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Orange
Orange enables open source data visualisation and data analysis for novice and expert. It provides a large toolbox to create interactive workflows to analyse and visualise data. Orange is packed with different visualisations, from scatter plots, bar charts, trees, to dendrograms, networks and heat maps
Type of content:
Assets
Type of asset:
Tool
Big data potential
Yes
Policy domains:
Innovation, Science & Technology
Phase in the policy cycle:
Policy Design and Analysis
TRL
8
Open license availability
Yes
Website
Tags:
Smart work
Real time information
Addresses:
| SWOT Analysis for Orange |
||
|---|---|---|
| Helpful | Harmful | |
Internal |
Strengths• Interactive Data Visualization • -component-based data mining and machine learning software suite, • Visual programming front-end for explorative data analysis and visualization, • Python bindings and libraries for scripting. • Includes a set of components for data pre-processing, feature scoring and filtering, modelling, model evaluation, and exploration techniques. • It is implemented in C++ and Python. |
Weaknesses• To achieve full functionality from Orange, additional add-ons, known as widgets, have to be obtained and added to the program. • In order to have API functionality, additional libraries and routines must be downloaded and added to the software • Little built-in support for other database systems. • Users must be able to understand and work with SQL documents and statements in order to import database files. Any database files in other formats are much more difficult to import into the system, if some of them can be imported at all • The visualization support within Orange is somewhat limited. While visualization is certainly available, and users are able to visualize data, processes, and results, the visualization is not as appealing to the eye or easy to work with as other data mining packages. |
External |
Opportunities• Open platforms, their very committed users and their advanced ecosystems will bring about the most interesting breakthroughs in data-driven innovation. • Increase of the number of large global organizations and institutions that actively consider and adopt open platforms for their data science teams |
Threats• Competition • Intellectual property and patents issues are complicated. • Licenses are complex – there is over 60 different licenses that comply with the open source definition • Migration of data -Retraining personnel |
