scholarly journals A WEB-BASED INTERACTIVE PLATFORM FOR CO-CLUSTERING SPATIO-TEMPORAL DATA

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 175-179
Author(s):  
X. Wu ◽  
A. Poorthuis ◽  
R. Zurita-Milla ◽  
M.-J. Kraak
Keyword(s):  
Clustering Algorithm ◽  
Temporal Patterns ◽  
Temporal Data ◽  
Web Based ◽  
Server Side ◽  
Exploratory Data ◽  
Spatio Temporal ◽  
Client Side ◽  
Concurrent Analysis ◽  
Selection Of

Since current studies on clustering analysis mainly focus on exploring spatial or temporal patterns separately, a co-clustering algorithm is utilized in this study to enable the concurrent analysis of spatio-temporal patterns. To allow users to adopt and adapt the algorithm for their own analysis, it is integrated within the server side of an interactive web-based platform. The client side of the platform, running within any modern browser, is a graphical user interface (GUI) with multiple linked visualizations that facilitates the understanding, exploration and interpretation of the raw dataset and co-clustering results. Users can also upload their own datasets and adjust clustering parameters within the platform. To illustrate the use of this platform, an annual temperature dataset from 28 weather stations over 20 years in the Netherlands is used. After the dataset is loaded, it is visualized in a set of linked visualizations: a geographical map, a timeline and a heatmap. This aids the user in understanding the nature of their dataset and the appropriate selection of co-clustering parameters. Once the dataset is processed by the co-clustering algorithm, the results are visualized in the small multiples, a heatmap and a timeline to provide various views for better understanding and also further interpretation. Since the visualization and analysis are integrated in a seamless platform, the user can explore different sets of co-clustering parameters and instantly view the results in order to do iterative, exploratory data analysis. As such, this interactive web-based platform allows users to analyze spatio-temporal data using the co-clustering method and also helps the understanding of the results using multiple linked visualizations.

scholarly journals CLUSTERING-BASED APPROACHES TO THE EXPLORATION OF SPATIO-TEMPORAL DATA

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 1387-1391 ◽  
Author(s):  
X. Wu ◽  
R. Zurita-Milla ◽  
M.-J. Kraak ◽  
E. Izquierdo-Verdiguier
Keyword(s):  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Temporal Patterns ◽  
Data Cube ◽  
Data Matrix ◽  
Temporal Data Mining ◽  
Temporal Data ◽  
Complex Patterns ◽  
Spatio Temporal ◽  
Temporal Dimensions

As one spatio-temporal data mining task, clustering helps the exploration of patterns in the data by grouping similar elements together. However, previous studies on spatial or temporal clustering are incapable of analysing complex patterns in spatio-temporal data. For instance, concurrent spatio-temporal patterns in 2D or 3D datasets. In this study we present two clustering algorithms for complex pattern analysis: (1) the Bregman block average co-clustering algorithm with I-divergence (BBAC_I) which enables the concurrent analysis of spatio-temporal patterns in 2D data matrix, and (2) the Bregman cube average tri-clustering algorithm with I-divergence (BCAT_I) which enables the complete partitional analysis in 3D data cube. Here the use of the two clustering algorithms is illustrated by Dutch daily average temperature dataset from 28 weather stations from 1992 to 2011. For BBAC_I, it is applied to the averaged yearly dataset to identify station-year co-clusters which contain similar temperatures along stations and years, thus revealing patterns along both spatial and temporal dimensions. For BCAT_I, it is applied to the temperature dataset organized in a data cube with one spatial (stations) and two nested temporal dimensions (years and days). By partitioning the whole dataset into clusters of stations and years with similar within-year temperature similarity, BCAT_I explores the spatio-temporal patterns of intra-annual variability in the daily temperature dataset. As such, both BBAC_I and BCAT_I algorithms, combined with suitable geovisualization techniques, allow the exploration of complex spatial and temporal patterns, which contributes to a better understanding of complex patterns in spatio-temporal data.

scholarly journals Creating and evaluating web-based visualizations of multi-dimensional spatiotemporal data

2019 ◽  
Vol 2 ◽  
pp. 1-8
Author(s):  
Jan Wilkening ◽  
Keni Han ◽  
Mathias Jahnke
Keyword(s):  
Spatiotemporal Data ◽  
Weather Data ◽  
Temporal Data ◽  
Web Based ◽  
Animated Maps ◽  
Different Types ◽  
Weather Parameters ◽  
Spatio Temporal ◽  
The Web

<p><strong>Abstract.</strong> In this article, we present a method for visualizing multi-dimensional spatio-temporal data in an interactive web-based geovisualization. Our case study focuses on publicly available weather data in Germany. After processing the data with Python and desktop GIS, we integrated the data as web services in a browser-based application. This application displays several weather parameters with different types of visualisations, such as static maps, animated maps and charts. The usability of the web-based geovisualization was evaluated with a free-examination and a goal-directed task, using eye-tracking analysis. The evaluation focused on the question how people use static maps, animated maps and charts, dependent on different tasks. The results suggest that visualization elements such as animated maps, static maps and charts are particularly useful for certain types of tasks, and that more answering time correlates with less accurate answers.</p>

scholarly journals Coastal Change Patterns from Time Series Clustering of Permanent Laser Scan Data

2020 ◽  
Author(s):  
Mieke Kuschnerus ◽  
Roderik Lindenbergh ◽  
Sander Vos
Keyword(s):  
Time Series ◽  
Laser Scanning ◽  
Clustering Algorithm ◽  
Coastal Areas ◽  
Small Scale ◽  
Temporal Data ◽  
Agglomerative Clustering ◽  
Data Set ◽  
Deformation Processes ◽  
Spatio Temporal

Abstract. Sandy coasts are constantly changing environments governed by complex interacting processes. Permanent laser scanning is a promising technique to monitor such coastal areas and support analysis of geomorphological deformation processes. This novel technique delivers 3D representations of a part of the coast at hourly temporal and centimetre spatial resolution and allows to observe small scale changes in elevation over extended periods of time. These observations have the potential to improve understanding and modelling of coastal deformation processes. However, to be of use to coastal researchers and coastal management, an efficient way to find and extract deformation processes from the large spatio-temporal data set is needed. In order to allow data mining in an automated way, we extract time series in elevation or range and use unsupervised learning algorithms to derive a partitioning of the observed area according to change patterns. We compare three well known clustering algorithms, k-means, agglomerative clustering and DBSCAN, and identify areas that undergo similar evolution during one month. We test if they fulfil our criteria for a suitable clustering algorithm on our exemplary data set. The three clustering methods are applied to time series of 30 epochs (during one month) extracted from a data set of daily scans covering a part of the coast at Kijkduin, the Netherlands. A small section of the beach, where a pile of sand was accumulated by a bulldozer is used to evaluate the performance of the algorithms against a ground truth. The k-means algorithm and agglomerative clustering deliver similar clusters, and both allow to identify a fixed number of dominant deformation processes in sandy coastal areas, such as sand accumulation by a bulldozer or erosion in the intertidal area. The DBSCAN algorithm finds clusters for only about 44 % of the area and turns out to be more suitable for the detection of outliers, caused for example by temporary objects on the beach. Our study provides a methodology to efficiently mine a spatio-temporal data set for predominant deformation patterns with the associated regions, where they occur.

User requirements for geo-collaborative work with spatio-temporal data in a web-based virtual globe environment

2013 ◽  
Vol 44 (6) ◽  
pp. 929-939 ◽  
Author(s):  
Zornitza Yovcheva ◽  
Corné P.J.M. van Elzakker ◽  
Barend Köbben
Keyword(s):  
Collaborative Work ◽  
User Requirements ◽  
Temporal Data ◽  
Web Based ◽  
Virtual Globe ◽  
Spatio Temporal

A Web-Based Embedded Sequential Controller with USB 1-N Wireless I/O Modules

2006 ◽  
Vol 505-507 ◽  
pp. 535-540
Author(s):  
Yung Hoh Sheu ◽  
Wu Jeng Li ◽  
Yen Chao Chen ◽  
Jheng Yi Yang
Keyword(s):  
Data Acquisition ◽  
Control Program ◽  
Control Software ◽  
Core System ◽  
Web Based ◽  
Linux Operation System ◽  
Server Side ◽  
Sequential Control ◽  
Multiple Data ◽  
Client Side

This paper designs web-based USB 1-N wireless I/O modules embedded sequential controller. The controller consists of ARM-based core system, a set of USB 1-N wireless I/O data acquisition modules, and sequential control software. The ARM-based core system running Linux operation system forms the basic hardware/software foundation of the controller. The set of USB devices used as I/O interface (sensor and actuator) of thecontroller. With the use of RF chip, the USB I/O is cascaded by wireless 1-N channel such that multiple data acquisition modules can communicate with the controller by a USB port. The device driver of the USB set for the ARM-base Linux system is developed. The sequential control software is designed as client/server structure. The server-side program and client-side program communicate through the Internet. The server-side control program, mainly a PLC interpreter, is an application developed in C++ in the Linux system. The client-side control program is developed in Java and put under a web server of the controller such that the program can be easily deployed by network and run in remote computer. The client program is also used as GUI of the controller.

scholarly journals Prosedur efektif pengembangan aplikasi basis data

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Zulkarnaen Hatala
Keyword(s):  
Web Application ◽  
Specific Requirement ◽  
Application System ◽  
Web Based ◽  
Database Application ◽  
Server Side ◽  
The Right ◽  
Client Side ◽  
The Web

Abstract—Efficient and quick procedure to build a web application is presented. The steps are intended to build a database application system with hundreds of tables. The procedure can minimize tasks needed to write code and doing manual programming line by line. The intention also to build rapidly web-based database application. In this method security concerning authentification and authorization already built in ensuring the right and eligible access of the user to the system. The end result is ready to use the web-based 3-tier application. Moreover, the application is still flexible to be customized and to be enhanced to suit more specific requirement in part of each module of the software both the server-side and client-side programming codes. Abstrak—Pada penelitian kali ini diusulkan prosedur cepat dan efisien pengembangan aplikasi basis data menggunakan generator aplikasi. Bertujuan untuk meminimalisir penulisan bahasa pemograman. Keuntungan dari prosedur ini adalah bisa digunakan untuk mengembangkan aplikasi basis data secara cepat terutama dengan sistem basis data yang terdiri dari banyak tabel. Hak akses dan prosedur keamanan standar telah disediakan sehingga setiap user terjamin haknya terhadap entitas tertentu di basis data. Hasil generasi adalah aplikasi basis data berbasis web yang siap pakai. Sistem aplikasi yang terbentuk masih sangat lentur untuk untuk dilakukan penyesuaian setiap komponen aplikasi baik di sisi server maupun di sisi client.

scholarly journals AtmoVis: Visualization of Air Quality Data

2021 ◽  
Author(s):  
◽  
Benjamin Powley
Keyword(s):  
Air Quality ◽  
User Study ◽  
Poor Quality ◽  
Quality Data ◽  
Temporal Data ◽  
Multiple Sources ◽  
Web Based ◽  
Visualization System ◽  
Spatio Temporal ◽  
Temporal Data Analysis

<p>Air quality has an adverse impact on the health of people living in areas with poor quality air. Monitoring is needed to understand the effects of poor air quality. It is difficult to compare measurements to find trends and patterns between different monitoring sites when data is contained in separate data stores. Data visualization can make analyzing air quality more effective by making the data more understandable. The purpose of this research is to design and build a prototype for visualizing spatio-temporal data from multiple sources related to air quality and to evaluate the effectiveness of the prototype against criteria by conducting a user study. The prototype web based visualization system, AtmoVis, has a windowed layout with 6 different visualizations: Heat calendar, line plot, monthly rose, site view, monthly averages and data comparison. A pilot study was performed with 11 participants and used to inform the study protocol before the main user study was performed on 20 participants who were air quality experts or experienced with Geographic Information Systems (GIS). The results of the study demonstrated that the heat calendar, line plot, site view, monthly averages and monthly rose visualizations were effective for analyzing the air quality through AtmoVis. The line plot and the heat calendar were the most effective for temporal data analysis. The interactive web based interface for data exploration with a window layout, provided by AtmoVis, was an effective method for accessing air quality visualizations and inferring relationships among air quality variables at different monitoring sites. AtmoVis could potentially be extended to include other datasets in the future.</p>

Mining Flow Patterns in Spatio-Temporal Data

2008 ◽  
pp. 157-188
Author(s):  
Wynne Hsu ◽  
Mong Li Lee ◽  
Junmei Wang
Keyword(s):  
Temporal Patterns ◽  
Flow Patterns ◽  
Optimization Techniques ◽  
Temporal Data ◽  
Generation Algorithm ◽  
Spatio Temporal

In this chapter, we describe flow patterns and the design of the algorithm called FlowMiner to find such flow patterns. FlowMiner incorporates a new candidate generation algorithm and employs various optimization techniques for better efficiency. The discovery of generalized spatio-temporal patterns will be described in the next chapter.

A step by step procedure for multivariate modeling

2021 ◽  
Author(s):  
Claudia Cappello ◽  
Sandra De Iaco ◽  
Monica Palma ◽  
Sabrina Maggio
Keyword(s):  
Covariance Structure ◽  
Space Time ◽  
Temporal Data ◽  
Multivariate Geostatistics ◽  
Correlated Variables ◽  
Multiple Data ◽  
Step Procedure ◽  
Complete Procedure ◽  
Spatio Temporal ◽  
Selection Of

&lt;p&gt;&lt;span&gt;&lt;span&gt;In environmental sciences, it is very common to observe spatio-temporal multiple data concerning several correlated variables which are measured in time over a monitored spatial domain. In multivariate Geostatistics, the analysis of these correlated variables requires the estimation and modelling of the spatio-temporal multivariate covariance structure.&lt;br&gt;In the literature, the linear coregionalization model (LCM) has been widely used, in order to describe the spatio-temporal dependence which characterizes two or more variables. In particular, the LCM model requires the identification of the basic independent components underlying the analyzed phenomenon, and this represents a tough task. In order to overcome the aforementioned problem, this contribution provides a complete procedure where all the necessary steps to be followed for properly detect the basic space-time components for the phenomenon under study, together with some computational advances which support the selection of an ST-LCM.&lt;br&gt;The implemented procedure and the related algorithms are applied on a space-time air quality dataset.&lt;br&gt;Note that the proposed procedure can help practitioners to reproduce all the modeling stages and to replicate the analysis for different multivariate spatio-temporal data.&lt;/span&gt;&lt;/span&gt;&lt;/p&gt;

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