DataMelt: Difference between revisions

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|field_name=Computation and Visualization Environment
|field_name=Computation and Visualization Environment
|field_developers=DataMelt community. Led by S.Chekanov
|field_developers=DataMelt community. Led by S.Chekanov
|field_website=http://jwork.org/dmelt/
|field_website=https://datamelt.org/
|field_data_tool_type=Application software
|field_data_tool_type=Application software
|field_license_type=Free&Open source
|field_license_type=Free&Open source
|field_free_software_licence=GPL / GNU for non commercial use. Commercial-friendly license is available
|field_free_software_licence=GPL / GNU for non commercial use. Commercial-friendly license is available
|field_last_release=2018/05/10
|field_last_release=2019/02/02
|field_last_version=2.2
|field_last_version=2.4
|field_description='''DataMelt is a software environment for numeric calculations, statistics and data analysis '''
|field_description='''DataMelt is a software environment for numeric calculations, statistics and data analysis '''


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DMelt creates high-quality vector-graphics images (SVG, EPS, PDF etc.) that can be included in LaTeX and other text-processing systems.
DMelt creates high-quality vector-graphics images (SVG, EPS, PDF etc.) that can be included in LaTeX and other text-processing systems.
The program runs on Windows, Linux, Mac OS. The package for the Android is called AWork.
The program runs on Windows, Linux, Mac OS.  
 
 
|field_analysis_orientation=General analysis
|field_analysis_orientation=General analysis
|field_data_manipulation_type=Data transformation, Data analysis, Data visualisation
|field_data_manipulation_type=Data transformation, Data analysis, Data visualisation
Line 33: Line 35:
|field_last_edition=2018/05/23
|field_last_edition=2018/05/23
}}
}}
==History==
==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]]<ref>
DataMelt has its roots in particle physics where data mining is a primary task. It was created as jHepWork project in 2005 <ref>HEP data analysis using jHepWork and Java, arXiv:0809.0840v2, ANL-HEP-CP-08-53 preprint. CERN preprint, [https://arxiv.org/abs/0809.0840/ arXiv:0809.0840v2]
HEP data analysis using jHepWork and Java, arXiv:0809.0840v2, ANL-HEP-CP-08-53 preprint. CERN preprint, [https://arxiv.org/abs/0809.0840/ arXiv:0809.0840v2]
</ref> 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 SCaVis. In 2015 it became a community-supported project
</ref> using the [[Java (programming language)|Java]] software concept for [[International linear collider|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.
and was renamed to DataMelt.
The main source of the reference is the book "Scientific Data analysis using Jython Scripting and Java"
 
DataMelt is hosted by the jWork.ORG portal<ref>jWork.ORG Community Portal focused on Java scientific software. [http://jwork.org/main/].</ref>.
 
==Documentation==
 
In 2018, DataMelt web page hosted 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. The DataMelt documentation includes  certain restrictions  for general public due to the proprietorial nature of the documentation project.
 
 
jHepWork was described in the book "Scientific Data analysis using Jython Scripting and Java"
<ref>
<ref>
Scientific Data analysis using Jython Scripting and Java. Book. By S.V.Chekanov, Springer-Verlag,  ISBN 978-1-84996-286-5, [https://www.springer.com/us/book/9781849962865]
Scientific Data analysis using Jython Scripting and Java. Book. By S.V.Chekanov, Springer-Verlag,  ISBN 978-1-84996-286-5, [https://www.springer.com/us/book/9781849962865]</ref>. Later it was also discussed in the  German Java SPEKTRUM journal  
</ref>
which discusses  data-analysis methods using [[Java (programming language)|Java]] and [[Jython]] scripting.  
Later it was also discussed in the  German Java SPEKTRUM journal  
<ref>
<ref>
DataMelt – Werkbank für technisch-wissenschaftliche Berechnungen und Visualisierungen mit Java und Jython. by Rohe Klaus.  Java SPEKTRUM. (in German) volume 5 (2013) 26-28 [https://www.sigs-datacom.de/fachzeitschriften/javaspektrum/archiv/artikelansicht/artikel-titel/integrationsspektrum-scavis-werkbank-fuer-technisch-wissenschaftliche-berechnungen-und-visualis.html]
DataMelt – Werkbank für technisch-wissenschaftliche Berechnungen und Visualisierungen mit Java und Jython. by Rohe Klaus.  Java SPEKTRUM. (in German) volume 5 (2013) 26-28 [https://www.sigs-datacom.de/fachzeitschriften/javaspektrum/archiv/artikelansicht/artikel-titel/integrationsspektrum-scavis-werkbank-fuer-technisch-wissenschaftliche-berechnungen-und-visualis.html]
</ref>.
</ref>.
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]] ('''S'''cientific '''C'''omputation and '''Vis'''ualization Environment). This project existed for 3 years before it was renamed to DataMelt (or, in short, DMelt).
More recently, the DataMelt program was described in the book <ref>Numeric Computation and Statistical Data Analysis on the Java Platform (Book). By  S.V.Chekanov, Springer, (2016)  ISBN 978-3-319-28531-3, 700 pages, [https://www.springer.com/gp/book/9783319285290]</ref>. According to the Springer International, this book was top 25% most downloadable books in 2016 and 2017 in the category "Advanced Information and Knowledge Processing".
 
Comparisons of DataMelt with other similar packages for statistical and numeric analysis are given in these resources
<ref>Comparative Analysis of Information Extraction Techniques for Data Mining, by Amit Verma et al. Indian Journal of Science and Technology, Vol 9, March 2016 [http://www.indjst.org/index.php/indjst/article/download/80464/67992]
</ref>
<ref>Evaluation and comparison of open source software suites for data mining and knowledge discovery. A.H. Altalhi et al.  Wiley Online Library (2017) [https://onlinelibrary.wiley.com/doi/full/10.1002/widm.1204]
</ref>
<ref>Brief Review of Educational Applications Using Data Mining  and Machine Learning, [https://redie.uabc.mx/redie/article/viewFile/1305/1586], by A. Berenice Urbina Nájera, Jorgede la Calleja Mora, Redie ISSN 1607-4041. Revista Electrónica de Investigación Educativa, 19(4), 84-96
</ref>
<ref>Analysis of Data Using Data Mining tool Orange. Maqsud S.Kukasvadiya et. al. [https://www.ijedr.org/papers/IJEDR1702288.pdf] (2017) IJEDR,  Volume 5, Issue 2, ISSN: 2321-9939
</ref>
<ref>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) [http://www.ijsrt.us/vol2issue1/pdf%209.pdf]
</ref>
.

Latest revision as of 22:27, 15 March 2020

Dm logo125px.png


Computation and Visualization Environment 2.4 (2019/02/02)

Datamelt 2dhisto.jpeg

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

Tool.png

The last edition of this page was on: 2018/05/23

The Completion level of this page is : Medium


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. The program runs on Windows, Linux, Mac OS.


TOOL CHARACTERISTICS

Usability

Authors of this page consider that this tool is rather easy to use.

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 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 can perform:

  • Data extraction of type:
  • Transformation of type: Mathematical transformation of data for analysis
  • Data analysis of type: Basic statistics and data summarization, Data mining methods and algorithms
  • Data visualisation of type: Sequential Graphic, Chart/Diagram, Map (These visualisations can be interactive and updated in "real time")



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 2.4 2019/02/02
(blank line)

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

Datamelt 2dhisto.jpeg

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. The program runs on Windows, Linux, Mac OS.

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 rather easy to use.
Data import format Data export format
. .
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

Can perform data extraction of type:


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


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


Can perform data visualisation of type:
Sequential Graphic, Chart/Diagram, Map (These visualisations can be interactive and updated in "real time")


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

Datamelt 2dhisto.jpeg
Datamelt 2dhisto.jpeg
Dm logo125px.png
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
2019/02/02
2.4
https://datamelt.org/
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. The program runs on Windows, Linux, Mac OS.

General analysis
Students/Learners/Consumers, Teachers/Tutors/Managers, Developers/Designers, Researchers, Organisations/Institutions/Firms
Advanced
Basic
None
Medium
Application software
Data transformation, Data analysis, Data visualisation
Basic statistics and data summarization, Data mining methods and algorithms
Mathematical transformation of data for analysis
Sequential Graphic, Chart/Diagram, Map
rather easy to use
Medium


History

DataMelt has its roots in particle physics where data mining is a primary task. It was created as jHepWork project in 2005 [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 SCaVis. In 2015 it became a community-supported project and was renamed to DataMelt.

DataMelt is hosted by the jWork.ORG portal[2].

Documentation

In 2018, DataMelt web page hosted 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. The DataMelt documentation includes certain restrictions for general public due to the proprietorial nature of the documentation project.


jHepWork was described in the book "Scientific Data analysis using Jython Scripting and Java" [3]. Later it was also discussed in the German Java SPEKTRUM journal [4]. More recently, the DataMelt program was described in the book [5]. According to the Springer International, this book was top 25% most downloadable books in 2016 and 2017 in the category "Advanced Information and Knowledge Processing".

Comparisons of DataMelt with other similar packages for statistical and numeric analysis are given in these resources [6] [7] [8] [9] [10] .

  1. ↑ HEP data analysis using jHepWork and Java, arXiv:0809.0840v2, ANL-HEP-CP-08-53 preprint. CERN preprint, arXiv:0809.0840v2
  2. ↑ jWork.ORG Community Portal focused on Java scientific software. [1].
  3. ↑ Scientific Data analysis using Jython Scripting and Java. Book. By S.V.Chekanov, Springer-Verlag, ISBN 978-1-84996-286-5, [2]
  4. ↑ DataMelt – Werkbank für technisch-wissenschaftliche Berechnungen und Visualisierungen mit Java und Jython. by Rohe Klaus. Java SPEKTRUM. (in German) volume 5 (2013) 26-28 [3]
  5. ↑ Numeric Computation and Statistical Data Analysis on the Java Platform (Book). By S.V.Chekanov, Springer, (2016) ISBN 978-3-319-28531-3, 700 pages, [4]
  6. ↑ Comparative Analysis of Information Extraction Techniques for Data Mining, by Amit Verma et al. Indian Journal of Science and Technology, Vol 9, March 2016 [5]
  7. ↑ Evaluation and comparison of open source software suites for data mining and knowledge discovery. A.H. Altalhi et al. Wiley Online Library (2017) [6]
  8. ↑ Brief Review of Educational Applications Using Data Mining and Machine Learning, [7], by A. Berenice Urbina Nájera, Jorgede la Calleja Mora, Redie ISSN 1607-4041. Revista Electrónica de Investigación Educativa, 19(4), 84-96
  9. ↑ Analysis of Data Using Data Mining tool Orange. Maqsud S.Kukasvadiya et. al. [8] (2017) IJEDR, Volume 5, Issue 2, ISSN: 2321-9939
  10. ↑ 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) [9]
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