On 2/12/15 8:13 AM, Kingsley Idehen wrote:
> On 2/12/15 6:30 AM, Erick Antezana wrote:
>> I need some help to better define the line (sometimes apparently
>> grey) between master data and ontologies.
>> We all, at least in this forum, know that there are several
>> definitions for both terms.
>> I guess most of us are familiar with Gruber's one: a formal
>> specification of a shared conceptualization.
>> In the case of master data:
>> - 'entities, relationships, and attributes that are critical for an
>> enterprise and foundational to a key business process and application
>> or among others:
>> - 'is the consistent and uniform set of identifiers and extended
>> attributes that describes the core entities of an enterprise'.
>> What are the key components to differenciate master data and ontologies?
>> What is common to both artefacts?
>> From what I have seen, sometimes the border between them seems indeed
>> relatively grey... which seems to be the product of having ontologies
>> as glue components of disparate master data. Also, there seems to be
>> a continuum between them (as in the databases and knowledge base
>> thread in this forum). Anyway, I would appraciate reading your
>> thoughts about it.
> The problem is that MDM is a marketing moniker, just like "Big Data",
> SOA, and the like. All of these monikers are deliberately generic and
> borderline meaningless (by design). An ontology (in my eyes) is the
> antithesis of fluffy marketing buzzwords since they force you to look
> at the actual characteristics of an entity and define said
> characteristics meaningfully.
> MDM is fundamentally about holistic views of heterogeneous data
> sources. Basically, heterogeneous data source virtualization.
> Most MDM narratives pay lip service to "semantics" and rarely address
> the practical realities of implementing enterprise-wide heterogeneous
> data virtualization, which cannot really manifest without an ontology
> of terms that defines the nature of entities and relations, in a given
> Anyway, ontologies are a powerful tool for addressing the fundamental
> challenges associated with practical virtualization of heterogeneous
> data sources, be it at the private enterprise level or even across the
> many data-silos that now constitute the world wide web (e.g.,
> Facebook, Google+, Twitter, LinkedIn etc..).
> BTW -- I've recently published a number of blog posts about data
> de-silo-fication and heterogeneous data virtualization.
> -- Deceptively Simple Conceptual Data Virtualization
> -- From Open Database Connectivity to Open Data Connectivity
> -- Oracle Data De-Silo-Fication .
In addition to the above, here's an example of data integration that
takes the form of an article/post description.
Note: what follows is the use of nanotation  to embed Linked Open
Data into this post i.e., make the post a data source integrated into
the Linked Open Data cloud. (02)
## Description of a recent Data Integration post by David S. Lintichum (03)
rdfs:label "The next generation of data integration technology for
the changing cloud market" ;
foaf:primaryTopic <https://twitter.com/hashtag/DataIntegration#this> ;
dcterms:subject <https://twitter.com/hashtag/DataIntegration#this> ;
schema:about <https://twitter.com/hashtag/DataIntegration#this> ;
rdfs:comment """Data integration becomes a much more important issue
with the use of both public and private cloud-based platforms.
Information that was once stored locally may now
be spread across several public cloud providers, and that information
needs to be shared with most, if not all, enterprise
systems that exist locally or within public clouds. -- by @David S.
dcterms:description """What’s most interesting about the path of
data integration and the rapidly growing cloud computing space?
The traditional approach to data
integration, meaning simple data replication and mediation, will become
an outdated concept over the next 10 years or
so. Data integration technology will make rapid moves in new directions,
in light of the needs and the value of cloud
computing, which will provide the enterprise with new capabilities, as well
as largely disrupt the existing data
integration marketplace. Also keep in mind that some data integration
will reach their 20th birthday in the next
<https://twitter.com/hashtag/SlowWeb#this>, <> . (08)
foaf:name "David S. Linthicum" ;
foaf:homepage <http://research.gigaom.com/analyst/david-linthicum> . (09)
# Definitions (010)
owl:sameAs <http://dbpedia.org/resource/Data_integration> ;
rdfs:label "Data Integration" ;
is foaf:primaryTopic of
is schema:about of
is dcterms:subject of
<https://twitter.com/hashtag/SlowWeb#this> . (014)
 http://kidehen.blogspot.com/2014/07/nanotation.html -- About
Nanotation . (017)
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