A hardware-assisted scalable solution for interactive volume rendering of time-varying data

In this paper we present a hardware-assisted rendering technique coupled with a compression scheme for the interactive visual exploration of time-varying scalar volume data. A palette-based decoding technique and an adaptive bit allocation scheme are developed to fully utilize the texturing capability of a commodity 3-D graphics card. Using a single PC equipped with a modest amount of memory, a texture capable graphics card, and an inexpensive disk array, we are able to render hundreds of time steps of regularly gridded volume data (up to 45 millions voxels each time step) at interactive rates, permitting the visual exploration of large scientific data sets in both the temporal and spatial domain.

Both interactive visualization and computational analysis methods are useful for data studies and an integration of both approaches is promising to successfully combine the benefits of both methodologies. In interactive data exploration and analysis workflows, we need successful means to quantitatively externalize results from data studies, amounting to a particular challenge for the usually qualitative visual data analysis. In this paper, we propose a hybrid approach in order to quantitatively externalize valuable findings from interactive visual data exploration and analysis, based on local linear regression models. The models are built on user-selected subsets of the data, and we provide a way of keeping track of these models and comparing them. As an additional benefit, we also provide the user with the numeric model coefficients. Once the models are available, they can be used in subsequent steps of the workflow. A model-based optimization can then be performed, for example, or more complex models can be reconstructed using an inversion of the local models. We study two datasets to exemplify the proposed approach, a meteorological data set for illustration purposes and a simulation ensemble from the automotive industry as an actual case study.

Non-human eukaryotic genome assemblies and annotations are frequently dispersed across repositories, supplementary materials, or remain unpublished. Their access often requires technical expertise and large downloads, hindering usability. The Swedish Reference Genome Portal*1 (SRGP) offers a web platform to easily find, explore, and share genomic data from non-human eukaryotic species studied in Sweden. The SRGP gathers assemblies and annotations into species-specific pages with an interactive JBrowse2 genome browser, metadata, and download links (Fig. 1). This web interface provides user-friendly visualisations aimed at broad user communities, particularly in biodiversity and evolution. Unlike global resources such as Ensembl or the UCSC Genome Browser, which focus on model organisms, the SRGP showcases species studied in Swedish institutions. It fosters community-driven data sharing and broadens access to genomic annotations that are rarely published (e.g., from population genomics), while adhering to Findable, Accessible, Interoperable and Reusable (FAIR) principles. In doing so, it bridges global infrastructures with local research needs, enabling both specialists and non-specialists to make better use of genomic data. The technical architecture of the SRGP (Fig. 2) is designed to make the service easy to use and reuse by others: Simple and secure : Built with Hugo, a static site generator that requires no databases or heavy servers, enabling lightweight maintenance. Lightweight and fast : Composed of prebuilt pages and files for quick loading and easy scalability. Open and reusable : Fully open-source, allowing others to adapt and extend the code. Easy to update : New species or datasets can be added by editing configuration files and rebuilding the site. Portable : Packaged with Docker for easy deployment anywhere, including Kubernetes clusters. User-friendly : Integrates JBrowse2 for interactive exploration of genomic data. Simple and secure : Built with Hugo, a static site generator that requires no databases or heavy servers, enabling lightweight maintenance. Lightweight and fast : Composed of prebuilt pages and files for quick loading and easy scalability. Open and reusable : Fully open-source, allowing others to adapt and extend the code. Easy to update : New species or datasets can be added by editing configuration files and rebuilding the site. Portable : Packaged with Docker for easy deployment anywhere, including Kubernetes clusters. User-friendly : Integrates JBrowse2 for interactive exploration of genomic data. The SRGP is designed for simple deployment and maintenance. Genome data and annotations are processed with automated scripts in Docker containers to ensure consistency and reproducibility. The website is built with Hugo, which generates fast, lightweight pages requiring no database. Both data and pages are bundled into Docker images and deployed on a Kubernetes cluster, with ArgoCD handling updates automatically. This setup provides reliable operation on standard infrastructure, easy scaling for new datasets, and secure, low-maintenance access to genomic resources (Fig. 2). In summary, the SRGP makes previously hidden genomic data, especially from local or specialised communities, easy to find and explore through a single, user-friendly browser. It lowers access barriers while remaining simple, fast, and scalable. By unifying scattered resources, it provides a reusable model for other countries, institutions, or research groups. This presentation is available in (presentación disponible en español en) Fuentes‐Pardo et al. (2025).

As video cameras proliferate, the ability to scalably capture and search their data becomes important. Scalability is improved by performing video analytics on cloudlets at the edge of the Internet, and only shipping extracted index information and meta-data to the cloud. In this setting, we describe interactive data exploration (IDE), which refers to human-in-the-loop content-based retrospective search using predicates that may not have been part of any prior indexing. We also describe a new technique called just-in-time indexing (JITI) that improves response times in IDE.

Interactive data exploration plays a fundamental role in analyzing three dimensional scientific data. Occlusion management and context preservation are among the key factors to ensure effective identification and extraction of three-dimensional features. In this paper, we present an interactive data exploration system that utilizes a physically based deformation method to investigate hidden structures of data in three dimensional data sets. While non-physically based methods are popular for visual analytic applications due to their lower computational cost, physically based deformation methods can often better preserve features and their context. Our physically based deformation method preserves data features by setting the mesh properties according to interesting data attributes. We design effective and intuitive interfaces by using a metaphor of virtual retractor, which reflects the cutting and splitting of data that our system is simulating. We demonstrate case studies on multiple particle datasets and volume datasets, and present feedback from a domain user.

An increasing number of emerging web database applications deal with large georeferenced data sets. However, exploring these large data sets through spatial queries can be very time and resource intensive. The need for interactive spatial queries has arisen in many applications such as Geographic Information Systems (GIS) for efficient decision-support. In this paper, we propose a new interactive spatial query processing technique for GIS. We present a family of the Incremental Refining Spatial Join (IRSJ) algorithms that can be used to report incrementally refined running estimates for aggregate queries while simultaneously displaying the actual query result tuples of the data sets sampled so far. Our goal is to minimize the time until an acceptably accurate estimate of the query result is available (to users) measured by a confidence interval. Our approach enables more interactive data exploration and analysis. While similar work has been done in relational databases, to the best of our knowledge, this is the first work using this approach in GIS. We investigate and evaluate different sampling methodologies through extensive experimental performance comparisons. Experiments on both real and synthetic data show an order of magnitude response time improvement relative to the final answer obtained when using a full R-tree join. We also show the impact of different index structures on the performance of our algorithms using three known sampling methods.

Passive acoustic monitoring (PAM) is being adopted in a range of contexts. Emerging methods facilitate analysis of large-scale data sets, but ecological interpretation of acoustic indices is not straightforward. In addition, the technical and logistical requirements of using emerging AI methods for big data mean that conservation actors increasingly adopt third-party analysis solutions. We argue that these compounding factors undermine robust ecological inference, clouding insight and decision making. To address this, we present echo-dash, an accessible, interactive dashboard that facilitates rapid, interactive exploration of spatiotemporal soundscape data by conservation actors. Developed through participatory design, echo-dash is built on the simple premise that the potential of PAM can best be realised by keeping human ecological knowledge in the analysis loop. Five key functions to facilitate analysis and interpretation of PAM data were identified and implemented: 1) Calculating soundscape descriptors and probability of species presence; 2) Checking data integrity; 3) Exploring data interactively and in spatiotemporal, environmental contexts; 4) Filtering data by extreme weather, outliers or clusters; 5) Exporting data subsets, plots and code to generate plots. By supporting integration of human, situated ecological knowledge with large scale spatiotemporal data sets, echo-dash bolsters PAM’s potential to transform ecological monitoring for applied and fundamental ecoacoustics.

BackgroundThe use of RNA-sequencing (RNA-seq) in molecular biology research and clinical settings has increased significantly over the past decade. Despite its widespread adoption, there is a lack of simple and interactive tools to analyze and explore RNA-seq data. Many established tools require programming or Unix/Bash knowledge to analyze and visualize results. This requirement presents a significant barrier for many researchers to efficiently analyze and present RNA-seq data.ResultsHere we present BEAVR, a Browser-based tool for the Exploration And Visualization of RNA-seq data. BEAVR is an easy-to-use tool that facilitates interactive analysis and exploration of RNA-seq data. BEAVR is developed in R and uses DESeq2 as its engine for differential gene expression (DGE) analysis, but assumes users have no prior knowledge of R or DESeq2. BEAVR allows researchers to easily obtain a table of differentially-expressed genes with statistical testing and then visualize the results in a series of graphs, plots and heatmaps. Users are able to customize many parameters for statistical testing, dealing with variance, clustering methods and pathway analysis to generate high quality figures.ConclusionBEAVR simplifies analysis for novice users but also streamlines the RNA-seq analysis process for experts by automating several steps. BEAVR and its documentation can be found on GitHub at https://github.com/developerpiru/BEAVR. BEAVR is available as a Docker container at https://hub.docker.com/r/pirunthan/beavr.

KNAVE-II is an intelligent interface to a distributed web-based architecture that enables users (e.g., physicians) to query, visualize and explore clinical time-oriented databases. Based on prior studies, we have defined a set of requirements for provision of a service for interactive exploration of time oriented clinical data. The main requirements include the visualization, interactive exploration and explanation of both raw data and multiple levels of concepts abstracted from these data; the exploration of clinical data at different levels of temporal granularity along both absolute (calendar-based) and relative (clinically meaningful) time-lines; the exploration and dynamic visualization of the effects of simulated hypothetical modifications of raw data on the derived concepts; and the provision of generic services (such as statistics, documentation, fast search and retrieval of clinically significant concepts, amongst others). KNAVE-II has been implemented and is currently evaluated by expert clinicians in several medical domains, such as oncology, involving monitoring of chronic patients.

Digital data have become a torrent engulfing every area of business, science and engineering disciplines. In the age of Big Data, deriving values and insights from large amounts of data using rich analytics becomes an important differentiating capability for competitiveness, success and leadership in every field. Scientists and engineers of many different domains are increasingly clamouring for mechanisms to manage and analyse the massive quantities of information now available in order to obtain new answers and extract from it maximum value. Computational modelling and simulation is the central technology to numerous of these domains. Molecular Dynamics (MD) is a computational simulation technique that describes the physical forces and movements of interacting microscopic elements such atoms and molecules. MD has important applications in the fields of chemistry, biotechnology, pharmaceutical industry, energy, climate or materials science, among others. Advanced MD algorithms include not only Molecular Mechanics (MM), but also Quantum Mechanics (QM) approaches, raising important big data challenges still to be sorted out. MD simulations perform an iterative process generating large amounts of data in streaming. Current software technology is far from being able to manage, analyze and visualize the extremely large and complex data sets generated by important molecular processes. This paper analyzes the current big data limits in the Computational Chemistry field, especially in the MD processes. To overcome these challenging situations, this work provide guidance for future research including advances in scalable algorithms for data analysis, dynamic query technology, data models and storage strategies, parallel executions, I/O optimization, and interactive visual exploration and analysis of MD data.

SemaPlorer—Interactive semantic exploration of data and media based on a federated cloud infrastructure

Interactive data exploration at scale remains constrained by 1) weak adaptability to shifting query workloads, 2) limited and post hoc error guarantees, 3) poor scalability under dynamic, high-dimensional data, 4) sparse user guidance during query formulation, and 5) non-trivial system overheads from learned or probabilistic components. We propose an end-to-end, privacy-aware framework that dynamically forms SQL queries for multi-dimensional data using randomized signals derived from personal web usage. The method integrates: 1) on-device user modeling that converts browsing interactions into preference embeddings under local differential privacy; 2) a constrained-randomization layer that enforces coverage and diversity to avoid filter bubbles while remaining responsive to user intent; 3) a contextual bandit policy (with optional deep reinforcement learning extension) that selects or completes query templates using signals from user profiles, session context, and data synopses; and 4) an error-aware AQP executor combining stratified/pilot sampling, synopsis reuse, and confidence-interval gating with automatic sample escalation. This design directly addresses the above limitations: the bandit adapts online to workload shifts; the AQP layer provides pre-execution feasibility checks and per-query error control; synopsis reuse and AB-tree–style random sampling maintain low latency under updates; and a guidance module (predictive autocompletion with information-gain scoring) reduces user effort while preserving exploration diversity. To evaluate effectiveness, we introduce a privacy-preserving training regimen (federated updates over DP-noised profiles) and a novel benchmark protocol measuring time-to-insight, error compliance under differential privacy, session diversity, and latency against strong baselines. The result is an ML-driven exploration loop that achieves error-bounded interactivity, robust personalization, and scalable performance on evolving, high-dimensional datasets, while providing evaluation metrics that capture both user experience and privacy-preserving guarantees.

Many Geographic Information Systems (GIS) handle large geospatial datasets stored in raster representation. Spatial joins over raster data are important queries in GIS for data analysis and decision support. However, evaluating spatial joins can be very time intensive due to the size of these datasets. In this paper we propose a new interactive framework that allows users to get approximate answers in near instantaneous time, thus allowing for truly interactive data exploration. Our method utilizes two proposed statistical approaches: probabilistic join and sampling based join. Our probabilistic join method provides speedup of two orders of magnitude with no correctness guarantee, while our sampling based method provides an order of magnitude improvement over the full quad-tree join and also provides running confidence intervals. We propose a framework that combines the two approaches to allow end users to tradeoff speed versus bounded accuracy. The two approaches are evaluated empirically with real and synthetic datasets.

With higher energy prices as well as the desired shift towards sustainability, improving the efficiency of their operation is becoming a major challenge of the pulp & paper industry. To achieve this, producers are prioritizing energy savings, optimized machine washing cycles as well as timely fault detection across their plants. Compressors are one of the key parts of paper production lines. This manuscript therefore presents an approach to address the aforementioned goals, focusing on compressors and their interaction with other critical components in the plant. To accommodate industry requirements for confidentiality, we explore methods that rely on minimal system knowledge, allowing clients to protect proprietary information. This study explores three approaches to re-adapt human-labeled machine operations and implement automatic labeling using clustering techniques. All methods, including an autoencoder, achieved strong performance with F1 scores above 0.92 and an ARI score above 0.94. The autoencoder compressed data by a factor of eight, retaining accuracy while enabling robust clustering and capturing non-linear relationships. The results shown by the tests indicate that it is possible to integrate automated analysis with visual analytics, laying the foundation for future advancements in interactive data exploration and in some circumstances safe automated labeling. This integration aims to enhance anomaly detection, predictive maintenance, and process optimization in industrial systems.

Network inference utilizes experimental high-throughput data for the reconstruction of molecular interaction networks where new relationships between the network entities can be predicted. Despite the increasing amount of experimental data, the parameters of each modeling technique cannot be optimized based on the experimental data alone, but needs to be qualitatively assessed if the components of the resulting network describe the experimental setting. Candidate list prioritization and validation builds upon data integration and data visualization. The application of tools supporting this procedure is limited to the exploration of smaller information networks because the display and interpretation of large amounts of information is challenging regarding the computational effort and the users' experience. The Ondex software framework was extended with customizable context-sensitive menus which allow additional integration and data analysis options for a selected set of candidates during interactive data exploration. We provide new functionalities for on-the-fly data integration using InterProScan, PubMed Central literature search, and sequence-based homology search. We applied the Ondex system to the integration of publicly available data for Aspergillus nidulans and analyzed transcriptome data. We demonstrate the advantages of our approach by proposing new hypotheses for the functional annotation of specific genes of differentially expressed fungal gene clusters. Our extension of the Ondex framework makes it possible to overcome the separation between data integration and interactive analysis. More specifically, computationally demanding calculations can be performed on selected sub-networks without losing any information from the whole network. Furthermore, our extensions allow for direct access to online biological databases which helps to keep the integrated information up-to-date.