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interactive data analysis in a manufacturing setting - A case study

COMPUTATIONAL STATISTICS 1999年第1期14卷 147-159页

作者： James, DA AT&T Bell Labs Lucent Technol Murray Hill NJ 07974 USA

We present a case study in the application of interactive data analysis in the context of semiconductors and optical fiber manufacturing. Our approach to interactive graphics is based on well-known principles of data analysis and interactive graphics (Chambers, Cleveland, Kleiner & Tukey 1983, Cleveland 1993, Tufte 1983, Tufte 1990, Stuetzle 1987, Becker, Cleveland & Well 1988b, Becker, Eick & Wilks 1991, Young & Lubinsky 1995, Buja, Cook & Swayne 1996), and its implementation is written in the S-language for data analysis and graphics (Becker, Chambers & Wilks 1988a, Chambers & Hastie 1992). The case study also highlights the need for improved interactive software tools in S-based systems to support the type of programmable interactive graphics that we envision. These limitations have motivated us to explore some recent developments in software technology, such as S Version 4 (Chambers 1998), Java, LispStat (Tierney 1996), XploRe (Haerdle, Klinke, Turlach & Ardle 1995), CORBA (Siegel 1996), and PVM (Geist, Beguelin, Dongarra, Jiang, Mancheck & Sunderam 1994), and asses how they can be used to alleviate some of the deficiencies we see in existing tools.

关键词： interactive data analysis integrated circuits optical fiber linked plots object-based programming

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Next-Step Suggestions for Modern interactive data analysis Platforms 18

Next-Step Suggestions for Modern Interactive Data Analysis P...

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24th ACM SIGKDD Conference on Knowledge Discovery and data Mining (KDD)

作者： Milo, Tova Somech, Amit Tel Aviv Univ Tel Aviv Israel

ISBN: (纸本)9781450355520

Modern interactive data analysis Aplatforms, Splunk, and Tableau, are gradually replacing traditional OLAP/SQL tools, as they allow for easy-to-use data exploration, visualization, and mining, even for users lacking SQL and programming skills. Nevertheless, data analysis is still a difficult task, especially for non-expert users. To that end we present REACT, a recommender system designed for modern IDA platforms. In these platforms, analysis sessions interweave high-level actions of to multiple types and operate over diverse datasets. REACT identifies and generalizes relevant (previous) sessions to generate personalized next-action suggestions to the user. We model the user's analysis context using a generic tree based model, where the edges represent the user's recent actions, and the nodes represent their result 'screens". A dedicated context similarity metric is employed for efficient indexing and retrieval of relevant candidate next-actions. These are then generalized to abstract actions that convey common fragments, then adapted to the specific user context. To prove the utility of REACT we performed an extensive online and offline experimental evaluation over real world analysis logs from the cyber security domain, which we also publish to serve as a benchmark dataset for future work.

关键词： interactive data analysis analysis Action Recommendation

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DEBrowser: interactive differential expression analysis and visualization tool for count data

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BMC GENOMICS 2019年第1期20卷 1-12页

作者： Kucukural, Alper Yukselen, Onur Ozata, Deniz M. Moore, Melissa J. Garber, Manuel Univ Massachusetts Sch Med Bioinformat Core Worcester MA 01605 USA Univ Massachusetts Sch Med Program Mol Med Worcester MA 01605 USA Univ Massachusetts Sch Med RNA Therapeut Inst Worcester MA 01605 USA

BackgroundSequencing data has become a standard measure of diverse cellular activities. For example, gene expression is accurately measured by RNA sequencing (RNA-Seq) libraries, protein-DNA interactions are captured by chromatin immunoprecipitation sequencing (ChIP-Seq), protein-RNA interactions by crosslinking immunoprecipitation sequencing (CLIP-Seq) or RNA immunoprecipitation (RIP-Seq) sequencing, DNA accessibility by assay for transposase-accessible chromatin (ATAC-Seq), DNase or MNase sequencing libraries. The processing of these sequencing techniques involves library-specific approaches. However, in all cases, once the sequencing libraries are processed, the result is a count table specifying the estimated number of reads originating from each genomic locus. Differential analysis to determine which loci have different cellular activity under different conditions starts with the count table and iterates through a cycle of data assessment, preparation and analysis. Such complex analysis often relies on multiple programs and is therefore a challenge for those without programming *** developed DEBrowser as an R bioconductor project to interactively visualize every step of the differential analysis, without programming. The application provides a rich and interactive web based graphical user interface built on R's shiny infrastructure. DEBrowser allows users to visualize data with various types of graphs that can be explored further by selecting and re-plotting any desired subset of data. Using the visualization approaches provided, users can determine and correct technical variations such as batch effects and sequencing depth that affect differential analysis. We show DEBrowser's ease of use by reproducing the analysis of two previously published data *** is a flexible, intuitive, web-based analysis platform that enables an iterative and interactive analysis of count data without any requirement of programming knowledge.

关键词： Differential expression data visualization interactive data analysis

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interactive analysis OF MULTIDIMENSIONAL data ON THE WEB BY USING TIME-TUNNEL

INTERACTIVE ANALYSIS OF MULTIDIMENSIONAL DATA ON THE WEB BY ...

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5th International Conference on Web Information Systems and Technologies

作者： Okajima, Seiji Okada, Yoshihiro Kyushu Univ Grad Sch Informat Sci & Elect Engn Nishi Ku Fukuoka 8190395 Japan

ISBN: (纸本)9789898111814

In recent years, the Internet has become popular in various application fields so that a huge number of data records are generated and stored on the web. In this situation, we need any tool that helps us to analyze such multidimensional data for obtaining new findings from those data. In this paper, we introduce a visual and interactive analysis tool for multidimensional data called Time-tunnel. Time-tunnel visualizes any number of time series numerical data records as individual charts each of which is displayed on an individual rectangular plane called data-wing in a 3D virtual space. Through direct manipulations on a computer screen, the user easily puts more than two data-wings overlapped together to compare their charts in order to recognize the similarity or the difference among those data records. Simultaneously a radar chart among those data at any time point is displayed to recognize the similarity and the correlation among them. This time, the authors extended Time-tunnel to make it available on the web and this paper clarifies the usefulness of web-version Time-tunnel by showing practical analysis examples.

关键词： data and knowledge visualization interactive data analysis Web application

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Visual analysis of Time Series data for Multi-Agent Systems Driven by Large Language Models 24

Visual Analysis of Time Series Data for Multi-Agent Systems ...

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3rd International Conference on Signal Processing, Computer Networks and Communications, SPCNC 2024

作者： Xu, Chao Zhang, Qi Li, Baiyan Wang, Anmin Bao, Jingsong College of Computer Science and Technology Donghua University Shanghai China College of Mechanical Engineering Donghua University Shanghai China

ISBN: (纸本)9798400710834

The application of digitalization in manufacturing involves using sensors to collect and transmit large amounts of data in real-time. These complex, timestamped sequence data require effective analytical support to drive the generation of analysis summaries. Visual analysis helps researchers identify hidden patterns by intuitively displaying complex data, but its implementation relies on specialized knowledge and a deep understanding of data analysis techniques. Moreover, the analysis process often requires users to recall and process large amounts of information, which increases the complexity of the analysis. Therefore, there is an increasing demand for intelligent visual analysis technology. Large language models, with their powerful reasoning, summarization, and code generation capabilities, provide the possibility of visual analysis for general users. We propose a large language model-based multi-agent framework that integrates domain knowledge to achieve an automated data analysis workflow, from data acquisition to analysis summarization, helping users extract valuable analytical results from complex data and improving both efficiency and experience. © 2024 Copyright held by the owner/author(s).

关键词： interactive data analysis

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Seismic Intelligence Tool: an extensive multipurpose software for seismic signal analysis

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APPLIED COMPUTING AND GEOSCIENCES 2025年 26卷

作者： Bono, Andrea INGV Rome Italy

Over the past five years, the Istituto Nazionale di Geofisica e Vulcanologia (INGV) has started a technological transformation of its real-time seismic monitoring capabilities. This comprehensive restructuring initiative represents a pivotal moment in the Institute's commitment to advancing seismic research and enhancing public safety. At the heart of this transformation lies the development and deployment of the integrated system known as Caravel. Caravel stands as a testament to INGV's dedication to cutting-edge seismic monitoring technology and its mission to provide timely and accurate seismic information to researchers, emergency responders, and the general public. It represents a leap forward in real-time seismic monitoring, integrating state-of-the-art technologies and methodologies to detect, analyze, and disseminate seismic data with unprecedented efficiency and precision. This development reflects INGV's commitment to staying at the forefront of seismic research and hazard mitigation. This integrated system not only improves the accuracy of earthquake detection but also enhances our ability to rapidly assess the potential impact of seismic events, enabling more informed decisionmaking during emergency situations. Seismic Intelligence Tool (SIT) emerges as a software fork from one of Caravel's components previously known as PickFX. The reason behind this fork is to share with the scientific community a robust, multi-platform and freely accessible data analysis tool that adheres to current standards for representing seismic data while removing all INGV specific customizations from PickFX. The decision to fork the original software and release SIT underscores a commitment to democratizing access to advanced seismic analysis tools. By offering this resource at no cost, the scientific community gains access to a platform that is fully compatible with contemporary seismic data representation standards and that can become very powerful with time and cooperation.

关键词： Earthquake continuous monitoring interactive data analysis FDSN web services and standards Earthworm QuakeML

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Design Concerns for Integrated Scripting and interactive Visualization in Notebook Environments

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IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2024年第9期30卷 6572-6585页

作者： Scully-Allison, Connor Lumsden, Ian Williams, Katy Bartels, Jesse Taufer, Michela Brink, Stephanie Bhatele, Abhinav Pearce, Olga Isaacs, Katherine E. Univ Utah SCI Inst Salt Lake City UT 84112 USA Univ Tennessee Knoxville TN 37996 USA Univ Tennessee Dept Elect Engn & Comp Sci Knoxville TN 37996 USA Davidson Coll Math & Comp Sci Davidson NC 28035 USA Rincon Res Tucson AZ 85711 USA Larence Livermore Natl Lab Livermore CA 94550 USA Univ Maryland Dept Comp Sci College Pk MD 20742 USA Univ Maryland Parallel Software & Syst Grp College Pk MD 20742 USA

interactive visualization can support fluid exploration but is often limited to predetermined tasks. Scripting can support a vast range of queries but may be more cumbersome for free-form exploration. Embedding interactive visualization in scripting environments, such as computational notebooks, provides an opportunity to leverage the strengths of both direct manipulation and scripting. We investigate interactive visualization design methodology, choices, and strategies under this paradigm through a design study of calling context trees used in performance analysis, a field which exemplifies typical exploratory data analysis workflows with Big data and hard to define problems. We first produce a formal task analysis assigning tasks to graphical or scripting contexts based on their specificity, frequency, and suitability. We then design a notebook-embedded interactive visualization and validate it with intended users. In a follow-up study, we present participants with multiple graphical and scripting interaction modes to elicit feedback about notebook-embedded visualization design, finding consensus in support of the interaction model. We report and reflect on observations regarding the process and design implications for combining visualization and scripting in notebooks.

关键词： Computational notebooks exploratory data analysis hybrid visualization-scripting interactive data analysis visualization design Computational notebooks exploratory data analysis hybrid visualization-scripting interactive data analysis visualization design

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GeneTonic: an R/Bioconductor package for streamlining the interpretation of RNA-seq data

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BMC BIOINFORMATICS 2021年第1期22卷 1-19页

作者： Marini, Federico Ludt, Annekathrin Linke, Jan Strauch, Konstantin Johannes Gutenberg Univ Mainz Univ Med Ctr Ctr Thrombosis & Hemostasis CTH Langenbeckstr 1 D-55131 Mainz Germany Johannes Gutenberg Univ Mainz Univ Med Ctr Inst Med Biostat Epidemiol & Informat IMBEI Obere Zahlbacher Str 69 D-55131 Mainz Germany

Background: The interpretation of results from transcriptome profiling experiments via RNA sequencing (RNA-seq) can be a complex task, where the essential information is distributed among different tabular and list formats-normalized expression values, results from differential expression analysis, and results from functional enrichment analyses. A number of tools and databases are widely used for the purpose of identification of relevant functional patterns, yet often their contextualization within the data and results at hand is not straightforward, especially if these analytic components are not combined together efficiently. Results: We developed the GeneTonic software package, which serves as a comprehensive toolkit for streamlining the interpretation of functional enrichment analyses, by fully leveraging the information of expression values in a differential expression context. GeneTonic is implemented in R and Shiny, leveraging packages that enable HTML-based interactive visualizations for executing drilldown tasks seamlessly, viewing the data at a level of increased detail. GeneTonic is integrated with the core classes of existing Bioconductor workflows, and can accept the output of many widely used tools for pathway analysis, making this approach applicable to a wide range of use cases. Users can effectively navigate interlinked components (otherwise available as flat text or spreadsheet tables), bookmark features of interest during the exploration sessions, and obtain at the end a tailored HTML report, thus combining the benefits of both interactivity and reproducibility. Conclusion: GeneTonic is distributed as an R package in the Bioconductor project (https://***/packages/GeneTonic/) under the MIT license. Offering both bird's-eye views of the components of transcriptome data analysis and the detailed inspection of single genes, individual signatures, and their relationships, GeneTonic aims at simplifying the process of interpretation of comp

关键词： RNA-Seq Functional enrichment analysis data interpretation interactive data analysis data visualization Transcriptomics R Bioconductor Shiny Reproducible research

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GeoBrick: exploration of spatiotemporal data

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VISUAL COMPUTER 2019年第2期35卷 191-204页

作者： Park, Ji Hwan Nadeem, Saad Kaufman, Arie SUNY Stony Brook Dept Comp Sci Stony Brook NY 11794 USA

We present GeoBrick, an interactive technique for exploring spatiotemporal data. In GeoBrick, each region is comprised of multivariate data, which is encoded into simple shapes with colors. Additionally, users can adjust the resolution of data values to get an overview as well as details of the data. GeoBrick allows users to (1) juxtapose data and spatial profiles of discontiguous regions, (2) identify temporal patterns of user-defined classes of regions, and (3) comparatively evaluate across distinct configurations of regions. We demonstrate the effectiveness and efficacy of GeoBrick using two case studies.

关键词： Visual analytics Spatiotemporal visualization Multivariate data interactive data analysis

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ProSecCo: progressive sequence mining with convergence guarantees

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KNOWLEDGE AND INFORMATION SYSTEMS 2020年第4期62卷 1313-1340页

作者： Servan-Schreiber, Sacha Riondato, Matteo Zgraggen, Emanuel MIT CSAIL Cambridge MA USA Amherst Coll Dept Comp Sci Amherst MA 01002 USA

We present ProSecCo, an algorithm for the progressive mining of frequent sequences from large transactional datasets: It processes the dataset in blocks and it outputs, after having analyzed each block, a high-quality approximation of the collection of frequent sequences. ProSecCo can be used for interactive data exploration, as the intermediate results enable the user to make informed decisions as the computation proceeds. These intermediate results have strong probabilistic approximation guarantees and the final output is the exact collection of frequent sequences. Our correctness analysis uses the Vapnik-Chervonenkis (VC) dimension, a key concept from statistical learning theory. The results of our experimental evaluation of ProSecCo on real and artificial datasets show that it produces fast-converging high-quality results almost immediately. Its practical performance is even better than what is guaranteed by the theoretical analysis, and ProSecCo can even be faster than existing state-of-the-art non-progressive algorithms. Additionally, our experimental results show that ProSecCo uses a constant amount of memory, and orders of magnitude less than other standard, non-progressive, sequential pattern mining algorithms.

关键词： Approximation algorithms interactive data analysis Pattern mining Sampling VC-dimension

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