DataMelt

DM & LA Portal > Form: Data mining and learning analytics tools

Computation and Visualization Environment 2.2 (2018/05/10)

Developed by: DataMelt community. Led by S.Chekanov
License: GPL / GNU for non commercial use. Commercial-friendly license is available
Web page : Tool homepage
Tool type : Application software

The last edition of this page was on: 2014/02/26

The Completion level of this page is : Medium

SHORT DESCRIPTION

[[has description::DataMelt is a software environment for numeric calculations, statistics and data analysis

DataMelt, or DMelt, is an environment for numeric computation, data analysis and data visualization. DMelt is designed for analysis of large data volumes ("big data"), data mining, statistical analyses and math computations. The program can be used in many areas, such as natural sciences, engineering, modeling and analysis of financial markets.

DMelt is a computational platform: It can be used with different programming languages on different operating systems. Unlike other statistical programs, DataMelt is not limited by a single programming language. Data analysis and statistical computations can be done using high-level scripting languages (Python/Jython, Groovy, etc.), as well as a lower-level language, such as JAVA. It incorporates many open-source JAVA packages into a coherent interface using the concept of dynamic scripting.

DMelt creates high-quality vector-graphics images (SVG, EPS, PDF etc.) that can be included in LaTeX and other text-processing systems.

History

DataMelt has its roots in particle physics where data mining is a primary task. It was created as jHepWork project in 2005 and it was initially written for data analysis for particle physics^[1] using the Java software concept for International Linear Collider project developed at SLAC. Later versions of jHepWork were modified for general public use (for scientists, engineers, students for educational purpose) since the International Linear Collider project has stalled. In 2013, jHepWork was renamed to DataMelt and become a general-purpose community-supported project. The main source of the reference is the book "Scientific Data analysis using Jython Scripting and Java" ^[2] which discusses data-analysis methods using Java and Jython scripting. Later it was also discussed in the German Java SPEKTRUM journal ^[3]. The string "HEP" in the project name "jHepWork" abbreviates "High-Energy Physics". But due to a wide popularity outside this area of physics, it was renamed to SCaViS (Scientific Computation and Visualization Environment). This project existed for 3 years before it was renamed to DataMelt (or, in short, DMelt).

DataMelt is hosted by the jWork.ORG portal^[4]

Supported platforms

DataMelt runs on Windows, Linux, Mac and the Android platforms. The package for the Android is called AWork.

Documentation

DataMelt is extensively documented. In 2018, the web page of this project contained about 600 examples written in Jython, Java, Groovy, JRuby, covering a number of fields, from general mathematics to data mining and data visualization. The Java API documentation includes the description of more than 40,000 Java classes. In addition, there is a wiki documentation. The documentation includes certain restrictions for general public due to the proprietorial nature of the documentation project.

License terms

DataMelt is licensed by Freemium license. The core source code of the numerical and graphical libraries is licensed by the GNU General Public License. The interactive development environment (IDE) used by DataMelt has some restrictions for commercial usage since language files, documentation files, examples, installer, code-assist databases, interactive help are licensed by the creative-common license. Full members of the DataMelt project have several benefits, such as: the license for a commercial usage, access to the source repository, an extended help system, a user script repository and an access to the complete documentation.

The commercial licenses cannot apply to source code that was imported or contributed^[5] to DataMelt from other authors.

Examples

Jython scripts

Here is an example of how to show 2D bar graphs by reading a CVS file downloaded from the World Bank web site.

from jhplot.io.csv import *
from java.io import *
from jhplot import *

d = {}
reader = CSVReader(FileReader("ny.gdp.pcap.cd_Indicator_en_csv_v2.csv"));
while True:
    nextLine = reader.readNext()
    if nextLine is None:
        break
    xlen = len(nextLine)
    if xlen < 50:
        continue
    d[nextLine[0]] = float(nextLine[xlen-2]) # key=country, value=DGP

c1 = HChart("2013",800,400)
#c1.setGTitle("2013 Gross domestic product  per capita")
c1.visible()
c1.setChartBar()
c1.setNameY("current US $")
c1.setNameX("")
c1.setName("2013 Gross domestic product  per capita")

name1 = "Data Source: World Development Indicators"

set_value = lambda name: c1.valueBar(d[name], name, name1)

set_value(name="Russia")
set_value(name="Poland")
set_value(name="Romania")
set_value(name="Bulgaria")
set_value(name="Belarus")
set_value(name="Ukraine")
c1.update()

The execution of this script plots a bar chart in a separate window. The image can be saved in a number of formats.

Here is another simple example which illustrates how to fill a 2D histogram and display it on a canvas. The script also creates a figure in the PDF format. This script illustrates how to glue and mix the native JAVA classes (from the package java.util) and DataMelt classes (the package jhplot) inside a script written using the Python syntax.

from java.util import Random
from jhplot import *

c1 = HPlot3D("Canvas") # create an interactive canvas
c1.setGTitle("Global title")
c1.setNameX("X")
c1.setNameY("Y")
c1.visible()
c1.setAutoRange()

h1 = H2D("2D histogram", 25, -3.0, 3.0, 25, -3.0, 3.0)
rand = Random()
for i in range(200):
    h1.fill(rand.nextGaussian(), rand.nextGaussian())
c1.draw(h1)
c1.export("jhplot3d.eps") # export to EPS Vector Graphics

This script can be run either using DataMelt IDE or using a stand-alone Jython after specifying classpath to DataMelt libraries. The output is shown below:

3D histogram

Groovy scripts

The same example can also be coded using the Groovy programming language which is supported by DataMelt.

import java.util.Random
import jhplot.*

c1 = new HPlot3D("Canvas")  //  create an interactive canvas
c1.setGTitle("Global title")
c1.setNameX("X")
c1.setNameY("Y")
c1.visible()
c1.setAutoRange()

h1 = new H2D("2D histogram",25,-3.0, 3.0,25,-3.0, 3.0)
rand = Random()
(1..200).each{ // or (0..<200).each{
// or Java: for (i=0; i<200; i++){
// if argument is required, you cann access it through "it" inside the loop:
// (0..<200).each{ println "step: ${it+1}" }
     h1.fill(rand.nextGaussian(),rand.nextGaussian())
}
c1.draw(h1);
c1.export("jhplot3d.eps") // export to EPS Vector Graphics

Groovy is better integrated with Java and can be a factor three faster for long loops over primitives compared to Jython.

Reviews

DataMelt and its earlier versions, SCaVis (2013-2015) and JHepWork (2005-2013), which are still available from DataMelt archive repository, are described in these articles: ^[6] ^[7] ^[8] ^[9] The program was compared with other similar frameworks in these resources ^[10] ^[11] ^[12] ^[13] ^[14] ^[15] ^[16] .

The DataMelt (2015-), a new development of the JHepWork and SCaVis programs. Comparisons of DataMelt with other popular packages for statistical and numeric analysis are given in these resources ^[17] ^[18] ^[19] ^[20] ^[21] .

Popularity

jHepWork, SCaVis/DatMelt are part of the software library of National Institutes of Health Library ^[22], Mathematical support of Institute for Nuclear Research of Russian academy of Sciences^[23] and others. On a commercial site, DataMelt is provided as a service on Amazon EC2 clouds by the Miri Infotech IT Solution Provider company ^[24].

It is difficult to judge how many users use DataMelt since download information from the main resource [1] is not available. Sourceforge, which provides an alternative download option, quotes 300 monthly downloads [2] (May 2018).

One estimate can be done by looking at the popularity of the book ^[25] which is an introduction to the DataMelt program. According to the Springer International, this book is top 25% most downloadable books in 2016 and 2017 in the category "Advanced Information and Knowledge Processing" ^[26]. Since the publication of the book, Springer detects 26k chapter downloads until May 2018^[27], about 1500 per chapter. The previous book describing jHepWork had a similar popularity ^[28]. Bookmetrix estimates 140 readers of the DataMelt book.

== References ==]]

TOOL CHARACTERISTICS

Usability

Authors of this page consider that this tool is '.

Tool orientation

This tool is designed for general purpose analysis.

Data mining type

This tool is made for '.

Manipulation type

This tool is designed for Data transformation, Data analysis, Data visualisation.

IMPORT FORMAT :

EXPORT FORMAT :

Tool can perform:

Transformation of type: Mathematical transformation of data for analysis
Data analysis of type: Data mining methods and algorithms, Basic statistics and data summarization
Data visualisation of type: Sequential Graphic, Chart/Diagram, Map

ABOUT USERS

Tool is suitable for:

Students/Learners/Consumers

Teachers/Tutors/Managers

Researchers

Developers/Designers

Organisations/Institutions/Firms

Others

Required skills:

STATISTICS: Advanced

PROGRAMMING: Basic

SYSTEM ADMINISTRATION: None

DATA MINING MODELS: Medium

FREE TEXT

Tool version : Computation and Visualization Environment 2.2 2018/05/10
(blank line)

Developed by : DataMelt community. Led by S.Chekanov
(blank line)
Tool Web page : http://jwork.org/dmelt/
(blank line)
Tool type : Application software
(blank line)
License:GPL / GNU for non commercial use. Commercial-friendly license is available

SHORT DESCRIPTION

DataMelt is a software environment for numeric calculations, statistics and data analysis

DataMelt, or DMelt, is an environment for numeric computation, data analysis and data visualization. DMelt is designed for analysis of large data volumes ("big data"), data mining, statistical analyses and math computations. The program can be used in many areas, such as natural sciences, engineering, modeling and analysis of financial markets.

DMelt is a computational platform: It can be used with different programming languages on different operating systems. Unlike other statistical programs, DataMelt is not limited by a single programming language. Data analysis and statistical computations can be done using high-level scripting languages (Python/Jython, Groovy, etc.), as well as a lower-level language, such as JAVA. It incorporates many open-source JAVA packages into a coherent interface using the concept of dynamic scripting.

DMelt creates high-quality vector-graphics images (SVG, EPS, PDF etc.) that can be included in LaTeX and other text-processing systems.

History

DataMelt has its roots in particle physics where data mining is a primary task. It was created as jHepWork project in 2005 and it was initially written for data analysis for particle physics^[1] using the Java software concept for International Linear Collider project developed at SLAC. Later versions of jHepWork were modified for general public use (for scientists, engineers, students for educational purpose) since the International Linear Collider project has stalled. In 2013, jHepWork was renamed to DataMelt and become a general-purpose community-supported project. The main source of the reference is the book "Scientific Data analysis using Jython Scripting and Java" ^[2] which discusses data-analysis methods using Java and Jython scripting. Later it was also discussed in the German Java SPEKTRUM journal ^[3]. The string "HEP" in the project name "jHepWork" abbreviates "High-Energy Physics". But due to a wide popularity outside this area of physics, it was renamed to SCaViS (Scientific Computation and Visualization Environment). This project existed for 3 years before it was renamed to DataMelt (or, in short, DMelt).

DataMelt is hosted by the jWork.ORG portal^[4]

Supported platforms

DataMelt runs on Windows, Linux, Mac and the Android platforms. The package for the Android is called AWork.

Documentation

DataMelt is extensively documented. In 2018, the web page of this project contained about 600 examples written in Jython, Java, Groovy, JRuby, covering a number of fields, from general mathematics to data mining and data visualization. The Java API documentation includes the description of more than 40,000 Java classes. In addition, there is a wiki documentation. The documentation includes certain restrictions for general public due to the proprietorial nature of the documentation project.

License terms

DataMelt is licensed by Freemium license. The core source code of the numerical and graphical libraries is licensed by the GNU General Public License. The interactive development environment (IDE) used by DataMelt has some restrictions for commercial usage since language files, documentation files, examples, installer, code-assist databases, interactive help are licensed by the creative-common license. Full members of the DataMelt project have several benefits, such as: the license for a commercial usage, access to the source repository, an extended help system, a user script repository and an access to the complete documentation.

The commercial licenses cannot apply to source code that was imported or contributed^[5] to DataMelt from other authors.

Examples

Jython scripts

Here is an example of how to show 2D bar graphs by reading a CVS file downloaded from the World Bank web site.

from jhplot.io.csv import *
from java.io import *
from jhplot import *

d = {}
reader = CSVReader(FileReader("ny.gdp.pcap.cd_Indicator_en_csv_v2.csv"));
while True:
    nextLine = reader.readNext()
    if nextLine is None:
        break
    xlen = len(nextLine)
    if xlen < 50:
        continue
    d[nextLine[0]] = float(nextLine[xlen-2]) # key=country, value=DGP

c1 = HChart("2013",800,400)
#c1.setGTitle("2013 Gross domestic product  per capita")
c1.visible()
c1.setChartBar()
c1.setNameY("current US $")
c1.setNameX("")
c1.setName("2013 Gross domestic product  per capita")

name1 = "Data Source: World Development Indicators"

set_value = lambda name: c1.valueBar(d[name], name, name1)

set_value(name="Russia")
set_value(name="Poland")
set_value(name="Romania")
set_value(name="Bulgaria")
set_value(name="Belarus")
set_value(name="Ukraine")
c1.update()

The execution of this script plots a bar chart in a separate window. The image can be saved in a number of formats.

Here is another simple example which illustrates how to fill a 2D histogram and display it on a canvas. The script also creates a figure in the PDF format. This script illustrates how to glue and mix the native JAVA classes (from the package java.util) and DataMelt classes (the package jhplot) inside a script written using the Python syntax.

from java.util import Random
from jhplot import *

c1 = HPlot3D("Canvas") # create an interactive canvas
c1.setGTitle("Global title")
c1.setNameX("X")
c1.setNameY("Y")
c1.visible()
c1.setAutoRange()

h1 = H2D("2D histogram", 25, -3.0, 3.0, 25, -3.0, 3.0)
rand = Random()
for i in range(200):
    h1.fill(rand.nextGaussian(), rand.nextGaussian())
c1.draw(h1)
c1.export("jhplot3d.eps") # export to EPS Vector Graphics

This script can be run either using DataMelt IDE or using a stand-alone Jython after specifying classpath to DataMelt libraries. The output is shown below:

3D histogram

Groovy scripts

The same example can also be coded using the Groovy programming language which is supported by DataMelt.

import java.util.Random
import jhplot.*

c1 = new HPlot3D("Canvas")  //  create an interactive canvas
c1.setGTitle("Global title")
c1.setNameX("X")
c1.setNameY("Y")
c1.visible()
c1.setAutoRange()

h1 = new H2D("2D histogram",25,-3.0, 3.0,25,-3.0, 3.0)
rand = Random()
(1..200).each{ // or (0..<200).each{
// or Java: for (i=0; i<200; i++){
// if argument is required, you cann access it through "it" inside the loop:
// (0..<200).each{ println "step: ${it+1}" }
     h1.fill(rand.nextGaussian(),rand.nextGaussian())
}
c1.draw(h1);
c1.export("jhplot3d.eps") // export to EPS Vector Graphics

Groovy is better integrated with Java and can be a factor three faster for long loops over primitives compared to Jython.

Reviews

DataMelt and its earlier versions, SCaVis (2013-2015) and JHepWork (2005-2013), which are still available from DataMelt archive repository, are described in these articles: ^[6] ^[7] ^[8] ^[9] The program was compared with other similar frameworks in these resources ^[10] ^[11] ^[12] ^[13] ^[14] ^[15] ^[16] .

The DataMelt (2015-), a new development of the JHepWork and SCaVis programs. Comparisons of DataMelt with other popular packages for statistical and numeric analysis are given in these resources ^[17] ^[18] ^[19] ^[20] ^[21] .

Popularity

jHepWork, SCaVis/DatMelt are part of the software library of National Institutes of Health Library ^[22], Mathematical support of Institute for Nuclear Research of Russian academy of Sciences^[23] and others. On a commercial site, DataMelt is provided as a service on Amazon EC2 clouds by the Miri Infotech IT Solution Provider company ^[24].

It is difficult to judge how many users use DataMelt since download information from the main resource [3] is not available. Sourceforge, which provides an alternative download option, quotes 300 monthly downloads [4] (May 2018).

One estimate can be done by looking at the popularity of the book ^[25] which is an introduction to the DataMelt program. According to the Springer International, this book is top 25% most downloadable books in 2016 and 2017 in the category "Advanced Information and Knowledge Processing" ^[26]. Since the publication of the book, Springer detects 26k chapter downloads until May 2018^[27], about 1500 per chapter. The previous book describing jHepWork had a similar popularity ^[28]. Bookmetrix estimates 140 readers of the DataMelt book.

References

TOOL CHARACTERISTICS

Tool orientation	Data mining type	Usability
This tool is designed for general purpose analysis.	This tool is designed for .	Authors of this page consider that this tool is .

Data import format	Data export format
.	.

Can perform data transformation of type:
Mathematical transformation of data for analysis

Can perform data analysis of type:
Data mining methods and algorithms, Basic statistics and data summarization

Can perform data visualisation of type:
Sequential Graphic, Chart/Diagram, Map

ABOUT USER

Tool is suitable for:
Students/Learners/Consumers:☑	Teachers/Tutors/Managers:☑	Researchers:☑	Organisations/Institutions/Firms:☑	Others:☑

Required skills:
Statistics: ADVANCED	Programming: BASIC	System administration: NONE	Data mining models: MEDIUM

OTHER TOOL INFORMATION


Scavis.png
Scavis logo.jpg
Computation and Visualization Environment
GPL / GNU for non commercial use. Commercial-friendly license is available"GPL / GNU for non commercial use. Commercial-friendly license is available" is not in the list (Artistic License 2.0, Berkeley Database License, Boost Software License, BSD license (modified version), BDL / BSD Documentation License, CeCILL (CEA CNRS INRIA Logiciel Libre), Cryptix General License, Eclipse Distribution License, EUPL - European Union Public License, GPL / GNU General Public License, ...) of allowed values for the "Free software license" property.
Free&Open source
DataMelt community. Led by S.Chekanov
2018/05/10
2.2
http://jwork.org/dmelt/
[[has description::DataMelt is a software environment for numeric calculations, statistics and data analysis DataMelt, or DMelt, is an environment for numeric computation, data analysis and data visualization. DMelt is designed for analysis of large data volumes ("big data"), data mining, statistical analyses and math computations. The program can be used in many areas, such as natural sciences, engineering, modeling and analysis of financial markets. DMelt is a computational platform: It can be used with different programming languages on different operating systems. Unlike other statistical programs, DataMelt is not limited by a single programming language. Data analysis and statistical computations can be done using high-level scripting languages (Python/Jython, Groovy, etc.), as well as a lower-level language, such as JAVA. It incorporates many open-source JAVA packages into a coherent interface using the concept of dynamic scripting. DMelt creates high-quality vector-graphics images (SVG, EPS, PDF etc.) that can be included in LaTeX and other text-processing systems. History DataMelt has its roots in particle physics where data mining is a primary task. It was created as jHepWork project in 2005 and it was initially written for data analysis for particle physics^[1] using the Java software concept for International Linear Collider project developed at SLAC. Later versions of jHepWork were modified for general public use (for scientists, engineers, students for educational purpose) since the International Linear Collider project has stalled. In 2013, jHepWork was renamed to DataMelt and become a general-purpose community-supported project. The main source of the reference is the book "Scientific Data analysis using Jython Scripting and Java" ^[2] which discusses data-analysis methods using Java and Jython scripting. Later it was also discussed in the German Java SPEKTRUM journal ^[3]. The string "HEP" in the project name "jHepWork" abbreviates "High-Energy Physics". But due to a wide popularity outside this area of physics, it was renamed to SCaViS (Scientific Computation and Visualization Environment). This project existed for 3 years before it was renamed to DataMelt (or, in short, DMelt). DataMelt is hosted by the jWork.ORG portal^[4] Supported platforms DataMelt runs on Windows, Linux, Mac and the Android platforms. The package for the Android is called AWork. Documentation DataMelt is extensively documented. In 2018, the web page of this project contained about 600 examples written in Jython, Java, Groovy, JRuby, covering a number of fields, from general mathematics to data mining and data visualization. The Java API documentation includes the description of more than 40,000 Java classes. In addition, there is a wiki documentation. The documentation includes certain restrictions for general public due to the proprietorial nature of the documentation project. License terms DataMelt is licensed by Freemium license. The core source code of the numerical and graphical libraries is licensed by the GNU General Public License. The interactive development environment (IDE) used by DataMelt has some restrictions for commercial usage since language files, documentation files, examples, installer, code-assist databases, interactive help are licensed by the creative-common license. Full members of the DataMelt project have several benefits, such as: the license for a commercial usage, access to the source repository, an extended help system, a user script repository and an access to the complete documentation. The commercial licenses cannot apply to source code that was imported or contributed^[5] to DataMelt from other authors. Examples Jython scripts Here is an example of how to show 2D bar graphs by reading a CVS file downloaded from the World Bank web site. from jhplot.io.csv import * from java.io import * from jhplot import * d = {} reader = CSVReader(FileReader("ny.gdp.pcap.cd_Indicator_en_csv_v2.csv")); while True: nextLine = reader.readNext() if nextLine is None: break xlen = len(nextLine) if xlen < 50: continue d[nextLine[0]] = float(nextLine[xlen-2]) # key=country, value=DGP c1 = HChart("2013",800,400) #c1.setGTitle("2013 Gross domestic product per capita") c1.visible() c1.setChartBar() c1.setNameY("current US $") c1.setNameX("") c1.setName("2013 Gross domestic product per capita") name1 = "Data Source: World Development Indicators" set_value = lambda name: c1.valueBar(d[name], name, name1) set_value(name="Russia") set_value(name="Poland") set_value(name="Romania") set_value(name="Bulgaria") set_value(name="Belarus") set_value(name="Ukraine") c1.update() The execution of this script plots a bar chart in a separate window. The image can be saved in a number of formats. Here is another simple example which illustrates how to fill a 2D histogram and display it on a canvas. The script also creates a figure in the PDF format. This script illustrates how to glue and mix the native JAVA classes (from the package java.util) and DataMelt classes (the package jhplot) inside a script written using the Python syntax. from java.util import Random from jhplot import * c1 = HPlot3D("Canvas") # create an interactive canvas c1.setGTitle("Global title") c1.setNameX("X") c1.setNameY("Y") c1.visible() c1.setAutoRange() h1 = H2D("2D histogram", 25, -3.0, 3.0, 25, -3.0, 3.0) rand = Random() for i in range(200): h1.fill(rand.nextGaussian(), rand.nextGaussian()) c1.draw(h1) c1.export("jhplot3d.eps") # export to EPS Vector Graphics This script can be run either using DataMelt IDE or using a stand-alone Jython after specifying classpath to DataMelt libraries. The output is shown below: 3D histogram Groovy scripts The same example can also be coded using the Groovy programming language which is supported by DataMelt. import java.util.Random import jhplot.* c1 = new HPlot3D("Canvas") // create an interactive canvas c1.setGTitle("Global title") c1.setNameX("X") c1.setNameY("Y") c1.visible() c1.setAutoRange() h1 = new H2D("2D histogram",25,-3.0, 3.0,25,-3.0, 3.0) rand = Random() (1..200).each{ // or (0..<200).each{ // or Java: for (i=0; i<200; i++){ // if argument is required, you cann access it through "it" inside the loop: // (0..<200).each{ println "step: ${it+1}" } h1.fill(rand.nextGaussian(),rand.nextGaussian()) } c1.draw(h1); c1.export("jhplot3d.eps") // export to EPS Vector Graphics Groovy is better integrated with Java and can be a factor three faster for long loops over primitives compared to Jython. Reviews DataMelt and its earlier versions, SCaVis (2013-2015) and JHepWork (2005-2013), which are still available from DataMelt archive repository, are described in these articles: ^[6] ^[7] ^[8] ^[9] The program was compared with other similar frameworks in these resources ^[10] ^[11] ^[12] ^[13] ^[14] ^[15] ^[16] . The DataMelt (2015-), a new development of the JHepWork and SCaVis programs. Comparisons of DataMelt with other popular packages for statistical and numeric analysis are given in these resources ^[17] ^[18] ^[19] ^[20] ^[21] . Popularity jHepWork, SCaVis/DatMelt are part of the software library of National Institutes of Health Library ^[22], Mathematical support of Institute for Nuclear Research of Russian academy of Sciences^[23] and others. On a commercial site, DataMelt is provided as a service on Amazon EC2 clouds by the Miri Infotech IT Solution Provider company ^[24]. It is difficult to judge how many users use DataMelt since download information from the main resource [5] is not available. Sourceforge, which provides an alternative download option, quotes 300 monthly downloads [6] (May 2018). One estimate can be done by looking at the popularity of the book ^[25] which is an introduction to the DataMelt program. According to the Springer International, this book is top 25% most downloadable books in 2016 and 2017 in the category "Advanced Information and Knowledge Processing" ^[26]. Since the publication of the book, Springer detects 26k chapter downloads until May 2018^[27], about 1500 per chapter. The previous book describing jHepWork had a similar popularity ^[28]. Bookmetrix estimates 140 readers of the DataMelt book. == References ==]]
General analysis


Advanced
Basic
None
Medium
Application software



Data transformation, Data analysis, Data visualisation
Data mining methods and algorithms, Basic statistics and data summarization
Mathematical transformation of data for analysis




Sequential Graphic, Chart/Diagram, Map



Medium

↑ HEP data analysis using jHepWork and Java, arXiv:0809.0840v2, ANL-HEP-CP-08-53 preprint. CERN preprint, arXiv:0809.0840v2
↑ Scientific Data analysis using Jython Scripting and Java. Book. By S.V.Chekanov, Springer-Verlag, ISBN 978-1-84996-286-5, [7]
↑ DataMelt – Werkbank für technisch-wissenschaftliche Berechnungen und Visualisierungen mit Java und Jython. by Rohe Klaus. Java SPEKTRUM. (in German) volume 5 (2013) 26-28 [8]
↑ jWork.ORG Community Portal focused on Java scientific software. [9]
↑ "Contributed Packages (DataMelt Manual)".
↑ Data Analysis and Data Mining Using Java, Jython and jHepWork Blog. 2010. Oracle.com. [10]
↑ DataMelt – Werkbank für technisch-wissenschaftliche Berechnungen und Visualisierungen mit Java und Jython. by Rohe Klaus. Java SPEKTRUM. (in German) volume 5 (2013) 26-28 [11]
↑ HEP data analysis using jHepWork and Java. Proceedings of the HERA-LHC workshops (2007-2008), DESY-CERN [12]
↑ Suitability analysis of data mining tools and methods. [13]. S.Kovac, Bachelor's thesis (in English), jHepWork is reviewed on page 39-42, Masaryk University.
↑ A Review: Comparative Study of Diverse Collection of Data Mining Tools. By S. Sarumathi, N. Shanthi, S. Vidhya, M. Sharmila. International Journal of Computer, Control, Quantum and Information Engineering. 2014; 8(6). 7.
↑ jHepWork – full-featured multiplatform data-analysis framework. [14]
↑ Java Applications: Weka, Svnkit, Jhepwork, Fiji, Memoranda, Livegraph, Dirsync Pro, Moneydance, Rachota Timetracker, Data Mapping Engine. de LLC Books (2010) (Redactor) [15]
↑ A Study of Tools, Techniques, and Trends for Big Data Analytics. By R.Shireesha et al. (2016) International Journal of Advance Computing Technique and Applications (IJACTA), ISSN : 2321-4546, Vol 4, Issue 1 [16]
↑ Comparison of Various Tools for Data Mining. By P.Kaur etc. IJERT ISSN: 2278-0181 Vol. 3 Issue 10 (2010) [[17]]
↑ Advanced Web and Network Technologies, and Applications. By Heng Tao Shen et al. Springer Science & Business Media - 2006-01-09
↑ SCaVis 2.1. Review by Pete Daniel (2014) [18]
↑ Comparative Analysis of Information Extraction Techniques for Data Mining, by Amit Verma et al. Indian Journal of Science and Technology, Vol 9, March 2016 [19]
↑ Evaluation and comparison of open source software suites for data mining and knowledge discovery. A.H. Altalhi et al. Wiley Online Library (2017) [20]
↑ Brief Review of Educational Applications Using Data Mining and Machine Learning, [21], by A. Berenice Urbina Nájera, Jorgede la Calleja Mora, Redie ISSN 1607-4041. Revista Electrónica de Investigación Educativa, 19(4), 84-96
↑ Analysis of Data Using Data Mining tool Orange. Maqsud S.Kukasvadiya et. al. [22] (2017) IJEDR, Volume 5, Issue 2, ISSN: 2321-9939
↑ Big Data - A Survey of Big Data Technologies. By P.Dhavalchandra, M.Jignasu, R.Amit. International Journal of Science and Technology. Volume 2, p45-50 (2016) [23]
↑ Data Sciences Workstation: SCaVis. By Lisa Federer. National Institutes of Health Library [24]
↑ The DataForge, Sector for Mathematical Support of Institute for Nuclear Research of Russian academy of Sciences [25]
↑ Miri Infotech. A Complete IT Solution Provider. DataMelt deployment
↑ Numeric Computation and Statistical Data Analysis on the Java Platform (Book). S.V.Chekanov, Springer, (2016) ISBN 978-3-319-28531-3, 700 pages, [26]
↑ Springer Book Performance Report [27]
↑ Springer download Statistics of the book "Numeric Computation and Statistical Data Analysis on the Java Platform" 2016 [28]
↑ Springer download Statistics of the book "Scientific Data Analysis using Jython Scripting and Java" [29]

[1] HEP data analysis using jHepWork and Java, arXiv:0809.0840v2, ANL-HEP-CP-08-53 preprint. CERN preprint, arXiv:0809.0840v2

[2] Scientific Data analysis using Jython Scripting and Java. Book. By S.V.Chekanov, Springer-Verlag, ISBN 978-1-84996-286-5, [7]

[3] DataMelt – Werkbank für technisch-wissenschaftliche Berechnungen und Visualisierungen mit Java und Jython. by Rohe Klaus. Java SPEKTRUM. (in German) volume 5 (2013) 26-28 [8]

[4] Work.ORG Community Portal focused on Java scientific software. [9]

[5] "Contributed Packages (DataMelt Manual)".

[6] Data Analysis and Data Mining Using Java, Jython and jHepWork Blog. 2010. Oracle.com. [10]

[7] DataMelt – Werkbank für technisch-wissenschaftliche Berechnungen und Visualisierungen mit Java und Jython. by Rohe Klaus. Java SPEKTRUM. (in German) volume 5 (2013) 26-28 [11]

[8] HEP data analysis using jHepWork and Java. Proceedings of the HERA-LHC workshops (2007-2008), DESY-CERN [12]

[9] Suitability analysis of data mining tools and methods. [13]. S.Kovac, Bachelor's thesis (in English), jHepWork is reviewed on page 39-42, Masaryk University.

[10] A Review: Comparative Study of Diverse Collection of Data Mining Tools. By S. Sarumathi, N. Shanthi, S. Vidhya, M. Sharmila. International Journal of Computer, Control, Quantum and Information Engineering. 2014; 8(6). 7.

[11] HepWork – full-featured multiplatform data-analysis framework. [14]

[12] Java Applications: Weka, Svnkit, Jhepwork, Fiji, Memoranda, Livegraph, Dirsync Pro, Moneydance, Rachota Timetracker, Data Mapping Engine. de LLC Books (2010) (Redactor) [15]

[13] A Study of Tools, Techniques, and Trends for Big Data Analytics. By R.Shireesha et al. (2016) International Journal of Advance Computing Technique and Applications (IJACTA), ISSN : 2321-4546, Vol 4, Issue 1 [16]

[14] Comparison of Various Tools for Data Mining. By P.Kaur etc. IJERT ISSN: 2278-0181 Vol. 3 Issue 10 (2010) [[17]]

[15] Advanced Web and Network Technologies, and Applications. By Heng Tao Shen et al. Springer Science & Business Media - 2006-01-09

[16] SCaVis 2.1. Review by Pete Daniel (2014) [18]

[17] Comparative Analysis of Information Extraction Techniques for Data Mining, by Amit Verma et al. Indian Journal of Science and Technology, Vol 9, March 2016 [19]

[18] Evaluation and comparison of open source software suites for data mining and knowledge discovery. A.H. Altalhi et al. Wiley Online Library (2017) [20]

[19] Brief Review of Educational Applications Using Data Mining and Machine Learning, [21], by A. Berenice Urbina Nájera, Jorgede la Calleja Mora, Redie ISSN 1607-4041. Revista Electrónica de Investigación Educativa, 19(4), 84-96

[20] Analysis of Data Using Data Mining tool Orange. Maqsud S.Kukasvadiya et. al. [22] (2017) IJEDR, Volume 5, Issue 2, ISSN: 2321-9939

[21] Big Data - A Survey of Big Data Technologies. By P.Dhavalchandra, M.Jignasu, R.Amit. International Journal of Science and Technology. Volume 2, p45-50 (2016) [23]

[22] Data Sciences Workstation: SCaVis. By Lisa Federer. National Institutes of Health Library [24]

[23] The DataForge, Sector for Mathematical Support of Institute for Nuclear Research of Russian academy of Sciences [25]

[24] Miri Infotech. A Complete IT Solution Provider. DataMelt deployment

[25] Numeric Computation and Statistical Data Analysis on the Java Platform (Book). S.V.Chekanov, Springer, (2016) ISBN 978-3-319-28531-3, 700 pages, [26]

[26] Springer Book Performance Report [27]

[27] Springer download Statistics of the book "Numeric Computation and Statistical Data Analysis on the Java Platform" 2016 [28]

[28] Springer download Statistics of the book "Scientific Data Analysis using Jython Scripting and Java" [29]

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Tool objective(s) in the field of Learning Sciences
☑ Analysis & Visualisation of data ☑ Predicting student performance ☑ Student modelling ☑ Social Network Analysis (SNA) ☑ Constructing courseware	☑ Providing feedback for supporting instructors: ☑ Recommendations for students ☑ Grouping students: ☑ Developing concept maps: ☑ Planning/scheduling/monitoring ☑ Experimentation/observation

Tool objective(s) in the field of Learning Sciences
☑ Analysis & Visualisation of data ☑ Predicting student performance ☑ Student modelling ☑ Social Network Analysis (SNA) ☑ Constructing courseware	☑ Providing feedback for supporting instructors: ☑ Recommendations for students ☑ Grouping students: ☑ Developing concept maps: ☑ Planning/scheduling/monitoring ☑ Experimentation/observation

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