Matching

1
Matching

• Lecture 10

2
Topics

• ID parade Frames
• Matching Examples
• Fuzzy Matching
• Scales of measurement

3
ID Parade Frames

• Classifying volunteers as clean
• Matching suspect to volunteers
• Reservation of parade facility, officers,
  volunteers
• Managing long-running process from decision to
  hold parade to payment of volunteers
• Accounting payment to volunteers and billing of
  police authorites
• Historical record and analysis

4
Merging multiple frames

• Each frame produces its own model of the actors.
• E.g. Models of volunteer
• For matching with suspect
• For classification
• For payment
• For reservation
• For database, problem is called view
  integration

5
Miscellaneous Matching applications

• Many systems have a matching task at their core
• Shazam sound sample matching
• De-duping mailing lists
• CD DB - CD recognition
• COTS selection
• IS development selection
• fingerprint matching
• patient/donor matching for transplant surgery
• blood typing and matching
• patients to clinical trials
• interns to placements in hospitals
• DNA samples
• search request to locate relevant documents
• incoming news items to information subscribers
• number plate recognition in Londons Congestion
  Congestion Charging System
• speech and writing recognition
• patterns to material to minimise wastage

6
Shazam - 2580

• Shazam is a mobile phone application
• It can recognise 1.7 million tracks from a 30 sec
  sample new tracks added at 5,000 a week
• The track details are texted back within about
  30secs
• It costs 50p 9p call charge (surcharge only if
  successful)
• Your personal page shows the tracks you have
  tagged
• www.shazam.com

7
De-duping
A catalogue from OReilly
C Wallace West England University Coldharbour
Lane Frenchay Bristol BS16 1QY
Ms C Wallace Univ. of the West of
England Frenchay Campus Coldharbour
Lane Bristol BS16 1QY
One person or two?
Mailing lists are reported with 25 40
duplicates.
8
CD DB

• Database of 2.5 million CDs, track details and
  supporting matter run by gracenote
  (www.gracenote.com)
• Used by media players to obtain track info
• Player sends signature of CD sequence of track
  lengths in 1/4sec to match against the database
  (via HTTP)
• Application searches DB for best match and
  returns track info to media player.
• Matching algorithm described in US Patent
  6,061,680

9
Commercial Of the Shelf Software (COTS)

• Software exists for most business needs
• payroll
• order processing
• general ledger
• human resources
• e-commerce
• e.g. SAP, SAGE ..
• but analysts need to match business needs to COTS
  capability, and customise generic software for
  local business rules.

10
Method selection

• DSDM Product Design Assistant
• The Product design Assistant (PDA) provides the
  practitioner with an approach to determine which
  mechanisms and techniques are appropriate for
  their project
• Table 1 Mechanism selection
• Table 2 Technique selection

11
Police ID parade

• Currently
• Suspect matched to Volunteers visually by officer
• Information System
• Suspect and Volunteers modelled in database
• System provides list of matching volunteers

12
Matching in general

• Matching task typically involve
• two sets of individuals e.g.
• the suspect / sampled track / DNA sample - The
  Requirement
• the volunteers / 1.7 million stored tracks / DNA
  on file The Resource
• adequate representations of both
• a fitness function which calculates how well
  matched a Resource is to the Requirement
• Matching processes
• Single or Batch?
• Single One Req to many Resources
• Batch Many Reqs to many Resources (e.g. cutting)
• Automatic, Interactive, Assistive
• Automatic Matching fully automated
• Interactive User makes final selection, adjusts
  weights
• Assistive Computer produces analyses which aid
  human selection

13
Single Allocation

• Allocation to a single Requirement
• long list the Resources - eliminate the
  obviously unsuitable
• compute fitness between Requirement and each
  remaining Resource
• rank the Resources in fitness order for a short
  list
• ? user selection from short list on basis of
  additional information unknown to system
• Interactive
• User adjusts
• description of Requirement (e.g the search term
  in Google)
• fitness function (e.g. the weights in the ID
  parade)
• and retries

14
Single attribute Matching

• Fuzzy String Matching
• Levenshtein distance
• Soundex and Metaphone
• Age difference
• Scales of measurement

15
Fuzzy String Matching

• How close are two strings words, DNA sequences?
  
• Levenshtein distance
• is the number of single character edits required
  to change one to the other
• insert a letter
• delete a letter
• replace a letter
• E.g.
• Receipt reciept tecept - distance 2
• Need a theory of why the strings are different
• Better theory for typing would be to count
  transposition as 1 edit instead of 2
• mutations in DNA matching

16
Soundex and Metaphone

• Surnames in English have multiple spellings for
  similar sounds
• Wallace and Wallis, Smith and Smythe
• Errors caused by similar phonetics having
  different spelling
• Useful where sound-text transliteration occurs in
  data capture
• e.g. Smith and Smythe
• Soundex (Odell and Rusell 1922) reduces every
  word to a letter and 3 digits S530 for both
• Metaphone (Philips 1990) smarter about English
  phonetics SM0 for both
• Not perfect
• Kris (K620 and XRS)
• Chris(C620 and KRS)

17
Fuzzy matching

• How close are two ages?
• Is the answer different for the identity parade
  and a dating agency?

Non fitness
0.0
Suspect
age
Volunteer
Ldeal Person
Date
18
Multi-attribute Matching

• How to combine multiple attributes to create a
  single fitness measure?
• Age and Height are different to Build,
  Eye-colour, Gender and Ethnic origin.
• Distance in 2-D space

Sqrt(dx2 dy2)
dy
y
dx
x
19
Multi-attribute matching

• Extract shows a simple Excel spreadsheet
  containing a suspect age, weight and gender, and
  the same attributes for 10 volunteers
• Representation
• Age is measured in years
• Height in cm
• Gender is M or F
• Fitness function
• Calculate difference between suspect and
  volunteer attributes
• Normalise differences to 01
• Multiple by weights to express importance of each
  attribute
• Sum of squared differences as Fitness function
• Best fit volunteer has minimum value for Fitness

20
Scales of Measurement

• Nominal names or categories
• E.g. Eye-colour, Ethnic origin, Telephone number,
  ISBN
• Valid operations , not
• Partly Ordered Scales e.g. grandparent, parent,
  uncle, child, cousin
• Pairs are ordered but no overall ordering
• Ordinal ranks
• E.g. 1,2,3 in Derby, 1st ,2.1, 2.2, 3rd class,
  slight, medium heavy build
• Valid operations lt, , gt
• Invalid operations , - ( gap between 1 and
  2, is not the same as between 2 and 3)
• Non-parametric statistics may apply
• Interval - arbitrary zero value
• E.g. Temperature in degrees F, date in Julian
  Calendar
• Valid Op - (minus)
• Invalid , (but differences are Ratio)
• Ratio
• E.g. Length, age
• Valid Ops , , /, standard statistical
  operations
• Multi-dimensional scales (index numbers)
• E.g. Miles/gallon, IQ

21
Suspect/Volunteer attributes

• Nominal names or codes
• Ordinal ranks
• Interval - no zero value
• Ratio

22
Transforming and Scaling

• To combine different attributes, we need to
  transform Nominal, Ordinal and Interval values to
  Ratio scales
• This cannot be done objectively, so judgement
  involved
• Scaling and weights need to be adjustable to
  fine-tune matching
• gt Learning Frame (later)

23
Sensitivity Analysis

• Arbitrary weights can be adjusted to see what
  effect their variation has on the final selection
• ? How much would each weight have to change
  before the first choice is demoted?
• Excel analysis

24
Tutorial Questions

• Explain the users interaction with Shazam to tag
  a track using a sequence diagram.
• Choose a matching problem with which you are
  familiar (or choose one from the list)
• Identify the requirement and the resources and
  suggest appropriate representations of each
• Identify a suitable fitness function for this
  problem