Digging deeper into data processing with emphasis on computational and microstructure data_f

The Socio-Technological
Integrator And Innovator
Digging Deeper into Data Processing with
Emphasis on Compositional and Microstructure
Data: Machine Learning in Support of
Archaeological Analysis
Liza Charalambous
liza.charalambous@eurocyinnovations.com
charalambous.elisavet@ucy.ac.cy

Overview
1. Introduction
 Archaeological Process
 Data in many forms and types
2. Part I: Compositional Data
 Pre-Processing Practices
 Case Study & Experimental Results
3. Part II: Microstructure Data
 Microstructure Analysis
 Pattern Recognition for the Characterization of
Microstructure Data
4. Data Analysis Remarks
 Data Idiosyncrasies

Profile and Background
 Real-time Monitoring
 Communication systems
 Security and Error Protection systems
 Research interests and Background
 Digital Signal Processing
 Artificial Intelligence
 Machine Learning
 Audio Coding
 PhD student in Computer Engineering at University of Cyprus
 In cooperation with the KIOS Research Center for Intelligent Systems and Networks
 NARNIA ITN ESR08 (starting date 01/11/2011)
 Educational Background
 BSc in IT and Multimedia Communications (2007-2010), Lancaster University, UK
 MSc in Digital Signal Processing and Intelligent Systems (2010-2011), Lancaster University,
UK

Archaeological Data
Then Now
SKETCHES
STRATIGRAPHY LOGS
PETROGRAPHIC ANALYSIS
RELATIONAL
DATABASES
DIGITAL REPRESENTATIONS
3D RECONSTRUCTIONS
ELEMENTAL CONCENTRATIONS SPECTRA

Gather Samples/
Artifacts
Technologies
Available
Methods
Data Analysis
Form Archaeological
Question
Interpretation
of Results
Analyze Objectives: What needs to
be proved?
Determine and gather the artifacts
of interest (based on the previously
formed question)
List available technologies for
deployment and analyze effectiveness
List available analysis methods
compatible to the selected technology
Application of Clustering/ Classification
algorithms so as to increase data
manageability
ARCHAEOLOGICAL PROCESS:
Steps

“Too much and overly complicated data”
 Data analysis in archaeology, is sometimes
believed to take the form of:
 Simple projection of data (a feature against another)
 Employment of very simple clustering or other
dimensionality reduction methods
 Much attention is given when:
 Sampling
 Data preprocessing
Available Methods
belief that good data
will speak for themselves

Technologies
 Analysis comes in different forms and shapes
 The result is usually in the form of:
 Peak elemental measurements → as a result of spectrum analysis
 Pictures or other schematic representations → commonly based on
the sample’s microstructure
 Each technology is dictated by its own characteristics,
integration of multiple technologies may not always be
beneficiary

Part I:
Compositional Data
Cu MnMg Ca
Ti
K
Fe SCr Al
 Compositional data are defined as
vectors of proportions
 strictly positive components
 constant sum; a restriction not always
maintained
 Chemical analysis is not really involved
in measuring, but in enumerating, or
counting, the number of each type of
atoms in a sample
 The results are usually given in relative
numbers (usually in % or ppm).
a) elemental concentrations are frequencies
of nominal or categorical classes (atoms)
of a classificatory concept (matter)
b) chemistry is usually interested not in
frequencies, but in relative frequencies.

Part I:
Pre-Processing Practices
 General Belief:
The more precise and accurate the bulk chemical
determinations, the better the chance of making more plausible
and refined estimations.
 Reproducibility and comparability of results, is commonly
assured by adopting one of the following practices:
a) Transformation of the relative concentrations into base 10 values
b) Sub-compositional data: the dataset of interest only contains
proportions of the components constituting a sample
c) Calculation of averages
d) Elimination of chemical elements dominated by noisy readings or
incomplete measuring

Part I:
Ceramics Case Study & Experimental Results
 Study the impact of pre-processing on datasets obtained from
ceramics with the use of NAA
 Investigations on the effect of the following parameters:
 Raw Vs. Log: the transformation of raw data into the equivalent 10-
base logarithm increased data separation (especially for the
heterogeneous ceramics)
 Sub-compositional data (with the addition of an extra column): has
not influenced in any significant way the product of analysis; practice
currently deployed in the archaeology domain
 Calculation of averages: reduced the variance of clusters between
successive runs; particularly useful for the analysis of homogeneous
material.
 Standardized and Normalized Data: no significant impact on the
commonly used analysis methods

Part II:
Microstructure Data
Involves the study of silicate and carbonate-based artifacts
which may be relatively unmodified from their original
geological parent raw materials
 Microstructure analysis is critical in extracting manufacturing
knowledge
 Can achieve resolution better than 1nm
 Can provide high quality imaging facilities together with
quantitative elemental analysis; using an energy dispersive
spectrometer

Part II:
Microstructure Data Analysis
 Classification by taking into consideration how ceramics are
processed
 Related to the impact on material durability
 The nature of the ceramic microstructure, as a function of
temperature, can be related to the composition of the clay source
exploited
 Issues that an archaeological scientist may require to address
through SEM:
 Characterization of origin material
 Reconstruction of the technology involved in manufacture
 Influence of the place of manufacture or source of raw materials
 Changes that have occurred in the object during burial or storage

Part II:
PR for the Characterization of Microstructure Data
Estimation of
Annealing
Temperature
Degree of
Vitrification
Porosity/
outer-
connection of
particles
Microstructure
Data
Evaluation of the sophistication of
firing process
Knowing the various nuances of materials and processing systems can
be overwhelming and confusing
 Properties of crystals
 Average size
 Orientation/Alignment
 Coarseness and depth of
primitive elements
 Vitrification Stage
 Identification of crystals
and degree of fusion
 Porosity
 Spread of pores
 Shape/size

Part II:
PR for the Characterization of Microstructural Data
PIXEL POINT & GROUP PROCESSING
 Edge related operations
 Enhancement
 Segmentation
 Detection
 Texture Analysis
 Co-occurrence matrix: captures numerical
features which can be used to represent,
compare, and classify textures.
 Auto and cross correlation: can be used to
detect repetitive patterns of textures
 Estimation of patch similarity: gives the
ability to compare image regions
 Promoting of unit invariant measures
 Perforation
 Shape Factors
SHAPE FACTORS
 Aspect Ratio: function of the
largest and the smallest
diameters perpendicular to
each other
 Circularity: a function of the
perimeter and the area
 Elongation: ratio of minor axis
width to major axis length
ratio
 Compactness: measure of
object roundness area to
perimeter ratio
 Waviness shape factor of the
perimeter: often related to
fracture toughness of metals
and ceramics

 Not all features should be treated equally
 Artifacts are characterized by primary, secondary and
supplementary elements
 All artifacts regardless physical characteristics are treated
the same
 Size, shape, texture, contamination, aperture upon exposure
 Preprocessing steps and methodology
 Preprocessing of the data usually complies to the disciplines of
certain fixed procedures
 Effectiveness of an analysis method may be greatly influenced
by data preparation routines
 Important to maintain consistency
Data Analysis Remarks

Problems of Archaeological Data
“THE VALUE OF DATA IS GIVEN BY THE ABILITY TO
EXTRACT INFORMATION.”
 Scarce and incomplete data
 High amounts of uncertainty and subjectivity
 Characterised by high degrees of redundancy
 Complex interactions between variables
 Analysis of findings with the use of different
technologies and analysts may result to be inaccurate
and imprecise

 Barely affected by alteration or deterioration during burial,
they generally present the original trading goods, as far as
their material properties and composition are concerned.
 Very helpful in providing classification among ceramic assemblages
 Often giving information about their provenance or origin of
production
 Ceramics of the same production series may reveal a
characteristic elemental composition, usually distinct from
ceramics from other production places or series. Due to the:
 Geochemical diversity of raw material sources
 The variation in the pottery manufacturing process
DATA IDIOSYNCRASIES:
Ceramics

 Prone to corrosion
 Different raw materials are
corroded at different rates and
degrees
 Corrosion is not uniform
 Assuming that the sample is
representative is not always trivial
 Sampling requires cleaning the outer surface
 Usually involves removing the outer coat
 Issues with licensing
 Due to the material’s flexibility, most of metal objects are not flat
 Alloys are challenging
Metals

 Notoriously homogeneous
 Very rarely found in large quantities
 Highly fragile
 Their usually thin structure makes artifact analysis a challenge
 Artifact in whole form are very rare to find
 Contamination over time
 Their analysis usually requires the use of acidic substances, for
cleaning the extra coating
 Sometimes alters some of their characteristics
 Against legislation restrictions
Glass

“I am enough of an artist to draw freely upon my imagination.
Imagination is more important than knowledge.
Knowledge is limited. Imagination encircles the world.”
Albert Einstein
Thank you for the attention!
Comments and Questions are Welcome!

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