Enhanced Project Management Tool

Context Based Configuration Management (CBCM) is a hybrid tool-suite that directly supports the dynamic, distributed strategic planning and decision making environment. The CBCM system marries Decision Map technology with Commercial Off-the-Shelf (COTS) configuration management work flow (Xerox Docushare), embedded component models (events models, configuration item models, and feedback models) all on top of a web based online collaboration technology (e.g., NASA/Xerox Netmark middleware engine). CBCM drives an enterprise management configuration system with built-in analysis functions, tightly interoperable with other enterprise management systems (through middleware connections) that deliver integrated reports and enable enterprise-wide inputs on decisions/actions/events, and present context-based query-driven views on configuration management information. The theory of operation flows from the most senior level of decision making and creates a master Configuration Decision Map. This map track events, configuration objects, and contexts. Additional Configuration Decision Maps can be created independently and/or as a result of the Master Configuration Decision Map by successive organizations and/or individuals in the entire enterprise. The integrated icon-objects have intelligent triggers embedded within them configurable by the users to provide automatic analysis, forecasts, and reports. All information is stored in an object-relational database that provides robust query and reporting tools to help analyze and support past and current decisions as well as track the enterprise baseline and future potential vectors.

A complex project has many tasks and sub-tasks, many phases and many collaborators, and will often have an associated database with many users, each of which has a limited need to know that does not extend to all information in the database. This invention relates to selective access to simultaneous editing of different portions of a database. It provides selective access to as many as 2N-1 (or 2N) mutually exclusive portions of a database by different subgroups of N users. One or more members of an access subgroup can edit a document or other information collection, to which the members have access simultaneously. In this system the database receives information from one or more information sources and is queried by a plurality of users. The database permits selective access of a given user to different portions of the database, depending upon the users identity and access permissions. Optionally, members of the same access subgroup can be assigned different numerical priorities in a queue so that, as between first and second users in the subgroup, draft editing of a document by the first user will subsequently be reviewed, declined, or either partly entered or wholly entered by the second user. User access to different portions of the database is initially determined when a user account is set up. User access can be subsequently changed according to the circumstances.

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.

The Inductive Monitoring System (IMS) software provides a method of building an efficient system health monitoring software module by examining data covering the range of nominal system behavior in advance and using parameters derived from that data for the monitoring task. This software module also has the capability to adapt to the specific system being monitored by augmenting its monitoring database with initially suspect system parameter sets encountered during monitoring operations, which are later verified as nominal. While the system is offline, IMS learns nominal system behavior from archived system data sets collected from the monitored system or from accurate simulations of the system. This training phase automatically builds a model of nominal operations, and stores it in a knowledge base. The basic data structure of the IMS software algorithm is a vector of parameter values. Each vector is an ordered list of parameters collected from the monitored system by a data acquisition process. IMS then processes select data sets by formatting the data into a predefined vector format and building a knowledge base containing clusters of related value ranges for the vector parameters. In real time, IMS then monitors and displays information on the degree of deviation from nominal performance. The values collected from the monitored system for a given vector are compared to the clusters in the knowledge base. If all the values fall into or near the parameter ranges defined by one of these clusters, it is assumed to be nominal data since it matches previously observed nominal behavior. The IMS knowledge base can also be used for offline analysis of archived data.