DBC & Data Science - Where to go and why?
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Transcript of DBC & Data Science - Where to go and why?
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Bo Weymann
DBC & data science – where to go and why?
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So why do we produce a knowledge system,
recommenders, automagic metadata,…..
Using Math…...
As Christian told you about..........
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A combined vision – BIG META DATA and replication on librarian skillsbased on Machine Learning, Datascienceand librarians
We could for a moment call it Librarian computing
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librarian skills are valuable in many contexts - the problem is that there are so few of them
Datascience as a strategic tool for libraries can compensate this and maybe even bring librarian skills in to situations and in ways that are innovative
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library users want digital solutions and services in the same way as dominating media giant do through solutions with cognitive understanding - but libraries do not need to know and help the user from a commercial aim
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In DBC we produce a lot of metadata - BUTTo create and aggregate metadata in those amounts as library users need only through intellectual processes and librarians m/w – are a NO GO
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So we felt a scence of necessityAcademic networking, courses and experiments in: Machine learning, datascience
Inspiration from commercial
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LIBRARIAN COMPUTING
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Librarian Computingshall be used in
production - in end user interfaces as well as production of
metadata and metadata systems
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Information specialist’s canStructure the knowledge &Navigating the large amounts of it
the librarian canrecommend it best in context &communicate and conveyher commitment
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The information specialist
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SkillsCreate and aggregate metadata - Cognitive machine based on large amounts of data and BIG DATA Create a new taxonomi from a data setCan seek out new relevant data setsCan connect taxonomies…........
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librarian intermediary
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Skillsempathize with the user's needsbe criticaltell why she recommends something to you
Cognitive "search engine” and recommender system based on Machine Learning and datascience, existing web services, user feedback, user behavior, taxonomies, metadata, data sets from social media, etc. gives users the best possible content depending on context…..with transparancy