Interactive Pattern Search in Time Series (2004)

The need for pattern discovery in long time series data led researchers to develop algorithms for similarity search. Most of the literature about time series focuses on algorithms that index time series and bring the data into the main storage, thus providing fast information retrieval on large time series. This paper reviews the state of the art in visualizing time series, and focuses on techniques that enable users to interactively query time series. Then it presents TimeSearcher 2, a tool that enables users to explore multidimensional data using coordinated tables and graphs with overview+detail, filter the time series data to reduce the scope of the search, select an existing pattern to find similar occurrences, and interactively adjust similarity parameters to narrow the result set. This tool is an extension of previous work, TimeSearcher 1, which uses graphical timeboxes to interactively query time series data

Visualization of Time-Series Data in Parameter Space for Understanding Facial Dynamics

Over the past decade, computer scientists and psychologists have made great efforts to collect and analyze facial dynamics data that exhibit different expressions and emotions. Such data is commonly captured as videos and are transformed into feature-based time-series prior to any analysis. However, the analytical tasks, such as expression classification, have been hindered by the lack of understanding of the complex data space and the associated algorithm space. Conventional graph-based time-series visualization is also found inadequate to support such tasks. In this work, we adopt a visual analytics approach by visualizing the correlation between the algorithm space and our goal – classifying facial dynamics. We transform multiple feature-based time-series for each expression in measurement space to a multi-dimensional representation in parameter space. This enables us to utilize parallel coordinates visualization to gain an understanding of the algorithm space, providing a fast and cost-effective means to support the design of analytical algorithms

From movement tracks through events to places : extracting and characterizing significant places from mobility data

Best VAST 2011 paperInternational audienceWe propose a visual analytics procedure for analyzing movement data, i.e., recorded tracks of moving objects. It is oriented to a class of problems where it is required to determine significant places on the basis of certain types of events occurring repeatedly in movement data. The procedure consists of four major steps: (1) event extraction from trajectories; (2) event clustering and extraction of relevant places; (3) spatio-temporal aggregation of events or trajectories; (4) analysis of the aggregated data. All steps are scalable with respect to the amount of the data under analysis. We demonstrate the use of the procedure by example of two real-world problems requiring analysis at different spatial scales

Concurrent time-series selections using deep learning and dimension reduction

The objective of this work was to investigate from a user perspective linkage between a 1D time-series view of data and a 2D representation provided by dimension reduction techniques. Our hypothesis is that when such interaction happens seamlessly, the use of these linked views, compared to only interacting with the 1D time-series view, for the ubiquitous task of selection and labelling, is more efficient and effective both in terms of performance and user experience. To this end we examine different dimension reduction techniques (UMAP, t-SNE, PCA and Autoencoder) and evaluate each technique within our experimental setting. Results demonstrate that there is a positive impact on speed and accuracy through augmenting 1D views with a dimension reduction 2D view when these views are linked and linkage is supported through coordinated interaction

ChronoView: Visualization Technique for Many Temporal Data

Abstract-This paper presents a method of visualizing data that contains temporal information, such as a human's behavior and the time at which it occurs. A feature of the data is that each event may have one or more time-stamps. By analyzing this kind of data, we are able to find some behavioral patterns and obtain knowledge applicable to many fields, such as marketing research and security. We develop ChronoView, a visualization technique to support the analysis of data with temporal information. ChronoView represents an event with a set of time-stamps as a position inside a circle, similar to the dial of an analog clock. By representing each event as a position on a two-dimensional plane, we can simultaneously visualize many events and easily compare their occurrence patterns. We implement a tool based on ChronoView, which is enriched with additional functions and overcomes the drawbacks of the original system. A use case involving tweet data from Twitter illustrates the use and practicality of ChronoView

Methods of visualization and analysis of cardiac depolarization in the three dimensional space

The master thesis presents methods for intellectual analysis and visualization 3D EKG in order to increase the efficiency of ECG analysis by extracting additional data. Visualization is presented as part of the signal analysis tasks considered imaging techniques and their mathematical description. Have been developed algorithms for calculating and visualizing the signal attributes are described using mathematical methods and tools for mining signal. The model of patterns searching for comparison purposes of accuracy of methods was constructed, problems of a clustering and classification of data are solved, the program of visualization of data is also developed. This approach gives the largest accuracy in a task of the intellectual analysis that is confirmed in this work. Considered visualization and analysis techniques are also applicable to the multi-dimensional signals of a different kind

Visualização de informação

O relatório está dividido em duas partes. Na primeira parte, é abordado o problema da visualização exactamente no que diz respeito à subtil correlação existente entre as técnicas (e respectivas metáforas), o utilizador e os dados. Na segunda parte, são analisadas algumas aplicações, projectos, ferramentas e sistemas de Visualização de Informação. Para categorizalos, serão considerados sete tipos de dados básicos subjacentes a eles: unidimensional, bidimensional, tridimensional, multi-dimensional, temporal, hierárquico, rede e workspace.O tema deste relatório é a visualização da informação. Esta é uma área actualmente muito activa e vital no ensino, na pesquisa e no desenvolvimento tecnológico. A ideia básica é utilizar imagens geradas pelo computador como meio para se obter uma maior compreensão e apreensão da informação que está presente nos dados (geometria) e suas relações (topologia). É um conceito simples, porém poderoso que tem criado imenso impacto em diversas áreas da engenharia e ciência.The theme of this report is information visualization. Nowadays, this is a very active and vital area of research, teaching and development. The basic idea of using computer generated pictures to gain information and understanding from data and relationships is the key concept behind it. This is an extremely simple, but very important concept which is having a powerful impact on methodology of engineering and science. This report is consisted of two parts. The first one, is an overview of the subtle correlation between the visual techniques, the user perception and the data. In the second part, several computer applications, tools, projects and information visualization systems are analyzed. In order to categorize them, seven basic types of data are considered: onedimensional, two- dimensional, three-dimensional, multidimensional, temporal, hierarchic, network and workspace

Scalable analysis of movement data for extracting and exploring significant places

Place-oriented analysis of movement data, i.e., recorded tracks of moving objects, includes finding places of interest in which certain types of movement events occur repeatedly and investigating the temporal distribution of event occurrences in these places and, possibly, other characteristics of the places and links between them. For this class of problems, we propose a visual analytics procedure consisting of four major steps: 1) event extraction from trajectories; 2) extraction of relevant places based on event clustering; 3) spatiotemporal aggregation of events or trajectories; 4) analysis of the aggregated data. All steps can be fulfilled in a scalable way with respect to the amount of the data under analysis; therefore, the procedure is not limited by the size of the computer's RAM and can be applied to very large data sets. We demonstrate the use of the procedure by example of two real-world problems requiring analysis at different spatial scales

Deep Time-Series Clustering: A Review

We present a comprehensive, detailed review of time-series data analysis, with emphasis on deep time-series clustering (DTSC), and a case study in the context of movement behavior clustering utilizing the deep clustering method. Specifically, we modified the DCAE architectures to suit time-series data at the time of our prior deep clustering work. Lately, several works have been carried out on deep clustering of time-series data. We also review these works and identify state-of-the-art, as well as present an outlook on this important field of DTSC from five important perspectives

Anaerobic Membrane Gas Extraction Facilitates Thermolphilic H2 Production from Clostridium Thermocellum

Clostridium thermocellum is among the most efficient bacteria to convert cellulosic biomass into H₂ during dark fermentation. However, despite great progress the H₂ yield and rate are still not satisfactory for large scale applications. The purpose of this study was to evaluate whether in-situ gas extraction using membrane bioreactors would increase H₂ production from Clostridium thermocelllum when compared to a conventional anaerobic fermentation setup. Clostridium thermocelllum ATCC 27405, a cellulotyic, thermophilic bacterium was grown on cellobiose and Avicel in an anaerobic-fermentor (AF) and an anaerobic-membrane-bioreactor (AnMBR). Compared to the AF, the ANMBR increased cumulative H₂ production by 63%, from 25.8 to 42.1 mmols, increased the max H₂ rate by 24%, from 3.4 to 4.2 mmol/hr, and increased yield by 58%, from 0.43 to 0.68 mmol H₂/mmol hexose, on cellobiose. Likewise, on Avicel, the AnMBR increased cumulative H₂ production by 47%, from 46.8 to 68.6 mmols, increased the max H₂ rate by 87%, from 3.1 to 5.8 mmol/hr, and increased the yield by 46%, from 0.76 to 1.11 mmol H₂/mmol hexose.Overall, this study indicates that membrane gas extraction reduces feedback inhibition and makes AnMBR more efficient in converting biomass to renewable H₂.</p