Developing Archaeological Informatics: A Proposed Agenda for the Next Five Years

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Developing Archaeological Informatics A Proposed
Agenda for the Next Five Years

• Lohse E S, Schou C, Strickland A, Sammons D,
  Strickland J, Schlader RIdaho State University,
  USA

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Our Information Age

• All information is incomplete.
• Information widens the range of choices, not
  narrow it.
• Information is always subject to multiple
  interpretations and constructions.

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Data Information Knowledge

• Data
• Raw facts and observations
• Information
• Data given meaning and context
• Organized
• Knowledge
• Information you have internalized

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Information Production
1,000,000 terabytes 90 digital Growing 50 yearly
Gray and Szalay 2003
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Storing All that Information

• Schematized storage (metadata) can help
  organization and research
• Schematized XML data sets are a universal way to
  exchange data
• Data are objects, and so, need standard
  representation for classes and methods

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How to Keep Up

• Bring the analysis to the data
• Embed analyses in data systems
• Build smart data systems
• Build image-based data systems

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Analysis of Databases

• Create uniform samples
• Filter data
• Assemble subsets
• Estimate completeness
• Censor bad data
• Count and build histograms
• Generate Monte Carlo subsets
• Perform likelihood calculations
• Test hypotheses
• These tasks are best done inside databases
  (bring Mohamed to the mountain)

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Go for Smart Data

• Too much data to move around, so take analysis to
  the data
• Do all data manipulations inside the database
  (build custom procedures and functions in the
  database)
• Guaranteed automatic parallelism
• Easy to build custom functionality key (pixel
  processing, temporal and spatial indexing,
  unified databases and procedures)
• Easy to reorganize data (multiple views make
  optimal analyses)
• Scalable to Petabyte data sets

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Data Mining Images
We can discover new types of phenomena using
automated pattern recognition multiscale analyses
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Disseminating Datawill also change

• expectations and standards must change
• there will be exponential growth
• projects must become more responsible

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Archaeological Informatics

• Technical, Social Aspects of Information
  Technology

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Archaeological IT

• Quantitative methods
• Statistics and classification
• Archaeometry
• Visualization (imaging, CAD, multimedia and
  virtual reality)
• Expert systems
• Artificial intelligence
• GIS

• All require
• Digital archives
• Databases

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Access to Data

• Primary issue today
• Data are unsorted and unavailable
• Number of existing data are huge and daunting
• Technological fixes are available
• but implementation is a social problem

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Information Science

• Scientists want open access to data and
  information
• Use of electronic media to enhance scientific
  communication is a huge shift in the conduct of
  basic science
• Potential for cross-disciplinary and
  international collaborations is booming
• Needs include building adequate metadata,
  accessing migrating data, and controlling access
  to information

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Whats Out There?

• Social and political agendas
• Competing proprietary interests
• Competing to control dissemination of the digital
  archive

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Current Risks

• Open environment will not continue
• Not everyone will catch on to using e-media
  structures
• Not all current e-media initiatives are
  altruistic or problem-solving without
  self-interest

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Practical Problems

• Scientists and policy-makers do not have accepted
  theory for shaping IT
• Producers and users work within context-free
  models
• Lots of prototypes and inititiatives with high
  promise and withered funding

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Practical Problems

• RESULT
• wasted funding, and orphaned data left in
  marginal, decaying, dead systems and formats

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Assumptions for future

• E-media is better than traditional media
• E-communication will be less expensive
• Access to e-media will be easier and wider
• Systematic use of e-media will dramatically speed
  up scientific communication

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Social Implications

• Electronic Data
• Electronic access to primary data
• High speed of sharing
• Target audiences will be selected
• Professional status based on quality of data
  design and data sharing

• Traditional Print
• Print access to secondary information
• Slow speed publishing
• Broad dissemination
• Professional status based on quantity and venue
  of publication

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Market Forces

• Open Access (transparent)
• Closed Access (opaque)
• Which will dominate?

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Liberating Archaeological Data

• Many archaeologists, working under Federal and
  State mandates, remain outside any long term
  concern with data handling
• Data liberation runs afoul of insistence on
  fossilized traditional research practice, fueled
  by resource management contracts
• Internet impacts organization of archaeological
  knowledge, with a shift from hierarchical
  structures to network flows (Hodder 1998)

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Need for Re-thinking

• Archaeological classification practices will need
  to emphasize optimal structures for organization
  of archaeological data in an electronic
  environment
• Interpretive structures must admit variable ways
  of grouping data
• Higher order groupings (typologies) will have to
  be supplemented by alternative analytical
  groupings (material classes, deposition classes)
• Data structures will have to be flexible and
  analytical

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New Structures Must Recover Links

• Traditional databases (TDs) have disparate or
  unlinked compendiums (fields with specimen
  measurements but no link to grey literature
  reports)
• TDs typically are arranged to follow a rigid
  linear structure based on chronological groupings
  dictated by field recovery records and publishing
• This produces intractable data sets, where
  important data remain unavailable because
  reclamation costs are so high, there is a lack of
  integration for specialist data to be linked with
  overall data structure, and little potential for
  futrue synthesis

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A Theory of Information

• Proviso we cannot enter new data as old
  structures into new IT (HTML, interrelational
  databases, and GIS) and expect working databases
• Current data systems are outdated, evolved
  through expediency, not grounded in theory
• The theory-driven structure of the data must be
  revived

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e.g., Metadata

• Data about data, providing information essential
  to data use and reuse
• Can refer to agreed upon sets of fields and
  associated lexicons
• Can consist of detailed descriptions of
  measurement systems and rules for their
  application
• Data users need metadata to make intelligent
  decision in selecting, using, adding to, or
  translating databases

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Current Standards for Metatdata

• MARC, Machine Readable Catalog, library
  cataloging
• Text Encoding Initiative (TEI), standard
  descriptions of machine readable text
• Directory Interchange Format (DIF), metadata for
  satellite imagery
• U.S. National Spatial Data Infrastructure (NSDI),
  complex descriptions of spatial data
• And of course, the Dublin Core

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Metadata and Databasesin the future

• Will improve access to data
• will facilitate sharing and interoperability
• will characterize and index data
• Will operate under the principles described
  above
• Analysis embedded in data
• Smart data systems
• Image-based data systems

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Measures for Data Qualityfor the information of
the future

• Adequate description and meaning
• Specification of intended use and range of
  purposes and constraints
• Requirements for access and use
• Description and rationale for structure and
  design
• Global relationships to other databases
• Updated cycle information

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Data Models

• Data are a model of the real world
• The description is arbitrary and biased
• Data models incorporate different data views
• Key issues verification, validation and
  certification of data quality
• Measures objective correctness (accuracy and
  consistency) and appropriateness defined by
  intended purpose
• Required elements all data must be augmented
  with metadata to record information needed to
  assess data quality, record results of
  assessments, and support process control

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Problems Data Deterioration

• Limited media life
• Rapid obsolescence of software and hardware
• Use of graphics, hypertext and linked structures
  only accelerates decay rates
• Data files will become increasingly dependent on
  specific software for continued interpretation
• Record keeping paradigms are essential
  (compression is not an option annotated metadata
  must remain transparent)

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Preparing for the Future

• Archaeological data and information are growing
  exponentially
• New paradigms of data access and manipulation
  will be created
• Effects on theory and method will be extreme
• Effects on the culture of the discipline will
  prompt profound dislocations

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Preparing for the Future

• Not just more data and faster access
• Qualitative differences in
• data gathering methods
• social relationships between/among data, users,
  creators, and managers
• the disciplines expectations for publication
  and research
• AND

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Preparing for the Future

• methodological and theoretical paradigm changes
  driven by technological innovations and social
  interactions with the technology