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. (01)
Thanks,
Leo (02)
>-----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
>Twitter/Identi.ca handle: @kidehen
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>
>
>
> (03)
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