On 12/2/12 4:58 PM, Obrst, Leo J. wrote:
> Kingsley, I agree. Implicit semantics in procedural code and structural data
>models just don't get us where we need to go. Humans simply cannot do all the
>semantic interpretation, or you never break out of this bottleneck. Machines
>have to lend a hand, by doing some explicit semantic interpretation. Machine
>understanding? No. Don't go down that rabbit hole.
>
> Thanks,
> Leo (01)
Yep! (02)
Today's "Big Data" meme is just the latest in a series of buzzwords (and
phrases) that just dance around the fact that entity relationship model
semantics can exists in self-describing structured data. Arguments about
formats are an eternal distraction that play into the hands of
marketeers that fall into two categories: (03)
1. understand the problem but muddy the waters for competitive reasons
2. are clueless about the matter and muddy the waters inadvertently. (04)
As clearly articulated by Peter Chen [1] circa. 1976. An entity
relationship model can be used to construct a unified view of disparate
shaped data. Similar clarity comes from John in his work relating to
conceptual graphs and the use of Logic as the conceptual schema for
driving data integration via semantics [2]. (05)
Where it all gets into trouble is when a single entity makes a
power-grab for all things relating to semantics, which is where RDF
drove itself into a horrible ditch, first time around. Of course, this
wasn't the fault of those who now much better (e.g., Pat Hayes and a few
others that worked on RDF), the problem arose from those who (for the
most part) discovered these concepts via RDF and failed to understand
that RDF was a tweak of what already existed, as is always the case in
an innovation continuum. The problem then got compounded when the Linked
Data meme took off. (06)
Links: (07)
1. http://slidesha.re/SbfHQG -- Integrating Semantic Systems (John Sowa)
2. http://bit.ly/YTdz3N -- Entity Relationship Model: Unified View of
Data (Peter Chen).
3. http://slidesha.re/UGg18k -- connecting Big Data, Linked Data, RDF,
and the Semantic Web vision (my attempt to connect these artificially
disconnected dots). (08)
Kingsley
>
>> -----Original Message-----
>> From: ontolog-forum-bounces@xxxxxxxxxxxxxxxx [mailto:ontolog-forum-
>> bounces@xxxxxxxxxxxxxxxx] On Behalf Of Kingsley Idehen
>> Sent: Sunday, December 02, 2012 4:22 PM
>> To: ontolog-forum@xxxxxxxxxxxxxxxx
>> Subject: Re: [ontolog-forum] Big Data Buzzwords From A to Z
>>
>> On 12/1/12 11:11 PM, John F Sowa wrote:
>>> I came across some slides with "Big Data Buzzwords From A to Z".
>>> So I browsed through them to see what terms the author (and his
>>> associates) consider important. See below for the list of terms
>>> and the URL of the slides.
>>>
>>> The words 'logic' and 'ontology' are not on their list. Tools
>>> developed and supported by the Apache Foundation are very well
>>> represented. The word for X is 'XML'. But none of the slides
>>> mention the Semantic Web or any of its notations and tools.
>> John,
>>
>> Which is why "Big Data" is typically pitched as being all about the
>> exponential effects:
>>
>> 1. data volume
>> 2. data velocity
>> 3. data variety.
>>
>> Unfortunately, for the proponent of this particular meme, they omit
>> "data verity" which is exactly where the virtues of machine
>> comprehensible semantics delivered by self-describing structured data
>> comes into play. Basically, they never hone into the real problem, one
>> that's 40+ years old, at the very least.
>>
>> When all is said an done, the mercurial pursuit (40+ years and
>> counting) is still all about agility driven by insights culled from
>> heterogeneously shaped data, accessible from disparate network locations.
>>
>> To me, meme labels and monikers just don't matter, substance of the
>> matter -- insightful data access, integration, and management -- is
>> ultimately the immutable item of relevance :-)
>>
>> Kingsley
>>> John
>>>
>> ___________________________________________________________
>> _________
>>> Big Data Buzzwords From A to Z
>>>
>>> by Rick Whiting
>>>
>>> Big data is one of the, well, biggest trends in IT today, and it has
>>> spawned a whole new generation of technology to handle it. And, with
>>> new technologies come new buzzwords: acronyms, technical terms,
>> product
>>> names, etc.
>>>
>>> Even the phrase "big data" itself can be confusing. Many think of
>>> "lots of data" when they hear it, but big data is much more than just
>>> data volume.
>>>
>>> Here, in alphabetical order, are some of the buzzwords we think you
>>> need to be familiar with.
>>>
>>> ACID, Atomicity, Consistency, Isolation and Durability.
>>> Big data.
>>> Columnar (or Column-Oriented) Database.
>>> Data Warehousing.
>>> Extract, transform and load (ETL).
>>> Flume, a technology in the Apache Hadoop family.
>>> Geospatial Analysis.
>>> Hadoop.
>>> In-Memory Database.
>>> Java.
>>> Kafka, a high-throughput, distributed messaging system.
>>> Latency.
>>> Map/reduce.
>>> NoSQL Databases.
>>> Oozie, an open-source workflow engine.
>>> Pig, a platform for analyzing huge data sets.
>>> Quantitative Data Analysis.
>>> Relational Database.
>>> Sharding, a form of database partitioning.
>>> Text Analytics.
>>> Unstructured Data.
>>> Visualization.
>>> Whirr, libraries for running big data cloud services.
>>> XML.
>>> Yottabyte.
>>> ZooKeeper, manage and coordinate Hadoop nodes.
>>>
>>> http://www.crn.com/slide-shows/data-center/240142568/big-data-
>> buzzwords-from-a-to-z.htm
>>>
>>>
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>>>
>>
>> --
>>
>> Regards,
>>
>> Kingsley Idehen
>> Founder & CEO
>> OpenLink Software
>> Company Web: http://www.openlinksw.com
>> Personal Weblog: http://www.openlinksw.com/blog/~kidehen
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>>
>>
>>
>>
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> (09)
-- (010)
Regards, (011)
Kingsley Idehen
Founder & CEO
OpenLink Software
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