Irbis Gallegos

DaProS: a data property specification tool to capture scientific sensor data properties

Environmental scientists have begun to use advanced technologies such as wireless sensor networks and robotic trams equipped with sensors to collect data, such as spectral readings and carbon dioxide, which is leading to a rapid increase in the amount of data being stored. This has resulted in a need to evaluate promptly the accuracy of the data, the meaning of the data, and the correct operation of the instrumentation in order to not lose valuable time and information. Performing such evaluations requires scientists to rely on their knowledge and experience in the field. Field knowledge is rarely shared or reused by other scientists mostly because of the lack of a well-defined methodology for sharing information and appropriate tool support. This work presents the Data Property Specification (DaProS) tool that assists practitioners in specification and refinement of properties that can be used to check data quality. The tool can be used to capture scientific knowledge about data processes in remote sensing systems through the use of decision trees and questionnaires that guide practitioners in the specification process. In addition, the tool uses Disciplined Natural Language (DNL) property representations for scientists to validate that the specifications capture the intended meaning.

JavaMaC and runtime monitoring for geoinformatics grid services

The Geoscience Network (GEON) project is a collaborative effort among numerous institutions to create the geoinformatics infrastructure. Because of the criticality of the grid services that is deployed, scientists need assurance of the integrity of the data supplied by the service and assurance that the workflows and other scientific applications behave as expected. This paper describes the efforts toward using the Kepler workflow system to define new applications, the prospec specification tool to define properties, and the JavaMac system to monitor grid services. In addition, it outlines the research questions and challenges that must be addressed to meet the requirements of the GEON community.

Towards near-real time data property specification and verification for Arctic hyperspectral sensor data

Environmental scientists, especially those conducting studies in remote areas such as the Arctic, can benefit from assessing data quality from autonomous sensors in near-real time. The Data Assessment Run-Time (DART) framework was developed to allow environmental scientists to specify and verify data properties associated with autonomous sensors. Data properties are logical statements about data values associated with sensors and their relationship with other sensor output or properties derived from historical data. The properties can be verified at near-real time, i.e., as the data are being collected in the field, or through post-processing routines after the data has been collected. This paper describes a case study that evaluates the specification of data properties associated with hyperspectral sensor data and how the DART framework was used to verify these data in both near-real time and through post-processing.

Towards a tool for generating aspects from MEDL and PEDL specifications for runtime verification

This paper describes an approach to generate AspectJ aspects from formal specifications written for the Monitoring and Checking (MaC) runtime verification tool. The aspects can serve as the foundation for instrumentation of programs that can be verified at runtime. To demonstrate the practicability of the proposed approach, the authors used a benchmark from the MaC research. The benchmark is based on a safety-critical railroad crossing system comprised of a train, a gate, and a controller. Finally, the paper describes the results from generating Java-MaCs specification scripts to AspectJ aspects, and it compares the proposed approach to related approaches and ones that use aspects.