| Title | A Visual Analytics Approach for the Diagnosis of Heterogeneous and Multidimensional Machine Maintenance Data |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Zhang, Xiaoyu, Fujiwara, Takanori, Chandrasegaran, Senthil, Brundage, Michael P., Sexton, Thurston, Dima, Alden, Ma, Kwan-Liu |
| Conference Name | 2021 IEEE 14th Pacific Visualization Symposium (PacificVis) |
| Date Published | apr |
| Keywords | Clustering algorithms, composability, data mining, Data visualization, dimensionality reduction, heterogeneous data, high-dimensional data, Human Behavior, Learning systems, machine learning, maintenance logs, Metrics, Organizations, pubcrawl, Scalability, text analytics, visual analytics |
| Abstract | Analysis of large, high-dimensional, and heterogeneous datasets is challenging as no one technique is suitable for visualizing and clustering such data in order to make sense of the underlying information. For instance, heterogeneous logs detailing machine repair and maintenance in an organization often need to be analyzed to diagnose errors and identify abnormal patterns, formalize root-cause analyses, and plan preventive maintenance. Such real-world datasets are also beset by issues such as inconsistent and/or missing entries. To conduct an effective diagnosis, it is important to extract and understand patterns from the data with support from analytic algorithms (e.g., finding that certain kinds of machine complaints occur more in the summer) while involving the human-in-the-loop. To address these challenges, we adopt existing techniques for dimensionality reduction (DR) and clustering of numerical, categorical, and text data dimensions, and introduce a visual analytics approach that uses multiple coordinated views to connect DR + clustering results across each kind of the data dimension stated. To help analysts label the clusters, each clustering view is supplemented with techniques and visualizations that contrast a cluster of interest with the rest of the dataset. Our approach assists analysts to make sense of machine maintenance logs and their errors. Then the gained insights help them carry out preventive maintenance. We illustrate and evaluate our approach through use cases and expert studies respectively, and discuss generalization of the approach to other heterogeneous data. |
| DOI | 10.1109/PacificVis52677.2021.00033 |
| Citation Key | zhang_visual_2021 |
A Visual Analytics Approach for the Diagnosis of Heterogeneous and Multidimensional Machine Maintenance Data