Search Course Outline

The deep diversity of modern-day data from many companies requires data scientists to master many technologies that rely on new principles to represent, describe, access, and analyse data. The course will provide insight into the rich landscape of big data modelling, management, and analysis in distributed and heterogeneous environments. Recommended preparation: COMPSCI 220, 351

The deep diversity of modern-day data from many companies requires data scientists to master many technologies that rely on new principles to represent, describe, access, and analyse data. The course will provide insight into the rich landscape of big data modelling, management, and analysis in distributed and heterogeneous environments. Recommended preparation: COMPSCI 220, 351

The deep diversity of modern-day data from many companies requires data scientists to master many technologies that rely on new principles to represent, describe, access, and analyse data. The course will provide insight into the rich landscape of big data modelling, management, and analysis in distributed and heterogeneous environments. Recommended preparation: COMPSCI 351.

The deep diversity of modern-day data from many companies requires data scientists to master many technologies that rely on new principles to represent, describe, access, and analyse data. The course will provide insight into the rich landscape of big data modelling, management, and analysis in distributed and heterogeneous environments. Recommended preparation: COMPSCI 351.

Big data modelling and management in distributed and heterogeneous environments. Sample topics include: representation languages for data exchange and integration (XML and RDF), languages for describing the semantics of big data (DTDs, XML Schema, RDF Schema, OWL, description logics), query languages for big data (XPath, XQuery, SPARQL), data integration (Mediation via global-as-view and local-as-view), large-scale search (keyword queries, inverted index, PageRank) and distributed computing (Hadoop, MapReduce, Pig), big data and blockchain technology (SPARK, cryptocurrency). Recommended preparation: COMPSCI 351 or equivalent.

Big data modelling and management in distributed and heterogeneous environments. Sample topics include: representation languages for data exchange and integration (XML and RDF), languages for describing the semantics of big data (DTDs, XML Schema, RDF Schema, OWL, description logics), query languages for big data (XPath, XQuery, SPARQL), data integration (Mediation via global-as-view and local-as-vie), large-scale search (keyword queries, inverted index, PageRank) and distributed computing (Hadoop, MapReduce, Pig), big data and blockchain technology (SPARK, cryptocurrency). Recommended preparation: COMPSCI 351 or equivalent.