Presentation by Gabriele Gattiglia, University of Pisa – MAPPA Lab EAA 2014 session: Open Access and Open Data in Archaeology Istanbul, Turkey 13 September 2013

1. Think big about data.
Archaeology and the Big Data challenge.
Gabriele Gattiglia, University of Pisa – MAPPA Lab

2. Think big about data: Archaeology and the Big Data challenge
is a research Lab of the University of Pisa, in which
archaeologists, mathematicians and geologists deal
with:
• mathematical models for archaeology
(www.mappaproject.org)
• open data (www.mappaproject.org/mod)
and now they are starting to explore risks and
potentiality of a Big Data approach to archaeology.
MAPPA Lab

3. Think big about data: Archaeology and the Big Data challenge
Index In this presentation we will illustrate:
1. What it is generally intended with Big Data
2. Which kind of risks are connected with a Big
Data approach
3. If a Big Data approach can be applied to
archaeology
4. The BAD project

4. Think big about data: Archaeology and the Big Data challenge
• Data are what economists call a “non-rivalrous” good: data
can be processed again and again and their value does not
diminish
• The value of data results from what they reveal in the
aggregate, i.e we can do innovative things by commingling
data in new ways
• If the data are open their value increase: unfortunately,
sometimes archaeological data are kept close in what we
could call “data tombs”.
We are not simply talking about Big Data, but
we are dreaming about Big Open Data
Introduction

5. Think big about data: Archaeology and the Big Data challenge
Usually defined as high volume, high velocity, and/or high
variety data, indeed:
 Big Data permit to learn things that we could not
comprehend using smaller amounts of data, thanks to
software, hardware and algorithms empowerment;
 Big Data are about prediction, i.e. about applying math to
huge quantities of data in order to infer probabilities;
 Big data are about seeing and understanding the relations
within and among pieces of information
 Big Data mean having the full (or close to the full) dataset,
this provides a lot more freedom to explore, to look at the
data from different angles or to look closer at certain
aspect of it.
Big Data paradigm

6. Think big about data: Archaeology and the Big Data challenge
N=all  The concept of sampling no more make much
sense as we can harness a huge amount of
data.
 Using all the available data makes it possibile
to spot connection that are otherwise cloacked
in the vastness of information.
 Big is not intended in an absolute term, but in a
relative way: relative to the comprehensive
dataset.
 This allows to explore more details and to
reach many levels of granularity.

7. Think big about data: Archaeology and the Big Data challenge
Messiness The obsession with exactness is an artifact of the
information-deprived analog era, so we must
accept messiness.
Messiness is created by:
 adding more data;
 combining different sources;
 the inconsistency of formatting;
 the extraction and the transformation of data

8. Think big about data: Archaeology and the Big Data challenge
Datafication Datafication is not digitisation, datafication refers
to transform a phenomenon in a quantified format
so it can be tabulated and analysed.
This allow us to use the information in new ways
such as the predictive analysis.
Datafication permit more sophisticated analyses
to identify non-linear relationships among data
The rise of the algorithmists

9. Think big about data: Archaeology and the Big Data challenge
Data-driven vs hypothesis-driven
In place of the hypothesis-driven approach,
we can use a data-driven one.
From causation to correlation
In a big data world we won’t
have to be fixated on casuality:
instead we can discover
patterns and correlations in the
data that offer us novel and
invaluable insights. The
correlations may not tell us
precisely why something is
happening, but they alert us
that is happening.

10. Think big about data: Archaeology and the Big Data challenge
We must be aware of the power but also
of limitations of Big Data:
 it’s a ‘cool’ topic;
 considering data = truth;
 Big Data will spell the end of theory;
 everything is permitted.
Risks
 Big Data will always need to be contextualized,
so we must decide if a Big Data approach is
useful or not for our purpose
 Big Data, no matter how comprehensive or well
analyzed, need to be complemented by big
judgment, so Big Data do not mean the end of
archaeologists
 Respecting intellectual property and ethical
issues.

11. Think big about data: Archaeology and the Big Data challenge
Are archaeologists ready for a Big Data approach?

12. Think big about data: Archaeology and the Big Data challenge
The BAD (Big Archaeological Data) project
A Big Archaeological Data
approach requires a new
theoretical approach that means
mainly a counterintuitive approach
to archaeology.
New archaeological approach

13. Think big about data: Archaeology and the Big Data challenge
Can archaeology theoretically fit a Big Data approach?
 The more the data from different disciplines are available,
the better we can describe the general pattern of a
phenomenon;
 Normally archaeology deals with the complexity of large
datasets, fragmentary data, data from a variety of sources
and disciplines, rarely in the same format or scale. We can
say that archaeological data are perfect for a Big Data
approach because they are messy and difficult to
structure;
 Archaeology in many case is easily datafiable as in the
case of tabular data of pottery quantifications, or in the
case of geolocation.
 Correlations are more useful for archaeological
interpretation, because they permit to reject the
deterministic dualism of cause and effect. Big Data
inform, rather than explain, they can expose the pattern for
archaeological interpretation.

14. Think big about data: Archaeology and the Big Data challenge
Data Capture : API, web scraping
Data storage: Hadoop Framework/NoSQL
databases.
Data analysis: Pig, Hive, Mahout, Giraph, R
Is it technologically possible to use a Big Data approach?

15. Think big about data: Archaeology and the Big Data challenge
Big Data analysis
historical/archaeological analysis
(Roman Mediterranean)
predictive models
(archaeological potential)
perception of archaeology
(sentiment analysis)

16. Thank you
Mappa Lab
info@mappaproject.org
Gabriele Gattiglia
g.gattiglia@arch.unipi.it
@g_gattiglia
http://pisa.academia.edu/GabrieleGattiglia
More info
@MappaProject
http://www.mappaproject.org
Think big about data: Archaeology and the Big Data challenge