Enabling Call Path Querying in Hatchet to Identify Performance Bottlenecks in Scientific Applications
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Computer science↗
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GraQL: A Query Language for High-Performance Attributed Graph Databases
Graph databases have gained increasing interest in the last few years due to the emergence of data sources which are not easily analyzable in traditional relational models or for which a graph data model is the natural representation. In order to understand the design and implementation choices for an attributed graph database backend and query language, we have started to design our infrastructure for attributed graph databases. In this paper, we describe the design considerations of our in-memory attributed graph database system with a particular focus on the data definition and query language components.
attributed graphs↗
PCalc User's Manual
PCalc is a software tool that computes travel-time predictions, ray path geometry and model queries. This software has a rich set of features, including the ability to use custom 3D velocity models to compute predictions using a variety of geometries. The PCalc software is especially useful for research related to seismic monitoring applications.
58 GEOSCIENCES↗
pCalc User's Manual
pCalc is a software tool that computes travel-time predictions, ray path geometry and model queries. This software has a rich set of features, including the ability to use custom 3D velocity models to compute predictions using a variety of geometries. The pCalc software is especially useful for research related to seismic monitoring applications. The pCalc software is available on the web at: www.sandia.gov/salsa3d/Software.html The software is packaged with this user's manual and a set of example datasets, the use of which is described in this manual.
97 MATHEMATICS AND COMPUTING↗
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↗
Swap Path Network for Robust Person Search Pre-training
This code corresponds to the WACV25 conference paper, "Swap Path Network for Robust Person Search Pre-training". In that paper, we introduce a new model for the person search task called the Swap Path Net (SPNet). The person search task is a problem in computer vision, where we locate and rank matches to an image of a query person in a set of other images where we want to find them. We also introduce a novel pre-training algorithm specific to the Swap Path Net architecture. The code implements pre-training and fine-tuning of the Swap Path Net (SPNet). This includes ingesting image datasets and updating the weights of the SPNet neural network to train it for the person search task. The repository contains code, configs, and instructions to reproduce all results from the paper.
Jaffe, LucasW [Lawrence Livermore National Laborat↗
In Situ Indexing and Query Processing of AMR Data
We have developed the first, holistic, Adaptive Mesh Refinement (AMR) capable, in situ query framework to ensure the tractability of the current- and next-generation AMR simulation analysis on the path to extreme scale. We have addressed key issues pertaining to in situ analysis, memory hierarchy, generalized parallel indexing, index-compression, and real-time access pattern detection. We have published the following research papers in high profile peer reviewed computer science conferences: (a) Parallel In Situ Detection of Connected Components in Adaptive Mesh Refinement Data; (b) AMR-aware In Situ Indexing and Scalable Querying; (c) Pattern-driven dynamic data layout optimization; (d) In situ Storage Layout Optimization for AMR Spatio-temporal Read Accesses; (e) AMRZone: A Runtime AMR Data Sharing Framework for Scientific Applications; (f) Exploring Memory Hierarchy and Network Topology for Runtime AMR Data Sharing Across Scientific Applications.
96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
Streamlining Stakeholder Data Accessibility Utilizing Web-Based GIS Capabilities - 18672
Environmental data is often complex, and it can be difficult to provide proper context to fully understand and comprehensively analyze the data. One tool identified to better present complicated data is the use of web-based Geographic Information Systems (GIS). Web-based GIS provides much-needed visual context to the complex data collected at Los Alamos National Laboratory (LANL). By overlaying environmental data atop relevant geographical features, such as topography, surface or subsurface geology, hydrogeological features, buildings, roads, and technical areas, LANL's diverse group of stakeholders are better able to understand environmental data collected at the LANL site within the framework of the surrounding geography. Since 2012, Los Alamos has utilized a web-based GIS module in the Intellus New Mexico data-sharing web site. This web site provides unfettered public access to LANL and New Mexico Environment Department (NMED) - Department of Energy (DOE) Oversight Bureau environmental data. Through the past five years of real world experience, LANL identified areas where questions and confusion arose, particularly for those not directly associated with the site's environmental cleanup mission. In order to query the millions of records in Intellus in a meaningful way, users had to have substantial knowledge of the data organization and nomenclature ahead of time. While this was not a challenge for LANL and NMED environmental scientists, it proved to be a substantial barrier for members of the general public to utilize Intellus. In 2017, LANL initiated an Intellus redesign with the goal of deconstructing the type of questions people asked and identifying the types of user personas accessing the site to engineer a simpler path to desired data sets. Greatly simplified click paths ('Quick Paths') were created that walk the user through the basics with a minimum of jargon and a strong reliance on mapping results for easy visualization. The improved mapping was enabled by an upgraded online GIS functionality that allowed LANL to easily integrate their own base-maps to provide users the most up to date views of their data. The integrated Intellus GIS was enhanced to assimilate with LANL's dynamic base maps and site information in relation to data collection activities, to avoid outdated presentations. A simplified user interface (UI) was added and easier data querying was designed. A more robust visual presentation was created that allowed better viewing of spatial data. All data queries in the new simplified 'Quick Path' give the users the option to map the results and save their results to their optional accounts. This brings the power of GIS visualization to all users without the need for GIS knowledge or extensive training. By integrating with Esri{sup R} technologies, base-map layers in ArcGIS Online{sup R} can be updated by LANL or other data providers at any time and reflected immediately in the Intellus mapping application called GIS+ 1. Users can even load additional map layers to GIS+ that contain their own spatial data or information shared from over a million users on ArcGIS Online{sup R}, such as ecological information from the Fish and Wildlife Service, or geology layers from the USGS. The results from the improved quick path search in Intellus can be viewed in GIS+ and analyzed using Esri's{sup R} Smart Mapping technology, which makes it easy to classify results with its visual representation of the results in an interactive histogram, where sliders can be dragged to look at various intervals for each color range for that query. With enhanced GIS+, Los Alamos National Laboratory is able to observe improve Stakeholder engagement and positive Stakeholder interactions within its public environmental database, Intellus New Mexico. (authors)
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
A Privacy First Path Analysis using Clickstream Data
In the modern digital economy, data-driven decision making is crucial for effectively meeting the ever-evolving demands of consumer engagement and satisfaction. Clickstream data has become invaluable for understanding customer behavior, yet concerns over privacy and security persist, especially with some internet service providers profiting from its sale. This article introduces an innovative methodology that blends experiential learning with advanced cryptographic techniques, including differential privacy and graph analytics. The core objective of this methodology is to estimate Customer Lifetime Value (CLV) by analyzing clickstream data, achieving an average prediction accuracy of 92.4% in user engagement levels while ensuring user anonymity through Recency, Frequency, and Monetary (RFM) analysis. Our study introduces the concept of a “data depositor” and a privacy manager, employing the composition theorem to merge non-adaptive queries effectively. Privacy budgets (? = 1.0, d = 10-5), sensitivity-specific techniques, and data partitioning were applied. Randomization and noise addition protect data integrity, with special handling for categorical values. This approach, differing from prior studies, offers a 12.6% improvement in privacy-preserving targeting accuracy while maintaining strict confidentiality, presenting a novel path forward in data-driven decision-making.
Frequency and Monetary (RFM) analysis↗
Differentially Private Map Matching (DPMM) v1.0
Human mobility trajectories provide valuable information for developing mobility applications, as they contain diverse and rich information about the users. User mobility data is valuable for various applications such as intelligent transportation systems (ITS), commercial business models, and disease-spread models. However, such spatio-temporal traces may pose a threat to user privacy. GPS trajectories in their raw form are not suitable for transportation studies, as they require matching locations with nearest road links — a process called map-matching. This software implements a differential privacy (DP)-based map-matching algorithm, called DPMM, that generates link-level location trajectories in a privacy-preserving manner to protect users' origin destinations (OD) and travel paths. OD privacy is achieved by injecting Planar Laplace noise to the user OD GPS points. Travel-path privacy is provided with randomized travel path construction using exponential DP mechanism. The injected noise level is selected adaptively, by considering the link density of the location and the functional category of the localized links. For path privacy, our mechanism samples waypoints and selects candidate paths between waypoints. DPMM provides privacy effectively with respect to link density instead of other trajectory samples in the database compared to other privacy mechanisms. Compared to the different baseline models our DP-based privacy model offers closer query responses to the raw data in terms of individual and aggregate trajectory-level statistics with an average at absolute deviation from the baseline for individual statistics on ϵ = 1.0. Beyond individual trajectory statistics, the DPMM outperforms the other benchmark DP-based mechanisms on different aggregate statistics with up to 8x improvement in utility.
Peisert, Sean [Lawrence Berkeley National Laborato↗
SlimIO: Lightweight I/O Path Design for Write Isolation in FDP-backed In-Memory Databases
In-Memory Databases (IMDBs) are widely used with HPC applications to manage transient data, often using snapshot-based persistence for backups. Redis, a representative IMDB, employs both snapshot and Write-Ahead Log (WAL) mechanisms, storing data on persistent devices via the traditional kernel I/O path. This method incurs syscall overhead, I/O contention between processes, and SSD garbage collection (GC) delays. To address these issues, we propose SlimIO, which adopts I/O passthru to minimize syscall overhead and inter-process I/O interference. Additionally, it leverages Flexible Data Placement (FDP) SSDs as backup storage to avoid performance degradation from SSD GC. Experimental results show that SlimIO reduces snapshot time by up to 25%, increases query throughput by up to 30% during non-snapshot periods, and lowers 99.9%-ile latency by up to 50%. Furthermore, it achieves a write amplification factor (WAF) of 1.00, indicating no redundant internal writes, thus extending SSD lifespan.
Lee, Sangyun [Sogang University]↗
ATcT — Active Thermochemical Tables Python Interface
SF-25-140 atct is a lightweight, Python client for the ATcT v1 API that enables programmatic access to high-accuracy thermochemical data and turnkey reaction-enthalpy analysis. The package implements full v1 endpoint coverage (species lookup by ATcT ID, name, formula, SMILES, InChI, CAS RN; covariance queries; health checks) with robust error handling, retries, and environment-based configuration for local/production endpoints. Beyond data retrieval, atct provides rigorously implemented reaction calculators that propagate uncertainties via either (i) a conventional independent-errors method (0 K or 298.15 K) or (ii) covariance-aware propagation using provided covariances at 298.15 K. Typed data classes ensure transparent, reproducible data structures and carry ATcT Thermochemical Network (TN) version identifiers for provenance. Dual import paths and comprehensive examples facilitate integration into research pipelines, enabling reproducible thermochemical calculations, automated validation, and downstream method development.
Bross, DavidHamilton [Argonne National Laboratory ↗
Visualizing the Uncertainty of Graph-based 2D Segmentation with Min-path Stability
Here, this paper presents a novel approach to visualize the uncertainty in graph-based segmentations of scalar data. Segmentation of 2D scalar data has wide application in a variety of scientific and medical domains. Typically, a segmentation is presented as a single unambiguous boundary although the solution is often uncertain due to noise or blur in the underlying data as well as imprecision in user input. Our approach provides insight into this uncertainty by computing the “min-path stability”, a scalar measure analyzing the stability of the segmentation given a set of input constraints. Our approach is efficient, easy to compute, and can be generally applied to either graph cuts or live-wire (even partial) segmentations. In addition to its general applicability, our new approach to graph cuts uncertainty visualization improves on the time complexity of the current state-of-the-art with an additional fast approximate solution. We also introduce a novel query enabled by our approach which provides users with alternate segmentations by efficiently extracting local minima of the segmentation optimization. Finally, we evaluate our approach and demonstrate its utility on data from scientific and medical applications.
97 MATHEMATICS AND COMPUTING↗
Toward Drilling the Perfect Geothermal Well: An International Research Coordination Network for Geothermal Drilling Optimization Supported by Deep Machine Learning and Cloud Based Data Aggregation
The EDGE project, supported by the U.S. Department of Energy Geothermal Technologies Office under award DE-EE0008793, established a data-driven framework for improving the efficiency, cost-effectiveness, and reliability of geothermal well drilling. The project focused on developing scalable data infrastructure, advanced machine learning and probabilistic models, and integrated analytics tools to support continuous drilling optimization. A central objective was to reduce geothermal drilling costs by up to seventy percent while minimizing the risk of well failure through predictive diagnostics and adaptive planning. Over the project period, a comprehensive data repository was designed and deployed, incorporating records from over one hundred geothermal wells across varied geological settings. This repository supported both structured and unstructured data and adhered to FAIR data principles, enabling provenance tracking, quality control, and standardized metadata. The project introduced automated ingestion pipelines and a cloud-hosted platform that facilitated access to raw, processed, and derived datasets. This infrastructure served as the foundation for model development and analysis. Machine learning workflows were developed to predict key drilling metrics including rate of penetration, non-productive time, and total drilling costs. Self-organizing maps and dimensionality reduction methods were used to uncover operational patterns and outliers, while supervised learning algorithms such as random forests and deep neural networks were applied to forecast performance outcomes. The models were validated on heterogeneous datasets from both U.S. and Icelandic fields, demonstrating variable but significant predictive accuracy. The results indicated that finer temporal resolution, inclusion of lithological data, and consistency in operational annotations could substantially improve model performance. The project also implemented process mining techniques to reconstruct state-transition models from drilling event logs. These models enabled the identification of deviations from optimal workflows and provided insights into recurring failure modes. Analysis of non-productive time highlighted the impact of equipment failures, geological challenges, and human factors, offering opportunities for targeted mitigation strategies. The EDGE Dashboard was developed as a web-based expert system integrating data visualization, model outputs, and user-driven queries. It provided an accessible interface for operators to explore historical data, evaluate predicted outcomes, and compare drilling scenarios. Initial feedback from project partners suggested that the dashboard could serve as a foundation for more advanced advisory and optimization tools. Overall, the EDGE project demonstrated the feasibility and value of applying modern data science techniques to geothermal drilling. It delivered a set of interoperable tools and models that can support more efficient, lower-risk well development. The findings point toward a viable path for transitioning from advisory analytics to semi-autonomous drilling systems, contingent on continued collaboration, expanded datasets, and field validation. The project results have immediate relevance for drilling operations, data management practices, and future geothermal R&D efforts aimed at achieving reliable, cost-competitive geothermal energy at scale.
15 GEOTHERMAL ENERGY↗
Inferring adversarial behaviour in cyber‐physical power systems using a Bayesian attack graph approach
Abstract Highly connected smart power systems are subject to increasing vulnerabilities and adversarial threats. Defenders need to proactively identify and defend new high‐risk access paths of cyber intruders that target grid resilience. However, cyber‐physical risk analysis and defense in power systems often requires making assumptions on adversary behaviour, and these assumptions can be wrong. Thus, this work examines the problem of inferring adversary behaviour in power systems to improve risk‐based defense and detection. To achieve this, a Bayesian approach for inference of the Cyber‐Adversarial Power System (Bayes‐CAPS) is proposed that uses Bayesian networks (BNs) to define and solve the inference problem of adversarial movement in the grid infrastructure towards targets of physical impact. Specifically, BNs are used to compute conditional probabilities to queries, such as the probability of observing an event given a set of alerts. Bayes‐CAPS builds initial Bayesian attack graphs for realistic power system cyber‐physical models. These models are adaptable using collected data from the system under study. Then, Bayes‐CAPS computes the posterior probabilities of the occurrence of a security breach event in power systems. Experiments are conducted that evaluate algorithms based on time complexity, accuracy and impact of evidence for different scales and densities of network. The performance is evaluated and compared for five realistic cyber‐physical power system models of increasing size and complexities ranging from 8 to 300 substations based on computation and accuracy impacts.
Sahu, Abhijeet↗
TripleGraph
RDF triplestores are great tools for online graph analytic processing (i.e., graph pattern query processing), but they do not provide graph mining capabilities (e.g., PageRank, connected-component analysis, node eccentricity, etc.). The software title “TripleGraph” is a graph analysis toolkit, which uses an RDF triplestore as its backend for creating, manipulating, mining, and programming with large scale property graphs. It allows users to run various graph mining algorithms easily. User can import edgelist-formatted (homogeneous graph) or JSON-formatted graph (property graph) into the RDF triplestore using the provided tool and perform various analysis such as (1) Node/edge retrieval and manipulation, (2) Pathfinding between two given nodes, (3) Running graph mining algorithms (PageRank/Personalized PageRank, Single Source Shortest Path/Multi-Source Shortest Path, Connected Component, Node Eccentricity, Peer Pressure Clustering). It supports standard graph data format and works with a standard SPARQL endpoint like Jena Fuseki. It allows users to perform online graph analytic processing and graph mining on the same platform (a triplestore).
Sangkeun, MattLee↗
Machine-Learning-Based Mapping and Modeling of Solar Energy with Ultra-High Spatiotemporal Granularity
Despite the rapid growth of solar energy, we still lack a dynamic, high-fidelity database that tracks the spatiotemporal variations of solar PVs and their associated infrastructures across different places at a spatially resolved scale. The absence of such data presents a barrier to various applications such as solar PV growth projection, solar energy integration, solar incentive design, and climate risk assessment. In this project, we aim to bridge this gap by developing AI-based algorithms to extract granular information about solar PV installations and their associated infrastructures (i.e., distribution grids) from widely available unstructured data like remote sensing images and street views. As a result, we have built the Solar Energy Atlas, a fine-grained, large-scale geospatial overlay of distributed solar PVs and distribution grids. On top of it, we have advanced the understanding of solar adoption and distribution grid vulnerability to climate-induced extremes. Our major contributions can be summarized as follow: (1) By developing new AI algorithms, we have built the most comprehensive solar PV spatiotemporal database covering the entire US. This is the first time we obtained the exact GPS locations, size, subtype, and installation year information for rooftop solar PVs across the US. This database can be used for solar PV growth projection, solar energy integration, solar energy policy analysis and design, and spatially-resolved climate risk assessment. (2) Leveraging this database, we have uncovered the socioeconomic driving factors that are correlated with earlier onset of solar adoption and higher saturated adoption levels. We have identified the heterogeneity in the effects of different types of financial incentives on solar adoption and provided implications for tailoring incentive design based on local income levels to promote equitable solar adoption. (3) We have developed a distribution grid GIS mapping algorithm which can obtain granular geospatial and topology information about distribution grids using multi-modal open data, reducing the dependency on hard-to-obtain smart meter data of conventional approaches. It shows effectiveness in both the U.S. and Sub-Saharan Africa. Using this algorithm, we have uncovered the non-uniform vulnerability of distribution grids to wildfires in California in the aspects of undergrounding protection and Distributed Energy Resources (DER) preparedness. This has provided important implications for improving the affordability and equity of grid adaptation approaches. (3) We have made our produced database publicly available and provided user-friendly interface to enable various stakeholders and the general public to interact with the data. We have also integrated the produced data into the Data Commons platform to enable the public to access the data and correlate it with other location-specific characteristics simply using natural language as queries. The impact of our project is three-fold: (1) New algorithms for mapping solar PVs and distribution grids across space and time, which are open source to facilitate researchers and industry; (2) New databases of solar PVs and distribution grids that have been made publicly available for engineering, social, and policy applications; (3) New understandings and actionable insights on the potential approaches to promoting solar adoption and reducing energy infrastructure vulnerabilities. In this report, we start by discussing the project background and motivation (section 5), followed by the overview of project objectives (section 6). Results and discussion for each task are presented in section 7. Significant accomplishments are summarized in section 8. This report will be concluded by discussing the paths forwards (section 9), products (section 10), and team roles (section 11).
14 SOLAR ENERGY↗
Estimating influenza incidence using search query deceptiveness and generalized ridge regression
Seasonal influenza is a sometimes surprisingly impactful disease, causing thousands of deaths per year along with much additional morbidity. Timely knowledge of the outbreak state is valuable for managing an effective response. The current state of the art is to gather this knowledge using in-person patient contact. While accurate, this is time-consuming and expensive. This has motivated inquiry into new approaches using internet activity traces, based on the theory that lay observations of health status lead to informative features in internet data. These approaches risk being deceived by activity traces having a coincidental, rather than informative, relationship to disease incidence; to our knowledge, this risk has not yet been quantitatively explored. We evaluated both simulated and real activity traces of varying deceptiveness for influenza incidence estimation using linear regression. We found that deceptiveness knowledge does reduce error in such estimates, that it may help automatically- selected features perform as well or better than features that require human curation, and that a semantic distance measure derived from the Wikipedia article category tree serves as a useful proxy for deceptiveness. This suggests that disease incidence estimation models should incorporate not only data about how internet features map to incidence but also additional data to estimate feature deceptiveness. By doing so, we may gain one more step along the path to accurate, reliable disease incidence estimation using internet data. This capability would improve public health by decreasing the cost and increasing the timeliness of such estimates.
59 BASIC BIOLOGICAL SCIENCES↗