| مجال التميز | تميز دراسي وبحثي |
البحوث المنشورة |
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| البحث (1): | |
| عنوان البحث: |
TimeCluster: dimension reduction applied to temporal data for visual analytics. |
| رابط إلى البحث: |
https://link.springer.com/article/10.1007%2Fs00371-019-01673-y |
| تاريخ النشر: |
09/05/2019 |
| موجز عن البحث: |
There is a need for solutions which assist users to understand long time-series data by observing its changes over time, finding repeated patterns, detecting outliers, and effectively labeling data instances. Although these tasks are quite distinct and are usually tackled separately, we present an interactive visual analytics system and approach that can address these issues in a single system. It enables users to visualize, understand and explore univariate or multivariate long time-series data in one image using a connected scatter plot. It supports interactive analysis and exploration for pattern discovery and outlier detection. Different dimensionality reduction techniques are used and compared in our system. Because of its power of extracting features, deep learning is used for multivariate time-series along with 2D reduction techniques for rapid and easy interpretation and interaction with large amount of time-series data. We deploy our system with different time-series datasets and report two real-world case studies that are used to evaluate our system. |
| البحث (2): | |
| عنوان البحث: |
Clustering and classification for time series data in visual analytics: A survey |
| رابط إلى البحث: | |
| تاريخ النشر: |
10/12/2019 |
| موجز عن البحث: |
Visual analytics for time series data has received a considerable amount of attention. Different approaches have been developed to understand the characteristics of the data and obtain meaningful statistics in order to explore the underlying processes, identify and estimate trends, make decisions and predict the future. The machine learning and visualization areas share a focus on extracting information from data. In this paper, we consider not only automatic methods but also interactive exploration. The ability to embed efficient machine learning techniques (clustering and classification) in interactive visualization systems is highly desirable in order to gain the most from both humans and computers. We present a literature review of some of the most important publications in the field and classify over 60 published papers from six different perspectives. This review intends to clarify the major concepts with which clustering or classification algorithms are used in visual analytics for time series data and provide a valuable guide for both new researchers and experts in the emerging field of integrating machine learning techniques into visual analytics. |
المؤتمرات العلمية |
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| المؤتمر (1): | |
| عنوان المؤتمر: |
International Symposium on Big Data Visual and Immersive Analytics (BDVA) |
| تاريخ الإنعقاد: |
17-19 Oct. 2018 |
| مكان الإنعقاد: |
Konstanz, Germany |
| طبيعة المشاركة: |
Paper presentation |
| عنوان المشاركة: |
Towards Visual Exploration of Large Temporal Datasets |
| ملخص المشاركة: |
We address the problem of visualizing and interacting with large multi-dimensional time- series data. We propose a visual analytics system and approach which aims to visualize, analyse, present and enable exploration of large temporal datasets. Our approach consists of three main stages which are pre-processing, dimensionality reduction, and visual exploration. It assists with finding the interesting features in the data which are often obscured in the line chart because of the visual compression that is required to render the large dataset to screen. Our approach helps to obtain an overview of the entire dataset and track changes over time. It enables the user to detect clusters and outliers and observe the transitions between data. The juxtaposed views are used to visualize and interact both with raw time series data and projected data. Different time series datasets are deployed on our system, and we demonstrate the utility and evaluate the results using a case study with two different datasets which show the effectiveness of our system. |
| الرابط: | |
