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The purpose of this paper is to discuss the implementation of a programmatic quality assurance discipline within the International Space Station Program and the resulting impact on safety. NASA culture has continued to stress safety at the expense of quality when both are extremely important and both can equally influence the success or failure of a Program or Mission. Although safety was heavily criticized in the media after Col~imbiaa, strong case can be made that it was the failure of quality processes and quality assurance in all processes that eventually led to the Columbia accident. Consequently, it is possible to have good quality processes without safety, but it is impossible to have good safety processes without quality. The ISS Program quality assurance function was analyzed as representative of the long-term manned missions that are consistent with the President s Vision for Space Exploration. Background topics are as follows: The quality assurance organizational structure within the ISS Program and the interrelationships between various internal and external organizations. ISS Program quality roles and responsibilities with respect to internal Program Offices and other external organizations such as the Shuttle Program, JSC Directorates, NASA Headquarters, NASA Contractors, other NASA Centers, and International Partner/participants will be addressed. A detailed analysis of implemented quality assurance responsibilities and functions with respect to NASA Headquarters, the JSC S&MA Directorate, and the ISS Program will be presented. Discussions topics are as follows: A comparison of quality and safety resources in terms of staffing, training, experience, and certifications. A benchmark assessment of the lessons learned from the Columbia Accident Investigation (CAB) Report (and follow-up reports and assessments), NASA Benchmarking, and traditional quality assurance activities against ISS quality procedures and practices. The lack of a coherent operational and sustaining quality assurance strategy for long-term manned space flight. An analysis of the ISS waiver processes and the Problem Reporting and Corrective Action (PRACA) process implemented as quality functions. Impact of current ISS Program procedures and practices with regards to operational safety and risk A discussion regarding a "defense-in-depth" approach to quality functions will be provided to address the issue of "integration vs independence" with respect to the roles of Programs, NASA Centers, and NASA Headquarters. Generic recommendations are offered to address the inadequacies identified in the implementation of ISS quality assurance. A reassessment by the NASA community regarding the importance of a "quality culture" as a component within a larger "safety culture" will generate a more effective and value-added functionality that will ultimately enhance safety.
This slide presentation reviews some of the issues in quality of remote sensing data. Data "quality" is used in several different contexts in remote sensing data, with quite different meanings. At the pixel level, quality typically refers to a quality control process exercised by the processing algorithm, not an explicit declaration of accuracy or precision. File level quality is usually a statistical summary of the pixel-level quality but is of doubtful use for scenes covering large areal extents. Quality at the dataset or product level, on the other hand, usually refers to how accurately the dataset is believed to represent the physical quantities it purports to measure. This assessment often bears but an indirect relationship at best to pixel level quality. In addition to ambiguity at different levels of granularity, ambiguity is endemic within levels. Pixel-level quality terms vary widely, as do recommendations for use of these flags. At the dataset/product level, quality for low-resolution gridded products is often extrapolated from validation campaigns using high spatial resolution swath data, a suspect practice at best. Making use of quality at all levels is complicated by the dependence on application needs. We will present examples of the various meanings of quality in remote sensing data and possible ways forward toward a more unified and usable quality framework.
High spatial and temporal resolution air quality estimation and forecasting can be enhanced by combining global data sources, like chemical transport models and satellite remote sensing, with local information from regulatory and low-cost air quality monitors. Successful integration of data from these diverse sources is complicated by many factors, however, including differences in spatial and temporal resolution, data availability and latency issues, varying data quality, and large computational and data storage requirements. This presentation will provide an overview of a NASA-funded effort to develop the foundation for future operationalization of air quality forecasting for world-wide end-users and integration into their air quality management decision processes, which will be achieved in future phases of this multi-year project. We will summarize our progress in developing a data fusion system using the Google Earth Engine platform which can integrate model, satellite, and surface-level monitoring datasets to enhance estimation and forecasting of air-quality-relevant pollutants at sub-daily and sub-city scales. The tool is being developed in close cooperation with several city- and regional-level air quality managers in the USA and around the world. Our end-goal is to provide these air quality managers with the information they need to assess and anticipate the impacts of poor air quality, track changes in air quality due to ongoing mitigation efforts and land use changes, and identify ways to improve their air quality monitoring strategies. This presentation will focus on recent advances achieved through the project, including integration of multiple air quality datasets in a prototype data fusion system in Google Earth Engine, the quantification of uncertainties associated with our data fusion approach, and the development of user interfaces and visualization tools to convey air quality information in a way which best meets end-user needs.
High spatial and temporal resolution air quality estimation and forecasting can be enhanced by combining global data sources, like chemical transport models and satellite remote sensing, with local information from regulatory and low-cost air quality monitors. Successful integration of data from these diverse sources is complicated by many factors, however, including differences in spatial and temporal resolution, data availability and latency issues, varying data quality, and large computational and data storage requirements. This presentation will provide an overview of a NASA-funded effort to develop the foundation for future operationalization of air quality forecasting for world-wide end-users and integration into their air quality management decision processes, which will be achieved in future phases of this multi-year project. We will summarize our progress in developing a data fusion system using the Google Earth Engine platform which can integrate model, satellite, and surface-level monitoring datasets to enhance estimation and forecasting of air-quality-relevant pollutants at sub-daily and sub-city scales. The tool is being developed in close cooperation with several city- and regional-level air quality managers in the USA and around the world. Our end-goal is to provide these air quality managers with the information they need to assess and anticipate the impacts of poor air quality, track changes in air quality due to ongoing mitigation efforts and land use changes, and identify ways to improve their air quality monitoring strategies. This presentation will focus on recent advances achieved through the project, including integration of multiple air quality datasets in a prototype data fusion system in Google Earth Engine, the quantification of uncertainties associated with our data fusion approach, and the development of user interfaces and visualization tools to convey air quality information in a way which best meets end-user needs.
The Information Quality Cluster (IQC) of the Federation of Earth Science Information Partners (ESIP) has been active since 2014 with membership from multiple organizations including NASA and NOAA. The purpose of this presentation is to foster collaboration between the IQC and the ESIP Usability Cluster. The IQC's activities are motivated partly by the guidelines on information quality from several federal agencies. The agencies developed the guidelines complying with a request in 2002 from the Office of Management and Budget (OMB). The OMB request resulted from a congressional mandate, namely, Section 515 of the Treasury and General Government Appropriations Act for Fiscal Year 2001 (Public Law 106-554; H.R. 5658). NASA's guidelines, for example, emphasize the need for high information quality indicating the various types of public users of information from NASA's missions and programs. The IQC's vision is to become an authoritative and responsive resource of information and guidance to data providers on how best to implement data quality standards and best practices, so that the implementations comply with the various agencies' guidelines, as well as provide users with the best quality of information possible. The IQC interacts with various national and international organizations and encourages collaboration for exchange of information. The IQC considers four aspects of information quality: Scientific Quality, Product Quality, Stewardship Quality and Service Quality. The IQC has considered several use cases to identify issues in capturing, describing, providing access to, and enabling use of information on quality. Several of these use cases point to issues about the usability of information. Collaboration between the IQC and Usability Cluster will be beneficial for arriving at solutions to such issues.
Due to the large number of product, project, and people parameters which impact large custom software development efforts, measurement of software product quality is a complex undertaking. Furthermore, the absolute perspective from which quality is measured (customer satisfaction) is intangible. While we probably can't say what the absolute quality of a software product is, we can determine the relative quality, the adequacy of this quality with respect to pragmatic considerations, and identify good and bad trends during development. While no two software engineers will ever agree on an optimum definition of software quality, they will agree that the most important perspective of software quality is its ease of change. We can call this flexibility, adaptability, or some other vague term, but the critical characteristic of software is that it is soft. The easier the product is to modify, the easier it is to achieve any other software quality perspective. This paper presents objective quality metrics derived from consistent lifecycle perspectives of rework which, when used in concert with an evolutionary development approach, can provide useful insight to produce better quality per unit cost/schedule or to achieve adequate quality more efficiently. The usefulness of these metrics is evaluated by applying them to a large, real world, Ada project.
The customers satisfaction depends not only on functional performance, it also depends on the quality characteristics of the software products. An examination of this quality aspect of software products will provide a clear, well defined framework for quality assurance functions, which improve the life-cycle activities of software development. Software developers must be aware of the following aspects which have been expressed by many quality experts: quality cannot be added on; the level of quality built into a program is a function of the quality attributes employed during the development process; and finally, quality must be managed. These concepts have guided our development of the following definition for a Software Quality Assurance function: Software Quality Assurance is a formal, planned approach of actions designed to evaluate the degree of an identifiable set of quality attributes present in all software systems and their products. This paper is an explanation of how this definition was developed and how it is used.
The purpose of this talk is to examine and discuss NASA's basic quality concept, the policy and procedures which define and implement this concept and subsequently explore the NASA-Industry quality relationships which are essential to the success of NASA's quality assurance concept. It is important to emphasize that NASA's Quality Assurance program is predicated on the concept of an individually tailored quality program for each significant NASA procurement - particularly, large space systems. As a result, NASA's Quality Assurance program - to be effective - must be responsive to various technologies, hardware systems, and space missions. In addition, the program must be responsive to changing policies and practices in research, engineering and procurement. Key issues in the NASA-Contractor quality relationships in the framework of an over all NASA-Industry partnership include: (1) the necessity for NASA to effectively discharge its responsibility for providing clear and complete definition of quality requirements at all phases of the procurement cycle; (2) the right of the contractor to expect an explicit definition of NASA quality requirements in each RFP and contract and to request same if it is not provided; (3) the necessity for the contractor to have a dynamic and responsive quality program which is directed towards assuring that the hardware meets all technical requirements and to accomplish this in an effective and efficient manner; and (4) the critical need for investigation and study of the economic aspects of quality and an evaluation of our policies and practices based upon the results of such a study.
Comprehensive measures of quality are a research imperative, yet the development of software quality models is a wicked problem. Definitive solutions do not exist and quality is subjective at its most abstract. Definitional measures of quality are contingent on a domain, and even within a domain, the choice of representative characteristics to decompose quality is subjective. Thus, the operationalization of quality models brings even more challenges. A promising approach to quality modeling is the use of hierarchies to represent characteristics, where lower levels of the hierarchy represent concepts closer to real-world observations. Building upon prior hierarchical modeling approaches, we developed the Platform for Investigative software Quality Understanding and Evaluation (PIQUE). PIQUE surmounts several quality modeling challenges because it allows modelers to instantiate abstract hierarchical models in any domain by leveraging organizational tools tailored to their specific contexts. Here, we introduce PIQUE; exemplify its utility with two practical use cases; address challenges associated with parameterizing a PIQUE model; and describe algorithmic techniques that tackle normalization, aggregation, and interpolation of measurements.
The Information Quality Cluster (IQC) within the Federation of Earth Science Information Partners (ESIP) was initially formed in 2011 and has evolved significantly over time. The current objectives of the IQC are to: 1. Actively evaluate community data quality best practices and standards; 2. Improve capture, description, discovery, and usability of information about data quality in Earth science data products; 3. Ensure producers of data products are aware of standards and best practices for conveying data quality, and data providers distributors intermediaries establish, improve and evolve mechanisms to assist users in discovering and understanding data quality information; and 4. Consistently provide guidance to data managers and stewards on how best to implement data quality standards and best practices to ensure and improve maturity of their data products. The activities of the IQC include: 1. Identification of additional needs for consistently capturing, describing, and conveying quality information through use case studies with broad and diverse applications; 2. Establishing and providing community-wide guidance on roles and responsibilities of key players and stakeholders including users and management; 3. Prototyping of conveying quality information to users in a more consistent, transparent, and digestible manner; 4. Establishing a baseline of standards and best practices for data quality; 5. Evaluating recommendations from NASA's DQWG in a broader context and proposing possible implementations; and 6. Engaging data providers, data managers, and data user communities as resources to improve our standards and best practices. Following the principles of openness of the ESIP Federation, IQC invites all individuals interested in improving capture, description, discovery, and usability of information about data quality in Earth science data products to participate in its activities.
Data quality plays a critical role in research and applications. The Earth Science Information Partners (ESIP) Information Quality Cluster (IQC) defines four aspects of information quality: Science, Product, Stewardship, and Services. The ESIP IQC has become internationally recognized as an authoritative and responsive resource of information and guidance to data producers and distributors on how to implement data quality standards and best practices for their science data systems, datasets, and data/metadata dissemination services. In recent years, cloud computing environments have provided scale-up capabilities such as data archives and services, enabling interdisciplinary science and applications. More value-added products are expected from data service providers, including Analysis Ready Data (ARD). ARD refers to data that has been preprocessed into a form that allows immediate analysis by the end user, processed to a minimum set of requirements and provides interoperability over time and across multiple datasets. Once a dataset has been developed from its original form to produce ARD, what quality characteristics should the derived dataset or ARD possess? Also, is it safe to assume that the quality of the ARD is consistent with the quality of the source data, or are there special attributes to an ARD that would warrant a secondary, independent quality assessment? What provenance (also called “data lineage”) information needs to be included in ARD? It is important to answer these questions, especially given the ease of use of ARD, and the consequent temptation by users to trust ARD without understanding the limitations or possible variations in quality compared to the source data. In this presentation, we will discuss data quality challenges for ARD products and services and introduce IQC for participation.
The Airline Quality Rating (AQR) was developed and first announced in early 1991 as an objective method of comparing airline performance on combined multiple factors important to consumers. Development history and calculation details for the AQR rating system are detailed in The Airline Quality Rating 1991 issued in April, 1991, by the National Institute for Aviation Research at Wichita State University. This current report, Airline Quality Rating 1998, contains monthly Airline Quality Rating scores for 1997. Additional copies are available by contacting Wichita State University or University of Nebraska at Omaha. The Airline Quality Rating 1998 is a summary of month-by-month quality ratings for the ten major U.S. airlines operating during 1997. Using the Airline Quality Rating system and monthly performance data for each airline for the calendar year of 1997, individual and comparative ratings are reported. This research monograph contains a brief summary of the AQR methodology, detailed data and charts that track comparative quality for major airlines domestic operations for the 12 month period of 1997, and industry average results. Also, comparative Airline Quality Rating data for 1991 through 1996 are included to provide a longer term view of quality in the industry.
The Airline Quality Rating (AQR) was developed and first announced in early 1991 as an objective method of comparing airline quality on combined multiple performance criteria. This current report, Airline Quality Rating 2001, reflects monthly Airline Quality Rating scores for 2000. AQR scores for the calendar year 2000 are based on 15 elements that focus on airline performance areas important to air travel consumers. The Airline Quality Rating 2001 is a summary of month-by-month quality ratings for the ten major U.S. airlines operating during 2000. Using the Airline Quality Rating system of weighted averages and monthly performance data in the areas of on-time arrivals, involuntary denied boardings, mishandled baggage, and a combination of 12 customer complaint categories, major airlines comparative performance for the calendar year of 2000 is reported. This research monograph contains a brief summary of the AQR methodology, detailed data and charts that track comparative quality for major airlines domestic operations for the 12 month period of 2000, and industry average results. Also, comparative Airline Quality Rating data for 1999 are included for each airline to provide historical perspective regarding performance quality in the industry.
The Airline Quality Rating (AQR) was developed and first announced in early 1991 as an objective method of comparing airline quality on combined multiple performance criteria. This current report, Airline Quality Rating 2002, reflects monthly Airline Quality Rating scores for 2001. AQR scores for the calendar year 2001 are based on 15 elements that focus on airline performance areas important to air travel consumers. The Airline Quality Rating 2002 is a summary of month-by-month quality ratings for the 11 largest U.S. airlines operating during 2001. Using the Airline Quality Rating system of weighted averages and monthly performance data in the areas of on-time arrivals, involuntary denied boardings, mishandled baggage, and a combination of 12 customer complaint categories, airlines comparative performance for the calendar year of 2001 is reported. This research monograph contains a brief summary of the AQR methodology, detailed data and charts that track comparative quality for domestic airline operations for the 12-month period of 2001, and industry average results. Also, comparative Airline Quality Rating data for 2000 are included for each airline to provide historical perspective regarding performance quality in the industry.
The Airline Quality Rating (AQR) was developed and first announced in early 1991 as an objective method of comparing airline quality on combined multiple performance criteria. This current report, the Airline Quality Rating 2003, reflects monthly Airline Quality Rating scores for 2002. AQR scores for the calendar year 2002 are based on 15 elements that focus on airline performance areas important to air travel consumers. The Airline Quality Rating 2003 is a summary of month-by-month quality ratings for the 10 largest U.S. airlines operating during 2002. Using the Airline Quality Rating system of weighted averages and monthly performance data in the areas of ontime arrivals, involuntary denied boardings, mishandled baggage, and a combination of 12 customer complaint categories, airlines comparative performance for the calendar year of 2002 is reported. This research monograph contains a brief summary of the AQR methodology, detailed data and charts that track comparative quality for domestic airline operations for the 12-month period of 2002, and industry average results. Also, comparative Airline Quality Rating data for 2001 are included for each airline to provide historical perspective regarding performance quality in the industry.
The Airline Quality Rating (AQR) was developed and first announced in early 1991 as an objective method of comparing airline quality on combined multiple performance criteria. This current report, the Airline Quality Rating 2004, reflects monthly Airline Quality Rating scores for 2003. AQR scores for the calendar year 2003 are based on 15 elements in four major areas that focus on airline performance aspects important to air travel consumers. The Airline Quality Rating 2004 is a summary of month-by-month quality ratings for U.S. airlines that have at least 1 % of domestic passenger volume during 2003. Using the Airline Quality Rating system of weighted averages and monthly performance data in the areas of on-time arrivals, involuntary denied boardings, mishandled baggage, and a combination of 12 customer complaint categories, airlines comparative performance for the calendar year of 2003 is reported. This research monograph contains a brief summary of the AQR methodology, detailed data and charts that track comparative quality for domestic airline operations for the 12-month period of 2003, and industry results. Also, comparative Airline Quality Rating data for 2002 are included, where available, to provide historical perspective regarding performance quality in the industry.
Calibrations to existing cost of doing business in space indicate that to establish human presence on the Moon and Mars with the Space Exploration Initiative (SEI) will require resources, felt by many, to be more than the national budget can afford. In order for SEI to succeed, we must actually design and build space systems at lower cost this time, even with tremendous increases in quality and performance requirements, such as extremely high reliability. This implies that both government and industry must change the way they do business. Therefore, new philosophy and technology must be employed to design and produce reliable, high quality space systems at low cost. In recognizing the need to reduce cost and improve quality and productivity, Department of Defense (DoD) and National Aeronautics and Space Administration (NASA) have initiated Total Quality Management (TQM). TQM is a revolutionary management strategy in quality assurance and cost reduction. TQM requires complete management commitment, employee involvement, and use of statistical tools. The quality engineering methods of Dr. Taguchi, employing design of experiments (DOE), is one of the most important statistical tools of TQM for designing high quality systems at reduced cost. Taguchi methods provide an efficient and systematic way to optimize designs for performance, quality, and cost. Taguchi methods have been used successfully in Japan and the United States in designing reliable, high quality products at low cost in such areas as automobiles and consumer electronics. However, these methods are just beginning to see application in the aerospace industry. The purpose of this paper is to present an overview of the Taguchi methods for improving quality and reducing cost, describe the current state of applications and its role in identifying cost sensitive design parameters.