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.

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.

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.

Validation of synthetic daily Landsat NDVI time series data generated by the improved spatial and temporal data fusion approach

2018 ◽  
Vol 40 ◽  
pp. 34-44 ◽  
Author(s):  
Mingquan Wu ◽  
Wenjiang Huang ◽  
Zheng Niu ◽  
Changyao Wang ◽  
Wang Li ◽  
...  
Keyword(s):  
Time Series ◽  
Data Fusion ◽  
Time Series Data ◽  
Series Data ◽  
Temporal Data ◽  
Ndvi Time Series ◽  
Fusion Approach

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.

The Digital Earth Viewer: A new visualization approach for geospatial time series data

2021 ◽  
Author(s):  
Valentin Buck ◽  
Flemming Stäbler ◽  
Everardo Gonzalez ◽  
Jens Greinert
Keyword(s):  
Time Series Data ◽  
Environmental Data ◽  
Series Data ◽  
Multiple Sources ◽  
Data Types ◽  
New Approach ◽  
Digital Earth ◽  
Spatio Temporal ◽  
Diverse Data

<p>The study of the earth’s systems depends on a large amount of observations from homogeneous sources, which are usually scattered around time and space and are tightly intercorrelated to each other. The understanding of said systems depends on the ability to access diverse data types and contextualize them in a global setting suitable for their exploration. While the collection of environmental data has seen an enormous increase over the last couple of decades, the development of software solutions necessary to integrate observations across disciplines seems to be lagging behind. To deal with this issue, we developed the Digital Earth Viewer: a new program to access, combine, and display geospatial data from multiple sources over time.</p><p>Choosing a new approach, the software displays space in true 3D and treats time and time ranges as true dimensions. This allows users to navigate observations across spatio-temporal scales and combine data sources with each other as well as with meta-properties such as quality flags. In this way, the Digital Earth Viewer supports the generation of insight from data and the identification of observational gaps across compartments.</p><p>Developed as a hybrid application, it may be used both in-situ as a local installation to explore and contextualize new data, as well as in a hosted context to present curated data to a wider audience.</p><p>In this work, we present this software to the community, show its strengths and weaknesses, give insight into the development process and talk about extending and adapting the software to custom usecases.</p>

A comparison of benchmark task and insight evaluation methods for information visualization

2011 ◽  
Vol 10 (3) ◽  
pp. 162-181 ◽  
Author(s):  
Chris North ◽  
Purvi Saraiya ◽  
Karen Duca
Keyword(s):  
User Interfaces ◽  
Time Series Data ◽  
Evaluation Methodology ◽  
Series Data ◽  
Task Structure ◽  
Graph Visualization ◽  
Data Types ◽  
Match Task ◽  
Visual Grouping ◽  
Multiple Data

This study compares two different empirical research methods for evaluating information visualizations: the traditional benchmark-task method and the insight method. The methods are compared using criteria such as the conclusions about the visualization designs provided by each method, the time participants spent during the study, the time and effort required to analyse the resulting empirical data, and the effect of individual differences between participants on the results. The study compares three graph visualization alternatives that associate bioinformatics microarray time series data to pathway graph vertices in order to investigate the effect of different visual grouping structures in visualization designs that integrate multiple data types. It is confirmed that visual grouping should match task structure, but interactive grouping proves to be a well-rounded alternative. Overall, the results validate the insight method’s ability to confirm results of the task method, but also show advantages of the insight method to illuminate additional types of tasks. Efficiency and insight frequently correlate, but important distinctions are found. Categories: H.5.2 [Information Interfaces and Presentation]: User Interfaces – evaluation/methodology.

scholarly journals Deep learning classification of temporal data in ecology

2020 ◽  
Author(s):  
César Capinha ◽  
Ana Ceia-Hasse ◽  
Andrew M. Kramer ◽  
Christiaan Meijer
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Data ◽  
Meteorological Data ◽  
Species Distribution Modelling ◽  
Series Data ◽  
Temporal Data ◽  
Climate Time Series ◽  
Biological Entities

AbstractTemporal data is ubiquitous in ecology and ecologists often face the challenge of accurately differentiating these data into predefined classes, such as biological entities or ecological states. The usual approach transforms the temporal data into static predictors of the classes. However, recent deep learning techniques can perform the classification using raw time series, eliminating subjective and resource-consuming data transformation steps, and potentially improving classification results. We present a general approach for time series classification that considers multiple deep learning algorithms and illustrate it with three case studies: i) insect species identification from wingbeat spectrograms; ii) species distribution modelling from climate time series and iii) the classification of phenological phases from continuous meteorological data. The deep learning approach delivered ecologically sensible and accurate classifications, proving its potential for wide applicability across subfields of ecology. We recommend deep learning as an alternative to techniques requiring the transformation of time series data.

scholarly journals The effect of network convergence on digital culture industry: Model construction of network industrial economics and empirical study on China

INFORMASI ◽  
2022 ◽  
Vol 51 (2) ◽  
pp. 195-226
Author(s):  
Panqiang Niu ◽  
Anang Masduki ◽  
Xigen Li ◽  
Filosa Gita Sukmono
Keyword(s):  
Empirical Study ◽  
Time Series Data ◽  
Culture Industry ◽  
Mediating Effect ◽  
Digital Culture ◽  
Series Data ◽  
Time Range ◽  
Data Types ◽  
Network Convergence ◽  
The Impact

This paper constructs the model of network economics to study the effect of different levels of network convergence on the digital culture industry. Then uses regression models and mediating effect models to test the effect mechanism of network convergence on the digital culture industry of China.  This paper used panel data to conduct an empirical study. The data in this paper were quarterly. The time range was from the first quarter of 2009 to the third quarter of 2013 for 19 quarters.The three data types in econometrics are time series data, cross-sectional data, and panel data.The main conclusions are as follows. Network convergence brings positive policy effects and adverse capital effects. The impact of network convergence on firm performance of the digital culture industry is not statistically significant, and this effect also has no indirect effects on the test of mediating effect. However, network convergence indirectly leads to the reduction of operating costs of the digital culture industry. The indirect effect is brought by the chain mediating effect of policy effect and capital effect. The study could provide a reference for other countries and regions. Meanwhile, it can be used to analyze the impact of different media convergence on digital industries.

scholarly journals Analisis Dampak Penanaman Modal Asing Dan Tenaga Kerja Asing Tiongkok terhadap Perekonomian Indonesia

2021 ◽  
Vol 10 (3) ◽  
pp. 159-167
Author(s):  
Neli Aida ◽  
Ukhti Ciptawaty ◽  
Toto Gunarto ◽  
Syarifah Aini
Keyword(s):  
Foreign Investment ◽  
Time Series Data ◽  
Secondary Data ◽  
Positive Influence ◽  
Least Square ◽  
Series Data ◽  
Foreign Workers ◽  
Data Types ◽  
Project Scheme ◽  
Study Results

This study will discuss the influence of the influx of foreign investment and Chinese foreign workers on the Indonesian economy, where cooperation between the two countries uses a turnkey project scheme. This study uses secondary data with time-series data types and is sourced from the Central Statistics Agency, the Investment Coordinating Board, and the Ministry of Manpower for the 2010-2019 period. The method used in this research is quantitative and statistical descriptive using multiple linear regression or OLS (Ordinary Least Square). The study results show a positive influence of Chinese foreign investment on the Indonesian economy and Chinese foreign workers who positively impact the Indonesian economy. Although both are below 1 percent, the percentage of Chinese foreign workers' influence on the Indonesian economy is greater than that of Chinese foreign investment.

Sign in / Sign up

Export Citation Format

Share Document