Data transformations.

Data Shapes and Data Transformations

Nowadays, information management systems deal with data originating from different sources including relational databases, NoSQL data stores, and Web data formats, varying not only in terms of data formats, but also in the underlying data model. Integrating data from heterogeneous data sources is a time-consuming and error-prone engineering task; part of this process requires that the data has ...

Transformations of Data

Robust Data Transformations

Background. Massively parallel data processing systems are ubiquitous in today’s big data era. Examples include Hadoop, Spark, Stratosphere, and a number of tools developed on top of them. Users of these systems upload their datasets to a distributed le system and run their analysis in a distributed fashion. However, several analyses require a variety of data preparation steps in order to perf...

Data Representations and Transformations

To permit visualization, analysis, and reporting, data must be transformed from their original raw state into a form, or representation, that is suitable for manipulation. These data representations and transformations are the foundation on which visual analytics is built. Data representations are structured forms suitable for computer-based transformations. These structures must exist in the o...

Data and Metadata Transformations

The advantages of collecting structured metadata are presented and various ways of storing metainformation are discussed. An example of a statistical metadata model is given using the Unified Modelling Language (UML) and properties and semantics of this model are further examined. The simultaneous manipulation of both data and metadata is discussed by introducing a set of transformations, inclu...