Summary notes of the course

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Data Integration Introduction Data Integration: is the problem of combining data coming from different data sources, providing the user with a unified vision of the data, detecting correspondences between similar concepts that come from different sources, and conflicting solving. The aim of Data Integration is to set up a system where it is possible to query different data sources as if they were a unique one (through a global schema). Data integration is needed because of a need for interoperability among SW applications, services and information managed by different organizations: - find information and processing tools, when they are needed, independently of physical location - understand and employ the discovered information and tools, no matter what platform supports them, whether local or remote - evolve a processing environment for commercial use without being constrained to a single vendor’s offerings. Data heterogeneities: - same data model, different systems —> technological heterogeneity - different data models OR semi- or unstructured data (HTML, XML, multimedia..)—> model heterogeneity - same data model, different query languages —> language heterogeneity The four V’s of Big Data: - Volume: each data source contains a huge volume of data, and the number of data sources has grown. - Velocity: data is continuously made available and many of the data sources are very dynamic. - Variety: data sources are extremely heterogeneous both at the schema level, regarding how they structure their data, and at the instance level, regarding how they describe the same real world entity. - Veracity: is the degree to which data is accurate, precise and trusted. STEPS OF DATA INTEGRATION 1) Schema reconciliation (if the sources have a schema): mapping the data structure as in the…