Master in Informatics and Computing Engineering
Information Description, Storage and Retrieval
Instance: 2020/2021
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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. ¶

1. Project datasets, tasks and tools ¶
   • Datasets ¶
   • Formats ¶
   • Conceptual models for the data ¶
   • Dataset storage  ¶

• Scott Ambler, The Object Primer, Chapter 8: Conceptual Domain Modeling, Cambridge University Press, 3rd Edition, 2004 (Section 8.4) ¶
• OpenRefine, OpenRefine User Guide, last accessed September 2020 ¶
• Project Rules, September 2020 ¶

• Data Collection and Preparation ¶

• OpenRefine, OpenRefine Getting Started, last accessed September 2020 ¶
• The Apache Software Foundation, Apache Tika - a content analysis toolkit, last accessed September 2020 ¶
• The R Foundation, The R Project for Statistical Computing, last accessed September 2020 ¶

• 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 ¶

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

— MCR, JCL, SSN ¶

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