Ghislain Fourny. Information Systems for Engineers Fall Outlook
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1 Ghislain Fourny Information Systems for Engineers Fall Outlook
2 What we learned
3 Database System Database Management System (DBMS) Database system (Database + Database Management System) Database
4 Data Independence (Edgar Codd) Logical data model Lorem Ipsum Dolor sit amet Physical storage Consectetur Adipiscing Elit. In Imperdiet Ipsum ante
5 Relations (the math, for database scientists) A relation R is made of 1. A set of attributes Attributes ) Attributes ) S 2. An extension (set of tuples) Extension ) Extension ) S V such that: t Extension ), support t = Attributes )
6 Summary of standardized SQL types Kind Types Character Binary Number, exact Number, approximate Boolean Date and time Intervals char(p) varchar(p) clob binary(p) varbinary(p) blob numeric(p,s) decimal(p,s) smallint integer(p) bigint float(p) real double precision boolean date time time with time zone timestamp timestamp with time zone interval year to month interval day to second
7 DDL on tables + Create a table * Modify a table - Drop a table
8 Data Manipulation Language Inserting tuples Deleting tuples Updating tuples Querying tuples
9 Primary key / Foreign key taxes ahv varchar(13) fiscal residence char(2) CH AT GB persons ahv varchar(13) name text Albert Einstein Kurt Gödel Alan Turing
10 Relational algebra Union Intersection Substraction Set queries Selection Projection Filter queries Relation renaming Attribute renaming Renaming queries Cartesian product Natural join Theta join Binary queries
11 Joining A B A B A A A B
12 Grouping A B C D
13 Sorting
14 Languages Here's how to do what I want. Here's what I want. C Java C++ Python... Imperative languages Haskell CAML SQL XQuery... Declarative languages
15 SQL persons name middle_initial last_name century captain varchar(30) char(1) text integer boolean James T Kirk 23 TRUE Beverly C Crusher 24 FALSE Jean-Luc NULL Picard 24 TRUE Kathryn NULL Janeway 24 TRUE SELECT century AS c FROM persons GROUP BY century HAVING COUNT(*) > 2
16 Full outer joins persons first_name middle_initial last_name century captain varchar(30) char(1) text integer boolean James T Kirk 23 TRUE Beverly C Crusher 24 FALSE Kathryn NULL Janeway 24 TRUE spaceships warp spaceship_name captain_name code numeric varchar(30) text integer 5 USS Enterprise A Kirk NCC-1701-A 4 USS Enterprise Kirk NCC USS Enterprise D Picard NCC-1701-D USS Voyager Janeway NCC SELECT * FROM persons FULL OUTER JOIN spaceships ON persons.name = spaceships.captain_name first_name middle_initial last_name captain century warp spaceship_name captain_name code varchar(30) char(1) text boolean integer numeric varchar(30) text integer James T Kirk TRUE 23 5 USS Enterprise A Kirk NCC-1701-A James T Kirk TRUE 23 4 USS Enterprise Kirk NCC-1701 NULL NULL NULL NULL NULL 9.2 USS Enterprise D Picard NCC-1701-D Beverly C Crusher 24 FALSE NULL NULL NULL NULL Kathryn NULL Janeway TRUE USS Voyager Janeway NCC-74656
17 Database Design Theory products product price varchar(30) char(1) Phone 800 Laptop 2000 HDTV 1000 USB Stick 10 sales product customer quantity varchar(30) text date Phone John 1 Phone Peter 2 Phone Mary 1 Laptop John 3 Laptop Mary 1 HDTV Mary 2 customers customer text John Peter Mary Bill
18 Integrity Schema sales product price customer quantity varchar(30) numeric text integer All sets of partial functions P (S V) All tables (identical support) T [fundamental integrity] Tables with the desired attributes [attribute integrity] Tables with the desired domains [domain integrity] Tables with further constraints [check/unique integrity] Normal forms
19 Functional dependency sales A1 A2 C B1 B2 text text text boolean integer = = foo bar lorem true 1 foo bar ipsum true 1 = bar foobar false 1 bar foobar true 1 (A1, A2) R (B1, B2)
20 Closure of a set of attributes: example a c a, b c a, b d a, c b a, d e f, g h a, b, c d a, b, c i i j i k c, j l a, b a, b, c, d, e, i, j, k, l { a, b } { a, b } + = { a, b, c, d, e, i, j, k, l }
21 Minimal basis: example a c b d a, b e a, b f a c a, b, c c, d b d a a a, b, c, d a, d, e, f a, b b
22 Boyce-Codd Normal Form A C D a foo true a bar false b foobar true A relation R is in Boyce-Codd Normal Form if for any non-trivial Functional Dependency F R G F is a super-key. (FDs such as AC R A can still hold but are trivial, and irrelevant)
23 Impossibility triangle Boyce-Codd normal form Lossless join Dependency preserving
24 Three-tier stack Internet Web servers Application servers Database servers Database
25 Indices On intuitionistic arithmetic and number theory Drawn from the Phenomena of Capillarity On the Possibility of a New Test of the Relativity Principle A Theory of the Foundations of Thermodynamics Mechanical Intelligence An Example of a New Type of Cosmological Solutions of Einstein's Field Equations of Gravitation Mathematical Logic On the Electrodynamics of Moving Bodies On the General Molecular Theory of Heat On the Theory of Brownian Motion The Theory of Relativity
26 Data models Relational model (SQL) In this course Data cube model (OLAP) NoSQL Arborescent model (XML, JSON) Graph model (RDF,...) Object-oriented model (programming)
27 Slicers and Dicers Dicers Slicers Servers World USD Peter 1,000,000$ 1,500,000$ 1,400,000$ Mary 2,000,000$ 2,300,000$ 2,200,000$
28 Materializing a full (rolled up) cube (SELECT t.year, p.brand, SUM(s.Quantity) FROM Sales s, Time t, Product p WHERE s.date = t.date AND s.product = p.name GROUP BY CUBE (t.year, p.brand) Year Brand Quantity 2017 Apple Samsung Apple Samsung Apple Samsung NULL NULL NULL 9 NULL Apple 20 NULL Samsung 14 NULL NULL 34
29 Database Architecture Cache (CPU), level 1 and 2 Memory (RAM) Disk (Secondary storage) Tapes, DVDs (Tertiary storage)
30 Exam Material Slides Recordings Textbook Theoretical exercises Practical exercises Clicker questions
31 Big Data Lectures Computer Science Data Science CBB MSc with CS background Big Data Other departments Information Systems for Engineers Big Data (for Engineers) Fall 2018 Spring 2019
32 Data Structure Lorem ipsum dolor sit amet, consectetur adipiscing elit. Etiam vel erat nec dui aliquet vulputate sed quis nulla. Donec eget ultricies magna, eu dignissim elit. Nullam sed urna nec nisl rhoncus ullamcorper placerat et enim. Integer varius ornare libero quis consequat. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Aenean eu efficitur orci. Aenean ac posuere tellus. Ut id commodo turpis. Unstructured data Semi-structured data Structured data
33 2000s: The NoSQL Era foo Triple stores bar foobar Key-value stores Column stores Document stores
34 The Three Vs Volume TB ZB Big Data Variety Velocity
35 What is Big Data (my definition)? Big Data is a portfolio of technologies that were designed to store, manage and analyze data that is too large to fit on a single machine while accommodating for the issue of growing discrepancy between capacity, throughput and latency.
36 Lecture scope AI Machine Learning Databases
37 Lecture Overview Data in the large Key-value stores (S3, DynamoDB) Distributed file systems (HDFS) Distributed query processing (MapReduce, Spark) Resource management (YARN) Column stores (HBase) Data in the small Document stores (MongoDB) Syntax (XML, JSON) Data models, Schemas, Querying Data in the very small Graph databases (RDF)
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