There are three innovations in this proposed SWIFT project, all of which were identified during earlier effort. The first innovation involves the development of a web-accessible model invocation engine (the "Web-based" part of SWIFT) which was prototyped earlier, demonstrated to NASA/Ames in December 2016, and will be fully developed in the current proposed projectI. This model invocation engine can be used as a front end to SMART-NAS and can potentially transforms NAS simulations into a Software as a Service (SaaS) model. This transformation will make it practical for NAS analyses to be run anywhere, and its design is compatible with the Data Distribution System (DDS) engine inside of SMART-NAS as well as with the Sherlock database system maintained by NASA. The second innovation, which is coupled with the first, is a standard modeling language, which we call the Predictive Query Language, or PQL (the "Interrogable" part of SWIFT). PQL is a powerful language for coordinating model runs across the distributed SMART-NAS environment, or any other model-based infrastructure. The final innovation involves developing applications ("app") that run on both Apple IOS and Google Android smart phones that enable commercial pilots to easily access the status of the NAS (the "Stakeholder" part of SWIFT). This app can access the current state stored in SMART-NAS or any other NAS data repository.

Our proposed research work significantly enhances the state-of-the-art in aviation data analytics by providing, for the first time, a one-stop resource for meeting data analysis needs of aviation researchers, analysts and practitioners. The resulting Cloud-based Aviation Big Data Analytics Platform benefits multiple NASA projects: RSSA real-time safety assessment, SMARTNAS test-bed, and the Sherlock ATM data warehouse. Our innovation is researched through achievement of five objectives and associated work efforts. The first objective is the refinement of use cases for the big data application. We draw upon our knowledge gained in Phase I research and continued interactions with aviation stakeholders to narrow the use cases to specific applications that are a challenge to NASA and the broader aviation community related to RSSA, SMARTNAS, and other ATM research efforts. The second objective is to create a Big Data technology-driven architecture and processing capabilities for the more specific use cases developed to meet objective 1. The third objective is to achieve a subcomponent demonstration for each refined use case so that we can measure the benefit of using these techniques to solve ATM analytics challenges. The fourth objective is to tie together the demonstration components developed as part of objective 3, into an overall architecture offering a one-stop-shop for both at-rest and in-motion analytics to meet a variety of research needs. Finally, our fifth objective is to pursue commercialization via outreach to government and industry stakeholders. Most current aviation research focuses on smaller datasets or specific data-types. A massive amount of data thus sits un-analyzed and potentially holds a rich set of undiscovered trends that may be valuable for aviation safety-assurance and NAS efficiency-enhancement. Our SBIR will greatly contribute to the advancement of aviation research by enabling truly big data analytics on this massive, un-tapped data.

Bruner Farm Study for Resilient Economic Agricultural Practices in Ames, Iowa Soil P analyzed by Bray P extractant. Soil K analyzed by Ammonium Acetate extractant. For MeasSoilCover estimates: Photos were taken with a Canon, 12.3 megapixel EOS Rebel T3 mounted on a Van-Guard QS-46 large format quick shoe. The monopod that the amera/shoe assembly was attached to is a Wooster Sherlock model R056 telescoping pole. A mounting racket and spirit level (part #’s MSRMB and MSRSLA respectively, available from Cropscan inc. http://www.cropscan.com/mpscs.html) were attached to the pole in a manner so that when the camera is mounted, and the bubble in the spirit level is centered, then the focal plane of the camera is perpendicular to the ground resulting in a nadir image of the ground beneath the camera. The fully extended pole camera assembly was carried diagonally across each plot in field 70/71 an East to West or West to East transect with photos being taken every 9 paces. The unit was angled in such a way that the shadow from the pole and camera was not included in the photo. In the larger plots at the Uthe farm and the Poets facility, 30 to 40 paces were taken between each photo. The self timer on the camera was set for a 10 second delay and the camera was focused before initiating the timer. In most instances, 10 seconds was ample time to maneuver the camera into position for the photo. The cameras motion stabilizer function was turned on and the18mm zoom setting was always used to capture the maximum amount of area in each photo. Cold days, and wind speeds in excess of 30mph present a challenge. Photos were downloaded from the camera with the EOS utility software supplied with the camera and analyzed with sample point version 1.51 with a grid size of 10x10 chosen. A newer version of sample point is available at http://www.samplepoint.org/. Resources in this dataset:Resource Title: Bruner Farm Study for Resilient Economic Agricultural Practices in Ames, Iowa. File Name: BrunerFarmStudy.zip

Full-motion video clips automatically identified in two PC Engine/TurboGrafx-16 games: Sherlock Holmes: Consulting Detective (1991) Kūsō Kagaku Sekai Gulliver Boy (1995)