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.https://www.polistudy.me/enhttps://www.polistudy.me/tis/prerequisites/https://www.polistudy.me/tis/prerequisites/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.entity-relationship modelentityrelationshipcardinality constraintidentifierweak entitygeneralizationUML class diagrammultiplicityassociation classrelational modelrelation schemarelation instancecandidate keyprimary keyforeign keyreferential integrityNULLintensional levelextensional levelER to relational translationbridge tablefunctional dependencyclosurenormalizationthird normal formBoyce-Codd normal formdenormalizationrelational algebraselectionprojectionjoinouter joingroupingSQLGROUP BYHAVINGviewhttps://www.polistudy.me/tis/data-integration/https://www.polistudy.me/tis/data-integration/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.information systemfour V's of big datadata pipelinedata integrationsource autonomyheterogeneitymaterialized vs virtual integrationdata exchangemediatorwrapperglobal schemaviewsmultidatabaseconflict analysispervasive data managementpay-as-you-gomashupshttps://www.polistudy.me/tis/gav-lav-mappings/https://www.polistudy.me/tis/gav-lav-mappings/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.global-as-viewlocal-as-viewGLAVquery unfoldinganswering queries using viewsconjunctive viewssound mappingcomplete mappingexact mappingopen-world assumptionclosed-world assumptionKeyGenquery rewritingGAV view operatorshttps://www.polistudy.me/tis/record-linkage-fusion/https://www.polistudy.me/tis/record-linkage-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.record linkageentity resolutionstring similarityedit distanceLevenshteinJaccardTF/IDFSoundexblockingrule-based matchingprobabilistic matchingdata fusionresolution functionhttps://www.polistudy.me/tis/semistructured-integration/https://www.polistudy.me/tis/semistructured-integration/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.semistructured dataXMLDTDgraph data modelOEMJSONNoSQLreverse engineeringmediatorTSIMMISwrapperautomatic wrapper generationinformation extractionontologyRDFOWLLinked Open DataSPARQLhttps://www.polistudy.me/tis/data-quality/https://www.polistudy.me/tis/data-quality/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.fitness for useGIGOTDQMaccuracycompletenessconsistencytimelinessdata profilingdata cleaningstandardizationimputationoutlier detectionduplicate detectiondata fusionbig data qualityhttps://www.polistudy.me/tis/data-warehouses-olap/https://www.polistudy.me/tis/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.data warehouseOLTP vs OLAPsubject-orientedintegratedtime-variantnon-volatileETLdata martmultidimensional cubedimensionmeasurehierarchyroll-updrill-downslice and dicepivotingMOLAPROLAPWITH CUBEWITH ROLLUPhttps://www.polistudy.me/tis/dfm-conceptual-design/https://www.polistudy.me/tis/dfm-conceptual-design/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.Dimensional Fact Modelfactdimensionmeasuredimensional attributehierarchyprimary eventsecondary eventdescriptive attributeoptional edgecross-dimensional attributeconvergencemultiple edgeadditivityaggregabilityflow measurelevel measureunitary measureattribute treepruninggraftingglossaryfact schemahttps://www.polistudy.me/tis/dw-logical-design/https://www.polistudy.me/tis/dw-logical-design/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.MOLAPROLAPHOLAPstar schemasnowflake schemasurrogate keydenormalizationbridge tablemultiple edgeconstellation schemamaterialized viewmultidimensional latticedistributive operatoralgebraic operatorholistic operatorview selectionWITH CUBEWITH ROLLUPgreatest-per-groupweighted re-aggregationhttps://www.polistudy.me/tis/modern-data-landscape/https://www.polistudy.me/tis/modern-data-landscape/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.DAMADMBoKdata governancegovernance organizational modelsmetadata managementbusiness glossarydata lineagedata virtualizationaugmented concept viewdata lakedata lakehousemedallion architecturedata lake federationdata meshFAIRuncertain datadata provenancecrowdsourcingethicsfairness