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UnifiedDL: Secure Metadata-enabled Intelligent Data Integration and Management Lake by AI-powered Graph Canonization and Parallel Processing

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N68335-23-C-0507
Agency Tracking Number: N231-015-0678
Amount: $139,998.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: N231-015
Solicitation Number: 23.1
Solicitation Year: 2023
Award Year: 2023
Award Start Date (Proposal Award Date): 2023-07-10
Award End Date (Contract End Date): 2024-01-09
Small Business Information
320 Whittington PKWY STE 117
Louisville, KY 40222-1111
United States
DUNS: 877380530
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: Yes
Principal Investigator
 Bin Xie
 (502) 321-2043
Business Contact
 Bin Xie
Phone: (502) 321-2043
Research Institution

The Navy has started integrating Battle Management Aid systems (BMA) on the ship platform to share heterogonous superset data from disparate data sources through a centralized data repository like Data Lake (DL). However, the existing approaches like a common data delivery occur asynchronously and take up available bandwidth for intra-application sharing multiple times. The optimization of shared data from the DL for reuse across BMAs has been limited without a unified data management strategy. For this issue, InfoBeyond advocates UnifiedDL (Secure Metadata-enabled Intelligent Data Integration and Management Lake by AI-powered Graph Canonization and Parallel Processing) for providing a secure and unified data management approach in the backend DL to effectively support microservices across BMAs. UnifiedDL utilizes graph canonization with an attention-based neural learning network for integrating heterogeneous datasets into a unified data model for microservices. Such a unified data model enables common data field search by microservices and further enhances the efficiency of data reuse. UnifiedDL also tackles the effectiveness of the multiple-request-format searches from microservices by developing a parallel-processing runtime-optimized aggregated query approach through the cluster-based multithreading execution. Meanwhile, the proposed multi-zone YANG-specified metadata strategy integrates the ingestion-time access control schema for enhancing data reuse, management, and security/privacy. In such a way, UnifiedDL provides effective data lifecycle management within the DL that minimizes the bandwidth of duplicate data traversing the network backbone.

* Information listed above is at the time of submission. *

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