat characterization in marine systems. However the comparison of the relative performance of different classifiers using acoustic data is limited. Characterization of acoustic backscatter data from MBES using four different supervised learning methods to generate benthic habitat maps is presented. Maximum Likelihood Classifier (MLC), Quick, Unbiased, Efficient Statistical Tree (QUEST), Random Forest (RF) and Support Vector Machine (SVM) were evaluated to classify angular backscatter response into habitat classes using training data acquired from underwater video observations. Results for biota classifications indicated that SVM and RF produced the highest accuracies, followed by QUEST and MLC, respectively. The most important backscatter data were from the moderate incidence angles between 30° and 50°. This study presents initial results for understanding how acoustic backscatter from MBES can be optimized for the characterization of marine benthic biological habitats. © 2012 by the authors.