====== L: 02/10/2020 ======

**Master in Informatics and Computing Engineering\\
Information Description, Storage and Retrieval\\
Instance: 2020/2021**
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====== Lecture #2 :: 02/10/2020 ======

===== Goals =====

By the end of this class, the student should be able to:
  * Characterize the project datasets, identifying some of their properties;
  * Obtain the conceptual model of the domain;
  * Identify suitable tools for the collection and storage of datasets;
  * Identify some retrieval tasks using the datasets.

===== Topics =====

  - Project datasets, tasks and tools
    * Datasets
    * Formats
    * Conceptual models for the data
    * Dataset storage 

===== Bibliography =====

  * Scott Ambler, //The Object Primer, Chapter 8: Conceptual Domain Modeling//, Cambridge University Press, 3rd Edition, 2004 (Section 8.4)
  * OpenRefine, [[http://github.com/OpenRefine/OpenRefine/wiki/Getting-Started|OpenRefine User Guide]], last accessed September 2020
  * [[..:project]], September 2020

===== Materials =====

  * {{ dapi2021-data.pdf |Data Collection and Preparation}}

  * OpenRefine, [[http://github.com/OpenRefine/OpenRefine/wiki/Getting-Started|OpenRefine Getting Started]], last accessed September 2020
  * The Apache Software Foundation, [[http://tika.apache.org/|Apache Tika - a content analysis toolkit]], last accessed September 2020
  * The R Foundation, [[https://www.r-project.org/|The R Project for Statistical Computing]], last accessed September 2020

===== Tasks =====

  * Characterize the datasets
  * Obtain the conceptual model of the domain
  * Try available tools to work with datasets
  * Discuss the storage of datasets
  * Identify retrieval tasks using the datasets

===== Summary =====

  * Characterization of datasets of the project domain.
  * Conceptual domain modelling.
  * Datasets storage.
  * Identifications of retrieval tasks.

 --- //MCR, JCL, SSN//

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