DataMelt

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Software

DataMelt (or, in short, DMelt) a computation and visualization environment,[1] is an interactive framework for scientific computation, data analysis and data visualization designed for scientists, engineers and students. DataMelt is multiplatform since it is written in Java, thus it runs on any operating system where the Java virtual machine can be installed.

The program is designed for statistical data analysis, curve fitting, data-mining algorithms, numeric computations and interactive scientific plotting in 2D and 3D. DataMelt uses high-level programming languages, such as Jython, Apache Groovy, JRuby, but Java coding can also be used to call DataMelt numerical and graphical libraries.

DataMelt is an attempt to create a data-analysis environment using open-source packages with a coherent user interface and tools competitive to commercial programs. The idea behind the project is to incorporate open-source mathematical and numerical software packages with GUI-type user interfaces into a coherent program in which the main user interface is based on short-named Java/Python classes. This was required to build an analysis environment using Java scripting concept. A typical example will be shown below.

Scripts and Java code (in case of the Java programming) can be run either in a GUI editor of DataMelt or as batch programs. The graphical libraries of DataMelt can be used to create applets. All charts (or "Canvases") used for data representation can be embedded into web browser.

DataMelt can be used for analysis of large numerical data volumes, data mining, statistical data analysis and mathematics are essential. The program can be used in natural sciences, engineering, modeling and analysis of financial markets. While the program falls into the category of open source software, it is not completely free for commercial usage (see below), no source code is available on the home page, and all documentation and even bug reporting requires "membership".

Overview

DataMelt has several features for data analysis:

  • uses Jython, BeanShell, Groovy, JRuby scripting, or the standard Java. The GNU Octave mode is also available for symbolic calculations;
  • can be integrated with the Web in forms of applets or Java Web-start applications, thus it is suited for distributed analysis environment via the Internet;
  • has a full-featured IDE with syntax highlighting, syntax checker, code completion and analyser. It includes a version of IDE for small-screen devices;
  • includes a help system with a code completion based on the Java reflection technology;
  • uses a platform-neutral I/O based on Google's Protocol Buffers. Data can be written in C++ and analyzed using Java/Jython. It has a browser for serialized objects and objects created using Google Protocol Buffers;
  • includes SQL-based and NoSQL databases
  • includes packages for statistical calculations, error (uncertainty) propagation using a linear expansion or a Monte Carlo approach for arbitrary function, symbolic calculations similar to those found in the GNU Octave project or MATLAB, but rewritten in Java;
  • includes a number of Neural networks, such as JOONE and Encog, which can be accesses via the GUI or using Java scripting

Data-analysis features

The package supports several mathematical, data-analysis and data mining features:

Symbolic and numeric calculations

Symbolic calculations use GNU Octave scripting language. The following methods are available:

Input and output

DataMelt includes the native Java and Python methods for file input and outputs. In addition, it allows to write data in the following formats:

  • The HFile format based on Java serialization. Optionally, compression and XML serialization are supported. Data can be written sequentially or using the key-value maps.
  • The PFile format based on the Protocol Buffers engine for multiplatform input output
  • The HBook format, which is a simplified XML format to write large data structures without XML tags
  • Arbitrary data structure can be written into object databases with file system as back-end. This allows writing large data collections to files which normally do not fit into the computer memory.
  • Several SQL database engines are included as external packages
  • AIDA (computing) file format (read only)
  • ROOT file format (read only)

Data stored in external files can be viewed using browsers for convenient visualization.

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[2] 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 reference is the book "Scientific Data analysis using Jython Scripting and Java"[3] and[1] which discuss in depth data analysis methods using Java and Jython scripting. More recently, DataMelt was discussed in German Java SPEKTRUM journal.[4]

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[5]

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[6] 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.

<syntaxhighlight lang="python"> 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)

  1. 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() </syntaxhighlight>

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.

<syntaxhighlight lang="python"> 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 </syntaxhighlight>

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.

<syntaxhighlight lang="groovy"> 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 </syntaxhighlight>

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

Reviews

Template:Excessive citations

DataMelt and its earlier versions, SCaVis (2013-2015) and JHepWork (2005-2013), which are still available from DataMelt archive repository, were discussed in these resources [7] [8] [9] [10] [11]. The program was compared to other similar packages in these articles [12] [13] [14] [15] [16] [17] [18] .

The DataMelt (2015-), a new development of the JHepWork and SCaVis programs, also is mentioned. Comparisons of DataMelt with other popular packages for statistical and numeric analysis are given in these reviews [19] [20] [21] [22] [23] .

Popularity

It is difficult to judge how many users use DataMelt since download information from the main resource [19] is not available. Sourceforge, which provides an alternative download option, quotes 250 monthly downloads [20] (May 2018).

One estimate can be done by looking at the popularity of the book[24] which, de facto, is a description of 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" [25]. Since the publication of the book, Springer detects 26k chapter downloads until May 2018[26], about 1500 per chapter. The previous book describing jHepWork had a similar popularity [27]. Bookmetrix estimates 140 readers of the DataMelt book.

References

  1. 1.0 1.1 Chekanov, Sergei V. (2016). Numeric Computation and Statistical Data Analysis on the Java Platform. Springer. p. 700. ISBN 978-3-319-28529-0. https://www.springer.com/gp/book/9783319285290. 
  2. "HEP data analysis using jHepWork and Java. arXiv:0809.0840v2, ANL-HEP-CP-08-53 preprint. CERN preprint". https://arxiv.org/abs/0809.0840/. 
  3. Chekanov, Sergei V. (2010). Scientific Data analysis using Jython Scripting and Java. Springer-Verlag. p. 497. ISBN 978-1-84996-286-5. https://www.springer.com/computer/book/978-1-84996-286-5. 
  4. Rohe, Klaus. "DataMelt – Werkbank für technisch-wissenschaftliche Berechnungen und Visualisierungen mit Java und Jython". Java SPEKTRUM Journal (5/2013): 25-28. 
  5. "jWork.ORG". Community Portal focused on Java scientific software. http://jwork.org. 
  6. "Contributed Packages (DataMelt Manual)". http://jwork.org/wiki/DMelt:Dev/Contributions. 
  7. Rohe, Klaus. "DataMelt – Werkbank für technisch-wissenschaftliche Berechnungen und Visualisierungen mit Java und Jython" (in German). Java SPEKTRUM (5/2013): 26-28. [1]
  8. Gestion del Conocimiento (in Spanish), Universidad del Magdalena, A.E.Gonzalez, A.M.Pertuz, S.Pimienta, A,Cana, A.Quiceno, [2] (p 20-25).
  9. Data Analysis and Data Mining Using Java, Jython and jHepWork. Oracle.com Community Article, May 2011. DOC-982931
  10. HEP data analysis using jHepWork and Java. Proceedings of the HERA-LHC workshops (2007-2008), DESY-CERN [3]
  11. Suitability analysis of data mining tools and methods. [4]. S.Kovac, Bachelor's thesis (in English), jHepWork is reviewed on page 39-42, Masaryk University
  12. 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.
  13. jHepWork – full-featured multiplatform data-analysis framework. [5]
  14. Java Applications: Weka, Svnkit, Jhepwork, Fiji, Memoranda, Livegraph, Dirsync Pro, Moneydance, Rachota Timetracker, Data Mapping Engine. de LLC Books (2010) (Redactor) [6]
  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 [7]
  16. Comparison of Various Tools for Data Mining. By P.Kaur etc. IJERT ISSN: 2278-0181 Vol. 3 Issue 10 (2010) [[8]]
  17. SCaVis 2.1. Review by Pete Daniel (2014) [9]
  18. K.Saravanpriya, A Study on Free Open Source Data mining tools. International Journal Of Engineering And Computer Science, ISSN 2319-7242, Volume 3 Issue 12, 2014, Page No. 9450. [10]
  19. Comparative Analysis of Information Extraction Techniques for Data Mining, by Amit Verma et al. Indian Journal of Science and Technology, Vol 9, March 2016 [11]
  20. Evaluation and comparison of open source software suites for data mining and knowledge discovery. A.H. Altalhi et al. Wiley Online Library (2017) [12]
  21. Brief Review of Educational Applications Using Data Mining and Machine Learning, [13], by A. Berenice Urbina Nájera, Jorgede la Calleja Mora, Redie ISSN 1607-4041. Revista Electrónica de Investigación Educativa, 19(4), 84-96
  22. Analysis of Data Using Data Mining tool Orange. Maqsud S.Kukasvadiya et. al. [14] (2017) IJEDR, Volume 5, Issue 2, ISSN: 2321-9939
  23. 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) [15]
  24. Chekanov, Sergei V. (2016). Numeric Computation and Statistical Data Analysis on the Java Platform. Springer. p. 700. ISBN 978-3-319-28529-0. https://www.springer.com/gp/book/9783319285290. 
  25. Springer Book Performance Report [16]
  26. Springer download Statistics of the book "Numeric Computation and Statistical Data Analysis on the Java Platform" 2016 [17]
  27. Springer download Statistics of the book "Scientific Data Analysis using Jython Scripting and Java" [18]