Polimi · 2025/26

Technologies for Information Systems

From multidimensional cubes and star schemas to data integration, data quality and semi-structured sources — the full Politecnico di Milano Technologies for Information Systems course, rebuilt as an interactive, exam-focused study guide.

10 chapters~6 h reading 33 past-exam questions

Practice past exams 33 questions from 2016–2022 papers — filter by chapter, year and difficulty, with instant grading and worked solutions.

After this course you can

  • Run the five-step multidatabase design method from source schemas to a global schema
  • Choose between GAV and LAV, write the mappings, and unfold a global query onto its sources
  • Resolve entities across sources with edit and Jaccard similarity, blocking, and fusion functions
  • Reverse-engineer XML and JSON sources into schemas and integrate them with relational ones
  • Design a data warehouse end to end, from attribute tree to fact schema to star or snowflake
  • Query a cube with roll-up, drill-down, slice and dice, and the CUBE and ROLLUP SQL patterns

Syllabus

  1. Prerequisites: Data Modelling, the Relational Model & SQL

    The database background TIS assumes on day one and never re-teaches — ER and UML conceptual modelling, the relational model and its constraints, the ER to relational translation, functional dependencies and normal forms, relational algebra, and the SQL needed to query a star schema — each tied to the exact chapter and Part II exercise that will demand it.

     55 minutes reading.

  2. The Data Integration Problem & Design Method

    Why heterogeneous sources cannot just be glued together, the two ways to integrate them (materialize vs leave-in-place), and the five-step method that turns a pile of independent schemas into one queryable global schema — the spine of every Part II integration exercise.

     high exam weight. 40 minutes reading. 7 past-exam questions.

  3. GAV & LAV: Mapping Global and Local Schemas

    The two ways to relate a global schema to its sources — define the global schema as views over the sources (GAV, answered by unfolding) or the sources as views over the global schema (LAV, answered by rewriting) — plus soundness and the GAV view operators every integration exercise leans on.

     high exam weight. 40 minutes reading. 1 past-exam question.

  4. Record Linkage, Similarity & Data Fusion

    Once schemas are reconciled, the same real-world entity still appears under different strings in different sources. This chapter covers how to measure string similarity (edit distance, Jaccard, Soundex), how to decide that two records match (rule-based, learned, probabilistic — with blocking for scale), and how to fuse conflicting values afterwards.

     25 minutes reading.

  5. Semistructured Integration, Mediators & Ontologies

    When sources speak different data models — XML, JSON/NoSQL, raw web pages — integration needs wrappers to expose them as relations and mediators to hold the global schema. This chapter walks the reverse-engineer → conflict → GAV exercise on a semistructured source, the wrapper machinery behind it, and the ontologies/RDF layer that adds machine-readable semantics.

     medium exam weight. 40 minutes reading. 4 past-exam questions.

  6. Data Quality: Dimensions, Cleaning & Profiling

    Garbage in, garbage out — the success of any data-driven decision rests on the quality of its data. This chapter defines data quality as fitness for use, names the four objective dimensions (accuracy, completeness, consistency, timeliness), and walks the cleaning pipeline: profiling, standardization, imputation, outlier and duplicate detection.

     medium exam weight. 30 minutes reading. 1 past-exam question.

  7. Data Warehouses & OLAP

    The second pillar of the course. What a data warehouse is (subject-oriented, integrated, time-variant, non-volatile), how OLAP differs from OLTP, the multidimensional cube and its hierarchies, the OLAP operations (roll-up, drill-down, slice-and-dice, pivot), and the WITH CUBE / WITH ROLLUP SQL that every Part II warehouse query is built from.

     medium exam weight. 35 minutes reading. 1 past-exam question.

  8. Conceptual DW Design: the Dimensional Fact Model

    The conceptual half of every Part II warehouse exercise. The Dimensional Fact Model — facts, dimensions, measures, hierarchies and their special cases; how measures aggregate (flow, level, unitary); and the top-down method that turns an operational ER schema into a fact schema via the attribute tree, pruning and grafting, and the glossary.

     high exam weight. 45 minutes reading. 11 past-exam questions.

  9. Logical DW Design: Star, Snowflake & OLAP SQL

    The logical and SQL half of every Part II warehouse exercise. Turning a fact schema into a ROLAP star (or snowflake) schema with surrogate keys and bridge tables, the four SQL patterns graded in every exercise (CUBE/ROLLUP, greatest-per-group, weighted re-aggregation of averages, bridge-table queries), and choosing which materialized views to precompute on the multidimensional lattice.

     high exam weight. 45 minutes reading. 1 past-exam question.

  10. Modern Data Landscape: Governance, Lakes & Ethics

    The course's closing survey of where data management is heading: DAMA data governance and metadata, data virtualization (the logical, Denodo-style approach), data lakes / lakehouse / data mesh and FAIR, and the frontier problems — uncertainty, provenance, crowdsourcing, and the ethics of data-driven decisions.

     30 minutes reading.