Selective Access and Editing in a Database

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.

A searchable skill set module lists a name of each worker employed by the company and/or employed by one or more companies that contract services for the company, and a list of skills possessed by each such worker. When the system receives a description of a skill set that is needed for a project, the skill set module is queried. The name and relevant skill(s) possessed by each worker that has at least one skill set provided in the received skill set list is displayed in a visually perceptible format. Provisions are provided for customizing and linking, where feasible, a subset of reports and accompanying illustrations for a particular user, and for adding or deleting other reports, as needed. This allows a user to focus on the 15 reports of immediate concern and to avoid sorting through reports and related information that is not of concern. Implementation of this separate-storage option would allow most or all users who have review access to a document to write, edit, and otherwise modify the original version, by storing the modified version only in the users own memory space. Where a user who does not have at least review-access to a report explicitly requests that report, the system optionally informs this user of the lack of review access and recommends that the user contact the system administrator. The system optionallystores preceding versions of a present report for the preceding N periods for historical purposes. The comparative analysis includes an ability to retrieve and reformatnumerical data for a contemplated comparison.

Previous virtual digital assistants (e.g., Siri®, Google Assistant®, Cortana®, Alexa®, etc.) allow users to speak requests to computers or mobile phones, which then perform speech-to-text recognition and execute web searches based on the content of the user's request. However, such systems cannot provide answers that are geospatially and space-time aware, for example, what the weather was like in San Francisco three days ago. In addition, typical virtual assistants only allow community users to indirectly influence system behavior through their queries. MATA provides a conversational assistant and associated computing to turn 40 years and hundreds of terabytes of Earth science of NASA Earth science data into usable knowledge. This system engages with users in an integrated, conversational manner using natural language dialogue, and invokes external web services, when appropriate, to obtain information and perform various actions on a variety of satellite and geospatial data to provide spatio-temporally aware answers. MATA is conversational computing, not just a conversational assistant. Broadly, this technology takes a query, translates that query into to an intent, then that intent invokes the right capability, which in turn invokes the right APIs for computations that take milliseconds. MATA does not simply retrieve an answer from a database, but it intelligently answers a user's question within its specific context.

NASA Armstrong collaborated with the U.S. Air Force to develop algorithms that interpret highly encoded large area terrain maps with geographically user-defined error tolerances. A key feature of the software is its ability to locally decode and render DTMs in real time for a high-performance airplane that may need automatic course correction due to unexpected and dynamic events. Armstrong researchers are integrating the algorithms into a Global Elevation Data Adaptive Compression System (GEDACS) software package, which will enable customized maps from a variety of data sources. How It Works The DTM software achieves its high performance encoding and decoding processes using a unique combination of regular and semi-regular geometric tiling for optimal rendering of a requested map. This tiling allows the software to retain important slope information and continuously and accurately represent the terrain. Maps and decoding logic are integrated into an aircraft's existing onboard computing environment and can operate on a mobile device, an EFB, or flight control and avionics computer systems. Users can adjust the DTM encoding routines and error tolerances to suit evolving platform and mission requirements. Maps can be tailored to flight profiles of a wide range of aircraft, including fighter jets, UAVs, and general aviation aircraft. The DTM and GEDACS software enable the encoding of global digital terrain data into a file size small enough to fit onto a tablet or other handheld/mobile device for next-generation ground collision avoidance. With improved digital terrain data, aircraft could attain better performance. The system monitors the ground approach and an aircraft's ability to maneuver by predicting several multidirectional escape trajectories, a feature that will be particularly advantageous to general aviation aircraft. Why It Is Better Conventional DTM encoding techniques used aboard high-performance aircraft typically achieve relatively low encoding process ratios. Also, the computational complexity of the decoding process can be high, making them unsuitable for the real-time constrained computing environments of high-performance aircraft. Implementation costs are also often prohibitive for general aviation aircraft. This software achieves its high encoding process ratio by intelligently interpreting its maps rather than requiring absolute retention of all data. For example, the DTM software notes the perimeter and depth of a mining pit but ignores contours that are irrelevant based on the climb and turn performance of a particular aircraft and therefore does not waste valuable computational resources. Through this type of intelligent processing, the software eliminates the need to maintain absolute retention of all data and achieves a much higher encoding process ratio than conventional terrain-mapping software. The resulting exceptional encoding process allows users to store a larger library of DTMs in one place, enabling comprehensive map coverage at all times. Additionally, the ability to selectively tailor resolution enables high-fidelity sections of terrain data to be incorporated seamlessly into a map.

The HSSLP employs a three-point architecture integrated with motorized jackscrews and an inertial measurement unit (IMU) to achieve automated leveling across uneven terrain. Each leg provides multiple degrees of freedom, enabling both rotational and translational adjustments to maintain a stable platform on inclines up to 15 degrees. Once positioned, the system can hold its orientation without continuous power, reducing energy consumption and improving reliability in remote or resource-limited environments. Redundancy is built into the design through multiple actuators and independent control capability, allowing the platform to maintain functionality even if one actuator fails. The design is inherently scalable, allowing adaptation for payloads ranging from small instruments to multi-ton structures without sacrificing stiffness or stability. Originally developed for a lunar crane on the Lightweight Surface Manipulation System (LSMS), the platform has been tested in relevant environments to validate its structural integrity and load-handling performance, including successful support of a 35 kg payload at a 2-meter reach. Beyond space applications, HSSLP offers significant advantages for terrestrial industries such as construction, surveying, renewable energy, and film production. By eliminating manual adjustments and providing automated, high-load leveling capability, this technology enables faster deployment, improved safety, and greater operational efficiency in challenging environments. The HSSLP is currently assessed at a TRL 6 and is available for patent licensing.