Acquiring and Managing Data in Support of the CBS Integrated Research Plan: Strategies and Lessons Learned
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International Space Station Alpha (ISSA) will accommodate a variety of user payloads investigating diverse scientific and technology disciplines on behalf of five international partners: Canada, Europe, Japan, Russia, and the United States. A combination of crew, automated systems, and ground operations teams will control payload operations that require complementary on-board and ground systems. This paper presents the current planning for the ISSA U.S. user payload operations concept and the functional architecture supporting the concept. It describes various NASA payload operations facilities, their interfaces, user facility flight support, the payload planning system, the onboard and ground data management system, and payload operations crew and ground personnel training. This paper summarizes the payload operations infrastructure and architecture developed at the Marshall Space Flight Center (MSFC) to prepare and conduct ISSA on-orbit payload operations from the Payload Operations Integration Center (POIC), and from various user operations locations. The authors pay particular attention to user data management, which includes interfaces with both the onboard data management system and the ground data system. Discussion covers the functional disciplines that define and support POIC payload operations: Planning, Operations Control, Data Management, and Training. The paper describes potential interfaces between users and the POIC disciplines, from the U.S. user perspective.
This paper examines the requirements and the design considerations of the Synthetic Aperture Radar (SAR) ground data system for the Alaska SAR Facility (ASF) at the University of Alaska in Fairbanks (UAF) for the new era of Radarsat/ERS-2 missions. These include a new data acquisition planning capability to manage more satellites with global planning and to manage more than one instrument mode; a new archive strategy that is cheaper, faster, and better; a product generation system to produce data on demand and to produce data for the varied instruments and modes; and a product verification ability for the new and old products. In response to these new functional requirements, JPL is using a design approach that emphasizes an open systems, client/server architecture based on industry standards and commercial off-the-shelf technology.
This is a progress report on an effort in which our goal is to demonstrate the effectiveness of automated data mining and planning for the daily management of Earth Science missions. Currently, data mining and machine learning technologies are being used by scientists at research labs for validating Earth science models. However, few if any of these advanced techniques are currently being integrated into daily mission operations. Consequently, there are significant gaps in the knowledge that can be derived from the models and data that are used each day for guiding mission activities. The result can be sub-optimal observation plans, lack of useful data, and wasteful use of resources. Recent advances in data mining, machine learning, and planning make it feasible to migrate these technologies into the daily mission planning cycle. We describe the design of a closed loop system for data acquisition, processing, and flight planning that integrates the results of machine learning into the flight planning process.
Guidelines and recommendations are presented for the collection of software development data. Motivation and planning for, and implementation and management of, a data collection effort are discussed. Topics covered include types, sources, and availability of data; methods and costs of data collection; types of analyses supported; and warnings and suggestions based on software engineering laboratory (SEL) experiences. This document is intended as a practical guide for software managers and engineers, abstracted and generalized from 5 years of SEL data collection.
An operational approach to shuttle mission planning during high flight frequency years (20 or more flights per year) is described wherein diverse mission planning functions interface via an interactive computer system and common data base. The Vehicle Management and Mission Planning System (VMMPS) is proposed as a means of helping to accomplish the mission planning function. The VMMPS will link together into an interactive system the major mission planning areas such as trajectory, crew, vehicle performance, and launch operations. A common data base will be an integral part of the system and the concept of standard mission types and phases will be used to minimize mission to mission uniqueness. The use of this system will eliminate much redundancy and replanning, shorten interface times between functions, and provide a means to evaluate unplanned events and modify schedules.
The Phoenix Pilot Program has been designed to make effective use of past experience in making land use maps and collecting land use information. Conclusions reached from the project are: (1) Land use maps and accompanying statistical information of reasonable accuracy and quality can be compiled at a scale of 1:250,000 from orbital imagery. (2) Orbital imagery used in conjunction with other sources of information when available can significantly enhance the collection and analysis of land use information. (3) Orbital imagery combined with modern computer technology will help resolve the problem of obtaining land use data quickly and on a regular basis, which will greatly enhance the usefulness of such data in regional planning, land management, and other applied programs. (4) Agreement on a framework or scheme of land use classification for use with orbital imagery will be necessary for effective use of land use data.
Data relating to hydrologic and water resource systems and subsystems management are reported. Systems models, user application, and remote sensing technology are covered. Parameters governing water resources include evaportranspiration, vegetation, precipitation, streams and estuaries, reservoirs and lakes, and unsaturate and saturated soil zones.
Listening to the case studies that were presented it became quite evident that the best data management systems were the ones where data managers and research scientists worked as a team developed in the early stages of project planning. Examples that were given included WOCE Data Assembly Centres e.g., Drifters, the Global Temperature Salinity Pilot Project (GTSPP) collaboration with Joint Analyses Centres in the U.S. and Australia, and JGOFS/BOFS development of Topical Centres. While each of these has some elements unique to the project, each had brought together 'teams' of Principal Investigators (PI's) and data management experts at an early stage of project development. Conversely, projects which had considered data management as a totally separate activity with lower priority often failed to provide the service required to meet scientific objectives. Therefore, the following actions should be brought to the attention of relevant groups within the IOC and other international organizations: (1) Publicize, at the national and international level, underway data/scientist collaborations that may be used as models in planning for the future. (2) Reduce adversarial situations where data managers and scientists appear to be in competition. (3) Colocation and other forms of collaboration often results in very high quality data sets and more timely data submission. Improved timeliness of data submissions was a common theme throughout the workshop and must be considered an important element in all future plans. Improvements in timely submission of data were noted. In order to continue this trend the advantages of timely submission of data must be stressed to those planning new ocean science projects.
During my internship I worked on two major projects, recommending improvements for the Center's Risk Management Workshop and helping with the strategic planning efforts for Safety and Mission Assurance (S&MA). The risk management improvements is the key project I worked on this semester through my internship, while the strategic planning is the secondary assignment. S&MA Business Office covers both aspects in its delegation, getting both spans some of the work done in the office. A risk is a future event with a negative consequence that has some probability of occurring. Safety and Mission Assurance identifies, analyzes, plans, and tracks risk. The directorate offers the Center a Risk Management Workshop, and part of the ongoing efforts of S&MA is to make continuous improvements to the RM Workshop. By using the Project Management Institute's (PMI) Standard for Risk Management, I performed a gap analysis to make improvements for our materials. I benchmarked the PMI's Risk Management Standard, compared our Risk Management Workshop materials to PMI's standard, and identified any gaps in our material. My major findings were presented to the Business Office of S&MA for a decision on whether or not to incorporate the improvements. These suggestions were made by attending JSC working group meetings, Health, Safety and Environment (HSE) panel reviews and various risk review meetings. The improvements provide better understanding of risk management processes and enhanced risk tracking knowledge and skills. Risk management is an integral part of any engineering discipline, getting exposed to this section of engineering will greatly help shape my career in the future. Johnson Space Center is a world leader in risk management processes; learning risk management here gives me a huge advantage over my peers, as well as understanding decision making in the context of risk management will help me to be a well-rounded engineer. Strategic planning is an area I had not previously studied. Helping with the strategic planning efforts in S&MA has taught me how organizations think and function as a whole. S&MA is adopting a balanced scorecard approach to strategic planning. As part of this planning method strategic themes, objectives, and initiatives are formed. I attended strategic theme team workshops that formed the strategy map for the directorate and gave shape to the plan. Also during these workshops the objectives were discussed and built. Learning the process for strategic planning has helped me better understand how organizations and businesses function, which also helps me to be a more effective employee. Other assignments I had during my internship included completing the Safety and Mission Assurance Technical Excellent Program (STEP) Level 1, as well as doing a two week rotation through the Space Exploration division in S&MA, specifically working with a thermal protection systems (TPS) engineer. While working there, I learned about the Orion capsule and the SpaceX Dragon cargo capsule. I attended meetings to prepare the engineers for the upcoming Critical Design Reviews for both capsules and reviewed test data. Learning risk management, strategic planning, and working in the Space Exploration division has taught me about many aspects of S&MA. My internship at NASA has given me new experiences and taught me numerous subjects that I would have otherwise not learned. This opportunity has expanded my educational horizons and is helping me to become a more useful engineer and employee.
An Earth Sciences Data and Information System (ESDIS) Project Management Plan (PMP) is prepared. An ESDIS Project Systems Engineering Management Plan (SEMP) consistent with the developed PMP is also prepared. ESDIS and related EOS program requirements developments, management and analysis processes are evaluated. Opportunities to improve the effectiveness of these processes and program/project responsiveness to requirements are identified. Overall ESDIS cost estimation processes are evaluated, and recommendations to improve cost estimating and modeling techniques are developed. ESDIS schedules and scheduling tools are evaluated. Risk assessment, risk mitigation strategies and approaches, and use of risk information in management decision-making are addressed.
A data reduction plan is being developed to efficiently process residual acceleration data from orbiting space laboratories. Implementation of the reduction plan will result in a useful, manageable accelerometer data base which can be readily employed by principal investigators during post-flight analysis of experimental results. The data reduction plan will also assist in the characterization of the acceleration environment of orbiters, which is important for the planning of future experimental missions. Prototype versions of the reduction plan are being tested using accelerometer data recorded during the Spacelab 3 (SL3) mission. Transient disturbances caused by shuttle and experiment operations and crew activities are being cataloged. The magnitudes of such disturbances can be as much as 0.01 g, but are rarely sustained for more than a fraction of a second and tend to vary greatly in orientation. It is found that the recorded frequency components of the SL3 disturbances do not exceed tolerance limits determined from modeling for a selected experiment set.
A new technology for producing finite capacity schedules has been developed in response to complex requirements for operating space systems such as the Space Shuttle, the Space Station, and the Deep Space Network for telecommunications. This technology has proven its effectiveness in manufacturing environments where popular scheduling techniques associated with Materials Resources Planning (MRPII) and with factory simulation are not adequate for shop-floor work planning and control. The technology has three components. The first is a set of data structures that accommodate an extremely general description of a factory's resources, its manufacturing activities, and the constraints imposed by the environment. The second component is a language and set of software utilities that enable a rapid synthesis of functional capabilities. The third component is an algorithmic architecture called the Five Ruleset Model which accommodates the unique needs of each factory. Using the new technology, systems can model activities that generate, consume, and/or obligate resources. This allows work-in-process (WIP) to be generated and used; it permits constraints to be imposed or intermediate as well as finished goods inventories. It is also possible to match as closely as possible both the current factory state and future conditions such as promise dates. Schedule revisions can be accommodated without impacting the entire production schedule. Applications have been successful in both discrete and process manufacturing environments. The availability of a high-quality finite capacity production planning capability enhances the data management capabilities of MRP II systems. These schedules can be integrated with shop-floor data collection systems and accounting systems. Using the new technology, semi-custom systems can be developed at costs that are comparable to products that do not have equivalent functional capabilities and/or extensibility.
The DIAMS program, initiated in 1986, led to the development of a prototype expert system, DIAMS-1 dedicated to the Telecom 1 Attitude and Orbit Control System, and to a near-operational system, DIAMS-2, covering a whole satellite (the Telecom 2 platform and its interfaces with the payload), which was installed in the Satellite Control Center in 1993. The refinement of the knowledge representation and reasoning is now being studied, focusing on the introduction of appropriate handling of incompleteness, uncertainty and time, and keeping in mind operational constraints. For the latest generation of the tool, DIAMS-3, a new architecture has been proposed, that enables the cooperative exploitation of various models and knowledge representations. On the same baseline, new solutions enabling higher integration of diagnostic systems in the operational environment and cooperation with other knowledge intensive systems such as data analysis, planning or procedure management tools have been introduced.
The need for a more flexible and efficient data file management system for mission planning in the Mission Operations Laboratory (EO) at MSFC has spawned the development of Silvabase. Silvabase is a new data file structure based on a B+ tree data structure. This data organization allows for efficient forward and backward sequential reads, random searches, and appends to existing data. It also provides random insertions and deletions with reasonable efficiency, utilization of storage space well but not at the expense of speed, and performance of these functions on a large volume of data. Mission planners required that some data be keyed and manipulated in ways not found in a commercial product. Mission planning software is currently being converted to use Silvabase in the Spacelab and Space Station Mission Planning Systems. Silvabase runs on a Digital Equipment Corporation's popular VAX/VMS computers in VAX Fortran. Silvabase has unique features involving time histories and intervals such as in operations research. Because of its flexibility and unique capabilities, Silvabase could be used in almost any government or commercial application that requires efficient reads, searches, and appends in medium to large amounts of almost any kind of data.
Single form presents clear picture of entire system. Form giving relational review of data flow well suited to information system planning, analysis, engineering, and management. Used to review data flow for developing system or one already in use.
This is an effort to assess the applicability of air and spaceborne photography toward providing data inputs to urban and regional planning, management, and research. Through evaluation of remote sensing inputs to urban change detection systems, analyzing an effort to replicate an existing urban land use data file using remotely sensed data, estimating population and dwelling units from imagery, and by identifying and evaluating a system of urban places ultilizing space photography, it was determined that remote sensing can provide data concerning land use, changes in commercial structure, data for transportation planning, housing quality, residential dynamics, and population density.