Normalisation. Royal Museum for Central Africa 5 June Edward Vanden Berghe
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1 Normalisation Royal Museum for Central Africa 5 June 2013 Edward Vanden Berghe
2 What is Normalisation? Theoretical: satisfying the requirements of the different Normal Forms, as spelled out by (mainly) E.F. Codd Practical: make sure data is in your database once and only once Repeated data go to separate table Relationships between the tables are part of the model of the database
3 Earlier example Species # legs # eyes place Country date Asterias rubens 5 0 Oostende Belgium 12/3/2004 Asterias rubens 5 0 Zeebrugge Belgium 13/3/2005 Asterias rubens 5 0 Zeebrugge Belgium 14/3/2005 Cancer pagurus 10 2 De Panne Belgium 12/3/2004 Cancer pagurus 10 2 Oostende Belgium 12/3/2004 Cancer pagurus 10 2 Zeebrugge Belgium 14/3/2004 Asterias rubens 5 0 Wimereux France 13/3/2005 Asterias rubens 5 0 Wimereux France 14/3/2005 Cancer pagurus 10 2 Wimereux France 12/3/2004
4 Why normalise Save space on disk by avoiding repetition But huge disk space makes this less important Zipping would replace repeated strings by a code Avoid modification anomalies Make model intuitive and informative Make database unbiased with respect to patterns of querying
5 Modification anomalies Update anomalies Potential source of conflicting data Insertion anomalies Some relevant data can t be stored Deletion anomalies Some relevant data are lost while deleting other data
6 Update anomalies If data is present more than once, it s possible to create conflicting information by updating one version of he data and not the other Species # legs # eyes place Country date Asterias rubens 6 0 Oostende Belgium 12/3/2004 Asterias rubens 5 0 Zeebrugge Belgium 13/3/2005 Asterias rubens 5 1 Zeebrugge France 14/3/2005
7 Insertion anomalies If two concepts are mixed in one table, we can t store information on new items of one type, unless we have at the same time information on the other Species # legs # eyes place Country date Asterias rubens 5 0 Oostende Belgium 12/3/2004 Asterias rubens 5 0 Zeebrugge Belgium 13/3/2005 Asterias arenata 5 0 <null> <null> <null>
8 Deletion anomalies If two concepts are mixed in one table, we loose information on a concept if the last instance of the other concept is deleted Species # legs # eyes place Country date Asterias rubens 5 0 Oostende Belgium 12/3/2004 Asterias rubens 5 0 Zeebrugge Belgium 13/3/2005 Asterias arenata 5 0 Zeebrugge Belgium 13/3/2005
9 Making model more intuitive A good model should reflect the reality it tries to mirror, including the relationships between the entities. Separate entities in real life (can be abstract) should be modelled separately Species # legs # eyes place Country date Asterias rubens 5 0 Oostende Belgium 12/3/2004 Asterias rubens 5 0 Zeebrugge Belgium 13/3/2005 Shared biological biogeographical
10 and robust Entries in a database should be atomic Should not be a combination of several smaller entities such as Oostende, Belgium Contain no qualifiers (such as Asterias cfr rubens; Asterias?rubens ) Not be dependent on the value of another field Not contain repeated values (e.g. several authors for a multi-author publication)
11 Avoid bias Asterias rubens Oostende, Belgium, 12/3 Zeebrugge, Belgium, 13/3 Wimereux, France, 13/3 Asterias arenata Den Osse, Netherlands, 17/3 Cancer pagurus Oostende, Belgium, 12/3 De Panne, Belgium, 12/3 Den Osse, Netherlands, 14/5 Abra alba Oostende, Belgium, 14/5 A nested list is easier to query on the grouping factor of the list. It is easy to find in which countries Asterias rubens occurs; to find out which species occur in say France, we must read our complete database
12 The formal process The key, The whole key, And nothing but the key So help me (E.F.) Codd
13 N1NF (non-1 Normal Form) Asterias rubens Oostende, Belgium, 12/3 Zeebrugge, Belgium, 13/3 Wimereux, France, 13/3 Asterias arenata Den Osse, Netherlands, 17/3 Cancer pagurus Oostende, Belgium, 12/3 De Panne, Belgium, 12/3 Den Osse, Netherlands, 14/5 Abra alba Oostende, Belgium, 14/5
14 N1NF Structure of the table : drs (species, legs, eyes, place1, country1, date1, place2, country2, date2, place3, country3, date3) Entries are not atomic, difficult to query What if we have a fourth distribution record??
15 1NF Species # legs # eyes place Country date Asterias rubens 5 0 Oostende Belgium 12/3/2004 Asterias rubens 5 0 Zeebrugge Belgium 13/3/2005 Asterias rubens 5 0 Zeebrugge Belgium 14/3/2005 Cancer pagurus 10 2 De Panne Belgium 12/3/2004 Cancer pagurus 10 2 Oostende Belgium 12/3/2004 Cancer pagurus 10 2 Zeebrugge Belgium 14/3/2004 Asterias rubens 5 0 Wimereux France 13/3/2005 Asterias rubens 5 0 Wimereux France 14/3/2005 Cancer pagurus 10 2 Wimereux France 12/3/2004
16 1NF: the key A distribution record (a line in our table) is unique when taking into account species, place and date drs (species, place, date, legs, eyes, country) Table names are usually plural, field (column) names singular. In this type of analysis keys are underlined
17 2NF: the whole key Moving repeating groups to separate entities, and looking for a key for that entity: remove entities that are dependent only on part of the compound key Distribution records (species, place, date) Species (species, legs, eyes) Places (place, country)
18 2NF: foreign keys The one original table was split in three Distribution records (drs), species, places Table drs and species share a field, species, that allow us to find related records Field species is foreign key in table drs Same with drs and places Species and places can be populated from reference tables (CoL; Gazetteer)
19 3NF: nothing but the key Moving attributes that are functionally dependent on non-key attribute Possible structure (in this case same as 2NF) Distribution records (species, place, date) Places (place, country) Species (species, legs, eyes)
20 Elaborating further: IDs Key of drs is compound, composed of three fields better to replace with a synthetic key (id autonumber or sequence ) Keys of places and species are names with real meaning; anything with meaning in real life can change, so also better to replace with artificial key
21 Elaborating further: traits Our database now has information on number of legs and number of eyes. What if we want to start storing colour? Requires rewrite of the database Alternative: split out data on biological traits in table with property/value pairs Species (id, species, author, parent_id ) Traits (species_id, trait, value)
22 Model
23 Remarks Sometimes it is better not to normalise completely Surname & first name as 1 attribute instead of 2 Calculated fields to speed up queries Sometimes it is better to denormalise completely Exchange formats such as Darwin Core
24 Final remarks Normalisation is a means, not a goal Intelligent denormalising is as much an art as normalising!
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