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All sources NASA NTRS DOE OSTI

NASA NTRS1990

An SQL query generator for CLIPS

As expert systems become more widely used, their access to large amounts of external information becomes increasingly important. This information exists in several forms such as statistical, tabular data, knowledge gained by experts and large databases of information maintained by companies. Because many expert systems, including CLIPS, do not provide access to this external information, much of the usefulness of expert systems is left untapped. The scope of this paper is to describe a database extension for the CLIPS expert system shell. The current industry standard database language is SQL. Due to SQL standardization, large amounts of information stored on various computers, potentially at different locations, will be more easily accessible. Expert systems should be able to directly access these existing databases rather than requiring information to be re-entered into the expert system environment. The ORACLE relational database management system (RDBMS) was used to provide a database connection within the CLIPS environment. To facilitate relational database access a query generation system was developed as a CLIPS user function. The queries are entered in a CLlPS-like syntax and are passed to the query generator, which constructs and submits for execution, an SQL query to the ORACLE RDBMS. The query results are asserted as CLIPS facts. The query generator was developed primarily for use within the ICADS project (Intelligent Computer Aided Design System) currently being developed by the CAD Research Unit in the California Polytechnic State University (Cal Poly). In ICADS, there are several parallel or distributed expert systems accessing a common knowledge base of facts. Expert system has a narrow domain of interest and therefore needs only certain portions of the information. The query generator provides a common method of accessing this information and allows the expert system to specify what data is needed without specifying how to retrieve it.

Snyder, James↗

DOE OSTI2015

Automatic building information model query generation

Energy efficient building design and construction calls for extensive collaboration between different subfields of the Architecture, Engineering and Construction (AEC) community. Performing building design and construction engineering raises challenges on data integration and software interoperability. Using Building Information Modeling (BIM) data hub to host and integrate building models is a promising solution to address those challenges, which can ease building design information management. However, the partial model query mechanism of current BIM data hub collaboration model has several limitations, which prevents designers and engineers to take advantage of BIM. To address this problem, we propose a general and effective approach to generate query code based on a Model View Definition (MVD). This approach is demonstrated through a software prototype called QueryGenerator. In conclusion, by demonstrating a case study using multi-zone air flow analysis, we show how our approach and tool can help domain experts to use BIM to drive building design with less labour and lower overhead cost.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Query Relaxation for LLM-Generated SPARQL Queries over Building Knowledge Graphs

When Knowledge Graph (KG) queries fail to match a pattern in a KG, they return no results. Identifying the statements causing these failures is tedious, especially for LLM-generated queries, which tend to be longer and more complex than queries written by hand. Query relaxation addresses this by systematically loosening query constraints until results are recovered. To evaluate the effectiveness of query relaxation against LLM generated queries, we propose a two-stage relaxation method combining triple deletion and path relaxation and test it against 1,823 failed queries for building KGs.

Paul, Lazlo↗

NASA NTRS2017

An Application Programming Interface for Synthetic Snowflake Particle Structure and Scattering Data

The work by Kuo and colleagues on growing synthetic snowflakes and calculating their single-scattering properties has demonstrated great potential to improve the retrievals of snowfall. To grant colleagues flexible and targeted access to their large collection of sizes and shapes at fifteen (15) microwave frequencies, we have developed a web-based Application Programming Interface (API) integrated with NASA Goddard's Precipitation Processing System (PPS) Group. It is our hope that the API will enable convenient programmatic utilization of the database. To help users better understand the API's capabilities, we have developed an interactive web interface called the OpenSSP API Query Builder, which implements an intuitive system of mechanisms for selecting shapes, sizes, and frequencies to generate queries, with which the API can then extract and return data from the database. The Query Builder also allows for the specification of normalized particle size distributions by setting pertinent parameters, with which the API can also return mean geometric and scattering properties for each size bin. Additionally, the Query Builder interface enables downloading of raw scattering and particle structure data packages. This presentation will describe some of the challenges and successes associated with developing such an API. Examples of its usage will be shown both through downloading output and pulling it into a spreadsheet, as well as querying the API programmatically and working with the output in code.

reflectivity properties of snowflakes↗

NASA NTRS2003

Developing A Web-based User Interface for Semantic Information Retrieval

While there are now a number of languages and frameworks that enable computer-based systems to search stored data semantically, the optimal design for effective user interfaces for such systems is still uncle ar. Such interfaces should mask unnecessary query detail from users, yet still allow them to build queries of arbitrary complexity without significant restrictions. We developed a user interface supporting s emantic query generation for Semanticorganizer, a tool used by scient ists and engineers at NASA to construct networks of knowledge and dat a. Through this interface users can select node types, node attribute s and node links to build ad-hoc semantic queries for searching the S emanticOrganizer network.

Berrios, Daniel C.↗

NASA NTRS1985

The Mystro system: A comprehensive translator toolkit

Mystro is a system that facilities the construction of compilers, assemblers, code generators, query interpretors, and similar programs. It provides features to encourage the use of iterative enhancement. Mystro was developed in response to the needs of NASA Langley Research Center (LaRC) and enjoys a number of advantages over similar systems. There are other programs available that can be used in building translators. These typically build parser tables, usually supply the source of a parser and parts of a lexical analyzer, but provide little or no aid for code generation. In general, only the front end of the compiler is addressed. Mystro, on the other hand, emphasizes tools for both ends of a compiler.

Collins, W. R.↗

NASA NTRS1985

KARL: A Knowledge-Assisted Retrieval Language

Data classification and storage are tasks typically performed by application specialists. In contrast, information users are primarily non-computer specialists who use information in their decision-making and other activities. Interaction efficiency between such users and the computer is often reduced by machine requirements and resulting user reluctance to use the system. This thesis examines the problems associated with information retrieval for non-computer specialist users, and proposes a method for communicating in restricted English that uses knowledge of the entities involved, relationships between entities, and basic English language syntax and semantics to translate the user requests into formal queries. The proposed method includes an intelligent dictionary, syntax and semantic verifiers, and a formal query generator. In addition, the proposed system has a learning capability that can improve portability and performance. With the increasing demand for efficient human-machine communication, the significance of this thesis becomes apparent. As human resources become more valuable, software systems that will assist in improving the human-machine interface will be needed and research addressing new solutions will be of utmost importance. This thesis presents an initial design and implementation as a foundation for further research and development into the emerging field of natural language database query systems.

Dominick, Wayne D.↗

DOE OSTI2023

CrossLink: Geometry API [Slides]

The mesh generation process is very challenging and time consuming when working with complex CAD models. The process of creating and sorting geometric entities into groups appropriate for meshing is labor intensive and prone to error. In addition, the common data exchange formats such as STEP and IGES do not propagate information such as entity names that may be defined in the original model. Finally, entity counts change frequently with parameter variation as a result of tolerance-based geometry operations. Thus, sorting by index does not provide a robust and repeatable means for grouping. xGeom is a geometry library that enables the creation of NURBS curves and surfaces via a python scripting interface. xGeom is ideal for studying relatively simple models and is fully integrated with CrossLink’s mesh generation capabilities. For more complex models, xCAD is a python-based Creo Parametric CAD model driver that enables the model to be generated, queried, parametrically modified, regenerated, and exported without data loss and in a fully repeatable manner.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2018

GeneLab Analysis Working Group Kick-Off Meeting

Goals to achieve for GeneLab AWG - GL vision - Review of GeneLab AWG charter Timeline and milestones for 2018 Logistics - Monthly Meeting - Workshop - Internship - ASGSR Introduction of team leads and goals of each group Introduction of all members Q/A Three-tier Client Strategy to Democratize Data Physiological changes, pathway enrichment, differential expression, normalization, processing metadata, reproducibility, Data federation/integration with heterogeneous bioinformatics external databases The GLDS currently serves over 100 omics investigations to the biomedical community via open access. In order to expand the scope of metadata record searches via the GLDS, we designed a metadata warehouse that collects and updates metadata records from external systems housing similar data. To demonstrate the capabilities of federated search and retrieval of these data, we imported metadata records from three open-access data systems into the GLDS metadata warehouse: NCBI's Gene Expression Omnibus (GEO), EBI's PRoteomics IDEntifications (PRIDE) repository, and the Metagenomics Analysis server (MG-RAST). Each of these systems defines metadata for omics data sets differently. One solution to bridge such differences is to employ a common object model (COM) to which each systems' representation of metadata can be mapped. Warehoused metadata records are then transformed at ETL to this single, common representation. Queries generated via the GLDS are then executed against the warehouse, and matching records are shown in the COM representation (Fig. 1). While this approach is relatively straightforward to implement, the volume of the data in the omics domain presents challenges in dealing with latency and currency of records. Furthermore, the lack of a coordinated has been federated data search for and retrieval of these kinds of data across other open-access systems, so that users are able to conduct biological meta-investigations using data from a variety of sources. Such meta-investigations are key to corroborating findings from many kinds of assays and translating them into systems biology knowledge and, eventually, therapeutics.

GeneLab↗

DOE OSTI2018

The SQL Query Used to Generate the Report

Marilyn mentioned that we will need to submit the SQL query to a review in order to ensure it is releasable to the IG. To whom should the SQL query be submitted? Please find the SQL query below.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1992

StarView: The object oriented design of the ST DADS user interface

StarView is the user interface being developed for the Hubble Space Telescope Data Archive and Distribution Service (ST DADS). ST DADS is the data archive for HST observations and a relational database catalog describing the archived data. Users will use StarView to query the catalog and select appropriate datasets for study. StarView sends requests for archived datasets to ST DADS which processes the requests and returns the database to the user. StarView is designed to be a powerful and extensible user interface. Unique features include an internal relational database to navigate query results, a form definition language that will work with both CRT and X interfaces, a data definition language that will allow StarView to work with any relational database, and the ability to generate adhoc queries without requiring the user to understand the structure of the ST DADS catalog. Ultimately, StarView will allow the user to refine queries in the local database for improved performance and merge in data from external sources for correlation with other query results. The user will be able to create a query from single or multiple forms, merging the selected attributes into a single query. Arbitrary selection of attributes for querying is supported. The user will be able to select how query results are viewed. A standard form or table-row format may be used. Navigation capabilities are provided to aid the user in viewing query results. Object oriented analysis and design techniques were used in the design of StarView to support the mechanisms and concepts required to implement these features. One such mechanism is the Model-View-Controller (MVC) paradigm. The MVC allows the user to have multiple views of the underlying database, while providing a consistent mechanism for interaction regardless of the view. This approach supports both CRT and X interfaces while providing a common mode of user interaction. Another powerful abstraction is the concept of a Query Model. This concept allows a single query to be built form a single or multiple forms before it is submitted to ST DADS. Supporting this concept is the adhoc query generator which allows the user to select and qualify an indeterminate number attributes from the database. The user does not need any knowledge of how the joins across various tables are to be resolved. The adhoc generator calculates the joins automatically and generates the correct SQL query.

Williams, J. D.↗

DOE OSTI2019

GRACC: GRid ACcounting Collector

The OSG has long maintained a central accounting system called Gratia. It uses small probes on each computing and storage resource in order to collect resource usage. The probes report to a central collector which stores the usage in a database. The database is then queried to generate reports. As the OSG aged, the size of the database grew very large. It became too large for the database technology to efficiently query to generate detailed reports. The design of a replacement requires data storage that could be queried efficiently to generate multi-year reports. Additionally, it requires flexibilityto add new attributes to the collected data.In this paper we will describe updates to the GRACC architecture in the last 18 months. GRACC uses modern web technologies that were designed for large data storage, query, and visualization. That includes the open source database Elasticsearch, message broker software RabbitMQ, and Grafana and Kibana as data visualization platforms. It uses multiple agents that perform operations on the data to transform it for easier querying and summarization.

Weitzel, Derek↗

DOE OSTI2018

Social media based NPL system to find and retrieve ARM data: Concept paper

Information connectivity and retrieval has a role in our daily lives. The most pervasive source of online information is databases. The amount of data is growing at rapid rate and database technology is improving and having a profound effect. Almost all online applications are storing and retrieving information from databases. One challenge in supplying the public with wider access to informational databases is the need for knowledge of database languages like Structured Query Language (SQL). Although the SQL language has been published in many forms, not everybody is able to write SQL queries. Another challenge is that it may not be practical to make the public aware of the structure of the database. There is a need for novice users to query relational databases using their natural language. To solve this problem, many natural language interfaces to structured databases have been developed. The goal is to provide more intuitive method for generating database queries and delivering responses. Social media makes it possible to interact with a wide section of the population. Through this medium, and with the help of Natural Language Processing (NLP) we can make the data of the Atmospheric Radiation Measurement Data Center (ADC) more accessible to the public. We propose an architecture for using Apache Lucene/Solr [1], OpenML [2,3], and Kafka [4] to generate an automated query/response system with inputs from Twitter5, our Cassandra DB, and our log database. Using the Twitter API and NLP we can give the public the ability to ask questions of our database and get automated responses.

Devarakonda, Ranjeet↗

DOE OSTI2026

datasight [SWR-26-045]

This software is an AI-powered data exploration with natural language. datasight connects an AI agent to your database and provides a web UI where you can ask questions in natural language. The agent writes SQL, runs queries, and generates interactive Plotly visualizations. Supports DuckDB, PostgreSQL, SQLite, and Flight SQL databases. Also queries local CSV and Parquet files directly — no database setup required. Supports Anthropic Claude (default), GitHub Models (open source), and Ollama (local) as LLM backends.

Thom, Daniel [National Laboratory of the Rockies (↗

DOE OSTI2024

ATCCfinder - Download and Search the ATCC Genome Portal

Much strain-specific sequence data exists in research conducted before the deployment of large sequencing repositories, making it challenging to identify and validate the identity of strains used in these studies through bioinformatics and phenotyping. The American Type Culture Collection (ATCC) is an organization that sells a wide variety of microbes with strain-level taxonomy classification and associated sequenced reference genomes. Currently, ATCC does not provide a method for searching for sequence similarity between a query sequence and their database of reference genomes. Here I propose the software ATCCfinder, which utilizes ATCC application interface software (API) to generate query-able databases from ATCC Genome resources.

Koehler, Samuel↗

DOE OSTI2018

Machine Learning and Social Media to Mine and Disseminate Big Scientific Data

One of the challenges in supplying the communities with wider access to scientific databases is the need for knowledge of database languages like Structured Query Language (SQL). Although the SQL language has been published in many forms, not everybody is able to write SQL queries. Another challenge is that it might not be practical to make the public aware of the structure of databases. There is a need for novice users to query relational databases using their natural language. To solve this problem, many natural language interfaces to structured databases have been developed. The goal is to provide a more intuitive method for generating database queries and delivering responses. Through social media, which makes it possible to interact with a wide section of the population, and with the help of natural language processing, researchers at the Atmospheric Radiation Measurement (ARM) Data Center at Oak Ridge National Laboratory (ORNL) have developed a concept to enable easy search and retrieval of data from several environmental data centers for the scientific community through social media.Using a machine learning framework that maps natural language text to thousands of datasets, instruments, variables, and data streams, the prototype system would allow users to request data through Twitter and receive a link (via tweet) to applicable data results on the project's search catalog tailored to their key words. This automated identification of relevant data from various petascale archives at ORNL could increase convenience, access, and use of the project's data by the broader community. In this paper we discuss how some data-intensive projects at ORNL are using innovative ways to help in data discovery.

Devarakonda, Ranjeet↗

DOE OSTI2025

Extraction and Analysis of Time Series Data from Building Automation Systems Using Large Language Models

Semantic schemas like Haystack 4, Brick and ASHRAE standard 223 enable the structured, standardized, and machine-readable representation of building data, facilitating interoperability, data integration, and advanced analytics. However, extracting information from these models requires specialized expertise in SPARQL and other programming languages, skills that are not commonly found among building professionals. Recent advancements in Large Language Models (LLMs), such as ChatGPT, enable the construction of queries using natural language, making it easier for individuals to interact with these systems in a manner that resembles everyday speech. However, these methods have not yet been tested on building semantic ontologies. This paper introduces a novel workflow and tool for enabling users to ask questions about a specific building's data, using natural language and receive answers automatically generated by GPT-4o. Our approach integrates semantic ontologies with advanced LLM capabilities to automate three critical steps: (1) generating SPARQL queries to retrieve time series references from ontological models, (2) extracting the corresponding time series data from the Building Automation System, and (3) performing computations and visualizations tailored to the user's query. The proposed method simplifies access to BAS data, allowing both domain experts and non-specialists to conduct sophisticated analyses without needing extensive technical knowledge of semantic web technologies. By demonstrating this pipeline, we facilitate more accessible and scalable data-driven decision-making in building operations and management.

Mulayim, Ozan Baris↗

DOE OSTI2025

Extraction and Analysis of Time Series Data from Building Automation Systems Using Large Language Models

Mulayim, Ozan Baris↗