OceanTEA: A Platform for Sharing Oceanographic Data and Analyses

Ocean observation systems, such as Argo floats or the modular ocean laboratory MoLab, produce an increasing amount of time series data. Both, statistical data mining techniques and manual exploration via visualization are necessary for oceanographers to extract scientific knowledge from such vast datasets. Therefore, scientists require a platform to explore and analyze data visually, supporting their collaboration and research. To deliver results and foster the impact of publications, such platform should facilitate automatic and interactive access to research results for scientists, their peers and the public. Our software platform OceanTEA (Oceanographic TimeSeries Exploration and Analysis) supports oceanographers in their research and publication efforts. The platform leverages modern web technology to support the interactive exploration and analysis of high-dimensional datasets. OceanTEA relies on a microservice architecture which can be deployed on desktops and on cloud computing infrastructure

Visual Queries for Finding Patterns in Time Series Data (2002)

Few tools exist for data exploration and pattern identification in time series data sets. Timeboxes are rectangular, direct-manipulation queries for studying time-series datasets. Timeboxes are the primary query tool in our Time- Searcher application, which supports interactive exploration via dynamic queries, along with overviews of query results and drag-and-drop support for query-by-example. This paper describes the TimeSearcher application and possible extensions to the timebox query model, along with a discussion of the use of TimeSearcher for exploring a time series data set involving gene expression profiles

Interactive time series analytics powered by ONEX

Modern applications in this digital age collect a staggering amount of time series data from economic growth rates to electrical household consumption habits. To make sense of it, domain analysts interactively sift through these time series collections in search of critical relationships between and recurring patterns within these time series. The ONEX (Online Exploration of Time Series) system supports effective exploratory analysis of time series collections composed of heterogeneous, variable-length and misaligned time series using robust alignment dynamic time warping (DTW) methods. To assure real-time responsiveness even for these complex and compute-intensive analytics, ONEX precomputes and then encodes time series relationships based on the inexpensive-to-compute Euclidean distance into the ONEX base. Thereafter, based on a solid formal foundation, ONEX uses DTW-enhanced analytics to correctly extract relevant time series matches on this Euclidean-prepared ONEX base. Our live interactive demonstration shows how our ONEX exploratory tool, supported by a rich array of visual interactions and expressive visualizations, enables efficient mining and interpretation of the MATTERS real data collection composed of economic, social, and education data trends across the fifty American states. © 2017 ACM

Longitudinal visualization for exploratory analysis of multiple sclerosis lesions

In multiple sclerosis (MS), the amount of brain damage, anatomical location, shape, and changes are important aspects that help medical researchers and clinicians to understand the temporal patterns of the disease. Interactive visualization for longitudinal MS data can support studies aimed at exploratory analysis of lesion and healthy tissue topology. Existing visualizations in this context comprise bar charts and summary measures, such as absolute numbers and volumes to summarize lesion trajectories over time, as well as summary measures such as volume changes. These techniques can work well for datasets having dual time point comparisons. For frequent follow-up scans, understanding patterns from multimodal data is difficult without suitable visualization approaches. As a solution, we propose a visualization application, wherein we present lesion exploration tools through interactive visualizations that are suitable for large time-series data. In addition to various volumetric and temporal exploration facilities, we include an interactive stacked area graph with other integrated features that enable comparison of lesion features, such as intensity or volume change. We derive the input data for the longitudinal visualizations from automated lesion tracking. For cases with a larger number of follow-ups, our visualization design can provide useful summary information while allowing medical researchers and clinicians to study features at lower granularities. We demonstrate the utility of our visualization on simulated datasets through an evaluation with domain experts.publishedVersio

A graphical data analysis tool for dataset enhancement and preprocessing

This thesis describes the development of DataMole, a new tool written in Python, equipped with a Qt-based graphical interface, that can support researchers during data exploration and preprocessing activities. Data transformation pipelines can be defined and executed within a simple, user-friendly graphical environment, effectively providing an intuitive approach to data manipulation. The tool also embeds functionalities for data visualisation through interactive plots, like scatterplots and line charts, and provides a specific feature for the extraction of time series from longitudinal dataset

An Augmented Visual Query Mechanism for Finding Patterns in Time Series Data (2002)

Relatively few query tools exist for data exploration and pattern identification in time series data sets. In previous work we introduced Timeboxes. Timeboxes are rectangular, direct-manipulation queries for studying time-series datasets. We demonstrated how Timeboxes can be used to support interactive exploration via dynamic queries, along with overviews of query results and drag-and-drop support for query-by-example. In this paper, we extend our work by introducing Variable Time Timeboxes (VTT). VTTs are a natural generalization of Timeboxes, which permit the specification of queries that allow a degree of uncertainty in the time axis. We carefully motivate the need for these more expressive queries, and demonstrate the utility of our approach on several data sets

TimeCluster: dimension reduction applied to temporal data for visual analytics

With the increase of temporal data, there is a growing need for advanced solutions which assist users to understand such data, observe its changes over the time, find repeated patterns, detect outliers, and effectively label data instances in long time-series data. 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