Information Systems for Engineers - Fall 2018

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This course provides the basics of information systems from the perspective of the user. The main focus is on relational databases, including tabular data, the relational algebra, the SQL query language, schema design, normal forms, physical architecture, indices. The course also covers support for data cubes on top of relational databases. 


Latest information

  • The first lecture is on Thursday, September 20th. The first exercise session is on September 27th.
  • The first graded exercise will be given on Thursday, October 25th. Students should upload their solutions to this polybox folder.
  • The second graded exercise is online on Moodle. The deadline is on 16.11.2018 14:00.
  • The third graded exercise is online on Moodle. The deadline is on 29.11.2018 14:00.



After visiting this course, students should be capable to:

  • Explain, in the big picture, how a relational database works and what it can do in their own words.
  • Explain the relational data model (tables, rows, attributes, primary keys, foreign keys), formally and informally, including the relational algebra operators (select, project, rename, all kinds of joins, division, cartesian product, union, intersection, etc).
  • Perform non-trivial reading SQL queries on existing relational databases, as well as insert new data, update and delete existing data.
  • Design a new relational schema to store data in accordance to the real world's constraints, such as relationship cardinality.
  • Adapt and improve an existing schema to make it more robust against anomalies, thanks to a very good theoretical knowledge of normal forms.
  • Understand how indices work (hash indices, B-trees), how they are implemented, and how to use them to make queries faster.
  • Access an existing relational database from a host language such as Java, using bridges such as JDBC.
  • Explain data independence.
  • Explain how a relational database is physically implemented.
  • Know and deal with the natural syntax for relational data, CSV.
  • Explain the data cube model including slicing and dicing.
  • Store data cubes in a relational database.
  • Map cube queries to SQL.
  • Slice and dice cubes in a UI.



Using a relational database

  • Introduction (historical overview, data independence, data shapes)
  • The relational model (data models, tables, relational algebra, CSV syntax)
  • The query language SQL (DML 1 of 2, SQL shell, counterpart of selection, projection, grouping, ordering, renaming)
  • Schema definitions (DDL, data types, SQL)
  • Updates (DML 2 of 2, insertion, deletion, updates, SQL)

Taking a relational database to the next level

  • Best practices and normal forms (update/insert/delete anomalies, first, second, third, BC, fourth)
  • Physical architecture of a relational database (storage levels, tuple storage)
  • Indices and optimization (ISAM, B-tree, B+-tree, hash)
  • Communicating with a SQL database from a host language (Java, JDBC)

Analytics on top of a relational database

  • Analytics, data warehousing, OLAP vs. OLTP, the data cube model (dimensions, algebra)
  • Storing and querying data cubes in a relational database (star schema, snowflake schema)
  • Data cube optimization (indices, bitmaps)


  • Limits of tables and cubes (decision points for when and when not to use them vs. trees, graphs)
  • Limits of scaling up a single machine and transition to Big Data (introduction to data denormalization on simple cases, "hacking" a relational database onto several machines and issues)



  • Lecture material (slides)
  • Book: "Database Systems: The Complete Book", H. Garcia-Molina, J.D. Ullman, J. Widom (It is not required to buy the book, as the library has it)


Prerequisites / Notice

  • For non-CS/DS students only, BSc and MSc
  • Elementary knowledge of set theory and logics
  • Knowledge as well as basic experience with a programming language such as Pascal, C, C++, Java, Haskell, Python