Developing Experimental Models for NASA Missions with ASSL

NASA's new age of space exploration augurs great promise for deep space exploration missions whereby spacecraft should be independent, autonomous, and smart. Nowadays NASA increasingly relies on the concepts of autonomic computing, exploiting these to increase the survivability of remote missions, particularly when human tending is not feasible. Autonomic computing has been recognized as a promising approach to the development of self-managing spacecraft systems that employ onboard intelligence and rely less on control links. The Autonomic System Specification Language (ASSL) is a framework for formally specifying and generating autonomic systems. As part of long-term research targeted at the development of models for space exploration missions that rely on principles of autonomic computing, we have employed ASSL to develop formal models and generate functional prototypes for NASA missions. This helps to validate features and perform experiments through simulation. Here, we discuss our work on developing such missions with ASSL.Comment: 7 pages, 4 figures, Workshop on Formal Methods for Aerospace (FMA'09

Mercury: using the QuPreSS reference model to evaluate predictive services

Nowadays, lots of service providers offer predictive services that show in advance a condition or occurrence about the future. As a consequence, it becomes necessary for service customers to select the predictive service that best satisfies their needs. The QuPreSS reference model provides a standard solution for the selection of predictive services based on the quality of their predictions. QuPreSS has been designed to be applicable in any predictive domain (e.g., weather forecasting, economics, and medicine). This paper presents Mercury, a tool based on the QuPreSS reference model and customized to the weather forecast domain. Mercury measures weather predictive services' quality, and automates the context-dependent selection of the most accurate predictive service to satisfy a customer query. To do so, candidate predictive services are monitored so that their predictions can be eventually compared to real observations obtained from a trusted source. Mercury is a proof-of-concept of QuPreSS that aims to show that the selection of predictive services can be driven by the quality of their predictions. Throughout the paper, we show how Mercury was built from the QuPreSS reference model and how it can be installed and used.Peer ReviewedPostprint (author's final draft

Exploring the Behavior of Highly Effective CIOs Using Video Analysis

Although recently several studies have addressed the required skills of effective CIOs, little is known of the actual behavior successful CIOs. In this study, we explore the behavior of highly effective CIOs by video-recording CIOs at work. The two CIOs videotaped were nominated as CIO of the year. We analyze the data in an innovative and systematic way by developing and using a behavioral leadership coding scheme. The analysis indicates that highly effective CIOs are good listeners. They also often verify previously made agreements; structure the conversation; and provide subordinates with factual information. We also compare the behavior of the highly effective CIOs to a sample of 25 highly effective middle managers. Whereas the CIOs spend little time defending themselves against their subordinates and are mostly involved in steering, middle-managers spend much more time defending themselves and show more support for their subordinates. We conclude that our new video observation-and-coding method is viable to analyze and better understand the behavior of CIOs

Implementing means-tested welfare systems in the United States

While targeting can effectively channel resources to the poor, implementation details matter tremendously to distributive outcomes. Several key factors affect performance, including: data collection processes; information management; household assessment mechanisms; institutional arrangements; and monitoring and oversight mechanisms. This report conducts an in-depth assessment of key design and implementation factors and their potential impact on outcomes for the household targeting system used in the United States to target social programs to the poor and vulnerable.