SBIR/STTR Research Award

Technologies for Sharing Network Performance Data (TEECODE)

Award identifier be4e68759308ddc3879c2ef855cd7b3397c0a1e0c7109dad4b75d1350bdbb806Awarding agency Department of Energy

Award amount

$256,499

Program and phase

SBIR · Phase I

Award year

2021

Explore This Research Award in CaptureIQ

Research the technology, evaluate the recipient and agency, identify related opportunities, and explore potential follow-on work with CaptureIQ.

Award details

Awarding agency
Department of Energy
Branch
Not reported
Recipient company
Reservoir Labs, Inc.
Proposal award date
2021-02-22
Contract end date
2021-11-21
UEI
J4DVUAMQJMW7
Solicitation number
Not reported
Topic code
05b

Research abstract

Government and commercial sectors need high-performance secure networks to carry out their missions and operations. High-performance networks that connect the DOE National Labs and their scientific facilities and scientific and academic institutions within and outside the nation are extremely critical to fulfill the mission of enabling scientists nationwide and globally to perform and deliver uninterrupted scientific discoveries. There is a critical need for tools that help manage and improve the performance of networks and these tools must correlate different forms of network data to present a unified picture of the network infrastructure and enable the network administrator to get holistic insights into network health and performance to take proper actions. We propose TEECODE as a solution that provides an effective, usable, and scalable correlation tool to bind multiple network data ensembles together and present actionable insights and holistic knowledge on the network to the network administrator. The approach used for deriving and presenting correlations to enable network visibility will involve an unsupervised machine learning technique for data analysis that is based on an advanced mathematical tool that can effectively extract coherent patterns and correlations in data. The proposed approach will result in an easy-to-use tool that is scalable to handle large volumes of diverse data ensembles that may be anonymized for security reasons and seamlessly provide visibility into network state and performance. In Phase I, we will develop a proof-of-concept prototype of a usable and scalable correlation analysis pipeline that can scalably and flexibly extract, transform, and load diverse network data ensembles, analyze the data ensembles and extract correlations, and present the results of the correlation analysis in an user-friendly form to the network analyst. We will extensively test and validate the solution on real network data. We will demonstrate the solution to potential early adopters and lay the groundwork for building a mature product and commercializing the solution. TEECODE will significantly help improve the performance, reliability, and operation of commercial, research, and Government (DOE, DoD, and other agencies) networks, and will help reduce the overall operational costs and risks of network operation. TEECODE will bring tangible benefits to commercial sectors including, but not limited to, telecommunications, healthcare, insurance, and finance sectors, by offering improved visibility, reliability, and protection to the networks that are critical for their operations and business. TEECODE will have a very positive impact on the scientific community and enable them to fully utilize the sophisticated infrastructure provided by high-speed high-performance networks, conduct high-end research, and discover new scientific innovations. Increasing the power of scientists by enabling them to do uninterrupted discovery will directly contribute to national and global prosperity.

Technology keywords

Not reported

Other awards by this recipient

Verified by the same UEI. These are other public SBIR/STTR award pages, not a merged company record.

Awards with the same topic code

Verified by an exact topic code match. This is not a text-similarity match.

Research funding history

71 SBIR/STTR records share this UEI (2004–2022). Reported award amounts total $38,801,244. Reported amounts are not federal obligations.

Phases: Phase I, Phase II

Agencies: Department of Defense, Department of Energy

Agency intelligence

Department of Energy

Back to SBIR/STTR Research Awards

Don't take our word for it. Ask AI about RFPCapture.

See what leading AI assistants have to say about RFPCapture and CaptureIQ.

Gemini opens without a prefilled question. The question is copied to your clipboard when the button works. You can also copy it here:

What is RFPCapture.com and its CaptureIQ platform from Smart Logic AI? How does it help federal contractors discover opportunities, evaluate bid/no-bid decisions, research government contracts, and prepare proposals? How does it compare with other federal capture platforms? Use independent sources where available.