Evan W. Patton

Democratizing mobile app development for disaster management

Smartphones are being used for a wide range of activities including messaging, social networking, calendar and contact management as well as location and context-aware applications. The ubiquity of handheld computing technology has been found to be especially useful in disaster management and relief operations. Our focus is to enable developers to quickly deploy applications that take advantage of key sources that are fundamental for todays networked citizens, including Twitter feeds, Facebook posts, current news releases, and government data. These applications will also have the capability of empowering citizens involved in crisis situations to contribute via crowdsourcing, and to communicate up-to-date information to others. We will leverage several technologies to develop this application framework, namely (i) Linked Data principles for structured data, (ii) existing data sources and ontologies for disaster management, and (iii) App Inventor, which is a mobile application development framework for non-programmers. In this paper, we describe our motivating use cases, our architecture, and our prototype implementation.

A Power Consumption Benchmark for Reasoners on Mobile Devices

We introduce a new methodology for benchmarking the performance per watt of semantic web reasoners and rule engines on smartphones to provide developers with information critical for deploying semantic web tools on power-constrained devices. We validate our methodology by applying it to three well-known reasoners and rule engines answering queries on two ontologies with expressivities in RDFS and OWL DL. While this validation was conducted on smartphones running Googles Android operating system, our methodology is general and may be applied to different hardware platforms, reasoners, ontologies, and entire applications to determine performance relevant to power consumption. We discuss the implications of our findings for balancing tradeoffs of local computation versus communication costs for semantic technologies on mobile platforms, sensor networks, the Internet of Things, and other power-constrained environments.

The Web is My Back-end: Creating Mashups with Linked Open Government Data

Governments around the world have been releasing raw data to their citizens at an increased pace. The mixing and linking of these datasets by a community of users enhances their value and makes new insights possible. The use of mashups — digital works in which data from one or more sources is combined and presented in innovative ways — is a great way to expose this value. Mashups enable end users to explore data that has a real tangible meaning in their lives. Although there are many approaches to publishing and using data to create mashups, we believe Linked Data and Semantic Web technologies solve many of the true challenges in open government data and can lower the cost and complexity of developing these applications. In this chapter we discuss why Linked Data is a better model and how it can be used to build useful mashups.

SANLab-CM: a tool for incorporating stochastic operations into activity network modeling.

The Stochastic Activity Network Laboratory for Cognitive Modeling (SANLab-CM) is a new tool that incorporates stochastic operations into activity network modeling (Schweickert, Fisher, & Proctor, 2003). In this article, we discuss the core functionality of SANLab-CM and walk through a case study that expands a previously published single, static path model of telephone operators interacting with customers via a workstation (from Gray, John, & Atwood, 1993) into a stochastic model that generates 55 unique paths with different frequencies and a variety of qualitative properties. Without SANLab-CM, it would have been easy to mistake some of the more frequent critical paths as evidence for alternative strategies for task completion. With SANLab-CM, these critical paths can be shown to be simple emergent properties of variability in elementary cognitive, perceptual, and motor processes.

SemantEco: A semantically powered modular architecture for integrating distributed environmental and ecological data

Abstract We aim to inform the development of decision support tools for resource managers who need to examine large complex ecosystems and make recommendations in the face of many tradeoffs and conflicting drivers. We take a semantic technology approach, leveraging background ontologies and the growing body of linked open data. In previous work, we designed and implemented a semantically enabled environmental monitoring framework called SemantEco and used it to build a water quality portal named SemantAqua. Our previous system included foundational ontologies to support environmental regulation violations and relevant human health effects. In this work, we discuss SemantEco’s new architecture that supports modular extensions and makes it easier to support additional domains. Our enhanced framework includes foundational ontologies to support modeling of wildlife observation and wildlife health impacts, thereby enabling deeper and broader support for more holistically examining the effects of environmental pollution on ecosystems. We conclude with a discussion of how, through the application of semantic technologies, modular designs will make it easier for resource managers to bring in new sources of data to support more complex use cases.

A semantic portal for next generation monitoring systems

We present a semantic technology-based approach to emerging monitoring systems based on our linked data approach in the Tetherless World Constellation Semantic Ecology and Environment Portal (SemantEco). Our integration scheme uses an upper level monitoring ontology and mid-level monitoring-relevant domain ontologies. The initial domain ontologies focus on water and air quality. We then integrate domain data from different authoritative sources and multiple regulation ontologies (capturing federal as well as state guidelines) to enable pollution detection and monitoring. An OWL-based reasoning scheme identifies pollution events relative to user chosen regulations. Our approach captures and leverages provenance to enable transparency. In addition, SemantEco features provenance-based facet generation, query answering, and validation over the integrated data via SPARQL. We introduce the general SemantEco approach, describe the implementation which has been built out substantially in the water domain creating the SemantAqua portal, and highlight some of the potential impacts for the future of semantically-enabled monitoring systems.

Tools for Predicting the Duration and Variability of Skilled Performance without Skilled Performers

Many devices are designed to allow skilled users to complete routine tasks quickly, often within a specified amount of time. Predictive human performance modeling has long been able to predict the mean time to accomplish a task, making it possible to compare device designs before building them. However, estimates of the variability of performance are also important, especially in real-time, safety-critical tasks. Until recently, the human factors community lacked tools to predict the variability of skilled performance. In this paper, we describe a combination of theory-based tools (CogTool and SANLab) that address this critical gap and that can easily be used by human factors practitioners or system designers. We describe these tools, their integration, and provide a concrete example of their use in the context of entering the landing speed into the Boeing 777 Flight Management Computer (FMC) using the Control and Display Unit (CDU).

SANLab-CM – The Stochastic Activity Network Laboratory for Cognitive Modeling

SANLab-CM is an activity network tool for the stochastic modeling of routine interactive behavior. Within the cognitive engineering community the best-known examples of activity networking modeling are the CPM-GOMS models of Project Ernestine (Gray, John, & Atwood, 1993). Project Ernestine showed that modeling the parallel use of cognitive, perceptual, and motor resources within an activity network formalism produces reliable and accurate predictions of expert performance times across alternative designs for the same task. SANLab-CM provides time predictions, but its essence is the prediction of procedural variability amidst strategic constancy: when expert human performers follow the same task strategy from trial to trial variability in the processing time of cognitive, perceptual, and motor resources is such as to produce different critical paths of performance and significantly different execution times. The stochastic component of SANLab-CM goes beyond current techniques to create a new means of assessing alternative designs based on the procedural variability expected in expert performance.

Towards semantically-enabled exploration and analysis of environmental ecosystems

We aim to inform the development of decision support tools for resource managers who need to examine large complex ecosystems and make recommendations in the face of many tradeoffs and conflicting drivers. We take a semantic technology approach, leveraging background ontologies and the growing body of open linked data. In previous work, we designed and implemented a semantically-enabled environmental monitoring framework called SemantEco and used it to build a water quality portal named SemantAqua. In this work, we significantly extend SemantEco to include knowledge required to support resource decisions concerning fish and wildlife species and their habitats. Our previous system included foundational ontologies to support environmental regulation violations and relevant human health effects. Our enhanced framework includes foundational ontologies to support modeling of wildlife observation and wildlife health impacts, thereby enabling deeper and broader support for more holistically examining the effects of environmental pollution on ecosystems. Our results include a refactored and expanded version of the SemantEco portal. Additionally the updated system is now compatible with the emerging best in class Extensible Observation Ontology (OBOE). A wider range of relevant data has been integrated, focusing on additions concerning wildlife health related to exposure to contaminants. The resulting system stores and exposes provenance concerning the source of the data, how it was used, and also the rationale for choosing the data. In this paper, we describe the system, highlight its research contributions, and describe current and envisioned usage.

Automated CPM-GOMS Modeling from Human Data

We present the Log Analyzer for generating CPM-GOMS models from human performance data. Built on top of the SANLab tool for stochastic CPM-GOMS modeling (Patton & Gray, 2010), the Log Analyzer uses event-driven parsing to map experimental log files into SANLab interactive routines used to generate CPM-GOMS activity networks. Identical models within and across participants are averaged to obtain estimates of performance times and variability, which are then used to drive stochastic simulations. In this report, we apply our tool to human data collected during a simple eyetracking calibration task and compare the resulting models to existing models in the literature. The generated models show good predictive performance and raise questions about di erent strategies not captured in the literature.