Patient identification (ID) errors occurring during the medication administration process can be fatal. The aim of this study is to determine whether differences in nurses' behaviors and visual scanning patterns during the medication administration process influence their capacities to identify patient ID errors. Nurse participants (n = 20) administered medications to 3 patients in a simulated clinical setting, with 1 patient having an embedded ID error. Error-identifying nurses tended to complete more process steps in a similar amount of time than non-error-identifying nurses and tended to scan information across artifacts (e.g., ID band, patient chart, medication label) rather than fixating on several pieces of information on a single artifact before fixating on another artifact. Non-error-indentifying nurses tended to increase their durations of off-topic conversations-a type of process interruption-over the course of the trials; the difference between groups was significant in the trial with the embedded ID error. Error-identifying nurses tended to have their most fixations in a row on the patient's chart, whereas non-error-identifying nurses did not tend to have a single artifact on which they consistently fixated. Finally, error-identifying nurses tended to have predictable eye fixation sequences across artifacts, whereas non-error-identifying nurses tended to have seemingly random eye fixation sequences. This finding has implications for nurse training and the design of tools and technologies that support nurses as they complete the medication administration process.

The conventional extracting-transforming-loading (ETL) system is typically operated on a single machine not capable of handling huge volumes of geospatial big data. To deal with the considerable amount of big data in the ETL process, we propose D_ELT (delayed extracting-loading -transforming) by utilizing MapReduce-based parallelization. Among various kinds of big data, we concentrate on geospatial big data generated via sensors using Internet of Things (IoT) technology. In the IoT environment, update latency for sensor big data is typically short and old data are not worth further analysis, so the speed of data preparation is even more significant. We conducted several experiments measuring the overall performance of D_ELT and compared it with both traditional ETL and extracting-loading-transforming (ELT) systems, using different sizes of data and complexity levels for analysis. The experimental results show that D_ELT outperforms the other two approaches, ETL and ELT. In addition, the larger the amount of data or the higher the complexity of the analysis, the greater the parallelization effect of transform in D_ELT, leading to better performance over the traditional ETL and ELT approaches.

With the rapid development of Internet of Things (IoT) technologies, the increasing volume and diversity of sources of geospatial big data have created challenges in storing, managing, and processing data. In addition to the general characteristics of big data, the unique properties of spatial data make the handling of geospatial big data even more complicated. To facilitate users implementing geospatial big data applications in a MapReduce framework, several big data processing systems have extended the original Hadoop to support spatial properties. Most of those platforms, however, have included spatial functionalities by embedding them as a form of plug-in. Although offering a convenient way to add new features to an existing system, the plug-in has several limitations. In particular, while executing spatial and nonspatial operations by alternating between the existing system and the plug-in, additional read and write overheads have to be added to the workflow, significantly reducing performance efficiency. To address this issue, we have developed Marmot, a high-performance, geospatial big data processing system based on MapReduce. Marmot extends Hadoop at a low level to support seamless integration between spatial and nonspatial operations of a solid framework, allowing improved performance of geoprocessing workflow. This paper explains the overall architecture and data model of Marmot as well as the main algorithm for automatic construction of MapReduce jobs from a given spatial analysis task. To illustrate how Marmot transforms a sequence of operators for spatial analysis to map and reduce functions in a way to achieve better performance, this paper presents an example of spatial analysis retrieving the number of subway stations per city in Korea. This paper also experimentally demonstrates that Marmot generally outperforms SpatialHadoop, one of the top plug-in based spatial big data frameworks, particularly in dealing with complex and time-intensive queries involving spatial index.

In the case of level 3 automated vehicles, in order to safely and quickly transfer control authority rights to manual driving, it is necessary that a study be conducted on the characteristics of human factors affecting the transition of manual driving. In this study, we conducted three experiments to compare the characteristics of human factors that influence the driver’s quality of response when re-engaging and stabilizing manual driving. The three experiments were conducted sequentially by dividing them into a normal driving situation, an obstacle occurrence situation in front, and an obstacle and congestion on surrounding roads. We performed a statistical analysis and classification and regression tree (CART) analysis using experimental data. We found that as the number of trials increased, there was a learning effect that shortened re-engagement times and increased the proportion of drivers with good response times. We found that the stabilization time increased as the experiment progressed, as obstacles appeared in front and traffic density increased in the surrounding lanes. The results of the analysis are useful for vehicle developers designing safer human–machine interfaces and for governments developing guidelines for automated driving systems.

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