Context Based Configuration Management System

The technology is an adaptive data management and integration platform designed for disparate data sources. It is built to support multitenancy, manage data governance, handle heterogeneous data formats and advance data democratization using a suite of connected, independent microservices. Each service can be used within an integrated environment, or as a standalone product, with a dedicated set of functionalities, such as metadata management, data versioning, access control, data tagging, link management, and analytics, among others. The platform includes APIs to query, navigate and analyze complex interconnections between data assets. The invention provides the capability to capture and manage domain knowledge as a graph schema. The microservices architecture provides services for data tagging, managing data ownership, managing data relationships by dynamically associating data assets based on their content, metadata cataloging and handling, data discovery by searching data across domains ingested from various data sources, while tracking the data’s lineage and provenance, and product lifecycle that data assets belong to. Such data management can integrate heterogeneous datasets, facilitating cross-domain Metadata Management Services (MMS), identifying and limiting unconnected data sources and other fragmented data, as well as reducing redundant data sources.

The Flight Awareness Collaboration Tool (FACT) user interface is a quad design with four areas. The Primary Map View shows the US with several traffic and weather overlays. The Surface Map View displays the selected airport with information on runway conditions and other factors. The Information View has specific data from various sources about the area of interest. This view also has a built-in algorithm that predicts the impact of the forecast winter weather on airport capacity. The Communication View supports messaging within the geographically-dispersed team that is using FACT. When an airport is selected in the Primary Map View, the information presented in the Surface Map and Information Views is focused on that choice. FACT is a web-based application using Node.js and MongoDB. It receives Java messages from the Federal Aviation Administration System Wide Information Management (SWIM) data repository. Data acquired from web pages and SWIM are tailored for FACTs Information View area. FACT is designed to reside on an existing workstation monitor to be put into use as needed.

NASA Ames has developed an Autonomous Situational Awareness Platform system for a UAS (ASAP-U), a traffic management system to incorporate Unmanned Aerial Systems (UASs) into the National Airspace System. The Autonomous Situational Awareness Platform (ASAP) is a system that combines existing navigation technology (both aviation and maritime) with new procedures to safely integrate Unmanned Aerial Systems (UASs) with other airspace vehicles. It uses a module called ASAP-U, which includes a transmitter, receivers, and various links to other UAS systems. The module collects global positioning system GPS coordinates and time from a satellite antenna, and this data is fed to the UAS's flight management system for navigation. The ASAP-U module autonomously and continuously sends UAS information via a radio frequency (RF) antenna using Self-Organized Time Division Multiple Access (SOTDMA) to prevent signal overlap. It also receives ASAP data from other aircraft. In case of transmission overload, priority is given to closer aircraft. Additionally, the module can receive weather data, navigational aid data, terrain data, and updates to the UAS flight plan. The collected data is relayed to the flight management system, which includes various databases and a navigation computer to calculate necessary flight plan modifications based on regulations, right-of-way rules, terrain, and geofencing. Conflicts are checked against databases, and if none are found, the flight plan is implemented. If conflicts arise, modifications can be made. The ASAP-U module continuously receives and transmits data, including UAS data and data from other aircraft, to detect conflicts with other aircraft, terrain, weather, and geofencing. Based on this information, the flight management system determines the need for course adjustments and the flight control system executes them for a safe flight route.

NASA’s newly developed software leverages flight operations data (e.g., SWIM Terminal Data Distribution System (STDDS) information), and with it, can predict aviation related risks, such as unstable approaches of flights. To do this, the software inputs the complex, multi-source STDDS data, and outputs novel prediction and outcome information. The software converts the relatively inaccessible SWIM data from its native format that is not data science friendly into a format easily readable by most programs. The converted, model friendly data are then input into machine learning algorithms to enable risk prediction capabilities. The backend software sends the machine learning algorithm results to the front end software to display the results in appropriate user interfaces. These user interfaces can be deployed on different platforms including mobile phones and desktop computers and efficiently update models based on changes in the data. To allow for visualization, the software uses a commercially available mapping API. The data are visualized in several different ways, including a heat map layer that shows the risk score, with higher risk in areas of higher flight density, a polyline layer, which shows flight paths, and markers that can indicate a flight’s location in real time, among other things. The related patent is now available to license. Please note that NASA does not manufacturer products itself for commercial sale.

As ever larger numbers of spacecraft seek to make use of Earth's limited orbital volume in increasingly dense orbital regimes, greater coordination becomes necessary to ensure these spacecraft are able to operate safely while avoiding physical collisions, radio-frequency interference, and other hazards. While efforts to date have focused on improving Space Situational Awareness (SSA) and enabling operator to operator coordination, there is growing recognition that a broader system for Space Traffic Management (STM) is necessary. The STM architecture forms the framework for an STM ecosystem, which enables the addition of third parties that can identify and fill niches by providing new, useful services. By making the STM functions available as services, the architecture reduces the amount of expertise that must be available internally within a particular organization, thereby reducing the barriers to operating in space and providing participants with the information necessary to behave responsibly. Operational support for collision avoidance, separation, etc., is managed through a decentralized architecture, rather than via a single centralized government-administered system. The STM system is based on the use of standardized Application Programming Interfaces (API) to allow easier interconnection and conceptual definition of roles to more easily allow suppliers with different capabilities to add value to the ecosystem. The architecture handles basic functions including registration, discovery, authentication of participants, and auditable tracking of data provenance and integrity. The technology is able to integrate data from multiple sources.