Data modeling using ER1 Entity relationship diagrams

Data modeling using ER2 Why analyze data? Insight in data - and structures of data. –Create a structure in “unstructured” data Prior to designing a database A part of the systems development process.

Data modeling using ER3 ER modeling ER modeling is a widely used technique for data analysis. Many notations (syntax) for ER diagrams –Different book  different notations –Diagrams can be drawn using Visio or a similar tool.

Data modeling using ER4 Entities with attributes Characteristics of an entity –An important ”thing” in the world we model. –Entities have attributes (data) –Similar to a class in OOA, but has no methods / functions. Notation (my version) –Entity: Rectangle –Attribute: Oval

Data modeling using ER5 Types of attributes Simple or composite attributes –simple (atomic) e.g. age Notation: Oval –composite address = street + number + ZIP code + town Notation: Oval connected to several ”sub” ovals.

Data modeling using ER6 Types of attributes II Single valued or multi-valued –Single valued e.g. CPR –Multi-valued e.g. phone number One person may have several phones Notation: Double oval –An attribute may be composite and multi- valued!

Data modeling using ER7 Types of attributes III Derived: Calculated from other attributes e.g. sum or average –Notation: Oval with a dashed border An attribute with no value has value NULL –Values does not exist. –Value unknown.

Data modeling using ER8 Entities An entity type is like a class –Naming convention: Substantive, singular Every “instance” of the entity must be unique –Identified by a key, one or more attributes Notation for keys: Underline the name of the attribute(s)

Data modeling using ER9 Relationships Relationships between entities. Notation: Rhomb Similar to association in OOA. Relationships may have attributes –Notation: Oval

Data modeling using ER10 Relationships II Binary relationship –Relationship between 2 entities (normal) Relationships, higher degree –Relationship between more entities (rare) Recursive relationships –Relationship of 2 entities belonging to the same entity type e.g. an employee with a relation to another to a boss (another employee)

Data modeling using ER11 Cardinality 1-1very rare –1 entity of type A has a relation to at most 1 entity of type B and vice versa 1 husband ~ 1 wife 1-many, 1-Nvery common –1 entity of type A may have relation to many entities of type B, not vice versa. 1 department ~ many employees

Data modeling using ER12 Cardinality II Many - many, N-Mcommon –1 relation of type A may have relations to many entities of type B and vice versa. 1 employee ~ many projects 1 project ~ many employees

Data modeling using ER13 Cardinality notation Step into the entity type and look at the other entity through the relation. –How many entities of the other type do you see? –Write the number on far end of the relation.

Data modeling using ER14 (min, max) cardinality Often you’re only concerned with max. cardinalities. Sometimes you want both (min, max) cardinality. –Note: Cardinalities placed in the other end of the relation!!!

Data modeling using ER15 History History: More generations of relations –Cardinality goes up 1 man ~ many wives [not simultaneously] –Put a timestamp / period on the relation (attribute on the relation) Relation marriage: start_date, end_date –end_date may be NULL :-)

Data modeling using ER16 Total participation Ordinary relation –An entity may be related to another entity. Total participation –En entity must be related to another entity. –Min. cardinality 1 (not 0) –Notation: Double line on the relation

Data modeling using ER17 Diagramming tools Drawing –Microsoft Visio [now a part of MS Office] Diagramming to generate tables –You draw ER, the tool generates tables Reverse engineering –You have the tables, the tools generates ER. Don’t use tools early in the process - use pen and paper!!!

Data modeling using ER18 Best practice Procedure (best practice) 1.find entities and [easy] attributes 2.relationships 3.cardinalities 4.keys 5.total participation