Data Integration (Sommersemester 2024)
Dozent:
Prof. Dr. Felix Naumann
(Information Systems)
,
Sebastian Schmidl
(Information Systems)
Website zum Kurs:
https://hpi.de/naumann/teaching/current-courses/ss-2024/data-integration.html
Allgemeine Information
- Semesterwochenstunden: 4
- ECTS: 6
- Benotet:
Ja
- Einschreibefrist: 01.04.2024-30.04.2024
- Prüfungszeitpunkt §9 (4) BAMA-O: 29.07.2024
- Lehrform: Vorlesung / Übung
- Belegungsart: Wahlpflichtmodul
- Lehrsprache: Englisch
Studiengänge, Modulgruppen & Module
- IT-Systems Engineering
- IT-Systems Engineering
- OSIS: Operating Systems & Information Systems Technology
- HPI-OSIS-K Konzepte und Methoden
- OSIS: Operating Systems & Information Systems Technology
- HPI-OSIS-T Techniken und Werkzeuge
- CODS: Complex Data Systems
- HPI-CODS-K Konzepte und Methoden
- CODS: Complex Data Systems
- HPI-CODS-T Techniken und Werkzeuge
- CODS: Complex Data Systems
- HPI-CODS-S Spezialisierung
- DASY: Data Systems
- HPI-DASY-K Konzepte und Methoden
- DASY: Data Systems
- HPI-DASY-T Techniken und Werkzeuge
- DASY: Data Systems
- HPI-DASY-S Spezialisierung
- HPI-SSE-S Systems Foundations
- DSYS: Data-Driven Systems
- HPI-DSYS-C Concepts and Methods
- DSYS: Data-Driven Systems
- HPI-DSYS-T Technologies and Tools
- DSYS: Data-Driven Systems
- HPI-DSYS-S Specialization
Beschreibung
Data integration is the merging of heterogeneous information from various data sources to a homogenous, clean dataset. Despite research and development over the past 40 years, collecting and integrating data from multiple sources remains an important and challenging task in any data-oriented or data science project. This lecture covers the basic technologies, such as distributed database architectures, techniques for virtual and materialized integration, data profiling, and data cleansing technologies. It thus combines the previous foundational lectures on information integration and data profiling to lay a foundation for handling unknown data.
Voraussetzungen
- Database knowledge (e.g. DBS I)
Lern- und Lehrformen
Lecture and exercises
Leistungserfassung
Lecture grading is based 100% on the written exam (approx. 3h) after the end of the teaching period. Requirements for exam admission are:
- "Passing" all four exercises
- At least one short presentation of an exercise solution
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