[{"id":328,"title":"Algorithms learning for large scale facilities","sha":"9a52fa2122dac91321c355073bcc25a3","user_id":null,"state":"published","body":"A large scale facility can be described as an object producing, as an output, datasets `D_i`, that scientists analyse to obtain results `R_i`. The ideal data analysis trajectory for an experiment is thus `D_i--\u003eR-i--\u003eP_i`, where `P_i` denotes the desired output: a publication.\r\n\r\nMost of the times, something goes wrong along the way: there is no time to analyse the data, the analysis is harder than expected... To speed up the process and make it more efficient, I suggest using a new approach, including machine learning methods. Once developed, it could be applied to various industries. \r\n\r\nWith time, a large set of algorithms `A_ij` is developed to analyse the data collected at the considered large scale facility. I propose to profile the algorithms, translating them to a high-level formal language, so to create a platform that \r\n\r\n- If the data collected during an experiment is similar to a previous dataset, suggests in a concise format which steps to follow.\r\n\r\n- If the experiment is new, it searches how similar data have been treated, and uses machine learning techniques to suggest a possible data analysis approach. \r\n\r\nThe system would help to speed up data analysis both for well developed and new techniques.","created_at":"2016-08-04T08:44:54.916Z","updated_at":"2016-08-16T06:00:24.570Z","zenodo_id":145365,"doi":"http://dx.doi.org/10.5281/zenodo.60165","subject":null,"tags":["machinelearning","large scale facilities","scientific programming","data analysis","data-manipulation"],"vote_count":0,"view_count":1,"score":0.1,"deleted":false,"muted":false,"tweeted":true,"attachment_file_name":null,"attachment_content_type":null,"attachment_file_size":null,"attachment_updated_at":null}]