This document discusses big data and applying semantics to unstructured data. It provides an overview of big data, including what big data is, why it is important now due to cheaper hardware and software, and examples like Hadoop. It also discusses NoSQL databases and semantic tools to help analyze and understand large, unstructured data sets. Key recommendations are to aim high and leverage the big data opportunities, build consensus on opportunities and risks, and focus on developing important human skills like critical thinking to help analyze large data sets.

1. Applying Semantics to Unstructured
Data (Big and Getting Bigger)
Wednesday, November 30, 2012
4:00 – 5:00
Bryan Bell
Vice President, Enterprise Solutions, Expert
System
Lynda Moulton,
Analyst & Consultant, LWM Technology Services
Peter O'Kelly
Principal Analyst, O'Kelly Associates

2. Overall Session Agenda
• Introduction and context-setting
• "Big Data" 101 for Business
• Semantics and the Big Data Opportunity
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3. Big Data 101 Agenda
• Big data in context
• Recap
• Risks
• Recommendations
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4. Big Data in Context
• What is “big data”?
– Unhelpfully, both “big data” and “NoSQL,” generally
considered a key part of the big data wave, are defined
more in terms of what they aren’t than what they are
– A typical big data definition (Wikipedia):
• “[…] data sets that grow so large that they become awkward to
work with using on-hand database management tools”
– Often associated with Gartner’s volume, variety (and
complexity), and velocity model
• Also value and veracity considerations
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5. Big Data in Context
• Why is big data a big deal now?
– Commoditized hardware, software, and networking
• Capability and price/performance curves that continue to
defy all economic “laws”
• Cloud services with radical new capability/cost equations
– Maturation and uptake of related open source
software, especially Hadoop
• Powerful and often no- or low-cost
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6. Big Data in Context
• Why is big data a big deal now (continued)?
– Market enthusiasm for “NoSQL” systems
– Useful and often “open source”/public domain data
sources and services
– Mainstreaming of semantic tools and techniques
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7. A Prime Minicomputer, c1982
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8. Fast-Forward to 2012
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9. Fast-Forward to 2012
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10. Fast-Forward to 2012
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11. Fast-Forward to 2012
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12. Fast-Forward to 2012
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13. Google BigQuery
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14. Hadoop
• Hadoop is often considered central to big data
– Originating with Google’s MapReduce architecture,
Apache Hadoop is an open source architecture for
distributed processing on networks of commodity
hardware
– From Wikipedia:
• “’Map’ step: The master node takes the input, divides it into
smaller sub-problems, and distributes them to worker nodes
• ‘Reduce’ step: The master node then collects the answers to
all the sub-problems and combines them in some way to
form the output – the answer to the problem it was
originally trying to solve”
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15. Hadoop
• Commercial application domains include (from
Wikipedia)
– Log and/or clickstream analysis of various kinds
– Marketing analytics
– Machine learning and/or sophisticated data mining
– Image processing
– Processing of XML messages
– Web crawling and/or text processing
– General archiving, including of relational/tabular data,
e.g. for compliance
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16. Hadoop
• Hadoop is popular and rapidly evolving
– Most leading information management vendors
have embraced Hadoop
– There is now a Hadoop ecosystem
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17. Meanwhile, Back in the Googleplex
• Dremel, BigQuery, Spanner, and other really
big data projects
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18. Meanwhile, Back in the Googleplex
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19. Google Now
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20. A NoSQL Taxonomy
• From the NoSQL Wikipedia article:
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21. A View of the NoSQL Landscape
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22. Another NoSQL Landscape View

23. NoSQL Perspectives
• The “NoSQL” meme confusingly conflates
– Document database requirements
• Best served by XML DBMS (XDBMS)
– Physical database model decisions on which only DBAs and
systems architects should focus
• And which are more complementary than competitive with DBMS
– Object databases, which have floundered for decades
• But with which some application developers are nonetheless
enamored, for minimized “impedance mismatch,” despite significant
information management compromises
– Semantic (e.g., RDF) models
• Also more complementary than competitive with RDBMS/XDBMS
• Also consider: the “traditional” DBMS players can leverage
the same underlying technology power curves
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24. Data as a Service
• The (single source of) truth is out there?...
– High-quality data sources are being commoditized
– Value is shifting to the ability to discern and leverage conceptual
connections, not just to manage big databases
• Some resources and developments to explore
– Social networking graphs and activities
– Data.com (Salesforce.com)
– Data.gov
– Google Knowledge Graph
– Linked Data
– Microsoft Windows Azure Data Marketplace
– Wikidata.org
– Wolfram Alpha
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25. Mainstreaming Semantics
• Tools and techniques applied in search of
more meaning, e.g.,
– Vocabulary management
– Disambiguation and auto-categorization
– Text mining and analysis
– Context and relationship analysis
• It’s still ideal to help people capture and apply
data and metadata in context
– Semantic tools/techniques are complementary
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26. Mainstreaming Semantics
• The Semantic Web is still more vision than reality
– But Google, Microsoft, and Yahoo, and Yandex, for
example, are improving Web searches by capturing
and applying more metadata and relationships via
schema.org schemas in Web pages
– And Google’s Knowledge Graph is about “things, not
strings,” with, as of mid-2012, “500 million objects, as
well as more than 3.5 billion facts about and
relationships between these different objects”
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27. Recap
• Commoditization and cloud
– Very significant new opportunities
• Hadoop and related frameworks
– Complementary to RDBMS and XDBMS
• NoSQL
– Likely headed for meme-bust…
• Data services
– Game-changing potential
• Semantic tools and techniques
– Rapidly gaining momentum
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28. Risks
• The potential for an ever-expanding set of information silos
– Focus on minimized redundancy and optimized integration
• GIGO (garbage in, garbage out) at super-scale
– New opportunities for unprecedented self-inflicted damage, for
organizations that don’t model or query effectively
• Cognitive overreach
– The potential for information workers to create and act on
nonsensical queries based on poorly-designed and/or
misunderstood information models
• Skills gaps can create competitive disadvantages
– Modeling, query formulation, and data analysis
– Critical thinking and information literacy
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29. Recommendations
• Aim high: big data is in many respects just
getting started…
– A lot of technology recycling but also
significant and disruptive innovation
• Work to build consensus among stake-
holders on the opportunities and risks
• Focus on human skills – e.g., critical
thinking and information literacy
– For now, an instance of the most creative and
powerful type of semantic big data processor
we know of is between your ears
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