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Student modelling Analysis & Visualisation of data Providing feedback for supporting instructors Predicting student performance Recommendations for students Grouping students Social Network Analysis (SNA) Developing concept maps Constructing courseware Planning/scheduling/monitoring Experimentation/observation
Once registered form, specified format(s) will appear in the selection list above and will become available for every users Add format(s):
Web crawler Screen scraping Flat file database/Logfile extractor Structured database extractor
Simple data format conversion Simple data transformation operations Advanced data transformation operations Mathematical transformation of data for analysis
Data mining methods and algorithms Basic statistics and data summarization
Map Sequential Graphic Slide Show Chart/Diagram Tag Cloud Model Drawing Video/Animation Photograph Poster Game
Visualisation is interactive Visualisation is updated in "real time"
Others Students/Learners/Consumers Teachers/Tutors/Managers Developers/Designers Researchers Organisations/Institutions/Firms
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]
</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
and was renamed to DataMelt.
DataMelt is hosted by the jWork.ORG portal<ref>jWork.ORG Community Portal focused on Java scientific software. [http://jwork.org/main/].</ref>.
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"
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
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]
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>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>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>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>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]
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