Enhance a Website: Automatically bringing scientists together through time series data
mín £5000 GBP
Pagado a la entrega
Automatically bringing scientists together through time series data
With Imperial College Mathematics
The following work is to enhance an existing website [login to view URL] with a drag-and-drop server-side data analysis capability.
This builds on work presented in these open access papers:
[login to view URL]
[login to view URL]
The basic idea is that each time series a user uploads is converted into a vector of features. The time series are compared by comparing these feature vectors - this requires a nearest neighbour search of a large set of feature vectors. Our core objective is to make it extremely easy for users to take single time series and (through drag and drop) have their data analysed centrally by us and then be provided with a clear, fast, and robust visualization that compares it to other data.
We are interested in competitive quotes for the following bundle of elements:
-- A drag-and-drop upload interface: users will be able to drag their time series files onto their browser (for at least the top four browsers) and have it automatically uploaded to our servers and analysed. We would be able to parse .csv, .xls or .txt and some .dat files.
-- Server side evaluation and scalable back-end implementation: Up to 200 pieces of fast C code will be run, in parallel on each time series - generating a 200-dimensional feature vector (we will provide example code for testing + see below). We would like to draw on Imperial's range of central computing resources, while considering cloud computing, to support our efforts so we can readily scale if faced with more user load.
-- Nearest neighbour search: after calculating the features of each time series our site can already compare the time series to our existing data, returning lists of similar real and simulated data. We require a fast nearest neighbour search as we currently have ~20k feature vectors and this might increase by an order of magnitude. We can suggest algorithms for this.
--On-site data analysis for single series: This part is the core project deliverable and must be realized to a high standard. Developers will use network visualization plug-ins to show a network of ~20 nearest neighbours as well as a list. We require a ``hover" function so that more metadata about each neighbour can be accessed without having to click-through. We want users to be able to filter this data by tags associated with the data. We also want to compare the uploaded data to pre-computed distributions of values for each feature and comment on whether a feature is notable e.g. "Your signal has a comparatively high autocorrelation".
Further extensions on which we would be interested in quotes for the different elements:
-- We would like to provide a capability for bulk upload of 1000s of series but we do not need to do this via the drag-and-drop interface.
-- Fast coding MATLAB files into C: We have a set of 50 pieces of MATLAB time series analysis code we would like to have rewritten in C. Each piece of MATLAB code is ~200 lines and many rely on Matlab toolboxes. All code files are already licenced as GNU. We will hold the licence for this new code and will likely pick an open access licence.
-- File format compatibility: We want to be able accept (and convert) audio types (.mp3, .wav). We will require truncation and resampling of very long time series based on a quick auto-correlation calculation. We can provide code for subsampling and autocorrelation.
Nº del proyecto: #7967933
Sobre el proyecto
41 freelancers están ofertando un promedio de £6544 por este trabajo
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