Ghislain Fourny. Information Systems for Engineers 1. Introduction
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1 Ghislain Fourny Information Systems for Engineers 1. Introduction
2 Data is like matter Study of the real world Physics Study of the data world Data Science 2
3 Why Data Science? A good decision is based on knowledge, not on numbers. - Plato 3
4 A Short History of Databases 4
5 Speaking/Singing 5
6 Writing Rosetta Stone Hans Hillewaert 6
7 Accounting Plimpton 322 (Public Domain) 7
8 Willi Heidelbach Printing8
9 Computers Ben Franske - DM IBM S360.jpg on en.wiki 9
10 1960s: File Systems Lorem Ipsum Dolor sit amet Consectetur Adipiscing Elit. In Imperdiet Ipsum ante 10
11 11
12 Mr. Databases: Edgar Codd Wikipedia 12
13 1970s: The Relational Era 13
14 1980s: The Object Era 14
15 2000s: The NoSQL Era foo Triple stores bar foobar Key-value stores Column stores Document stores 15
16 In short? We threw data at computers
17 In short? We threw computers at computers
18 In short? We threw computers at data
19 In short? We are throwing data at data. now 19
20 Lecture Scope 20
21 Lecture scope 21
22 Lecture scope AI Machine Learning Databases 22
23 Lecture scope: databases only AI Machine Learning Databases 23
24 Lecture Team Johannes De Fine Licht (TA) Ghislain Fourny Alexandros Ziogas (TA) 24
25 Lecture Overview Relational databases Relational model Using SQL to create and modify tables Relational algebra Using SQL to query tables 25
26 Lecture Overview Relational databases Relational model Using SQL to create and modify tables Relational algebra Using SQL to query tables Advance use of relational databases Database design theory Indices Architecture and Storage 26
27 Lecture Overview Relational databases Relational model Using SQL to create and modify tables Relational algebra Using SQL to query tables Advance use of relational databases Database design theory Indices Architecture and Storage Data cubes Storing cubes in a relational database Querying cubes 27
28 Lecture Overview Relational databases Relational model Using SQL to create and modify tables Relational algebra Using SQL to query tables Advance use of relational databases Database design theory Indices Architecture and Storage Data cubes Storing cubes in a relational database Querying cubes Outlook towards Big Data 28
29 What is expected Attendance of the weekly lecture (2 hours/w Thursday 10-12) Attendance of the exercise session (1 hours/w Fridays 13-15) Passing the written exam in the winter session (90 minutes in January-February) 4 KP 29
30 Data and Information 30
31 Information 31
32 Information Language (spoken, written) 32
33 Information Language (spoken, written) Drawings, diagramms, symbols 33
34 Information Language (spoken, written) Drawings, diagramms, symbols Model of the world 34
35 Data (raw) Facts that get stored 35
36 Data (raw) Facts that get stored and exchanged 36
37 Data and information in perspective Data 37
38 Data and information in perspective Data Information + meaning 38
39 Data and information in perspective Data Information Knowledge + meaning + meaningful application 39
40 Information system Information 40
41 Information system Information A program or set of programs that manages information. 41
42 Data structures 42
43 Data Shapes: Tables 43
44 Data Shapes: Trees 44
45 Data Shapes: Graphs 45
46 Data Shapes: Cubes 46
47 Data Shapes: Text 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. Praesent nec libero metus. Praesent at turpis placerat, congue ipsum eget, scelerisque justo. Ut volutpat, massa ac lacinia cursus, nisl dui volutpat arcu, quis interdum sapien turpis in tellus. Suspendisse potenti. Vestibulum pharetra justo massa, ac venenatis mi condimentum nec. Proin viverra tortor non orci suscipit rutrum. Phasellus sit amet euismod diam. Nullam convallis nunc sit amet diam suscipit dapibus. Integer porta hendrerit nunc. Quisque pharetra congue porta. Suspendisse vestibulum sed mi in euismod. Etiam a purus suscipit, accumsan nibh vel, posuere ipsum. Nulla nec tempor nibh, id venenatis lectus. Duis lobortis id urna eget tincidunt. 47
48 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 48
49 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 49
50 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 50
51 Structured data Company Last Change Bid vol Bid Ask Ask vol On volume ABB LTD N % 13' '981 3'603'794 ACTELION N % '698 ADECCO N % 2' ' '564 CS GROUP N % 20' '242 4'060'717 GEBERIT N % '847 GIVAUDAN N % '604 JULIUS BAER N % 1' '377 LAFARGEHOLCIM N % 7' '448 1'027'247 NESTLE N % 12' '300 2'956'191 NOVARTIS N % 25' '074 1'660'761 RICHEMONT N % 2' ' '687 ROCHE GS % ' '115 SGS N % '032 SWATCH GROUP I % '216 SWISS LIFE HOLDING AG N % 1' '871 SWISS RE N % 4' ' '097 SWISSCOM N % '694 SYNGENTA N % '368 UBS GROUP N % 47' '396 5'932'713 ZURICH INSURANCE N % 1' '893 51
52 Structured data: Schema Company Last Change Bid vol Bid Ask Ask vol On volume Regular and fixed structure defined in advance 52
53 Structured data Relational databases Spreadsheets 53
54 Unstructured data: Text Of all the problems which have been submitted to my friend, Mr. Sherlock Holmes, for solution during the years of our intimacy, there were only two which I was the means of introducing to his notice--that of Mr. Hatherley's thumb, and that of Colonel Warburton's madness. Of these the latter may have afforded a finer field for an acute and original observer, but the other was so strange in its inception and so dramatic in its details that it may be the more worthy of being placed upon record, even if it gave my friend fewer openings for those deductive methods of reasoning by which he achieved such remarkable results. The story has, I believe, been told more than once in the newspapers, but, like all such narratives, its effect is much less striking when set forth en bloc in a single half-column of print than when the facts slowly evolve before your own eyes, and the mystery clears gradually away as each new discovery furnishes a step which leads on to the complete truth. At the time the circumstances made a deep impression upon me, and the lapse of two years has hardly served to weaken the effect. 54
55 Unstructured data: Picture (bitmap) 55
56 Unstructured data: Audio jumbi / 123RF Stock Photo 56
57 Unstructured data: Video (sequence of bitmaps) 57
58 Unstructured data... is in a very raw form 58
59 Unstructured data No explicit structure but implicit features (characters, grammar, pixels, waves, etc) 59
60 Data models 60
61 A model is an abstraction from reality. 61
62 Models Maps Polylerus at the English language Wikipedia Scale models Physics models Economic models 62
63 Models Database schema 63
64 Data models Relational model (SQL) Data cube model (OLAP) Arborescent model (XML, JSON) Object-oriented model (programming) Graph model (RDF,...) 64
65 Data models In this course NoSQL Relational model (SQL) Data cube model (OLAP) Arborescent model (XML, JSON) Object-oriented model (programming) Graph model (RDF,...) 65
66 Information systems 66
67 Where are information systems used? Universities Information retrieval Chains of supermarkets Online newspapers Banks Social networks Flight booking systems Geographic Information Systems Online stores 67
68 Big Data 68
69 Big Data The Three Vs Volume TB ZB Variety Velocity 69
70 Prefixes (International System of Units) kilo (k) Mega (M) Giga (G) Tera (T) Peta (P) Exa (E) Zetta (Z) Yotta (Y) 1,000 (3 zeros) 1,000,000 (6 zeros) 1,000,000,000 (9 zeros) 1,000,000,000,000 (12 zeros) 1,000,000,000,000,000 (15 zeros) 1,000,000,000,000,000,000 (18 zeros) 1,000,000,000,000,000,000,000 (21 zeros) 1,000,000,000,000,000,000,000,000 (24 zeros) You must know this by! 70
71 Prefixes (International System of Units) kibi (ki) 1,024 (2 10 ) Mebi (Mi) 1,048,576 (2 20 ) Gibi (Gi) 1,073,741,824 (2 30 ) Tebi (Ti) 1,099,511,627,776 (2 40 ) Pebi (Pi) 1,125,899,906,842,624 (2 50 ) Exbi (Ei) 1,152,921,504,606,846,976 (2 60 ) Zebi (Zi) 1,180,591,620,717,411,303,424 (2 70 ) Yobi (Yi) 1,208,925,819,614,629,174,706,176 (2 80 ) You must NOT know this by! 71
72 Information system Application manages knows Data 72
73 Application (3-tier architecture) Persistency manages knows Data 73
74 Application (3-tier architecture) Business Logic Persistency manages knows Data 74
75 Application (3-tier architecture) User Interface Business Logic Persistency manages knows Data 75
76 Application (3-tier architecture) Application layers User Interface Business Logic Persistency manages knows Data 76
77 Information system: front-end 77
78 Typical setting (unfortunately) 78
79 Typical setting (unfortunately) Payroll Orders Products Stock 79
80 Typical setting (unfortunately) Redundancy 80
81 Typical setting (unfortunately) Inconsistency 81
82 Typical setting (unfortunately) Hard to combine 82
83 Why do we need a Database Management System? 5. Enforce integrity constraints Automatic checks across files and applications 83
84 Why do we need a Database Management System? 4. Support Multiple Users 84
85 Why do we need a Database Management System? 3. Avoids Data Loss 85
86 Why do we need a Database Management System? 2. Support Access Control dvarg / 123RF Stock Photo 86
87 Why do we need a Database Management System? 1. Save Development Costs 87
88 We can replace the file system... 88
89 ... with a central database Database (An organized collection of related data) 89
90 (hiding the actual file system) Database 90
91 (hiding the actual file system) (A piece of software to store, manage and query databases) Database Management System (DBMS) Database 91
92 (hiding the actual file system) Database Management System (DBMS) Database system (Database + Database Management System) Database 92
93 (hiding the actual file system) Database Management System (DBMS) Database system Database 93
94 Data Independence 94
95 Database System: Layers Logical layer Physical layer 95
96 Layers: logical Tables Relationships Logical layer Physical layer 96
97 Layers: physical Logical layer Physical layer (Or internal layer) Files Indices 97
98 Layers Data Independence Logical layer Physical layer Edgar Codd
99 Data Independence (Edgar Codd) Logical data model Lorem Ipsum Dolor sit amet Physical storage Consectetur Adipiscing Elit. In Imperdiet Ipsum ante 99
100 Data Independence (Edgar Codd) Logical data model Physical storage 100
101 Data Independence (Edgar Codd) Logical data model Physical storage 101
102 Logical Schema Physical Schema Directory layout File layout Indices Physical layer Changes very 102 rarely
103 Logical layer Physical Data Independence Conceptual layer Physical layer 103
104 Logical layer Logical Schema Logical Schema Structures Types Integrity Constraints Conceptual layer Physical layer Changes rarely 104
105 Logical layer Logical Data Independence External layer External layer Conceptual layer Physical layer 105
106 Logical layer View Application Masks Views External layer External layer Changes often Conceptual layer Physical layer 106
107 Logical layer Inside the logical layer External layer External layer Applications Conceptual layer Tables Physical layer Files 107
108 Database users and job positions 108
109 User: Database administrator DBMS Coordination Monitoring Access control Database 109
110 User: Database designer DBMS structure of content Database 110
111 User: Power user Programming (3-tier stack) DBMS Database 111
112 User: Data analyst Queries Updates DBMS Database 112
113 User: Parametric users Masks Menus (parameters) DBMS Database 113
114 Database architecture 114
115 Architecture DBMS Database 115
116 Architecture DBMS Database local hard drive of user 116
117 Architecture DBMS Database local hard drive of user or local hard drive of server 117
118 Architecture DBMS Database local hard drive of user or local hard drive of server or network drive, shared (LAN) 118
119 Architecture DBMS Database local hard drive of user or local hard drive of server or network drive, shared (LAN) or distributed file system (Big Data) 119
120 Data Data Manipulation Language (DML) Data Manager Data (files on disk) 120
121 Metadata (data on data) Data Definition Language (DDL) Includes: Schema Users Access control Metadata Manager Metadata (files on disk) 121
122 Overall architecture DDL + DML Interpreter Metadata Manager Data Manager Transaction Manager Data + metadata (files on disk) 122
123 Schemas (DDL) 123
124 Schemas Schema 124
125 Schemas Schema Structure 125
126 Schemas Schema Structure + Types 126
127 Schemas Schema Structure + Types + Integrity constraints 127
128 Schema (metadata) example Holding Summary Structure Customer ID Security ID Quantity Integer String Integer Integrity constraint > 0 Types 128
129 Instance (data) example Holding Summary Customer ID Security ID Quantity 1 CH CH CH CH CH CH
130 Validity of an instance against a schema Customer ID Security ID Quantity Integer String Integer Customer ID Security ID 1 CH CH CH CH CH CH Quantity Customer ID Security ID 1 CH foo CH CH CH CH CH Quantity 130
131 Lifecycle of an instance Customer ID Security ID Quantity Integer String Integer 131
132 Query languages (DML) 132
133 Languages Here's how to do what I want. Here's what I want. 133
134 Languages Here's how to do what I want. Here's what I want. Imperative languages Declarative languages 134
135 Languages Here's how to do what I want. Here's what I want. C Java C++ Python... Imperative languages Declarative languages 135
136 Languages Here's how to do what I want. Here's what I want. C Java C++ Python... Haskell CAML SQL XQuery... Imperative languages Declarative languages 136
137 Layers Declarative Language SQL,... Logical layer Imperative Language C++, Java,... Physical layer Assembly 137
138 SQL is a declarative language Logical model "What, not how" Physical execution 138
139 SQL is a declarative language Logical model "What, not how" Query Plan 139
140 SQL is a declarative language Logical model "What, not how" Parallelism 140
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