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All sourcesNASA NTRSDOE OSTI
DOE OSTI2012

Context-Enabled Business Intelligence

To truly understand context and apply it in business intelligence, it is vital to understand what context is and how it can be applied in addressing organizational needs. Context describes the facets of the environment that impact the way that end users interact with the system. Context includes aspects of location, chronology, access method, demographics, social influence/ relationships, end-user attitude/ emotional state, behavior/ past behavior, and presence. To be successful in making Business Intelligence content enabled, it is important to be able to capture the context of use user. With advances in technology, there are a number of ways in which this user based information can be gathered and exposed to enhance the overall end user experience.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

A Machine Learning Approach for Business Intelligence Analysis using Commercial Shipping Transaction Data

Business intelligence problems are particularly challenging due to the use of large volume and high velocity data in attempts to model and explain complex underlying phenomena. Incremental machine learning based approaches for summarizing trends and identifying anomalous behavior are often desirable in such conditions to assist domain experts in characterizing their data. The overall goal of this research is to develop a machine learning algorithm that enables predictive analysis on streaming data, detects changes and anomalies in the data, and can evolve based on the dynamic behavior of the data. Commercial shipping transaction data for the U.S. is used to develop and test a Naïve Bayes model that classifies several companies into lines of businesses and demonstrates an ability to predict when the behavior of these companies changes by venturing into other lines of businesses.

Bramer, Lisa M.↗
DOE OSTI2012

2020 Workplace and the Evolution of Business Intelligence

The work place is rapidly changing and will continue to change as the decade proceeds to 2020. Business Intelligence (BI) has many of the tools that will ensure that employees in this future work place can be successful and deal with these environmental changes. Ideas such as “swarming”, “hyper-connectedness”, “de-routinization of work” and “the collective” will morph employee engagement and those organizations with mature Business Intelligence practices will be prepared to support the success of their employees.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2014

Business intelligence for human resources. Toward a new paradigm for report development and delivery

As a corporate workforce grows, managers need more information close at hand to make decisions for the company. As every scientist knows, we can’t judge, let alone improve, something that can’t be measured. Thus, firms that are committed to constant improvement generate reports on various business metrics in order to make informed decisions. Sandia National Labs is no exception. Enterprise business intelligence (BI) is very important to keep such a large organization on the right track, as it signals any adjustments that may be required to stay the course. Like teams in other fields, Sandia’s HR Reporting wants to provide the most valuable Workforce BI possible, and it is considering a change in reporting paradigm to achieve that.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2016

The Cure for Ailing Self-Service Business Intelligence

There are many reasons that self-service models fail. Furthermore, these reasons are directly applicable in the management of self-service business inteligence modeling. Our article expands upon the reasons for failure and suggests how self-service models can be made successful through implementation of a centralized approach to development, testing, implementation and support for the delivery of decision making information.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2015

Watson and Siri: The Rise of the BI Smart Machine

Over the past few years, the industry has seen significant evolution in the area of human computer interaction. The era of the smart machines is upon us, with automation taking on a more advanced role than ever before, permeating into areas that have traditionally only been fulfilled by human beings. This movement has the potential of fundamentally altering the way that business intelligence is executed across the industry and the role that business intelligence has in all aspects of decision making.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2012

Consolidation and Centralization of Waste Operations Business Systems - 12319

This abstract provides a comprehensive plan supporting the continued development and integration of all waste operations and waste management business systems. These include existing systems such as ATMS (Automated Transportation Management System), RadCalc, RFITS (Radio Frequency Identification Transportation System) Programs as well as incorporating key components of existing government developed waste management systems and COTS (Computer Off The Shelf) applications in order to deliver a truly integrated waste tracking and management business system. Some of these existing systems to be integrated include IWTS at Idaho National Lab, WIMS at Sandia National Lab and others. The aggregation of data and consolidation into a single comprehensive business system delivers best practices in lifecycle waste management processes to be delivered across the Department of Energy facilities. This concept exists to reduce operational costs to the federal government by combining key business systems into a centralized enterprise application following the methodology that as contractors change, the tools they use to manage DOE's assets do not. IWITS is one efficient representation of a sound architecture currently supporting multiple DOE sites from a waste management solution. The integration of ATMS, RadCalc and RFITS and the concept like IWITS into a single solution for DOE contractors will result in significant savings and increased efficiencies for DOE. Building continuity and solving collective problems can only be achieved through mass collaboration, resulting in an online community that DOE contractors and subcontractors access common applications, allowing for the collection of business intelligence at an unprecedented level. This is a fundamental shift from a solely 'for profit' business model to a 'for purpose' business model. To the conventional-minded, putting values before profit is an unfamiliar and unnatural way for a contractor to operate - unless however; your objective is to build a strong, strategic alliance across the enterprise in order to execute an unprecedented change in waste management, transportation and logistical operations. The success of such an initiative can be achieved by creating a responsible framework by enabling key individuals to 'own' the sustainability of the program. This includes the strategic collaboration of responsible revolutionaries covering application developers, information owners and federal stakeholders to ensure compliance, security and risk management are 'baked' into the process and sustainability is fostered through continued innovation by both technology and application functionality. This ensures that working software can adapt to changing circumstances and is the principle measure of the success of the program. The consolidation of waste management business systems must be achieved in order to realize efficiencies in information technology portfolio management, data integrity, business intelligence and the lifecycle management of hazardous materials within the DOE enterprise architecture. By identifying best practices across the enterprise and aggregating computational and application development resources, you can provide a unified, holistic solution serviceable from a single location while being accessed from anywhere. The business impact of integrating and delivering a unified solution would reduce costs to the Department of Energy within the first year of deployment with increased savings annually. (author)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2012

Requirements for Predictive Analytics

It is important to have a clear understanding of how traditional Business Intelligence (BI) and analytics are different and how they fit together in optimizing organizational decision making. With tradition BI, activities are focused primarily on providing context to enhance a known set of information through aggregation, data cleansing and delivery mechanisms. As these organizations mature their BI ecosystems, they achieve a clearer picture of the key performance indicators signaling the relative health of their operations. Organizations that embark on activities surrounding predictive analytics and data mining go beyond simply presenting the data in a manner that will allow decisions makers to have a complete context around the information. These organizations generate models based on known information and then apply other organizational data against these models to reveal unknown information.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Radiology and Enterprise Medical Imaging Extensions (REMIX)

Radiology and Enterprise Medical Imaging Extensions (REMIX) is a platform originally designed to both support the medical imaging-driven clinical and clinical research operational needs of Department of Radiology of The Ohio State University Wexner Medical Center. REMIX accommodates the storage and handling of “big imaging data,” as needed for large multi-disciplinary cancer-focused programs. The evolving REMIX platform contains an array of integrated tools/software packages for the following: (1) server and storage management; (2) image reconstruction; (3) digital pathology; (4) de-identification; (5) business intelligence; (6) texture analysis; and (7) artificial intelligence. These capabilities, along with documentation and guidance, explaining how to interact with a commercial system (e.g., PACS, EHR, commercial database) that currently exists in clinical environments, are to be made freely available.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2020

The moving target of visualization software for an increasingly complex world

Visualization has evolved into a mature scientific field and it has also become widely accepted as a standard approach in diverse fields, including physics, life sciences, and business intelligence. However, despite its successful development, there are still many open research questions that require customized implementations in order to explore and establish concepts, and to perform experiments and take measurements. Many methods and tools have been developed and published but most are stand-alone prototypes and have not reached a mature state that can be used in a reliable manner by collaborating domain scientists or a wider audience. In this study, we discuss the challenges, solutions, and open research questions that affect the development of sophisticated, relevant, and novel scientific visualization solutions with minimum overheads. Furthermore, we summarize and discuss the results of a recent National Institute of Informatics Shonan seminar on these topics.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Inventory for Crystalline Silicon Module Recycling: Cooperative Research and Development (Final Report)

A critical challenge for the continued expansion of photovoltaics (PV) is to develop technically feasible, inexpensive and environmentally friendly practices for handling and recycling modules at the end of their usable life. The National Renewable Energy Laboratory (NREL) is requested by the Electric Power Research Institute (EPRI) to collect primary data regarding the environmental performance of currently operational PV module recycling facilities in Europe. Very little has been published regarding crystalline silicon (C-Si) module recycling. Thus, much effort will be needed in direct industry outreach, collection of information and other business intelligence strategies similar to NREL's approaches for developing cost models for PV manufacturing. The goal of this work effort is to produce a detailed inventory that accounts for physical (e.g., energy, water, materials) flows through each step of a C-Si recycling process. The inventory (a life cycle inventory, or LCI) shall be designed so that it can be extended to include an accounting of costs for each process step, inputs, etc. This work effort shall leverage prior LCI data collection NREL performed for the United States Department of Energy, Solar Energy Technologies Office, under the auspices of the U.S. contribution to International Energy Agency's Photovoltaics Power Systems (PVPS) Task 12 (Environmental Health and Safety), which SETO nominated NREL to chair. The primary purpose of this work effort is to augment the prior data collection to increase the sample size of manufacturers' primary data in the LCI.

14 SOLAR ENERGY↗
DOE OSTI2016

NDMAS implementation and data qualification for accident tolerant fuel experiments

In order to meet quality and regulatory requirements associated with the U.S. Department of Energy Accident Tolerant Fuels (ATF) experiments, all data and documentation associated with ATF component fabrication, characterization, traceability, testing and post irradiation examination are being managed using the Nuclear Data Management and Analysis System (NDMAS). NDMAS melds commercial off-the-shelf software to provide the full range of needed functionality. The core component of NDMAS is the SAS Enterprise Business Intelligence software, which provides data manipulation, statistical and graphical capabilities, and delivery of live content to web pages. Content delivery to both Idaho National Laboratory researchers and external users via a web interface is managed using SharePoint, which is access-controlled. Additionally, scripts, macros, and commercial wrappers may be used or written by the NDMAS team to control interfaces between SAS and the structured query language server, where much of the data reside. ATF experiment data and documentation stored in the NDMAS database may be utilized to support Nuclear Regulatory Commission licensing of ATF concepts for commercial Light Water Reactor demonstration. Therefore, Data Qualification Plans are being developed to screen and tag all document and data input for ATR water loop testing, Transient Reactor Testing (TREAT), and post irradiation examination. A project-wide document library has been created in the ATF NDMAS SharePoint Site for general unrestricted document and information access. Restricted libraries have also been created to allow individual customer access to restricted documents. Data from ATR water loop instrumented lead experimentation, TREAT, and post-irradiation examination will be available to program participants on the SharePoint site as they are collected. This offers a unique capability for team collaboration while these tests are being conducted. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

The Building Business Network (B-Biz): Addressing Gaps in the High-Performance Building Technology Market, Especially for Underserved Customers

Increased market intelligence and business model innovation is needed to build contractor confidence in high-performance building technologies in order to increase the speed and scale of adoption to meet the U.S. Department of Energy's building stock decarbonization goals. Although there have been important innovations in energy efficiency, affordability, and decarbonization of building technologies, consumers are not purchasing these technologies at the necessary speed and scale partially due to a gap in institutional understanding around barriers contractors face in providing and servicing these technologies. Small businesses in this market must mitigate risk around high-performance building technologies, limiting their opportunities in the market and impacting the rate of adoption. The Building Business Network (B-Biz) aims to provide high-performance building technology solutions to underserved customers by collaborating with local small businesses that provide and service high-performance building technologies in communities with the lowest rates of adoption. This research explores the current market, the importance of business models, and the opportunity to utilize small businesses to address market gaps in underserved communities.

B-Biz↗
DOE OSTI2018

Complex Network Analysis and Intelligent Monitoring Platform

Data Products has particular interest in developing an efficient network analysis and monitoring system that utilizes advanced machine learning methods to mine complex network data of the type generated within large data centers, with an objective to provide tangible business-usable intelligence. The Laboratory is currently the lead institution for a U.S. Department of Energy Office of Science (DOE/SC) network research project called BigData Express (BDE). The project seeks to provide an orchestration service for high-performance data movement of data sets for large-scale science. This collaboration is believed to potentially be a highly symbiotic one, with Data Products platform development benefiting from the rich sets of network data available in the BDE development environment, and BDE benefiting from the Data Products machine learning analysis of BDE-generated data movement.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Business Case Analysis for Artificial Intelligence-Large Language Model Technology Integration

AI-assisted processes are expected to enhance operational efficiency and improve decision-making, supporting the long-term economic viability of nuclear power plants. However, detailed business analyses of AI-generated cost savings are rarely performed. Given the recent industry interest in Large Language Model (LLM), the U.S. Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program has conducted a comprehensive business case analysis of LLM Artificial Intelligence (AI) implementation in nuclear plant engineering workflows. The research employed three complementary business case approaches to evaluate impact of an LLM, using three representative engineering processes as use-cases: Boric Acid Corrosion (BAC) Evaluations, Maintenance Rule Evaluations, and 10 CFR 50.59 Screenings. Through detailed workload analyses and structured interviews, the study quantified significant efficiency improvements ranging from 11% to 59% across these processes. The research further considers how these efficiency gains could translate into tangible reliability improvements through enhanced engineering capacity. Analysis of historical plant trip data indicates that enabling engineers to focus on proactive reliability activities could provide substantial financial benefits through avoided outages, potentially generating greater value than the direct efficiency improvements alone. By documenting successful applications, implementation challenges, and strategic opportunities, this research provides nuclear utilities with a practical framework for evaluating the value of AI technology to support long-term operations through advanced digital technologies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

DOE SBIR Phase II Final Technical Report - Assessing Climate Change Effects on Wind Energy

Specialized Vertum Partners software tools were prototyped, tested and commercialized to allow wind energy stakeholders to assess the uncertainties of climate change on wind power production and distribution. This project resulted in three commercially proven products and a marketing tool. The first was a Weather Research and Forecasting Model (WRF) based resource evaluation system. The second was a web-based service providing global 10m wind data from multiple sources to wind industry subscription customers. The third product addressed the needs of our utility clients looking at climate change effects on electricity distribution. For this we collaborated on the Santa Ana Wildfire Threat Index (SAWTi), which was released publicly last quarter. Finally to promote these products and educate potential users we released “Gust or Bust”, a graphic-novel styled marketing publication.

17 WIND ENERGY↗