", but we physically have no servers to host the data. Please also have a look at to get a first overview of the common Semantic Web technology stack. SPARQL is a standard data query language for Triple Stores (and thereby quite similar to SQL). That is why, data of a Triple Store can be requested and processed via a data query language (as it is usual on a database).
#Million song dataset hdf5 to csv Offline#
Generally, one can deploy Semantic Web knowledge representations online and offline (locally).
What you may especially have in mind are the Linked Data publishing principles that can be applied on datasets that are modelled with the help of Semantic Web knowledge representation languages and vocabularies. RDF Schema as knowledge representation language on top of RDF Model that introduces further concepts, e.g., class (rdfs:Class) and relations, e.g., sub property relation (rdfs:subPropertyOf). RDF Model as knowledge representation structureĢ. No, RDF is a knowledge representation framework that consists of two knowledge representation languages.ġ. "- RDF is intrinsically linked to a web platform" But I am not convinced that RDF suits the original purpose of the dataset. So, yes, it would be great to see the MSD as an RDF resource. I tried an online learning algorithm directly on the EN API a few months ago, and it took them less than two weeks to send me a friendly warning ) Querying the web for millions of fields / tracks is prohibitive (both in time and server load). ), but I'm not convinced they actually produced that much large-scale research as it is our goal here. Data accessible online has been around for a while (through RDF, APIs. I'm curious how much an RDF version would overlap with the current Echo Nest API, and how much more flexibility it would bring Some lab(s) would have to lend their resources RDF is intrinsically linked to a web platform, but we physically have no servers to host the data. Unfortunately I think we have no experience with it here at LabROSA, but the data is there, we would be pleased to help anyone that wants to transfer / convert the data. Thanks for the suggestion! yes, RDF should definitely be considered. I guess, the Music Ontology Specification Group can help when you plan to create a mapping from the conceptual schema of the Million Song Dataset to a Semantic Web ontology based one. From my point of view, the Million Song Dataset seems to be a perfect Linked Data usecase. These data can be stored in a Triple Store, e.g., Virtuoso, and/or published by following the principles of a Linked Data publishing guideline, e.g. What about RDF* Model and knowledge representation languages that are built on top of it, especially RDFS, OWL and SKOS? Since the Million Song Dataset deals with music metadata, the Music Ontology and its related ontologies, e.g., the Audio Features Ontology, might be a good choice to represent such knowledge. UPDATE: we are apparently part of the HDF5 definition now! I'm back to square one, I don't what else than HDF5 people use / want.! Please let us know what you think! The dataset is already difficult to get as it is! * An SQL database with all the audio features? MySQL? PostgreSQL? It might be faster, but think about the trouble of installing that massive database on local servers. And when I think large-scale, I might be wrong, but MATLAB does not come to mind. But even if the format is relatively open so Python can read it, I have a major issue with using a proprietary format for such a project. In fact, we made a converter to mat-files. * MATLAB files can definitely serve that purpose. But is it efficient, for a million files? It would be simple to understand, you decompress and get text. * XML or JSON, maybe compressed using gzip? It would be trivial to take the output of The Echo Nest API and put it in that format. HDF5 seemed to fit the bill, and as a bonus, there is a great python wrapper for it. * retrieval time should be reasonable, i.e. * we need it to be a minimum compressed, as our data is large * we need to save heterogeneous data (string, numbers, lists that might be empty.
So, one file per song, what format for that file? Requirements are important here: It also gives you an easy way to avoid collisions if you use parallel algorithms. This in itself is questionable, but we liked that every subset (set of files) would be an independent dataset on its own. Some background: we had pretty much settled on a "one file per song" format. It makes me wonder, what else could I have chosen? And now that it's done, what converter should I build? Since the beginning of the MSD project, Brian questioned the choice of HDF5 because it's.
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