N221-077 TITLE: DIGITAL ENGINEERING - Semantically-Driven Data Integration Software Solutions

OUSD (R&E) MODERNIZATION PRIORITY: General Warfighting Requirements (GWR)

TECHNOLOGY AREA(S): Weapons

The technology within this topic is restricted under the International Traffic in Arms Regulation (ITAR), 22 CFR Parts 120-130, which controls the export and import of defense-related material and services, including export of sensitive technical data, or the Export Administration Regulation (EAR), 15 CFR Parts 730-774, which controls dual use items. Offerors must disclose any proposed use of foreign nationals (FNs), their country(ies) of origin, the type of visa or work permit possessed, and the statement of work (SOW) tasks intended for accomplishment by the FN(s) in accordance with the Announcement. Offerors are advised foreign nationals proposed to perform on this topic may be restricted due to the technical data under US Export Control Laws.

OBJECTIVE: Develop a software solution to facilitate the integration of data across disparate electronic sources of technical information in accordance with a pre-defined ontology that semantically informs that integration through prescribed concepts and relationships. Solutions must directly address challenges associated with cross-tool/cross-vendor inoperability.

DESCRIPTION: As Strategic Systems Programs (SSP) transitions from traditional, document-based engineering processes to a digital engineering approach, more and more software tools and databases are being used to create, modify, and analyze massive amounts of data during all phases of a given system’s lifecycle. Though many domain-specific modeling tools and capabilities are quite mature and meet the needs of the day, the ability to associate/link technical data across domains in a semantically consistent manner is severely lacking. While commercial digital threading solutions exist, none of them are either semantically-driven in nature or have been validated in digital engineering environments representative of SSP’s. The absence of tools for rigorous integration poses a significant risk for large acquisition programs as inconsistencies in data across domain tool and database boundaries could lead to faulty analysis results and poor decisions during key stages during the engineering design process.

To support the development of its model-based and digital engineering environments, SSP needs novel software solutions to create a dynamic "data fabric" across disparate electronic sources of technical information in accordance with a pre-defined ontology. These semantically-driven "data integrators" should facilitate, either automatically or semi-automatically, the integration of data through tool-specific means that enforce adherence to the ontology and provide the following functionality:

• Retrieve data from the various data sources (e.g., model files, applications, and databases) collectively considered an authoritative source of truth (ASOT),
• Associate/link data across various data sources and convert one tool-specific language convention to another,
• Infer duplicate entities across and merge into single representations as needed,
• Push "round trip" changes between data sources through the integrator subject to certain synchronization triggers,
• Obfuscate data based on user/organization roles and access restrictions when required, and,
• Provide a means to traverse data across sources to understand and analyze the entire dataset.

Candidate data integration solutions must be compatible with the Web Ontology Language (OWL) files generated with the Protégé ontology and knowledge management system [Ref 3]. Minimum desired target data sources include IBM Rational DOORS for system requirements, No Magic Cameo Systems Modeler (CSM) for use cases and architecture (i.e., model-based system engineering), and PTC Windchill for various other artifacts including mechanical (mCAD) and electrical (eCAD) computer aided design models.

PHASE I: Develop an approach to semantically integrate targeted data sources based on SSP’s required capabilities and preliminary requirements. Phase I efforts shall articulate the functional design, algorithms, and framework required for interfacing with CSM, DOORS, and Windchill. Additionally, Phase I deliverables shall include detailed information regarding the software architecture and identification of a robust set of test cases that will be used to verify functionality. Licenses for all commercial software to be employed are required. The Phase I Option, if exercised, will include the initial design specifications and capabilities description to build a prototype solution in Phase II.

PHASE II: Develop a semantically-driven data integrator prototype that incorporates data from CSM, DOORS, and Windchill and demonstrate functionality with a sample data set. Phase II shall include testing based on test cases identified in Phase I (updated during prototype development, as needed). Lastly, the prototype shall demonstrate functionality to representative members of the SSP user community, implement prototype updates per test results and community feedback, and devise a set of use cases applicable to SSP and similar (dual use) digital engineering environments and workflows.

PHASE III DUAL USE APPLICATIONS: Transition the data integrator for Navy use by deploying the software on SSP’s "Blue" digital engineering environment. Support user testing in accordance with the use cases developed in Phase I and Phase II, implementing improvements in the tool based on user feedback, and providing product documentation, including installation and user guides. Successful deployment on the SSP Blue digital engineering environments will demonstrate dual use since the target data source tools here (CSM, DOORS, and Windchill) are ubiquitous in many industrial/engineering sectors such as aerospace, automotive, agriculture, mining, and oil/gas.

REFERENCES:

1. Noy, Natalya F. and McGuinness, Deborah L. "Ontology Development 101: A Guide to Creating Your First Ontology." May 11, 2021. https://protege.stanford.edu/publications/ontology_development/ontology101-noy-mcguinness.html.
2. Cruz, Isabel F. and Xiao, Huiyong. "The Role of Ontologies in Data Integration." Journal of Engineering Intelligent Systems, 13(4), December 2005. https://www.cs.uic.edu/~advis/publications/dataint/eis05j.pdf.
3. Protégé ontology and knowledge management system. https://protege.stanford.edu/.

KEYWORDS: Digital engineering; model-based engineering; model-based systems engineering; semantically-driven data integration; ontology; digital thread; data integration; semantic analysis.