Digital cultural heritage spring 2015 day 2

Seminar at IMT Lucca - Spring 2015
Prof. Stefano Gazziano sgazziano@johncabot.eu
Data, Value, People

Internet is a powerful a channel to spread info, and culture,
which power towards management of cultural heritages is
just being unleashed.
Topics
 Pros and cons of using internet in managing cultural
heritage assets.
 The "death of distance" and motivation to cross real
distances. "Being digital" helps increase real visits.
 Virtual Museums, Virtual reality, Augmented reality:
technologies and content to improve the user experience
of cultural heritage sites
 Internet platforms, on-site installations, mobile devices,
cloud computing platforms.
Stefano A Gazziano
sgazziano@johncabot.edu 2

Internet is a gold mine, users are the nuggets. Let us learn
how we can enrich culture.
Topics
 What is “Big data” and what use it is.
 “Analytics” or who are our internet visitors, what are they
looking for, and do they found it on our internet presence ?
 Data acquisition. Open data standards.
 Digital contact with users. Before and after the visit.
 Museum analytics, assessing user satisfaction. Case study.
Stefano A Gazziano

Internet has rules, netiquette, and we must conform and
be smart. A few “musts” to put cultural heritage on the
net.
Topics
 Search Engine Optimization. Content updates, internet
staff.
 Web reputation management.
 Search engine marketing: crawling, indexing, ranking.
 Analitycs and conversions of a web site.
Stefano A Gazziano

The web is really a wide world, and there is a lot more to
do than just publish a web site.
Topics
 Social networks: engagement techniques and online
tools.
 Going viral. Case study
Stefano A Gazziano

Internet is a gold mine, users are the nuggets. Let us learn
how we can enrich culture.
Topics
 What is “Big data” and what use it is.
 “Analytics” or who are our internet visitors, what are they
looking for, and do they found it on our internet presence ?
 Data acquisition. Open data standards.
 Digital contact with users.
 Museum analytics, assessing user satisfaction. Case study.
Stefano A Gazziano

 As a general reference: Head First Data Analysis - A learner's guide to big numbers,
statistics, and good decisions By Michael Milton Publisher: O'Reilly Media - July 2009
 SAS Institute, International Institute for Analytics. Big Data in Big Companies - May 2013
Authored by:Thomas H. Davenport, Jill Dyché. http://www.sas.com/resources/asset/Big-
Data-in-Big-Companies.pdf
 Web analytics on Wikipedia: http://en.wikipedia.org/wiki/Web_analytics
 Google Analytics Home Page http://www.google.com/analytics/
 Open Web analytics http://www.openwebanalytics.com/
 Open data Wikipedia page http://en.wikipedia.org/wiki/Open_data
 Opencultuurdata http://www.opencultuurdata.nl/english/ at the Rijksmuseum, the
Regionaal Archief Leiden and Visserijmuseum Zoutkamp, The Netherelands.
 The Rijksmuseum API (Application Programming Interface)
https://www.rijksmuseum.nl/en/api
 How the Rijksmuseum opened up its collection - a case study http://pro.europeana.eu/pro-
blog/-/blogs/how-the-rijksmuseum-opened-up-its-collection-a-case-study
 http://www.museumsandtheweb.com/mw2012/papers/sharing_cultural_heritage_the_lin
ked_open_data
 Museum Analytics http://www.museum-analytics.org/
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 Now: a
Video !!
 And a loong one on
visual overviews, just
in case (MIT video,
such stuff!)
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90% of world's data generated over last
two years

 There are few technology phenomena that have taken both the
technical and the mainstream media by storm than “big data.”
 From the analyst communities to the front pages of the most
respected sources of journalism, the world seems to be awash in big
data projects, activities, analyses, and so on.
 However, as with many technology fads, there is some murkiness in its
definition, which lends to confusion, uncertainty, and doubt when
attempting to understand how the methodologies can benefit the
organization. Therefore, it is best to begin with a definition of big data.
The analyst firm Gartner can be credited with the most-frequently
used (and perhaps, somewhat abused) definition:
Big data is high-volume, high-velocity and high-variety information assets
that demand cost-effective, innovative forms of information processing
for enhanced insight and decision making.
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 For the most part, in popularizing the big data concept, the
analyst community and the media have seemed to latch onto
the alliteration that appears at the beginning of the definition,
hyperfocusing on what is referred to as the “3Vs—volume,
velocity, and variety.” Others have built upon that meme to
inject additional Vs such as“value”or “variability,” intended to
capitalize on an apparent improvement to the definition.
 The challenge with Gartner’s definition is twofold. First, the
impact of truncating the definition to concentrate on the Vs
effectively distils out two other critical components of the
message:
1. “cost-effective innovative forms of information processing” (the
means by which the benefit can be achieved);
2. “enhanced insight and decision-making”(the desired outcome)
Stefano A Gazziano

 Big data is fundamentally about applying innovative
and cost-effective techniques for solving existing and
future business problems whose resource
requirements (for data management space,
computation resources, or immediate, inmemory
representation needs) exceed the capabilities of
traditional computing environments as currently
configured within the enterprise.
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Main » TERM » U » unstructured data Related Terms structured data data structuredata dynamic data structure static data structure SQL
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Stefano Gazziano <sgazziano@johncabot.edu> database database software ODBC - Open DataBase Connectivity cloud database By
Vangie Beal The phrase "unstructured data" usually refers to information that doesn't reside in a traditional row-column database. As
you might expect, it's the opposite of structured data -- the data stored in fields in a database. Unstructured data files often include text
and multimedia content. Examples include e-mail messages, word processing documents, videos, photos, audio files, presentations,
webpages and many other kinds of business documents. Note that while these sorts of files may have an internal structure, they are still
considered "unstructured" because the data they contain doesn't fit neatly in a database. Experts estimate that 80 to 90 percent of the
data in any organization is unstructured. And the amount of : with unstructured data. Big data refers to extremely large datasets that are
difficult to analyze with traditional tools. Big data can include both structured and unstructured data, but IDC estimates that 90 percent
of big data is unstructured data. Many of the tools designed to analyze big data can handle unstructured data. Implementing
Unstructured Data Management Organizations use of variety of different software tools to help them organize and manage
unstructured data. These can include the following: Big data tools: Software like Hadoop can process stores of both unstructured and
structured data that are extremely large, very complex and changing rapidly. Business intelligence software: Also known as BI, this is a
broad category of analytics, data mining, dashboards and reporting tools that help companies make sense of their structured and
unstructured data for the purpose of making better business decisions. Data integration tools: These tools combine data from disparate
sources so that they can be viewed or analyzed from a single application. They sometimes include the capability to unify structured and
unstructured data. Document management systems: Also called "enterprise content management systems," a DMS can track, store and
share unstructured data that is saved in the form of document files. Information management solutions: This type of software tracks
structured and unstructured enterprise data throughout its lifecycle. Search and indexing tools: These tools retrieve information from
unstructured data files such as documents, Web pages and photos. Unstructured Data Technology A group called the Organization for
the Advancement of Structured Information Standards (OASIS) has published the Unstructured Information Management Architecture
(UIMA) standard. The UIMA "defines platform-independent data representations and interfaces for software components or services
called analytics, which analyze unstructured information and assign semantics to regions of that unstructured information." Many
industry watchers say that Hadoop has become the de facto industry standard for managing Big Data. This open source project is
managed by the Apache Software Foundation. PREVIOUS unpackNEXT unusual software bug

Stefano A Gazziano

 The current work on e-Infrastructures relevant to digital
cultural heritage, such as DARIAH and CLARIN, and large-
scale aggregators of digital content, like Europeana,
changes the current landscape of digital cultural heritage.
 Better understanding of big data implications on content,
architectures, functionality of large digital collections and
the effects on the users, quality and policy aspects is
needed.
 The digital cultural heritage community forum to discuss
current work and theoretical advancements, and
consolidate state-of-the-art research, provide a forum to
discuss current experiences, and brainstorm future
developments in the area.
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• http://www.cut.ac.cy/euromed2014procee
dings/fullPapers_03_nov.html
• http://www.culturalheritage2014.eu/index.
php/workshops/
• http://www.slideshare.net/lljohnston/wolfr
am-2013-johnston
• http://mymeedia.com/stages/maxiculture/
post/4301413

 Being a new domain, it also requires an in-depth discussion on integrating aspects of
big data in curricula in librarianship, information science, archival science and a range of
Humanities disciplines. Novel research relates to big data in the following domains:
◦ Cultural heritage objects and big data:
◦ aspects of capture, storage, sharing, and analysis
◦ Visualisation of large digital cultural heritage collections
◦ Curation of big cultural heritage collections
◦ Searching big data: Information retrieval and data mining
◦ Natural language processing: statistical NLP in cultural heritage
◦ Semantic web technologies and large scales of cultural data
◦ Web intelligence Cultural cloud
◦ Issues of aggregation of vast resources
◦ Distributed service architectures: SaaS, PaaS, IaaS
◦ Big data economics and digital heritage
◦ Evaluation, usability and use
◦ Visualisation methods and tools
◦ e-Infrastructures and large digital resources
◦ Citizen science: the challenges of scale in engaging citizens
◦ Educational aspects: how to introduce big data aspects in digital humanities and in Library and
Information Science schools?
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 Justify and quantify NH impact to the communities they
serve while knowing relatively little about their visitors.
 Understanding of visitor behavior in museums significantly
lags common practice in the commercial sector to provide
adequate insight into how best to achieve the field’s
mission.
 Simple attendance statistics are not enough.
 Invest little in the detailed understanding of the actions,
experiences, and ongoing participation of visitors once they
enter the building.T
 Tools to know how to achieve long-term relevance.
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Data Acquisition
Digital contact with users
Assessing user satisfaction
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And open data standards, a little bit

Surveys v/s Digital interaction
The danger of garbage in / garbage out
Wrong email (misspelling), Incorrect
statistical sampling and “confounders“
The importance of digital
interaction
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 Actually, we’ll present a brief overview, just what is
necessary to interact then with a data analyst and not
look too dumb
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Our source
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 My problem ? Get more votes than others.
 A tough job that requires quantitative directions. The best
agency (progressive) is probably GQRR Research . I thank IPR
Marketing, who graciously allowed me to disclose this study
for IMT
 Get voters to the polls
 Create consensus on your proposal and candidate
Case study : Italian parliamentary 2013.
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Identify segments
of electorate
Survey voters
Target segments
with proper
message
Focus groups
Evaluate results
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Loyal voters
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Mobile voters
Swing voters
Non voters

Loyal voters
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Mobile voters
Swing voters
Non voters
Now: profile, profile and profile again (8 – 12)

We want to get as
much votes as
possible given the
campaign budget
Where to allocate
how much given
the data analysis
results ?
Constraints:
“profitability” of target by
segments
Total campaign budget
Time to election day
Decision variable:
How many ads to run
per target
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HFDA p 76

«Digital friends»
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Beyond paper: actual observational digital data
 Web site analytics, user experience
 Social networks engagement
 Direct contact by targeted mail
 Digital membership programs
 Online polls
 Newsletters
 Virtual / 3D museums
 Augmented reality
 Marketing & Upselling
 E-commerce
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 Sorry but the technicalia is exactly the same for a
Museum and a Supermarket
 Surveys are not enough, and are expensive
 Social networks and web site presence could offer a
deluge of data
 Day 3 will exactly be on how to produce content
suitable for data collection.
 Day 4 will focus on activity to engage prospects on
social networks
 Today we have a look at how selected CH institutions
assess user satisfaction
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Assessing user satisfaction
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 The ten largest museums in the world: off and online
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 The annual conference of Museums and the Web
◦ April 2-5, 2014 Baltimore, MD, USA
 MW2014: Museums and the Web 2014
 Tourist Satisfaction with Cultural Heritage destinations in India:
with special reference to Kolkata, West Bengal
 TOURIST SATISFACTION WITH CULTURAL / HERITAGE SITES: The
Virginia Historic Triangle
 A Study of Service Quality and Satisfaction for Museums - Taking
the National Museum of Prehistory as an Example
 The Contribution of Technology-Based Heritage Interpretation to
the Visitor Satisfaction in Museums
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Digital cultural heritage spring 2015 day 2

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