Sonification and Animation of Multivariate Data to Illuminate Dynamics of Geyser Eruptions

2020 ◽  
Vol 44 (1) ◽  
pp. 35-50
Author(s):  
Anna Barth ◽  
Leif Karlstrom ◽  
Benjamin K. Holtzman ◽  
Arthur Paté ◽  
Avinash Nayak
Keyword(s):  
Time Series Data ◽  
Multivariate Data ◽  
Rapid Identification ◽  
Series Data ◽  
Complex Data ◽  
Learning Tools ◽  
Call And Response ◽  
Eruptive Cycle ◽  
Nonstationary Data ◽  
Auditory Systems

Abstract Sonification of time series data in natural science has gained increasing attention as an observational and educational tool. Sound is a direct representation for oscillatory data, but for most phenomena, less direct representational methods are necessary. Coupled with animated visual representations of the same data, the visual and auditory systems can work together to identify complex patterns quickly. We developed a multivariate data sonification and visualization approach to explore and convey patterns in a complex dynamic system, Lone Star Geyser in Yellowstone National Park. This geyser has erupted regularly for at least 100 years, with remarkable consistency in the interval between eruptions (three hours) but with significant variations in smaller scale patterns between each eruptive cycle. From a scientific standpoint, the ability to hear structures evolving over time in multiparameter data permits the rapid identification of relationships that might otherwise be overlooked or require significant processing to find. The human auditory system is adept at physical interpretation of call-and-response or causality in polyphonic sounds. Methods developed here for oscillatory and nonstationary data have great potential as scientific observational and educational tools, for data-driven composition with scientific and artistic intent, and towards the development of machine learning tools for pattern identification in complex data.

Representing Multivariate Data by Optimal Colors to Uncover Events of Interest in Time Series Data

2020 ◽  
Author(s):  
Ding-Bang Chen ◽  
Chien-Hsun Lai ◽  
Yun-Hsuan Lien ◽  
Yu-Hsuan Lin ◽  
Yu-Shuen Wang ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Multivariate Data ◽  
Series Data

scholarly journals Microgenetic Learning Analytics Methods: Hands-on Sequence Analysis Workshop Report

2016 ◽  
Vol 3 (3) ◽  
pp. 96-114 ◽  
Author(s):  
Ani Aghababyan ◽  
Taylor Martin ◽  
Phillip Janisiewicz ◽  
Kevin Close
Keyword(s):  
Data Analysis ◽  
Learning Analytics ◽  
Pattern Mining ◽  
Time Series Data ◽  
Series Data ◽  
Complex Data ◽  
Digital Learning ◽  
Data Types ◽  
Development Environment ◽  
Analysis Techniques

Learning analytics is an emerging discipline and, as such, it benefits from new tools and methodological approaches.  This work reviews and summarizes our workshop on microgenetic data analysis techniques using R, held at the 2nd annual Learning Analytics Summer Institute in Cambridge, Massachusetts on June 30th, 2014. Specifically, this paper introduces educational researchers to our experience using data analysis techniques with the RStudio development environment to analyze temporal records of 52 elementary students’ affective and behavioral responses to a digital learning environment. In the RStudio development environment, we used methods such as hierarchical clustering and sequential pattern mining. We also used RStudio to create effective data visualizations of our complex data. The scope of the workshop, and this paper, assumes little prior knowledge of the R programming language, and thus covers everything from data import and cleanup to advanced microgenetic analysis techniques. Additionally, readers will be introduced to software setup, R data types, and visualizations. This paper not only adds to the toolbox for learning analytics researchers (particularly when analyzing time series data), but also shares our experience interpreting a unique and complex dataset.

scholarly journals An Innovative Concept for a Multivariate Plausibility Assessment of Simultaneously Recorded Data

Animals ◽  
2020 ◽  
Vol 10 (8) ◽  
pp. 1412
Author(s):  
André Mensching ◽  
Marleen Zschiesche ◽  
Jürgen Hummel ◽  
Armin Otto Schmitt ◽  
Clément Grelet ◽  
...  
Keyword(s):  
Time Series Data ◽  
Extreme Values ◽  
Blood Parameters ◽  
Management Tool ◽  
Series Data ◽  
Complex Data ◽  
Climate Data ◽  
Data Set ◽  
Continuous Measurements ◽  
Recorded Data

The aim of this work was to develop an innovative multivariate plausibility assessment (MPA) algorithm in order to differentiate between ‘physiologically normal’, ‘physiologically extreme’ and ‘implausible’ observations in simultaneously recorded data. The underlying concept is based on the fact that different measurable parameters are often physiologically linked. If physiologically extreme observations occur due to disease, incident or hormonal cycles, usually more than one measurable trait is affected. In contrast, extreme values of a single trait are most likely implausible if all other traits show values in a normal range. For demonstration purposes, the MPA was applied on a time series data set which was collected on 100 cows in 10 commercial dairy farms. Continuous measurements comprised climate data, intra-reticular pH and temperature, jaw movement and locomotion behavior. Non-continuous measurements included milk yield, milk components, milk mid-infrared spectra and blood parameters. After the application of the MPA, in particular the pH data showed the most implausible observations with approximately 5% of the measured values. The other traits showed implausible values up to 2.5%. The MPA showed the ability to improve the data quality for downstream analyses by detecting implausible observations and to discover physiologically extreme conditions even within complex data structures. At this stage, the MPA is not a fully developed and validated management tool, but rather corresponds to a basic concept for future works, which can be extended and modified as required.

scholarly journals Deep Temporal Convolution Network for Time Series Classification

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 603
Author(s):  
Bee Hock David Koh ◽  
Chin Leng Peter Lim ◽  
Hasnae Rahimi ◽  
Wai Lok Woo ◽  
Bin Gao
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Learning Algorithm ◽  
Multivariate Time Series ◽  
Classification Performance ◽  
Series Data ◽  
Temporal Context ◽  
Complex Data ◽  
Layer By Layer ◽  
The Right

A neural network that matches with a complex data function is likely to boost the classification performance as it is able to learn the useful aspect of the highly varying data. In this work, the temporal context of the time series data is chosen as the useful aspect of the data that is passed through the network for learning. By exploiting the compositional locality of the time series data at each level of the network, shift-invariant features can be extracted layer by layer at different time scales. The temporal context is made available to the deeper layers of the network by a set of data processing operations based on the concatenation operation. A matching learning algorithm for the revised network is described in this paper. It uses gradient routing in the backpropagation path. The framework as proposed in this work attains better generalization without overfitting the network to the data, as the weights can be pretrained appropriately. It can be used end-to-end with multivariate time series data in their raw form, without the need for manual feature crafting or data transformation. Data experiments with electroencephalogram signals and human activity signals show that with the right amount of concatenation in the deeper layers of the proposed network, it can improve the performance in signal classification.

scholarly journals Hybridized Gradient Descent Spectral Graph and Local-global Louvain Based Clustering of Temporal Relational Data

2020 ◽  
Vol 9 (3) ◽  
pp. 3515-3521
Keyword(s):  
Error Rate ◽  
Gradient Descent ◽  
Time Series Data ◽  
Series Data ◽  
Complex Data ◽  
Similar Data ◽  
Temporal Data ◽  
Data Types ◽  
Temporal Dimension ◽  
Spectral Graph

Temporal data clustering examines the time series data to determine the basic structure and other characteristics of the data. Many methodologies simply process the temporal dimension of data but it still faces the many challenges for extracting useful patterns due to complex data types. In order to analyze the complex temporal data, Hybridized Gradient Descent Spectral Graph and Local-Global Louvain Clustering (HGDSG-LGLC) technique are designed. The number of temporal data is gathered from input dataset. Then the HGDSG-LGLC technique performs graph-based clustering to partitions the vertices i.e. data into different clusters depending on similarity matrix spectrum. The distance similarity is measured between the data and cluster mean. The Gradient Descent function find minimum distance between data and cluster mean. Followed by, the Local-Global Louvain method performs the merging and filtering of temporal data to connect the local and global edges of the graph with similar data. Then for each data, the change in modularity is calculated for filtering the unwanted data from its own cluster and merging it into the neighboring cluster. As a result, optimal ‘k’ numbers of clusters are obtained with higher accuracy with minimum error rate. Experimental analysis is performed with various parameters like clustering accuracy ( ), error rate ( ), computation time ( ) and space complexity ( ) with respect to number of temporal data. The proposed HGDSG-LGLC technique achieves higher and lesser , minimum as well as than conventional methods.

Software Analysis Tools for Wave Sensors

2015 ◽  
Author(s):  
James Morrison ◽  
David Christie ◽  
Charles Greenwood ◽  
Ruairi Maciver ◽  
Arne Vogler
Keyword(s):  
High Performance ◽  
Time Series Data ◽  
Software Tools ◽  
Mixed Data ◽  
Series Data ◽  
Complex Data ◽  
Data Types ◽  
North West ◽  
Software Analysis ◽  
The North

This paper presents a set of software tools for interrogating and processing time series data. The functionality of this toolset will be demonstrated using data from a specific deployment involving multiple sensors deployed for a specific time period. The approach was developed initially for Datawell Waverider MKII/MKII buoys [1] and expanded to include data from acoustic devices in this case Nortek AWACs. Tools of this nature are important to address a specific lack of features in the sensor manufacturers own tools. It also helps to develop standard approaches for dealing with anomalous data from sensors. These software tools build upon an effective modern interpreted programming language in this case Python which has access to high performance low level libraries. This paper demonstrates the use of these tools applied to a sensor network based on the North West coast of Scotland as described in [2,3]. Examples can be seen of computationally complex data being easily calculated for monthly averages. Analysis down to a wave by wave basis will also be demonstrated form the same source dataset. The tools make use of a flexible data structure called a DataFrame which supports mixed data types, hierarchical and time indexing and is also integrated with modern plotting libraries. This allows sub second querying and the ability for dynamic plotting of large datasets. By using modern compression techniques and file formats it is possible to process datasets which are larger than memory datasets without the need for a traditional relational database. The software library shall be of use to a wide variety of industry involved in offshore engineering along with any scientists interested in the coastal environment.

scholarly journals Automation of Longitudinal Time Series Data Visualization using Plot Specifications obtained from XL file

2019 ◽  
Vol 8 (6) ◽  
pp. 3105-3109
Keyword(s):  
Decision Making ◽  
Time Series ◽  
Data Visualization ◽  
Time Series Data ◽  
R Package ◽  
Series Data ◽  
Complex Data ◽  
Business Decision ◽  
Business Requirement ◽  
Visualization Techniques

Longitudinal Time Series data visualization plays important role in all sector of business decision making [9]. With enormous amount of complex data [11] from cloud and business requirement, number of graphs needed for decision making increased many folds. Generating enormous number of plots manually with more human input is tedious, time consuming and error prone. To avoid these issues, suitable visualization techniques with solid design principles become very important. We conceptualized and designed a novel method for automation of these processes. R-GGPLOT2[7] package and XL specifications file were primarily used to achieve this goal. We here show as how we can create multiple plots from time series data, plots specifications-XL file and R package GGPLOT2[7] in a single run. Since all required information are entered in XL sheet, R function can be run with no modification. Multiple plots can be generated by using enormous data available in production and service sectors such as finance, healthcare, transportation and food industries etc.

scholarly journals Timeline Visualization Uncovers Gaps in Archived Tsunami Water Level Data

2021 ◽  
Vol 3 ◽  
Author(s):  
Aaron D. Sweeney
Keyword(s):  
Time Series ◽  
Water Level ◽  
Time Series Data ◽  
Deep Ocean ◽  
Rapid Identification ◽  
Series Data ◽  
Level Data ◽  
Water Level Data ◽  
Data Gaps ◽  
Coastal Water Level

We demonstrate that data abstraction via a timeline visualization is highly effective at allowing one to discover patterns in the underlying data. We describe the rapid identification of data gaps in the archival time-series records of deep-ocean pressure and coastal water level observations collected to support the NOAA Tsunami Program and successful measures taken to rescue these data. These data gaps had persisted for years prior to the development of timeline visualizations to represent when data were collected. This approach can be easily extended to all types of time-series data and the author recommends this type of temporal visualization become a routine part of data management, whether one collects data or archives data.

Kaleidomaps: A New Technique for the Visualization of Multivariate Time-Series Data

2007 ◽  
Vol 6 (2) ◽  
pp. 155-167 ◽  
Author(s):  
Kim Bale ◽  
Paul Chapman ◽  
Nick Barraclough ◽  
Jon Purdy ◽  
Nizamettin Aydin ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Rapid Identification ◽  
Series Data ◽  
Data Sets ◽  
Periodic Patterns ◽  
Large Complex ◽  
Visualization Tools ◽  
Glass Patterns

In this paper, we describe a new visualization technique that can facilitate our understanding and interpretation of large complex multivariate time-series data sets. ‘Kaleidomaps’ have been carefully developed taking into account research into how we perceive form and structure within Glass patterns. We have enhanced the classic cascade plot using the curvature of a line to alter the detection of possible periodic patterns within multivariate dual periodicity data sets. Similar to Glass patterns, the concentric nature of the Kaleidomap may induce a motion signal within the brain of the observer facilitating the perception of patterns within the data. Kaleidomaps and our associated visualization tools alter the rapid identification of periodic patterns not only within their own variants but also across many different sets of variants. By linking this technique with traditional line graphs and signal processing techniques, we are able to provide the user with a set of visualization tools that permit the combination of multivariate time-series data sets in their raw form and also with the results of mathematical analysis. In this paper, we provide two case study examples of how Kaleidomaps can be used to improve our understanding of large complex multivariate time dependent data.

scholarly journals Clustering Time Series Data Mining dengan Jarak Kedekatan Manhattan City

2019 ◽  
Vol 1 ◽  
pp. 1155
Author(s):  
Relita Buaton ◽  
Muhammad Zarlis ◽  
Herman Mawengkang ◽  
Syahril Effendi
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Data Sets ◽  
Complex Data ◽  
Media Technology ◽  
Time Series Data Mining ◽  
Storage Media ◽  
Complex Data Sets

The development of information technology is very rapid and is supported by the development of storage media technology and its application to all fields that produce huge amounts of data stacks generated from various sources, therefore need new techniques in managing data stacks. Data mining has become very important as an object and research study at this time because there are many data stacks found in agencies. Data mining is an analytical process of knowledge discovery in large and complex data sets. In this study the technique used is to conduct time series data mining clusters, using proximity to manhattan city. The time series graph is carried out by the sliding window to produce an analysis of the window for each cluster result. Based on cluster results, an analysis of knowledge transformation is carried out into new knowledge obtained from data mining time series data.

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