What’s all this cloud stuff, anyway? What kinds of problems do organizations set out to solve with ‘a cloud,’ or even ‘the cloud’? What are a few of the major government initiatives involving this technology? How does HLT in general, and Search in particular, fit? This talk will take a tour of the technology behind clouds and the sometimes-foggy ambitions of the projects that use them, and look in particular detail at the challenges of applying cloud technologies to Text Analytics. View more slides from the Human Language Technology Conference 2012 here: http://info.basistech.com/hlt-2012-slides

1. Clouds, Search or HLT The 'forecast'?
Benson Margulies
Executive Vice President and Chief Technology Officer
Basis Technology – Human Language Technology Conference 2012 1

2. Clouds, Search or HLT
The 'forecast'?
Basis Technology – Human Language Technology Conference 2012 2

3. Meteorology - or - Why Clouds
• Lie
on
the
grass
and
look
up
at
the
clouds
• Everyone
sees
something
different
• Computerized
Clouds
are
no
different
•
Applica;ons
Always
Available
•
Data
Always
Available
•
Tools
for
Processing
Big
Data
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4. Big Data and Clouds =~ Hadoop
• It's
not
just
a
maFer
of
size
• Hadoop
...
o Takes
in
structured
data
sets
o Op;mizes
stateless,
batch
processes
o Moves
computa3on
to
data
• All
of
which
is
great
if
that's
what
you
have
• The
world
is
more
complicated
than
that
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5. What it Doesn't Do So Easily
• On-­‐the-­‐fly
(non-­‐batch)
processing
• Stateful,
non-­‐local,
processing
• For
example,
consider
a
search
engine
o All
about
online:
a
document
arrives,
users
want
to
find
it.
o All
about
global
state:
relevancy
involves
global
data
across
the
whole
index.
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6. More on Search-in-a-Cloud
• Good
News:
'conven;onal'
technologies
scale
to
very
large
indices.
o Solr
o SolrCloud
o Elas;c
Search
o ...
• How?
Shards.
o 'hash'
to
split
docs
o queries
go
everywhere
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7. Search-in-a-Cloud less good news
• Alterna;ves
are
s;ll:
o Limited
o Research
o or
both
• Solandra
o Scaling
via
Cassandra
o 'just
another
sharded
solu;on'
o Just
the
thing
if
you
like
Cassandra
•
or
Accumulo
o So
far,
very
basic
inverted
index
o beFer
things
coming
Basis Technology – Human Language Technology Conference 2012 7

8. Other HLT tasks ...
• 'Extrac;on'
is
'straighZorward'
• Text
comes
in,
en;;es
or
rela;onships
come
out.
• Results
end
up
in
graph
DB
or
bigtable
or
...
• Scale
via
Hadoop
or
whatever
• The
Challenge
of
Mixing
and
Matching
• But
...
what
if
you
want
a
feedback
loop?
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9. Interoperation
• Lot's
of
focus
on
applica;ons
o e.g.
Ozone
Widgets
• Not
so
much
on
backend
processes
• What
good
is
'data
everywhere'
if:
o you
can't
deploy
processing
to
exploit
it?
o you
can't
fit
together
pieces
of
the
puzzle?
• A
stovepipe
in
a
cloud
is
s;ll........
• A
stovepipe
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10. Harder Unstructured Problems
• Imagine
you
wanted
to
cluster
...
• New
items
show
up
• Need
to
find
'best'
exis;ng
cluster
o It
could
be
'anywhere'
• Need
to
update
to
reflect
each
new
item
• (If
you're
wondering
what
we're
clustering
...)
Basis Technology – Human Language Technology Conference 2012 10

11. Rosette Concrete Examples
• Straight
Search
o RoseFe
Solr
Plugins
work
all
the
same
o SolrCloud
hashes/shards
o RoseFe
runs
on
the
target
node
• Extrac;on
and
similar
processes
o Same
story,
using
Update
Request
Processor
Basis Technology – Human Language Technology Conference 2012 11

12. Rosette and Hadoop
• Stateless
APIs
lead
to
simple
implementa;on
• Non-­‐code
resources
lead
to
some
issues
• Stateful
processes
(e.g.
RNI)
...
back
to
Solr
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