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FactExplorer: Fact Embedding-Based exploratory data analysis for Tabular data

INTERNATIONAL JOURNAL OF HUMAN-COMPUTER INTERACTION 2025年

作者： Jiang, Qi Sun, Guodao Dong, Yue Pan, Lvhan Chang, Baofeng Jiang, Li Liang, Haoran Liang, Ronghua Zhejiang Univ Technol Coll Comp Sci & Technol Hangzhou Peoples R China

Despite exploratory data analysis (EDA) is a powerful approach for uncovering insights from unfamiliar datasets, existing EDA tools face challenges in assisting users to assess the progress of exploration and synthesize coherent insights from isolated findings. To address these challenges, we present FactExplorer, a novel fact-based EDA system that shifts the analysis focus from raw data to data facts. FactExplorer employs a hybrid logical-visual representation, providing users with a comprehensive overview of all potential facts at the outset of their exploration. Moreover, FactExplorer introduces fact-mining techniques, including topic-based drill-down and transition path search capabilities. These features facilitate in-depth analysis of facts and enhance the understanding of interconnections between specific facts. Finally, we present a usage scenario and conduct a user study to assess the effectiveness of FactExplorer. The results indicate that FactExplorer facilitates the understanding of isolated findings and enables users to steer a thorough and effective EDA.

关键词： data insight data embedding exploratory data analysis human-computer interaction

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Enhancing Recurrent Neural Network Efficacy in Online Sales Predictions with exploratory data analysis

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2025年第2期16卷 282-289页

作者： Widiastuti, Erni Kusanti, Jani Sulistyowati, Herwin Univ Surakarta Fac Econ Surakarta Indonesia Univ Surakarta Fac Elect Informat Engn Surakarta Indonesia Univ Surakarta Fac Law Surakarta Indonesia

Online sales forecasting has become an essential aspect of effective business planning in the digital era. The widespread adoption of digital transformation has enabled companies to collect substantial datasets related to consumer behavior, market trends, and sales drivers. This study attempts to uncover patterns and predict sales growth by utilizing product images and their associated filenames as input. To achieve this, we use EDA combined with LSTM and Gated Recurrent Unit (GRU), which excel in processing sequential data. However, the performance of these networks is significantly affected by the quality of data and the preprocessing methods applied. This study highlights the importance of exploratory data analysis (EDA) and Ensemble Methods in enhancing the efficacy of RNNs for online sales forecasting. EDA plays a crucial role in identifying significant patterns such as trends, seasonality, and autocorrelation while addressing data irregularities such as missing values and outliers. These findings show that integrating EDA substantially improves the performance metrics of RNN, as indicated by the reduction in loss and mean absolute error (MAE) values across training epochs (e.g. loss: 0.0720, MAE: 0.1918 at epoch 15). These results indicate that EDA improves the accuracy, stability, and efficiency of the model, allowing RNN to provide more reliable sales predictions while minimizing the risk of overfitting.

关键词： exploratory data analysis recurrent neural networks online sales prediction sequential data trend patterns

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A Web Application for exploratory data analysis and classification of Parkinson's Disease patients using machine learning models on different datasets

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SOFTWARE IMPACTS 2025年 23卷

作者： da Silva, Daniel Hilario Souza, Leandro Rodrigues da Silva Brasileiro, Simone Hilario da Silva Andrade, Adriano de Oliveira Ribeiro, Caio Tonus Pereira, Adriano Alves Nardo, Jose Renato Munari Programa Posgraduacao Engn Biomed Uberlandia Brazil Inst Fed Goiano Campus Cristalina Cristalina Brazil Inst Fed Goiano Campus Rio Verde Rio Verde Brazil Inst Univ Una Catalao Catalao Brazil Fac Engn Elect Uberlandia Brazil

Automated biomedical data analysis tools are crucial in research and clinical practice;however, they are not always accessible to everyone. This paper introduces a web-based system that facilitates exploratory data analysis and machine learning, focusing on identifying audio and video data patterns. This system applies to various biomedical contexts, such as the study of Parkinson's disease. Developed using Python and the Streamlit framework, it offers an intuitive interface for data analysis, visualization, and automated classification. Its flexibility makes it a valuable resource for researchers and healthcare professionals, enabling meaningful insights and fostering advancements in biomedical research.

关键词： exploratory data analysis Machine learning Parkinson's disease Patient classification Early diagnosis

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exploratory data analysis in quality-improvement projects

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JOURNAL OF QUALITY TECHNOLOGY 2007年第4期39卷 301-311页

作者： de Mast, Jeroen Trip, Albert Univ Amsterdam Inst Business & Ind Stat IBIS UvA NL-1018 TV Amsterdam Netherlands

The aim of this paper is to provide a prescriptive framework for exploratory data analysis (EDA) in quality-improvement projects. The framework is developed on the basis of a large number of real-life applications. The three steps of EDA are described: display the data, identify salient features, and interpret salient features. Graphical display of data, Shewhart's assignable causes, the maximum entropy principle, abduction, and explanatory coherence all are part of the resulting framework. Furthermore, the roles of probabilistic reasoning and automatic statistical procedures in EDA are discussed.

关键词： discovery entropy exploratory data analysis graphical data analysis hypothesis generation pattern discovery

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exploratory data analysis for industrial safety application

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SENSORS AND ACTUATORS B-CHEMICAL 2008年第1期131卷 100-109页

作者： Vezzoli, M. Ponzoni, A. Pardo, M. Falasconi, M. Faglia, G. Sberveglieri, G. Univ Brescia CNR INFM Sensor LabDept Chem & Phys I-25123 Brescia Italy

We tested the detection properties of four MOX sensors toward different ozone mixtures to identify sets of sensing layers and interfering compounds concentrations most suitable for a reliable detection of ozone. The measurement campaign lasted I year divided in four sessions. We collected a substantial amount of measurements (more than 500) with diverse interfering gases: ammonia, ethanol, ethylene, carbon monoxide and humidity. Due to the dimension of the data set it could not be analyzed using the conventional methods generally applied for characterizing gas sensors: evaluating the sensor performance by visual inspection of the sensors responses is unfeasible. For this reason we systematically applied the exploratory data analysis methodology. We used some simple but effective statistical techniques to insight the data. This approach allows us to draw sound conclusions about the causes of variation in the data, e.g. time (sensors' long-term stability) or interfering effects of different chemical compounds. All the analysis techniques employed in this work are implemented in a software package developed at our laboratory. We concluded that the two best stable and sensitive sensors are based on WO3 and SnO2 (An catalyzed). We ranked the contributions of different gases on sensor responses, deducing that out sensors are suitable to detect steps of 50 ppb of ozone when ethylene is less than 10 ppm. Carbon monoxide does not affect the measurements still, the strongest interfering compound is humidity that needs to be controlled or parallely measured also in a preliminary stage. (c) 2008 Elsevier B.V. All rights reserved.

关键词： exploratory data analysis industrial application ozone detection sensors array

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exploratory data analysis Applied in Mapping Multi-element Soil Geochemical Anomalies for Drill Target Definition:A Case Study from the Unpha Layered Non-magmatic Hydrothermal Pb-Zn Deposit,DPR Korea

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Acta Geologica Sinica(English Edition) 2021年第4期95卷 1357-1365页

作者： JANG Gwang-Hyok WON Hyon-Chol HWANG Bo-Hyon CHOI Chol-Man Faculty of Geology Kim Il Sung UniversityPyongyangDemocratic People's Republic of Korea

A factor analysis was applied to soil geochemical data to define anomalies related to buried Pb-Zn mineralization.A favorable main factor with a strong association of the elements Zn,Cu and Pb,related to mineralization,was selected for *** median+2 MAD(median absolute deviation)method of exploratory data analysis(EDA)and C-A(concentration-area)fractal modeling were then applied to the Mahalanobis distance,as defined by Zn,Cu and Pb from the factor analysis to set the thresholds for defining multi-element *** a result,the median+2 MAD method more successfully identified the Pb-Zn mineralization than the C-A fractal *** soil anomaly identified by the median+2 MAD method on the Mahalanobis distances defined by three principal elements(Zn,Cu and Pb)rather than thirteen elements(Co,Zn,Cu,V,Mo,Ni,Cr,Mn,Pb,Ba,Sr,Zr and Ti)was the more favorable reflection of the ore *** identified soil geochemical anomalies were compared with the in situ economic Pb-Zn ore bodies for *** results showed that the median+2 MAD approach is capable of mapping both strong and weak geochemical anomalies related to buried Pb-Zn mineralization,which is therefore useful at the reconnaissance drilling stage.

关键词： factor analysis exploratory data analysis Mahalanobis distance multi-element Unpha

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exploratory data analysis of the electrical energy demand in the time domain in Greece

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ENERGY 2017年 134卷 902-918页

作者： Tyralis, Hristos Karakatsanis, Georgios Tzouka, Katerina Mamassis, Nikos Natl Tech Univ Athens Sch Civil Engn Dept Water Resources & Environm Engn Iroon Polythechniou 5 GR-15780 Zografos Greece

The electrical energy demand (EED) in Greece for the time period 2002-2016 is investigated. The aim of the study is to introduce a framework for the exploratory data analysis (EDA) of the EED in the time domain. To this end, the EED at the hourly, daily, seasonal and annual time scale along with the mean daily temperature and the Gross Domestic Product (GDP) of Greece are visualized. The forecast of the EED provided by the Greek Independent Power Transmission Operator (IPTO) is also visualized and is compared with the actual EED. Furthermore, the EED pricing system is visualized. The results of the study in general confirm and summarize the conclusions of previous relevant studies in Greece, each one treating a single topic and covering shorter and earlier time periods.. Furthermore, some unexpected patterns are observed, which if not considered carefully could result to dubious models. Therefore, it is shown that the EDA of the EED in the time domain coupled with weather-, climate-related and socioeconomic variables is essential for the building of a model for the short-, medium- and long-term EED forecasting, something not highlighted in the literature. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Electrical energy demand Energy forecasting exploratory data analysis Greece Gross domestic product Temperature

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exploratory data analysis and Classification of a New Arabic Online Extremism dataset

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IEEE ACCESS 2021年 9卷 161613-161626页

作者： Aldera, Saja Emam, Ahmed Al-Qurishi, Muhammad Alrubaian, Majed Alothaim, Abdulrahman King Saud Univ Coll Business Adm Management Informat Syst Dept Riyadh 11451 Saudi Arabia King Saud Univ Coll Comp & Informat Sci Informat Syst Dept Riyadh 11451 Saudi Arabia

The dissemination of extremist ideas and causes online has intensified over the last decade. Extremist organizations use social media to gain publicity and new recruits, often with little interference from network providers. New techniques are being developed to identify extremist content, ensuring it can be promptly removed and its authors blocked from network access. However, most techniques are only compatible with the English language, despite the fact that extremist propaganda is frequently shared in other languages, including Arabic. Since the most effective methods for automated linguistic analysis use deep learning and require large, high-quality datasets, creating specialised data samples containing examples of extremist communication is an essential step toward a practical solution. In this paper, we present a dataset compiled for this purpose and discuss the classification methods that can be used for extremism detection. The manually annotated Arabic Twitter dataset consists of 89,816 tweets published between 2011 and 2021. Using guidelines, three expert annotators labelled the tweets as extremist or non-extremist. exploratory data analysis was performed to understand the dataset's features. Classification algorithms were used with the dataset, including logistic regression, support vector machine, multinominal naive Bayes, random forest, and BERT. Among the traditional machine learning models, support vector machine with term frequency-inverse document frequency features achieved the highest accuracy (0.9729). However, BERT outperformed the traditional models with an accuracy of 0.9749. This dataset is expected to enhance the accuracy of Arabic online extremism classification in future research, and so we have made it publicly available.

关键词： Social networking (online) Blogs Support vector machines data analysis Radio frequency Feature extraction Psychology Extremism radicalization benchmark dataset exploratory data analysis machine learning bidirectional encoder representations from transformers

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exploratory data analysis for the cancellation of slot booking in intercontinental container liner shipping: A case study of Asia to US West Coast Service

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2019年 106卷 243-263页

作者： Zhao, Hui Meng, Qiang Wang, Yadong Natl Univ Singapore Dept Civil & Environm Engn Singapore 117576 Singapore Nanjing Univ Sci & Technol Sch Econ & Management Nanjing 210094 Jiangsu Peoples R China

In an intercontinental container liner service, container shipping operators reserve container slots for the customers who book capacity for their cargoes a few weeks before the ship depart from a particular port. In practice, some of these bookings are finally cancelled without loading any containers onto the ships, which leads to a low loading rate and revenue loss. However, deep analyses of container slot booking cancellation in container liner services rarely appear in the literature due to the lack of real data. The characteristics and patterns of the container slot booking cancellation are unclear from both academic and managerial perspectives. To fill this gap, this study first proposes a conceptual model for the container slot booking cancellation analysis in intercontinental shipping services. A case study on a container liner service between Asia and US west coast is then conducted based on the proposed model. The static and dynamic cancellation rates of voyage, the attributes of bookings, and the factors that may influence the cancellation behaviours are inspected and discussed. The primary findings of this study will benefit both the academic research on container shipping slot bookings and the practices of the slot cancellation control in container shipping company.

关键词： Container shipping exploratory data analysis Slot booking Cancellation rate

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exploratory data analysis of Earthquake Moment Tensor Catalog in Japan Using Non-linear Graph-Based Dimensionality Reduction

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PURE AND APPLIED GEOPHYSICS 2023年第7期180卷 2689-2703页

作者： Kubo, Hisahiko Kimura, Takeshi Shiomi, Katsuhiko Natl Res Inst Earth Sci & Disaster Resilience 3-1 Tennodai Tsukuba Ibaraki 3050006 Japan

The catalog of moment tensor solutions, which contains the information on spatial location, origin time and fault mechanism type of earthquakes, has been interpreted by researchers based on a wealth of past knowledge. However, the long-term routine analyses have led to the accumulation of a huge amount of data in the moment tensor catalog, and it is worth considering moving away from the artisanal approach. In this study, using dimensionality reduction of unsupervised machine learning, we performed exploratory data analysis of the moment tensor catalog in Japan to objectively obtain comprehensive images of seismic activity and to acquire knowledge on the spatial and temporal characteristics of the earthquake mechanism. Source parameters of the moment tensor catalog in Japan, spatial location (latitude, longitude, and focal depth) and source-mechanism diagram information were embedded in two-dimensional space via a non-linear graph-based dimensionality-reduction method, Uniform Manifold Approximation and Projection. On the embedding map, earthquakes in eastern and western Japan are distributed separately and are further embedded to reflect their characteristic fault mechanism and focal depth in each region. The similarity degree of the earthquakes can be obtained as the distance on the embedding map. This study demonstrates that the data visualization using dimensionality reduction is useful for intuitively and objectively understanding the regional characteristics of earthquake mechanisms. The embedding map can also be employed to visualize temporal changes in regional seismic activity and to perform a similarity search with a past event.

关键词： Earthquake moment tensor solutions exploratory data analysis dimensionality reduction earthquakes in Japan data visualization similarity search

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