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DataMelt
DataMelt or DMelt is a software for numeric computation, statistics, analysis of large data volumes ("big data") and scientific visualisation. The program can serve many areas, such as natural sciences, engineering, modelling and analysis of financial markets and (as it is a computational platform) it can be used with different programming languages on different operating systems
Type of content:
Assets
Type of asset:
Application / Tool
Big data potential
Yes
Policy domains:
Innovation, Science & Technology
Phase in the policy cycle:
Policy Design and Analysis
TRL
7
Open license availability
Yes
Website
Addresses:
| SWOT Analysis for DataMelt |
||
|---|---|---|
| Helpful | Harmful | |
Internal |
Strengths• Software for numeric computation, statistics, analysis of big data and scientific visualization. • It is not limited by a single programming language. • Is a computational platform. It can be used with different programming languages on different operating systems. DMelt runs on the Java platform, but can be used with the Python language too. Thus this software combines the word's most-popular enterprise language with the most popular scripting language used in data science. • Python programming can use more than 40,000 Java classes for numeric computation and scientific visualization. In addition, more than 4000 classes come with Java API, plus 500 native Python modules. Not to mention modules of Groovy and Ruby. • Creates high-quality vector-graphics images (SVG, EPS, PDF etc.) that can be included in LaTeX and other text-processing systems. • Runs on Windows, Linux, Mac and Android operating systems. Thus the software represents the ultimate analysis framework which can be used on any hardware, such as desktops, laptops, netbooks, production servers and android tablets. |
Weaknesses• Low ease of use • Poorly designed interface • Documentation for commercial applications comes with the full membership and DMelt activation. • TRL: System prototype demonstration in operational environment |
External |
Opportunities• The program can be used in many areas, such as natural sciences, engineering, modelling and analysis of financial markets • High demand on tools that help extracting insight from big data: The enterprise-generated data is expected to exceed 240 exabytes daily by 2020 |
Threats• Low ease of use • Poorly designed interface • Documentation for commercial applications comes with the full membership and DMelt activation. • TRL: System prototype demonstration in operational environment • Data quality |
