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ACM CHI Conference on Human Factors in Computing Systems (CHI)

作者： Barczewski, Antoine Bezerianos, Anastasia Boukhelifa, Nadia Univ Paris Saclay Univ Paris Sud CNRS INRIA Orsay France Univ Paris Saclay INRAE AgroParisTech UMR MIA Paris Paris France

ISBN: (纸本)9781450368193

exploratory data analysis is an open-ended iterative process, where the goal is to discover new insights. Much of the work to characterise this exploration stems from qualitative research resulting in rich findings, task taxonomies, and conceptual models. In this work, we propose a machine-learning approach where the structure of an exploratory analysis session is automatically learned. Our method, based on Hidden-Markov Models, automatically builds a storyline of past exploration from log data events, that shows key analysis scenarios and the transitions between analysts' hypotheses and research questions. Compared to a clustering method, this approach yields higher accuracy for detecting transitions between analysis scenarios. We argue for incorporating provenance views in exploratory data analysis systems that show, at minimum, the structure and intermediate results of past exploration. Besides helping the reproducibility of the different analyses and their results, this can encourage analysts to reflect upon and ultimately adapt their exploration strategies.

关键词： exploratory data analysis log analysis sensemaking provenance storytelling machine learning visualization

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Using exploratory data analysis to Support Implementation and Improvement of Education Technology Product 20th

Using Exploratory Data Analysis to Support Implementation an...

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20th International Conference on Artificial Intelligence in Education (AIED)

作者： Feng, Mingyu Brenner, Daniel Coulson, Andrew 400 Seaport Ct Redwood City CA 94063 USA 111 Acad Suite 100 Irvine CA 92617 USA

ISBN: (纸本)9783030232078;9783030232061

ST Math is a visual instructional game-based program that builds a deep conceptual understanding of mathematics through rigorous learning and creative problem solving. It is widely adopted in many elementary schools in the US. In this paper, we describe the exploratory data analysis we conducted on system log data of kindergarten students to discover patterns in students' interaction with the system, and to examine productivity and engagement of students with different profiles. The findings informed the implementation of the program in schools as well as improvement of individual games in the program to render more effective student learning.

关键词： ST Math Game-based learning Education technology exploratory data analysis

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Construct Hunting in GovTech Research: An exploratory data analysis 22nd

Construct Hunting in GovTech Research: An Exploratory Data A...

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22nd IFIP WG 8.5 International Conference on Electronic Government (EGOV) / International Conference on E-Democracy and Open Government (CeDEM) / 15th IFIP WG 8.5 International Conference on Electronic Participation (EPart)

作者： Svahn, Mattias Larsson, Aron Macedo, Eloisa Bandeira, Jorge Swedish Def Res Agcy S-16490 Stockholm Sweden Stockholm Univ Dept Comp & Syst Sci S-16440 Kista Sweden Mid Sweden Univ Dept Commun Qual Management & Informat Syst S-85170 Sundsvall Sweden Univ Aveiro TEMA Ctr Mech Technol P-3810193 Aveiro Portugal LASI Intelligent Syst Associate Lab Guimaraes Portugal

ISBN: (纸本)9783031411373;9783031411380

The concept of "GovTech" has emerged as a business-oriented model and practice for enabling the public sector to take advantage of digital solutions as service towards the citizen, while the private for-profit sector is responsible for innovation, development, and profitable maintenance of the GovTech services, hence making the whole area of solutions seemingly desirable to invest in. However, the current literature on stakeholder views of the GovTech market remains rather generic, less connected to concrete examples of GovTech solutions as these are perceived within a given and delimited GovTech domain. The objective of this paper is to apply exploratory quantitative data analysis for phenomena detection and evaluation. We explore to what extent the constructs of the area actually are disparate and ill-suited to use for quantitative GovTech research, and find five factors showing a degree of mistrust between the public and private sector and prescribe further research into developing constructs that can cut across the research area, enabling for a build-up of a stronger theoretical base for tech business in the public-private markets.

关键词： GovTech quantitative method constructs exploratory data analysis mobility-as-a-service MaaS

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Wavelet-PLS regression models for both exploratory data analysis and process monitoring

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JOURNAL OF CHEMOMETRICS 2000年第5-6期14卷 383-399页

作者： Teppola, P Minkkinen, P Lappeenranta Univ Technol Inorgan & Analyt Chem Lab Chemometr Grp FIN-53851 Lappeenranta Finland

Two novel approaches an presented which take into account the collinearity among variables and the different phenomena occurring at different scales. This is achieved by combining partial least squares (PLS) and multiresolution analysis (MRA). In this work the two novel approaches are interconnected. First, a standard exploratory PLS model is scrutinized with MRA. In this way, different events at different scales and latent variables are recognized. In this case, especially periodic seasonal fluctuations and long-term drifting introduce problems. These low-frequency variations mask and interfere with the detection of small and moderate-level transient phenomena. As a result, the confidence limits become too wide. This relatively common problem caused by autocorrelated measurements can be avoided by detrending. In practice, this is realized by using fixed-size moving windows and by detrending these windows Based on the MRA of the standard model, the second PLS model for process monitoring is constructed based on the filtered measurements. This filtering is done by removing the low-frequency scales representing low-frequency components, such as seasonal fluctuations and other long-term variations, prior to standard PLS modeling. For these particular data the results are shown to be superior compared to a conventional PLS model based on the non-filtered measurements. Often, model updating is necessary owing to non-stationary characteristics of the process and variables. As a big advantage, this new approach seems to remove any further need for model updating, at least in this particular case. This is because the presented approach removes low-frequency fluctuations and results in a more stationary filtered data set that is more suitable for monitoring. Copyright (C) 2000 John Wiley & Sons, Ltd.

关键词： paper and pulp industry activated sludge wastewater treatment plant chemometrics exploratory data analysis process monitoring partial least squares multiresolution analysis wavelets

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Integrating Statistics and Visualization: Case Studies of Gaining Clarity during exploratory data analysis 08

Integrating Statistics and Visualization: Case Studies of Ga...

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26th Annual CHI Conference on Human Factors in Computing Systems

作者： Perer, Adam Shneiderman, Ben Univ Maryland Human Comp Interact Lab College Pk MD 20742 USA

ISBN: (纸本)9781605580111

Although both statistical methods and visualizations have been used by network analysts, exploratory data analysis remains a challenge. We propose that a tight integration of these technologies in an interactive exploratory tool could dramatically speed insight development. To test the power of this integrated approach, we created a novel social network analysis tool, SocialAction, and conducted four long-term case studies with domain experts, each working on unique data sets with unique problems. The structured replicated case studies show that the integrated approach in SocialAction led to significant discoveries by a political analyst, a bibliometrician, a healthcare consultant, and a counter-terrorism researcher. Our contributions demonstrate that the tight integration of statistics and visualizations improves exploratory data analysis, and that our evaluation methodology for long-term case studies captures the research strategies of data analysts.

关键词： Information visualization statistics social networks evaluation case studies exploratory data analysis

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Examining the structural attributes of TRIZ contradiction Matrix using exploratory data analysis

International Journal of Systematic Innovation

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International Journal of Systematic Innovation 2024年第4期8卷 1-17页

作者： Shin, Won-Shik Choi, Youngjoon Hyun, Jung Suk CEO of SIXWEZ South Korea Department of Management Information Systems Jeju National University South Korea Department of International Office Administration Ewha Womans University South Korea

While the efficacy of TRIZ Contradiction Matrix (CM) is still being disputed, CM has been widely used due to its relative ease of use compared to other TRIZ methodologies. However, research on CM has been dominated by case studies of its use, the fundamental structure and components of CM have not been thoroughly explored. This study aims to enhance our understanding on CM by analyzing the relationships between its structural elements and constituents. To do so, exploratory data analysis (EDA) was utilized to explore the correlations between the parameters of improving and worsening features and inventive principles within the intersection boxes. Frequency analysis conjectured a considerable similarity between the same attribute parameters that possessed opposing features. Association rules analysis (ARA) was conducted to identify the structural features of CM. On average, 56.67% of similarity was observed in the inventive principles located within the symmetrical intersection boxes around the matrix’s main diagonal. Remarkably, 93.62% of the intersection boxes shared at least one common inventive principle. Propositional logic was adopted as a conceptual tool to interpret and understand the observed probabilities of inventive principles within CM’s symmetrical intersection boxes. The findings showed that both improving and worsening parameters tend to converge in function enhancement due to the inventive principles in the intersection boxes. Given that parameters symmetric with the CM’s main diagonal represented physical contradiction relations, this study suggests that the intersection box’s inventive principles could potentially offer solutions to these physical contradictions. By examining the correlations between 39 Parameters of CM and 40 inventive principles within the intersection boxes, this study provides meaningful insights to understand the complex mechanisms of CM. © (2024), (Society of Sytematic Innovation). All rights reserved.

关键词： 40 Inventive Principles exploratory data analysis TRIZ Contradiction Matrix

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Advances in exploratory data analysis, Visualisation and Quality for data Centric AI Systems 22

Advances in Exploratory Data Analysis, Visualisation and Qua...

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28th ACM SIGKDD Conference on Knowledge Discovery and data Mining (KKD)

作者： Patel, Hima Guttula, Shanmukha Mittal, Ruhi Sharma Manwani, Naresh Berti-Equille, Laure Manatkar, Abhijit IBM Res Hyderabad India Int Inst Informat Technol Hyderabad India Inst Rech Dev Paris France

ISBN: (纸本)9781450393850

It is widely accepted that data preparation is one of the most time-consuming steps of the machine learning (ML) lifecycle. It is also one of the most important steps, as the quality of data directly influences the quality of a model. In this tutorial, we will discuss the importance and the role of exploratory data analysis (EDA) and data visualisation techniques to find data quality issues and for data preparation, relevant to building ML pipelines. We will also discuss the latest advances in these fields and bring out areas that need innovation. To make the tutorial actionable for practitioners, we will also discuss the most popular open-source packages that one can get started with along with their strengths and weaknesses. Finally, we will discuss on the challenges posed by industry workloads and the gaps to be addressed to make data-centric AI real in industry settings.

关键词： visualization techniques machine learning data-centric ai exploratory data analysis large scale analysis

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Enhancing Drug Reviews Insights through exploratory data analysis and Sentiment analysis 28

Enhancing Drug Reviews Insights through Exploratory Data Ana...

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28th International Conference on Information Visualisation (IV)

作者： Pinto, Ana Sofia Pato, Matilde Datia, Nuno Politecn Lisboa Lisbon Sch Engn ISEL P-1959007 Lisbon Portugal Univ Lisbon IBEB Fac Ciencias P-1749016 Lisbon Portugal Univ Lisbon LASIGE Fac Ciencias P-1749016 Lisbon Portugal NOVA Sch Sci & Technol NOVA LINCS P-2829516 Largo Da Torre Caparica Portugal

ISBN: (纸本)9798350380170;9798350380163

The increasing volume of user-generated content across various online platforms has created vast datasets in multiple domains, including healthcare. This article explores the significant roles of data visualisation and sentiment analysis within the healthcare sector using the UCI ML Drug Review dataset. Our study highlights the value of exploratory data analysis and sentiment analysis in comprehending patient feedback, enriching insights from the dataset. data visualisation effectively elucidates the data's distribution and key characteristics, while sentiment analysis, performed using TextBlob and VADER, categorises the emotional tone of patient reviews. Our methodology aims to provide a deeper understanding of patient satisfaction and medication efficacy based on user-generated content.

关键词： NLP Sentiment analysis exploratory data analysis Drug Recommendation System Healthcare Support

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Towards Utilizing Domain Expertise for exploratory data analysis 19

Towards Utilizing Domain Expertise for Exploratory Data Anal...

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12th International Symposium on Visual Information Communication and Interaction (VINCI)

作者： Langer, Tristan Meisen, Tobias Rhein Westfal TH Aachen Inst Informat Management Mech Engn Aachen Germany Univ Wuppertal Inst Technol & Management Digital Transformat Wuppertal Germany

ISBN: (纸本)9781450376266

In exploratory data analysis, domain knowledge and experience play a central role in order to extract information from the data and to derive proof and knowledge. However, experienced domain experts are rarely the same people who carry out the data analyses. Therefore, utilizing domain expertise for guidance in analytic processes is a complex challenge. In recent years, machine learning has seen great advances. Increasing processing power and growth in data as well as affordable storage have led to more advanced algorithms. Therefore, with the emergence of applicable machine learning algorithms, there is now a method for preserving and making use even of complex knowledge. In this paper, we present a concept that allows to extract and utilize domain knowledge for exploratory data analysis. We introduce concepts of interaction store and analysis context store to record user interaction and context during an exploratory analysis. We use the recorded data to construct semantic interaction sequences and predict their potential insight. The prediction can then be used to guide other data scientist in their sensemaking while performing exploratory data analysis in similar domains and use cases. Furthermore, we discuss possible research opportunities and implications resulting from the presented concept.

关键词： User Interaction Visual Analytics Analytic Provenance exploratory data analysis Machine Learning

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A user friendly toolbox for exploratory data analysis of underwater sound

A user friendly toolbox for exploratory data analysis of und...

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Oceans 2007 Europe International Conference

作者： Pires, Femando J. Lobo, Victor IEEE Portuguese Naval Portugal Portuguese Naval Academy Portugal

ISBN: (纸本)9781424406340

The underwater acoustics research group at the Portuguese Naval academy has been working for some years on unsupervised classification of underwater sound [1-3]. Some software was developed using techniques developed by this group, but adapting the software developed during the research phase to the operational programs (that must be operated by less skilled personnel) proved to be difficult. So as to make the transition smoother, it was decided to use MATLAB both during the research phase and as development system for the operational program. By imposing some compliance rules during the exploratory phase, the same code can be seamlessly integrated with the operational program. Furthermore, the software developed constitutes a signal processing and classification toolbox that can be used by researchers or end-users with little effort. The toolbox is publicly available, and a tutorial was written so as to make the learning curve smoother. In this paper we present this toolbox, showing intended uses, available routines, and end-user programs.

关键词： underwater acoustics exploratory data analysis self-organizing maps spectograms

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