Version 14 (modified by mitar, 4 years ago) (diff)

Google Summer of Code Ideas

Here is a list of ideas for projects we thought up for what would be interesting and useful to do in a course of Google Summer of Code program for Orange. Of course you can propose also some other (your) idea(s). But of course connected with Orange, data mining, machine learning, artificial intelligence in general, bioinformatics and other fields in which we are interested in (or you can get us interested in).

Ideas are listed in no particular order.

Time-series analysis

Orange currently lacks any  time-series analysis tools. It would be great to develop some basic tools for dealing with them: reading, normalizing, basic pattern search, some (auto-)correlation and similar basic techniques, and so on. Research what other similar applications support and propose which features would be useful to have as a basic set of tools.

Useful skills: Python. Data analysis experience. Digital signal processing experience could also help.

Level from 1 (beginner) to 5 (professional): 4

Widgets in separate processes

Widgets in Orange Canvas currently run in a single process. As they are independent given their inputs, they could frequently work in parallel (in a  data-flow manner). The objective of this task would be to modify Orange Canvas so that each widget would run in its own process.

It would be also useful to separate GUI thread from main payload computation of widgets. Currently we are using also just one thread for everything (GUI thread) and we have, while widget is working, to repeatedly callback into the GUI to make it responsive. It would be great to have this separated so that code would be cleaner.

Useful skills: Python programming with multiple processes and threads. Qt and PyQt experience. Program design.

Level from 1 (beginner) to 5 (professional): 5

Anova regression

Implement Anova regression, which would support arbitrary models, similar to the R implementation.

Useful skills: Python. The candidate should be familiar with statistics and computation with matrices (numpy).

Level from 1 (beginner) to 5 (professional): 3

Support for parallel computation for scripting/backend

One other idea discusses the idea of making GUI process in parallel/separate processes. But this idea talks about having scripting part (backend part) of the Orange support (semi)automatic parallelisation/separation into processes and possible also processes over different computers. For example,  cross-validation with multiple folds is one simple example of easy parallelized technique, as each fold can be independently computed and then easily combined into the final result.

It would be good to analyze such opportunities for parallelization, find what they have in common and maybe devise a small helper library (possibly a wrapper for some existing grid computing system, like Xgrid) to use in code to easily make it run in parallel, if such environment is available, and run normally if not. And the of course move as much of already existing implementations to this new support for parallelization.

Useful skills: Python. Grid computing experience.

Level from 1 (beginner) to 5 (professional): 4

Check documentation

We have quite a lot of documentation for different aspects of Orange (widgets, scripting...) and of course a lot of it is still missing/incomplete/could be improved a lot. But some things could be done already on existing documentation, like checking the language itself (we are not English native speakers), proof-reading, checking if all examples (still) really work, find new and/or better examples and so on.

This could be also a good project if you would like to learn more about Orange and data mining and machine learning itself.

Useful skills: Proficiency in English (probably native speaker). Language/writing skills.

Level from 1 (beginner) to 5 (professional): 3

Benchmarking and optimizing Orange

It would be useful to test and benchmark different aspects of Orange and find bottle-necks. Furthermore, also find, propose and implement solutions for them. Orange implements various algorithms and some implementations are better than others. It would be useful to compare our implementations with others and see how they compare and if they should be improved.

Useful skills: Experience with testing and benchmarking software. Experience with common patterns which make programs run slowly.

Level from 1 (beginner) to 5 (professional): 3

Bridge between Orange and R

R contains many great methods/tools which would be also very useful in Orange. To prevent duplication of work (and implementation) it would be great to be able to use those methods/tools directly in Orange (so that it is not necessary to reimplement them in Orange).

The idea is to research possibilities for this and then implement a future-proof bridge between Orange and R.

Useful skills: Python. C/C++. Experience with R. Experience with program-to-program interfaces.

Level from 1 (beginner) to 5 (professional): 4