1. Relational databases
Introduction

2. Introduction
• Most common used data format next to
spreadsheets.
• Spreadsheets relatively easily
• Research projects mostly claim data to be
stored in relational database.
• Understanding a relational structure opens
the access to many data

3. Relational databases - Data mining
• Exploration of data
• Prerequisite: data should be available in a
minable format - database
• Database = electronic document storing data
– Non-relational: 1 bulk system with non-related
items (eg. Msexcel files, text-documents, non-
related-tables)
– Relational: all items (tables) are linked to each
other (see further)

4. Relational databases
Why using a database
• Relational database:
– All your data is stored in 1 file
• Easy to retrieve data
• Easy to backup
– Data and metadata stored together
• Data ...
• Metadata: data about the data (documentation)
– Many data-files contain undocumented values:
– Species A has an abundance of 17 ( meaning of value 17?)

5. Relational databases
Why using a database
• All data in a good relational designed database
is only stored once:
– Example: species list  typing errors
• Nudora thorakista
• Nudora thorrakista
• Nudora thorakhista
• Nudora thorakisa
– 1 species  species richness calculation: 4
– Solution: 1 table with each species 1 record and
use it as a reference

6. Why using a database
• Data is much more rigid ...
– More difficult to make errors
– E.g. Sorting in excell

7. Relational databases
Principle - Exercise
• A practical example to understand ...
– Make a list of 15 people you know
– Make a list of all genders
– Make a list of characters and indicate for each
character whether nice or not
– Make a list of countries
• Start coupling all your lists
• You made a relational database

8. Relational database - biology
Species
person
Places
Sample
Country
Density
Equipment

9. Species
person
Places
Sample
Country
Density
Equipment
Which person was present on samplings in sweden?

10. Species
person
Places
Sample
Country
Density
Equipment
Which species sampled with a core occur in densities higher than 40

11. Variable
Var_value
Taxonomy
Photo
Literature
...
...
...
...

12. Relational databases
Principles
• Think before you start ...
– Structure of a database is the key to a good
dataset
– Structure has to translate the whole concept
• One look at the structure (relational scheme) should
explain the database

13. Relational databases - components
• Tables
– Basic structures containing the data
– Structure of table important
– ID
• Relations
– Definition of how different tables are connected
and form a sense-full unit
• Queries
– Extractions of data from database

14. Table designs ...
• A table consists of a series of Columns ...
• Each record as such:
– Different fields
– Design of table must be done
before data is entered
– Each field: name, data type
– Each field can also by formatted  layout
Record
ColumnField

15. Table designs ...
• Field types:
– Numeric – integer/double
– Text
– Date/Time
– Memo
– Autonumber  ID
– Yes/No

16. Excercise on field types:
• 12
• 15 jan 1988
• hallo
• 12,456
• 12:56
• Azdazdazd azdda zda azdd dad zd dadazdzd
azdazddazdd azdazd azdazd dzdzdzzd ada zzd
azdaz dda azd da az d z azdzadazd a zd a azd
azd z dd da a z a z zd d ddaa zd
• 09:89

17. Special field in a table: key
• A key = a unique identifier for a record
– Example: pasport number:
• Number in a database which is unique and relates to all data
about you
– Each record in a table gets also a key
– This key is used to link tables to each other
– Example:
• Nudora sp1 – id: 123776
• Nudora sp2 – id: 34688
– Advantage: species name changes: linked taxa remain
linked

18. Linking tables through id’s
• Storing numbers is most effecient way to store
data:
• Nudora sp1 is found in the north sea with a
density of 32
• Species 123776 is found in station 2 (North
sea) with a density of 32
• Record in table density becomes:
123776 | 2 | 32

19. Setting up relations between tables
• Relations: links between tables
• Connecting tables through certain fields in a
rigid way to each other
• Advantage: database becomes a strong unity
• Types of relations:
– 1 to many
– Many to many ( = 2 times 1 to many)

20. Examples of relations
• Table places: field country (numeric)
• Table countries – list of countries,
each country has unique id
• Relation is made between:
– Field country in places
– Field id in country
• One to many relation: 1 record in table
country linked to multiple records in places
• No deleting of countries possible
Places
Country

21. Examples of relations
• Many to many
• Id of sample
• Id of species
• Table density: unique combination of sample,
species ...
Species
Sample
Density

23. Queries
• All data in database:
– Next step: get it out again
– Selections on 1 table: by using filters
– Selections on multiple tables: using queries
– Queries can be saved and reused
– Queries can be the basis for new queries

24. Sorting on tables
• Sorting

25. Filtering on tables

26. Making a simple selection Query
• Create ... Query in design view
• Switching between views:

27. Making a simple selection Query
• Select the tables and/or queries needed

28. Making a simple selection Query
• Select the fields needed for output/selection/sorting

29. Making a simple selection Query
• Select the fields needed for output/selection/sorting

30. Making a simple selection Query
• Select the fields needed for output/selection/sorting

31. Making a simple selection Query
• Select the fields needed for output/selection/sorting

32. Making a simple selection Query
• Set the criteria

33. Making a simple selection Query
• Select the values to out put and add sorting
options

34. Output the results
• Go to datasheet view

35. Making a simple selection Query
• Special options ...

36. Exporting data
• From msaccess it is possible to export to
different formats!
• Tables, queries, ...
• Exports can be used to do further data mining:
– Through MSExcell  making graphs
– To do statistical analysis

37. Exporting data

39. Step by step demonstration
• Open a database
• Different items in database
• Open tables, sorting, filtering
• Table design
• Relationships
• Queries

40. Query operators
= equals
> Larger than
< Smaller than
>= larger than or equals
Between ... And ...
Is null
Like ...
Not like ...

41. Query operators

42. Query operators
and both true
or at least 1 true
< Smaller than
>= larger than or equals
Between ... And ...
Is null
Like ...
Not like ... >"q*" and <"u*" VOORNAAM René, Robbie, Stefan, Stijn, Tim, Tristam
="r*" or "s*" VOORNAAM Robbie, Stefan, Stijn

43. Intermezzo ... Design a dataset
• Research project:
– You work with 3 persons on it
– You will sample 4 times on 3 locations
– You will measure 5 environmental characteristics
– You will identify all species
– You will count them
– Extra: you will measure each specimen
– Task: design on paper how your dataset will look
like