Dia 1

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Digital Media Technology
Week 9 XSLT 4
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Aims of todays Hackathon

• Improve your proficiency in XML and XSLT
• Computer-based research on the basis of secondary
  data (data produced by others)
• Possibilities and limitations of XSLT the
  language is not Turing-complete

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Charles Babbages Analytic Machine
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Artificial Intelligence

• Alan Turing, Computing Machinery and
  Intelligence
• Turing test
• The Universal Machine
• Computer can perform any activity which can be
  caught in an algorithm (state of being
  Turing-complete)

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Algorithms

• Term derived from Muhammad Al-Khwarizmi, who
  wrote Al-Kitab al-mukhtasar fi hisab al-jabr
  wa'l-muqabala (The book on Calculations using
  the Numbers of the Indians)
• Unambiguous description of the steps that need to
  followed to arrive at a specific result
• Also see John Searles Chinese Room Argument

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• Ray Kurzweil, The Age of Spiritual Machines

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• Assignment 2 is online
• Do ask for help if you have difficulties!
• Homework assignment

ltxslvalue-of select"count(body/letter)"/gtltxsl
value-of select"count(body/ letteryear lt
'1900')"/gt ltxslfor-each select"body/letter
recipient'Sijthoff, Albertus Willem "gt
lt/xslfor-eachgt
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Todays Seminar

• Exports from the Short Title Catalogue of the
  Netherlands
• Books printed in the Netherlands or in Dutch
  before 1801.
• Available as Linked Open Data
• Ca. 80.000 records
• Data encoded according to the MODS standard
  (Metadata Object Description Language)

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MODS record
ltmodsgt ltname type"personal"gtltnamePartgtMolière
(1622-1673)lt/namePartgt lt/namegt lttitleInfogtlttitlegtL
e Sicilien, ou L'amour peintre, comedie. / By
J.B.P. Molierelt/titlegtlt/titleInfogt ltlanguagegtltlang
uageTermgtfrelt/languageTermgtlt/languagegt ltoriginInfo
gt ltdateIssuedgt1674lt/dateIssuedgtltpublishergtElzevie
r, Daniellt/publishergt lt/originInfogt ltphysicalDescr
iptiongtltextentgtduodecimolt/extentgtlt/physicalDescrip
tiongt ltsubjectgtlttopicgtDramalt/topicgtlttopicgtPoetrylt/
topicgtlttopicgtFrench language and
literaturelt/topicgtlt/subjectgt lt/modsgt
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Big data research

• Number of publications in 16c vs number of
  publications in 17c
• Comparison of producti-vity of publishers
• Number of authors
• Historical developmentsin subjects
• Historical developmentsin formats

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XSLT and data processing

• Strengths
• Creation of lists
• Filtering a list
• Counting the number of items in a list
• Weaknesses
• Find the unique values in a list
• Count the number of items for all these unique
  values

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Todays Seminar

• Focus on specific publishers analyses of the
  number of publications, languages, subjects,
  formats
• XSLT enables you to make lists the analyses of
  these data will need to be done using other
  tools, e.g. Excel
• Visualisations can be made using SVG

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Organisation

• 1315 1500 Exploration and anaysis
• 1500 1530 Conclusions and preparation of
  presentation
• 1545 1700 Presentation and discussion of
  research results

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Teams

• 7 teams
• Useful to assign different roles
• A programmer
• A spokesperson in charge of making the
  presentation
• A project manager

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(No Transcript)
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Digital Humanities

• Focus on the various ways in which the computer
  can be used to investigate traditional questions
  in the humanities
• things that could not be done feasibly
  without the computational power and storage
  modern computers provide (Philip Burns, Day of
  DH 2013)

• GIS

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• 3D reconstructions

• GIS

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• Social network analysis

• GIS

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(No Transcript)
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Definition

• It brings computational techniques to bear on
  traditional humanistic questions
• It studies the phenomenon of computation from a
  humanities perspective, and aims to understand
  the epistemological and the methodological
  implications of using computers in humanities
  research

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Father Busas Index Thomasticus
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Differences with traditional research

• The work is of a practical nature
• It leads to different (non-textual) forms of
  output
• Difficult to make such results count in
  assessment of scholarly productivity
• Collaborative form of research
• New questions?

• GIS

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Big data

• Three Vs of big data Volume, Velocity, Variety
  (Laney)
• Issues of velocity and volume are not very
  pressing within the humanities
• Complexity Deluge