Course

Orga

• moodle for communication, incl. cancellations
• Präsenzlehre
• Folien online?
• Questions anytime during/after lectures
• Sprechstunde 13:40 - 14:00 (?)
• email
• Answers to Klausur can be in English weil die Folien sind in English

Prerequisites

• modern relationa DB
• SQL?
• /D Mengenlehre

Praktikum/Übung

• Solve tasks with NoQL db - data import, queries
• Computer pool with PostgresSQL and VMs
• Possible to test from home through VPN?

Score

• Points from Übungen?
  • she suggests to visit all of them
• Written (?) may change

Other

• Library - in slide
• Masterthesis possible
  • Current topics in data mgmt for DS (life sci. ontologies, semantic annotation)
  • PP - temporal data
  • DS/scientific workflows
  • “on my website I have topics of theses ich habe betreut”
    • TODO website

Content

• DBs, esp. NoSQL
  • NoSQL
    • categories, properties,
    • NoSQL vs relational DB
  • partitioning, consistency, repliction
  • storage, retrieval
• key-value and document stores
• wide column / record stores
• search in large data sets

Relevance & context

• relevance
  • relevant
• pipeline:
  • steps
    • /D daten-acquise - getting / generating
    • Cleaning
    • Integration
    • Analyse - classical ds/ml
    • Evaluierung
    • Interpretierung
  • cursive - 80%
• choosing
  • tradeoffs: sometimes you need to have it fast more than consistency etc.

Chapter 1. - Recap

Basics

• D/DB System
  • system to persistently store and manage large data sometimes
  • realization of data-intensive apps
• Why?
  • avoid data loss
  • many users
  • long-term storage

Data Model

• D/Data Model is a model that descriiract way how data is represented in an info or DB system
  • a system of concepts and their interrelatiosn
  • the “language” used to describe data
  • syntax and semantic
  • fundamental to other bits like integrity etc.
  • Example:
    • Java: objects of classes that have attributes and references to other obj + methods to access
    • relational db - structures tables of tuples with attrs, foreign keys, constraints etc.
      • tuple here is a row/Zeile in a DB?
      • TODO L/ Zeile, Spalte
    • there are also hierarchical etc.

Information system

• D/Database management system (DBMS) - software system to define, manage ,process and analyze DB data
  • (DB is the data itself)

• DB <-> [database management system (DBMS)]
  

• Example: File system for data management
  • why not files/folders?
    • two files, one with student name, the other with marks etc.
    • redundancy/inconsistency
      • updating / renaming names/Fächer is hard to do in multiple files
    • increased storage reqs
    • no central data storage
      • every application manages their own data independence
      • dep on file structure
      • programmers have to know about the storage and internal data repr.
    • multiple users working on the same data
    • no waranties wrt data protection / privacy
      • access rights
    • /IL “gentrennte Zugriff geben wollen würde”

Transaction

• Interface for transactions 1. begin of transaction (BOT) 2. commit transaction (COMMIT WORK in SQL) 3. rollback trans. (ROLLBACK WORK in SQL)
• D/DML (Data manipulation language)
  • has commands like INSERT, UPDATE, DELETE, etc. in SQL
• possible terminations of a transaction
  • BOT,
  • DML[1], DML[2], ... DML[k],
    • can be multiple, but capsuled as single transaction!
  • Two options:
    • Normal: COMMIT WORK
    • Abnormal:
      • ROLLBACK WORK (zB Integritätsbedingung verletzt)
      • Enforced rollback (Stromausfall)

ACID

• RDBMS ensure ACID properties for transactions
• D/ACID
  • Atomicity
    • “all or nothing” property
    • if one part fails, the entire transaction fails and the DB state is left unchanged
  • Consistency
    • A succesful transaction preserves DB consistency
    • /? Definition of integrity constraints
  • Isolation
    • Concurrent execution of transactions results in a system state as if they were executed serially
    • T. can’t rely on interm. or unfinished state.
    • Won’t it be slow?
      • Sometimes you can sacrifice part of this, esp. for Verteilte systeme

3-level schema architecture

• Slide ??

• TODO picture

• Describes abstraction steps:

  • /? TODO
  • D/Logical data independence: changes to the logical schema must not require a change to an application (external schema) based on the structure
    • If I renamed a Spalte, programs shouldn’t require changes
  • D/Physical data independence: -/- (how data is stored) shouldn’t require changes to the logical schema
    • Programs that use it shouldn’t have to get changes

• Example DB

  • External schema:
    • website or anw. that use/view parts of some tables
  • Conceptual:
    • Table “Booking” has columns like KdNr(int), you can then look up the Kunde in the table “Kunden” etc.
  • Internal
    • Customer[KdNr: int]