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Kingsley Uyi Idehen
Lexington, United States
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Virtuoso Linked Data Deployment 3-Step
Injecting Linked Data into the Web has been a major
pain point for those who seek personal, service, or
organization-specific variants of DBpedia. Basically, the sequence goes
something like this:
- You encounter DBpedia or the LOD Cloud Pictorial.
- You look around (typically following your nose from link to
link).
- You attempt to publish your own stuff.
- You get stuck.
The problems typically take the following form:
- Functionality confusion about the complementary Name and
Address functionality of a single URI abstraction
- Terminology confusion due to conflation and over-loading of
terms such as Resource, URL, Representation, Document, etc.
- Inability to find robust tools with which to generate Linked
Data from existing data sources such as relational databases, CSV
files, XML, Web Services, etc.
To start addressing these problems, here is a simple guide for
generating and publishing Linked Data using Virtuoso.
Step 1 - RDF Data Generation
Existing RDF data can be added to the Virtuoso RDF Quad Store
via a variety of built-in data loader utilities.
Many options allow you to easily and quickly generate RDF data
from other data sources:
- Install the Sponger Bookmarklet for the URIBurner
service. Bind this to your own SPARQL-compliant backend RDF database (in
this scenario, your local Virtuoso instance), and then Sponge some
HTTP-accessible resources.
- Convert relational DBMS data to RDF using the Virtuoso RDF
Views Wizard.
- Starting with CSV files, you can
- Place them at an HTTP-accessible location, and use the Virtuoso
Sponger to convert them to RDF or;
- Use the CVS import feature to import their content into
Virtuoso's relational data engine; then use the built-in RDF Views
Wizard as with other RDBMS data.
- Starting from XML files, you can
- Use Virtuoso's inbuilt XSLT-Processor for manual XML to RDF/XML
transformation or;
- Leverage the Sponger Cartridge for GRDDL, if there is a transformation service
associated with your XML data source, or;
- Let the Sponger analyze the XML data source and make a
best-effort transformation to RDF.
Step 2 - Linked Data Deployment
Install the Faceted Browser VAD package
(fct_dav.vad ) which delivers the following:
- Faceted Browser Engine UI
- Dynamic Hypermedia Resource Generator
- delivers descriptor resources for every entity (data object) in the Native or
Virtual Quad Stores
- supports a broad array of output formats, including
HTML+RDFa, RDF/XML, N3/Turtle, NTriples,
RDF-JSON, OData+Atom, and OData+JSON.
Step 3 - Linked Data Consumption & Exploitation
Three simple steps allow you, your enterprise, and your
customers to consume and exploit your newly deployed Linked Data
--
- Load a page like this in your browser:
http://<cname>[:<port>]/describe/?uri=<entity-uri>
-
<cname>[:<port>] gets replaced by the
host and port of your Virtuoso instance
-
<entity-uri> gets replaced by the URI you
want to see described -- for instance, the URI of one of the
resources you let the Sponger handle.
- Follow the links presented in the descriptor page.
- If you ever see a blank page with a hyperlink subject name in
the About: section at the top of the page, simply add the parameter
"&sp=1" to the URL in the browser's Address box, and hit
[ENTER]. This will result in an "on the fly" resource retrieval,
transformation, and descriptor page generation.
- Use the navigator controls to page up and down the data
associated with the "in scope" resource descriptor.
Related
10/29/2010 18:54 GMT-0500 |
Modified: 11/02/2010 11:57
GMT-0500 |
Virtuoso Linked Data Deployment In 3 Simple Steps
Injecting Linked Data into the Web has been a
major pain point for those who seek personal, service, or
organization-specific variants of DBpedia. Basically, the sequence goes
something like this:
- You encounter DBpedia or the LOD Cloud Pictorial.
- You look around (typically following your nose from link to
link).
- You attempt to publish your own stuff.
- You get stuck.
The problems typically take the following form:
- Functionality confusion about the complementary Name and
Address functionality of a single URI abstraction
- Terminology confusion due to conflation and over-loading of
terms such as Resource, URL, Representation, Document, etc.
- Inability to find robust tools with which to generate Linked
Data from existing data sources such as
relational databases, CSV files, XML, Web Services, etc.
To start addressing these problems, here is a simple guide for
generating and publishing Linked Data using Virtuoso.
Step 1 - RDF Data Generation
Existing RDF data can be added to the Virtuoso RDF Quad Store
via a variety of built-in data loader utilities.
Many options allow you to easily and quickly generate RDF data
from other data sources:
- Install the Sponger Bookmarklet for the URIBurner
service. Bind this to your own SPARQL-compliant backend RDF database (in
this scenario, your local Virtuoso instance), and then Sponge some
HTTP-accessible resources.
- Convert relational DBMS data to RDF using the Virtuoso RDF
Views Wizard.
- Starting with CSV files, you can
- Place them at an HTTP-accessible location, and use the Virtuoso
Sponger to convert them to RDF or;
- Use the CVS import feature to import their content into
Virtuoso's relational data engine; then use the built-in RDF Views
Wizard as with other RDBMS data.
- Starting from XML files, you can
- Use Virtuoso's inbuilt XSLT-Processor for manual XML to RDF/XML
transformation or;
- Leverage the Sponger Cartridge for GRDDL, if there is a transformation service
associated with your XML data source, or;
- Let the Sponger analyze the XML data source and make a
best-effort transformation to RDF.
Step 2 - Linked Data Deployment
Install the Faceted Browser VAD package
(fct_dav.vad ) which delivers the following:
- Faceted Browser Engine UI
- Dynamic Hypermedia Resource Generator
- delivers descriptor resources for every entity (data object) in the Native or
Virtual Quad Stores
- supports a broad array of output formats, including
HTML+RDFa, RDF/XML, N3/Turtle, NTriples,
RDF-JSON, OData+Atom, and OData+JSON.
Step 3 - Linked Data Consumption & Exploitation
Three simple steps allow you, your enterprise, and your
customers to consume and exploit your newly deployed Linked Data
--
- Load a page like this in your browser:
http://<cname>[:<port>]/describe/?uri=<entity-uri>
-
<cname>[:<port>] gets replaced by the
host and port of your Virtuoso instance
-
<entity-uri> gets replaced by the URI you
want to see described -- for instance, the URI of one of the
resources you let the Sponger handle.
- Follow the links presented in the descriptor page.
- If you ever see a blank page with a hyperlink subject name in
the About: section at the top of the page, simply add the parameter
"&sp=1" to the URL in the browser's Address box, and hit
[ENTER]. This will result in an "on the fly" resource retrieval,
transformation, and descriptor page generation.
- Use the navigator controls to page up and down the data
associated with the "in scope" resource descriptor.
Related
10/29/2010 18:54 GMT-0500 |
Modified: 11/02/2010 11:55
GMT-0500 |
Re-introducing the Virtuoso Virtual Database Engine
In recent times a lot of the commentary and focus re. Virtuoso
has centered on the RDF Quad Store and Linked Data. What sometimes gets overlooked
is the sophisticated Virtual Database Engine that provides the
foundation for all of Virtuoso's data integration
capabilities.
In this post I provide a brief re-introduction to this essential
aspect of Virtuoso.
What is it?
This component of Virtuoso is known as the Virtual Database
Engine (VDBMS). It provides transparent high-performance and secure
access to disparate data sources that are external to Virtuoso. It
enables federated access and integration of data hosted by any
ODBC- or JDBC-accessible RDBMS, RDF Store, XML database, or
Document (Free Text)-oriented Content Management System. In
addition, it facilitates integration with Web Services
(SOAP-based SOA RPCs or REST-fully accessible Web Resources).
Why is it important?
In the most basic sense, you shouldn't need to upgrade your
existing database engine version simply because your current DBMS
and Data Access Driver combo isn't compatible with ODBC-compliant
desktop tools such as Microsoft Access, Crystal Reports,
BusinessObjects, Impromptu, or other of ODBC, JDBC, ADO.NET, or OLE DB-compliant applications.
Simply place Virtuoso in front of your so-called "legacy database,"
and let it deliver the compliance levels sought by these tools
In addition, it's important to note that today's enterprise,
through application evolution, company mergers, or acquisitions, is
often faced with disparately-structured data residing in any number
of line-of-business-oriented data silos. Compounding the problem is
the exponential growth of user-generated data via new social
media-oriented collaboration tools and platforms. For companies to
cost-effectively harness the opportunities accorded by the
increasing intersection between line-of-business applications and
social media, virtualization of data silos must be achieved, and
this virtualization must be delivered in a manner that doesn't
prohibitively compromise performance or completely undermine
security at either the enterprise or personal level. Again, this is
what you get by simply installing Virtuoso.
How do I use it?
The VDBMS may be used in a variety of ways, depending on the
data access and integration task at hand. Examples include:
Relational Database Federation
You can make a single ODBC, JDBC, ADO.NET, OLE DB, or XMLA
connection to multiple ODBC- or JDBC-accessible RDBMS data sources,
concurrently, with the ability to perform intelligent distributed
joins against externally-hosted database tables. For instance, you
can join internal human resources data against internal sales and
external stock market data, even when the HR team uses Oracle, the Sales team uses Informix, and the Stock Market figures come
from Ingres!
Conceptual Level Data Access using the RDF Model
You can construct RDF Model-based Conceptual Views atop
Relational Data Sources. This is about generating HTTP-based
Entity-Attribute-Value (E-A-V) graphs
using data culled "on the fly" from native or external data sources
(Relational Tables/Views, XML-based Web Services, or User Defined
Types).
You can also derive RDF Model-based Conceptual Views from Web
Resource transformations "on the fly" -- the Virtuoso Sponger (RDFizing middleware component)
enables you to generate RDF Model Linked Data via a RESTful Web
Service or within the process pipeline of the SPARQL query engine (i.e., you simply use the
URL of a Web Resource in the FROM clause of a
SPARQL query).
It's important to note that Views take the form of HTTP links
that serve as both Data Source Names and Data Source Addresses.
This enables you to query and explore relationships across entities
(i.e., People, Places, and other Real World Things) via HTTP
clients (e.g., Web Browsers) or directly via SPARQL Query Language
constructs transmitted over HTTP.
Conceptual Level Data Access using ADO.NET Entity Frameworks
As an alternative to RDF, Virtuoso can expose ADO.NET Entity
Frameworks-based Conceptual Views over Relational Data Sources. It
achieves this by generating Entity Relationship graphs via its
native ADO.NET Provider, exposing all externally attached ODBC- and
JDBC-accessible data sources. In addition, the ADO.NET Provider
supports direct access to Virtuoso's native RDF database engine,
eliminating the need for resource intensive Entity Frameworks model
transformations.
Related
02/17/2010 16:38 GMT-0500 |
Modified: 02/17/2010 16:46
GMT-0500 |
Getting The Linked Data Value Pyramid Layers Right (Update #2)
One of the real problems that pervades all routes to Linked Data value prop. incomprehension stems
from the layering of its value pyramid; especially when
communicating with -initially detached- end-users.
Note to Web Programmers: Linked Data is
about Data (Wine) and not about Code (Fish).
Thus, it isn't a "programmer only zone", far from it. More than
anything else, its inherently inclusive and spreads its
participation net widely across: Data Architects, Data Integrators,
Power Users, Knowledge Workers, Information Workers, Data Analysts, etc..
Basically, everyone that can "click on a link" is invited to this
particular party; remember, it is about "Linked Data" not "Linked
Code", after all. :-)
Problematic Value Pyramid Layering
Here is an example of a Linked Data value pyramid that I am
stumbling across --with some frequency-- these days (note: 1 being
the pyramid apex):
-
SPARQL Queries
-
RDF Data Stores
- RDF Data Sets
-
HTTP scheme URIs
Basically, Linked Data deployment (assigning de-referencable
HTTP URIs to DBMS records, their attributes, and attribute values
[optionally] ) is occurring last. Even worse, this happens in the
context of Linked Open Data oriented
endeavors, resulting in nothing but confusion or inadvertent
perpetuation of the overarching pragmatically challenged "Semantic Web" stereotype.
As you can imagine, hitting SPARQL as your introduction to
Linked Data is akin to hitting SQL as
your introduction to Relational Database Technology, neither is an
elevator-style value prop. relay mechanism.
In the relational realm, killer demos always started with
desktop productivity tools (spreadsheets, report-writers, SQL QBE
tools etc.) accessing, relational data sources en route to
unveiling the "Productivity" and "Agility" value prop. that such
binding delivered i.e., the desktop application (clients) and the
databases (servers) are distinct, but operating in a mutually
beneficial manner to all, courtesy of a data access standards such
as ODBC (Open Database Connectivity).
In the Linked Data realm, learning to embrace and extend best
practices from the relational dbms realm remains a challenge, a lot
of this has to do with hangovers from a misguided perception that
RDF databases will somehow completely replace RDBMS engines, rather than compliment
them. Thus, you have a counter productive variant of NIH (Not
Invented Here) in play, taking us to the dreaded realm of: Break
the Pot and You Own It (exemplified by the 11+ year Semantic Web
Project comprehension and appreciation odyssey).
From my vantage point, here is how I believe the Linked Data value pyramid should be
layered, especially when communicating the essential value
prop.:
- HTTP URLs -- LINKs to documents (Reports) that users already
appreciate, across the public Web and/or Intranets
- HTTP URIs -- typically not visually distinguishable from the
URLs, so use the Data exposed by de-referencing a URL to show how each Data Item (Entity or Object) is uniquely identified by a
Generic HTTP URI, and how clicking on the said URIs leads
to more structured metadata bearing documents available in a
variety of data representation formats, thereby enabling flexible
data presentation (e.g., smarter HTML pages)
- SPARQL -- when a user appreciates the data representation and
presentation dexterity of a Generic HTTP URI, they will be more
inclined to drill down an additional layer to unravel how HTTP URIs
mechanically deliver such flexibility
- RDF Data Stores -- at this stage the user is now interested
data sources behind the Generic HTTP URIs, courtesy of natural
desire to tweak the data presented in the report; thus, you now
have an engaged user ready to absorb the "How Generic HTTP URIs
Pull This Off" message
- RDF Data Sets -- while attempting to make or tweak HTTP URIs,
users become curious about the actual data loaded into the RDF Data
Store, which is where data sets used to create powerful Lookup Data
Spaces (e.g., DBpedia) come into play such as those from
the LOD constellation as exemplified by
DBpedia (extractions from Wikipedia).
Related
01/31/2010 17:46 GMT-0500 |
Modified: 01/31/2010 17:47
GMT-0500 |
Getting The Linked Data Value Pyramid Layers Right (Update #2)
One of the real problems that pervades all routes to Linked Data value prop. incomprehension stems
from the layering of its value pyramid; especially when
communicating with -initially detached- end-users.
Note to Web
Programmers: Linked Data is about Data (Wine) and not about
Code (Fish). Thus, it isn't a "programmer only zone", far from it.
More than anything else, its inherently inclusive and spreads its
participation net widely across: Data Architects, Data Integrators,
Power Users, Knowledge Workers, Information Workers, Data Analysts, etc..
Basically, everyone that can "click on a link" is invited to this
particular party; remember, it is about "Linked Data" not "Linked
Code", after all. :-)
Problematic Value Pyramid Layering
Here is an example of a Linked Data value pyramid that I am
stumbling across --with some frequency-- these days (note: 1 being
the pyramid apex):
-
SPARQL Queries
-
RDF Data Stores
- RDF Data Sets
-
HTTP scheme URIs
Basically, Linked Data deployment (assigning de-referencable
HTTP URIs to DBMS records, their attributes, and attribute values
[optionally] ) is occurring last. Even worse, this happens in the
context of Linked Open Data oriented
endeavors, resulting in nothing but confusion or inadvertent
perpetuation of the overarching pragmatically challenged "Semantic Web" stereotype.
As you can imagine, hitting SPARQL as your introduction to
Linked Data is akin to hitting SQL as
your introduction to Relational Database Technology, neither is an
elevator-style value prop. relay mechanism.
In the relational realm, killer demos always started with
desktop productivity tools (spreadsheets, report-writers, SQL QBE
tools etc.) accessing, relational data sources en route to
unveiling the "Productivity" and "Agility" value prop. that such
binding delivered i.e., the desktop application (clients) and the
databases (servers) are distinct, but operating in a mutually
beneficial manner to all, courtesy of a data access standards such
as ODBC (Open Database Connectivity).
In the Linked Data realm, learning to embrace and extend best
practices from the relational dbms realm remains a challenge, a lot
of this has to do with hangovers from a misguided perception that
RDF databases will somehow completely replace RDBMS engines, rather than compliment
them. Thus, you have a counter productive variant of NIH (Not
Invented Here) in play, taking us to the dreaded realm of: Break
the Pot and You Own It (exemplified by the 11+ year Semantic Web
Project comprehension and appreciation odyssey).
From my vantage point, here is how I believe the Linked Data value pyramid should be
layered, especially when communicating the essential value
prop.:
- HTTP URLs -- LINKs to documents (Reports) that users already
appreciate, across the public Web and/or Intranets
- HTTP URIs -- typically not visually distinguishable from the
URLs, so use the Data exposed by de-referencing a URL to show how each Data Item (Entity or Object) is uniquely identified by a
Generic HTTP URI, and how clicking on the said URIs leads
to more structured metadata bearing documents available in a
variety of data representation formats, thereby enabling flexible
data presentation (e.g., smarter HTML pages)
- SPARQL -- when a user appreciates the data representation and
presentation dexterity of a Generic HTTP URI, they will be more
inclined to drill down an additional layer to unravel how HTTP URIs
mechanically deliver such flexibility
- RDF Data Stores -- at this stage the user is now interested
data sources behind the Generic HTTP URIs, courtesy of natural
desire to tweak the data presented in the report; thus, you now
have an engaged user ready to absorb the "How Generic HTTP URIs
Pull This Off" message
- RDF Data Sets -- while attempting to make or tweak HTTP URIs,
users become curious about the actual data loaded into the RDF Data
Store, which is where data sets used to create powerful Lookup Data
Spaces (e.g., DBpedia) come into play such as those from
the LOD constellation as exemplified by
DBpedia (extractions from Wikipedia).
Related
01/31/2010 17:44 GMT-0500 |
Modified: 02/01/2010 09:02
GMT-0500 |
5 Very Important Things to Note about HTTP based Linked Data
- It isn't World Wide Web Specific (HTTP != World
Wide Web)
- It isn't Open Data Specific
- It isn't about "Free" (Beer or Speech)
- It isn't about Markup (so don't expect to grok it via "markup
first" approach)
- It's about Hyperdata - the use of HTTP and REST to
deliver a powerful platform agnostic mechanism for Data Reference,
Access, and Integration.
When trying to understand HTTP based Linked Data, especially if you're well versed
in DBMS technology use (User, Power User, Architect, Analyst, DBA,
or Programmer) think:
- Open Database Connectivity (ODBC) without operating system, data model,
or wire-protocol specificity or lock-in potential
- Java Database Connectivity (JDBC) without programming language
specificity
-
ADO.NET without .NET runtime specificity and
.NET bound language specificity
- OLE-DB without Windows operating system & programming
language specificity
- XMLA without XML format specificity - with Tabular and
Multidimensional results formats expressible in a variety of data
representation formats.
- All of the above scoped to the Record rather than Container
level, with Generic HTTP scheme URIs associated with each Record,
Field, and Field value (optionally)
Remember the need for Data Access & Integration technology
is the by product of the following realities:
- Human curated data is ultimately dirty, because:
- our thick thumbs, inattention, distractions, and general
discomfort with typing, make typos prevalent
- database engines exist for a variety of data models - Graph,
Relational, Hierarchical;
- within databases you have different record container/partition
names e.g. Table Names;
- within a database record container you have records that are
really aspects of the same thing (different keys exist in a
plethora of operational / line of business systems that expose
aspects of the same entity e.g., customer data that spans
Accounts, CRM, ERP application databases);
- different field names (one database has "EMP" while another has
"Employee") for the same record
- .
- Units of measurement is driven by locale, the UK office wants
to see sales in Pounds Sterling while the French office prefers
Euros etc.
- All of the above is subject to context halos which can be quite granular re.
sensitivity e.g. staff travel between locations that alter locales
and their roles; basically, profiles matters a lot.
Related
01/31/2010 17:31 GMT-0500 |
Modified: 02/01/2010 09:00
GMT-0500 |
Virtuoso Chronicles from the Field: Nepomuk, KDE, and the quest for a sophisticated RDF DBMS.
For this particular user experience chronicle, I've simply
inserted the content of Sebastian Trueg's post titled: What We Did Last Summer (And the Rest of 2009)
– A Look Back Onto the Nepomuk Development Year ..., directly
into this post, without any additional commentary or
modification.
2009 is over. Yeah, sure, trueg, we know that, it has been
over for a while now! Ok, ok, I am a bit late, but still I
would like to get this one out - if only for my archive. So here
goes.
Let’s start with the major topic of 2009 (and also the beginning
of 2010): The new Nepomuk database backend: Virtuoso. Everybody who used Nepomuk had the
same problems: you either used the sesame2 backend which depends on Java and
steals all of your memory or you were stuck with Redland which had the
worst performance and missed some SPARQL features making important parts of
Nepomuk like queries unusable. So more than a year ago I had
the idea to use the one GPL’ed database server out there that
supported RDF in a professional manner: OpenLink’s
Virtuoso. It has all the features we need,
has a very good performance, and scales up to dimensions we will
probably never reach on the desktop (yeah, right, and 64k main
memory will be enough forever!). So very early I started
coding the necessary Soprano plugin which would talk to a locally
running Virtuoso server through ODBC. But since I ran into tons of small
problems (as always) and got sidetracked by other tasks I did not
finish it right away. OpenLink, however, was very interested in the
idea of their server being part of every KDE installation (why
wouldn’t they ;)). So they not only introduced a lite-mode which makes Virtuoso suitable
for the desktop but also helped in debugging all the problems that
I had left. Many test runs, patches, and a Virtuoso 5.0.12 release
later I could finally announce the Virtuoso
integration as usable.
Then end of last year I dropped the support for sesame2 and
redland. Virtuoso is now the only supported database backend. The
reason is simple: Virtuoso is way more powerful than the rest - not
only in terms of performance - and it is fully implemented in
C(++) without any traces of Java. Maybe even
more important is the integration of the full text index which
makes the previously used CLucene index unnecessary. Thus, we can
finally combine full text and graph queries in one SPARQL query.
This results in a cleaner API and way faster return of search
results since there is no need to combine the results from several
queries anymore. A direct result of that is the new Nepomuk Query API which I will discuss
later.
So now the only thing I am waiting for is the first bugfix
release of Virtuoso 6, i.e. 6.0.1 which will fix the bugs that make
6.0.0 fail with Nepomuk. Should be out any day now. :)
The Nepomuk Query API
Querying data in
Nepomuk pre-KDE-4.4 could be done in one of two ways: 1. Use the
very limited capabilities of the ResourceManager to list resources with
certain properties or of a certain type; or 2. Write your own
SPARQL query using ugly QString::arg
replacements.
With the introduction of Virtuoso and its awesome power we can
now do pretty much everything in one query. This allowed me to finally create a query API for KDE:
Nepomuk::Query::Query and friends. I won’t
go into much detail here since I did that before.
All in all you should remember one thing: whenever you think
about writing your own SPARQL query in a KDE application - have a
look at libnepomukquery. It is very likely that you can avoid the
hassle of debugging a query by using the query API.
The first nice effect of the new API (apart from me using it all
over the place obviously) is the new query interface in Dolphin.
Internally it simply combines a bunch of Nepomuk::Query::Term objects into a
Nepomuk::Query::AndTerm. All very readable
and no ugly query strings.
Dolphin Search Panel in KDE SC 4.4
Shared Desktop Ontologies
An important part of the Nepomuk
research project was the creation of a set of ontologies for describing desktop resources
and their metadata. After the Xesam
project under the umbrella of freedesktop.org had been convinced to use
RDF for describing file metadata they developed their own ontology.
Thanks to Evgeny (phreedom) Egorochkin and Antonie Mylka both the
Xesam ontology and the Nepomuk Information Elements Ontology were already
very close in design. Thus, it was relatively easy to merge the two
and be left with only one ontology to support. Since then not only
KDE but also Strigi and Tracker are using the Nepomuk ontologies.
At the Gran Canaria Desktop Summit I met some of the guys from
Tracker and we tried to come up with a plan to create a joint
project to maintain the ontologies. This got off to a rough start
as nobody really felt responsible. So I simply took the initiative
and released the shared-desktop-ontologies version 0.1 in
November 2009. The result was a s***-load of hate-mails and bug
reports due to me breaking KDE build. But in the end it was worth
it. Now the package is established and other projects can start to
pick it up to create data compatible to the Nepomuk system and
Tracker.
Today the ontologies (and the shared-desktop-ontologies package)
are maintained in the Oscaf project at Sourceforge. The situation
is far from perfect but it is a good start. If you need specific
properties in the ontologies or are thinking about creating one for
your own application - come and join us in the bug tracker…
Timeline KIO Slave
It was at the Akonadi meeting that Will Stephenson and myself
got into talking about mimicking some Zeitgeist functionality through Nepomuk.
Basically it meant gathering some data when opening and when saving
files. We quickly came up with a hacky patch for KIO and KFileDialog which covered most cases and
allowed us to track when a file was modified and by which
application. This little experiment did not leave that state though
(it will, however, this year) but another one did: Zeitgeist also
provides a fuse filesystem which allows to browse the files by
modification dates. Well, whatever fuse can do, KIO can do as well.
Introducing the timeline:/ KIO slave which
gives a calendar view onto your files.
Tips And Tricks
Well, I thought I would mention the Tips And Tricks section I wrote for the
techbase. It might not be a big deal but I
think it contains some valuable information in case you are using
Nepomuk as a developer.
Google Summer Of Code 2009
This time around I had the privilege to mentor two students in the Google Summer
of Code. Alessandro Sivieri and Adam Kidder did outstanding work on
Improved Virtual Folders and the Smart File Dialog.
Adam’s work lead me to some heavy improvements in the Nepomuk
KIO slaves myself which I only finished this week (more details on
that coming up). Alessandro continued his work on faceted file
browsing in KDE and created:
Sembrowser
Alessandro is following up on his work to make faceted file
browsing a reality in 2010 (and KDE SC 4.5). Since it was too late
to get faceted browsing into KDE SC 4.4 he is working on Sembrowser, a stand-alone faceted file
browser which will be the grounds for experiments until the code is
merged into Dolphin.
Faceted Browsing in KDE with
Sembrowser
Nepomuk Workshops
In 2009 I organized the first Nepomuk workshop in Freiburg,
Germany. And also the second one. While I reported properly on the first one I
still owe a summary for the second one. I will get around to that -
sooner or later. ;)
CMake Magic
Soprano gives us a nice command line tool to
create a C++ namespace from an ontology file: onto2vocabularyclass. It produces nice
convenience namespaces like Soprano::Vocabulary::NAO. Nepomuk adds
another tool named nepomuk-rcgen. Both were a bit clumsy to
use before. Now we have nice cmake macros which make it very simple
to use both.
See the techbase article on how to use the new
macros.
Bangarang
Without my knowledge (imagine that!) Andrew Lake created
an amazing new media player named Bangarang - a Jamaican word for noise,
chaos or disorder. This player is Nepomuk-enabled in the sense
that it has a media library which lets you browse your media files
based on the Nepomuk data. It remembers the number of times a song
or a video has been played and when it was played last. It allows
to add detail such as the TV series name, season, episode number,
or actors that are in the video - all through Nepomuk (I hope we
will soon get tvdb integration).
Edit metadata directly in Bangarang
Dolphin showing TV episode metadata
created by Bangarang
And of course searching for it works,
too...
And it is pretty, too...
I am especially excited about this since finally applications
not written or mentored by me start contributing Nepomuk data.
Gran Canaria Desktop Summit
2009 was also the year of the first Gnome-KDE joint-conference.
Let me make a bulletin for completeness and refer to my previous blog post reporting on my
experiences on the island.
Well, that was by far not all I did in 2009 but I think I
covered most of the important topics. And after all it is ‘just a
blog entry’ - there is no need for completeness. Thanks for
reading.
"
01/28/2010 11:14 GMT-0500 |
Modified: 02/01/2010 09:02
GMT-0500 |
BBC Linked Data Meshup In 3 Steps
Situation Analysis:
Dr. Dre is one of the artists in the Linked Data Space we host for the BBC. He
is also referenced in music oriented data spaces such as DBpedia, MusicBrainz and Last.FM (to name a few).
Challenge:
How do I obtain a holistic view of the entity "Dr. Dre" across the BBC, MusicBrainz,
and Last.FM data spaces? We know the BBC published Linked Data, but
what about Last.FM and MusicBrainz? Both of these data spaces only
expose XML or JSON data via REST APIs?
Solution:
Simple 3 step Linked Data Meshup courtesy of Virtuoso's in-built RDFizer Middleware
"the Sponger" (think ODBC Driver Manager for the Linked Data
Web) and its numerous Cartridges (think ODBC
Drivers for the Linked Data Web).
Steps:
- Go to Last.FM and search using pattern: Dr. Dre (you will end
up with this URL: http://www.last.fm/music/Dr.+Dre)
- Go to the Virtuoso powered BBC Linked Data Space home page and enter:
http://bbc.openlinksw.com/about/html/http://www.last.fm/music/Dr.+Dre
- Go to the BBC Linked Data Space home page and type full text
pattern (using default tab): Dr. Dre, then view Dr. Dre's metadata via the Statistics
Link.
What Happened?
The following took place:
- Virtuoso Sponger sent an HTTP GET to Last.FM
- Distilled the "Artist" entity "Dr. Dre" from the page, and made
a Linked Data graph
- Inverse Functional Property and sameAs reasoning handled the
Meshup (augmented graph from a conjunctive query processing
pipeline)
- Links for "Dr. Dre" across BBC (sameAs), Last.FM (seeAlso), via DBpedia
URI.
The new enhanced URI for Dr. Dre now provides
a rich holistic view of the aforementioned "Artist" entity. This
URI is usable anywhere on the Web for Linked Data Conduction
:-)
Related (as in NearBy)
06/12/2009 14:09 GMT-0500 |
Modified: 06/12/2009 16:38
GMT-0500 |
Time for RDBMS Primacy Downgrade is Nigh! (No Embedded Images Edition - Update 1)
As the world works it way through a "once in a generation"
economic crisis, the long overdue downgrade of the RDBMS, from its pivotal position at the
apex of the data access and data management pyramid is
nigh.
What is the Data Access, and Data Management Value
Pyramid?
As depicted below, a top-down view of the data access and data
management value chain. The term: apex, simply indicates value
primacy, which takes the form of a data access API based entry
point into a DBMS realm -- aligned to an underlying data model.
Examples of data access APIs include: Native Call Level Interfaces
(CLIs), ODBC, JDBC, ADO.NET, OLE-DB, XMLA, and Web Services.
See: AVF Pyramid Diagram.
The degree to which ad-hoc views of data managed by a DBMS can
be produced and dispatched to relevant data consumers (e.g.
people), without compromising concurrency, data durability, and
security, collectively determine the "Agility Value Factor" (AVF)
of a given DBMS. Remember, agility as the cornerstone of
environmental adaptation is as old as the concept of evolution, and
intrinsic to all pursuits of primacy.
In simpler business oriented terms, look at AVF as the degree to
which DBMS technology affects the ability to effectively implement
"Market Leadership Discipline" along the following pathways:
innovation, operation excellence, or customer intimacy.
Why has RDBMS Primacy has Endured?
Historically, at least since the late '80s, the RDBMS genre of
DBMS has consistently offered the highest AVF relative to other
DBMS genres en route to primacy within the value pyramid. The
desire to improve on paper reports and spreadsheets is basically
what DBMS technology has fundamentally addressed to date, even
though conceptual level interaction with data has never been its
forte.
See: RDBMS Primacy Diagram.
For more then 10 years -- at the very least -- limitations of
the traditional RDBMS in the realm of conceptual level interaction
with data across diverse data sources and schemas (enterprise, Web,
and Internet) has been crystal clear to many
RDBMS technology practitioners, as indicated by some of the quotes
excerpted below:
"Future of Database Research is excellent, but what is the
future of data?"
"..it is hard for me to disagree with the conclusions in this
report. It captures exactly the right thoughts, and should be a
must read for everyone involved in the area of databases and
database research in particular."
-- Dr. Anant Jingran, CTO, IBM Information Management Systems, commenting on
the 2007 RDBMS technology retreat attended by a
number of key DBMS technology pioneers and researchers.
"One size fits all: A concept whose time has come
and gone
-
They are direct descendants of System R and Ingres and were architected more than 25
years ago
-
They are advocating "one size fits all"; i.e. a single
engine that solves all DBMS needs.
-- Prof. Michael Stonebreaker, one of the founding
fathers of the RDBMS industry.
Until this point in time, the requisite confluence of
"circumstantial pain" and "open standards" based technology
required to enable an objective "compare and contrast" of RDBMS
engine virtues and viable alternatives hasn't occurred. Thus, the
RDBMS has endured it position of primacy albeit on a "one size fits
all basis".
Circumstantial Pain
As mentioned earlier, we are in the midst of an economic crisis
that is ultimately about a consistent inability to connect dots
across a substrate of interlinked data sources that transcend
traditional data access boundaries with high doses of schematic
heterogeneity. Ironically, in a era of the dot-com, we haven't been
able to make meaningful connections between relevant "real-world
things" that extend beyond primitive data hosted database tables
and content management style document containers; we've struggled
to achieve this in the most basic sense, let alone evolve our
ability to connect inline with the exponential rate at which the Internet & Web
are spawning "universes of discourse" (data spaces) that emanate
from user activity (within the enterprise and across the
Internet & Web). In a nutshell, we haven't been able to upgrade
our interaction with data such that "conceptual models" and
resulting "context lenses" (or facets) become concrete;
by this I mean: real-world entity interaction making its way into the
computer realm as opposed to the impedance we all suffer today when
we transition from conceptual model interaction (real-world) to
logical model interaction (when dealing with RDBMS based data
access and data management).
Here are some simple examples of what I can only best describe
as: "critical dots unconnected", resulting from an inability to
interact with data conceptually:
Government (Globally) -
Financial regulatory bodies couldn't effectively discern that a
Credit Default Swap is an Insurance policy in
all but literal name. And in not doing so the cost of an
unregulated insurance policy laid the foundation for
exacerbating the toxicity of fatally flawed mortgage backed
securities. Put simply: a flawed insurance policy was the fallback
on a toxic security that financiers found exotic based on
superficial packaging.
Enterprises -
Banks still don't understand that capital really does exists in
tangible and intangible forms; with the intangible being the
variant that is inherently dynamic. For example, a tech companies
intellectual capital far exceeds the value of fixture, fittings,
and buildings, but you be amazed to find that in most cases this
vital asset has not significant value when banks get down to the
nitty gritty of debt collateral; instead, a buffer of flawed
securitization has occurred atop a borderline static asset class
covering the aforementioned buildings, fixtures, and fittings.
In the general enterprise arena, IT executives continued to "rip
and replace" existing technology without ever effectively
addressing the timeless inability to connect data across disparate
data silos generated by internal enterprise applications, let alone
the broader need to mesh data from the inside with external data
sources. No correlations made between the growth of buzzwords and
the compounding nature of data integration challenges. It's 2009
and only a miniscule number of executives dare fantasize about
being anywhere within distance of the: relevant information at your
fingertips vision.
Looking more holistically at data interaction in general,
whether you interact with data in the enterprise space (i.e., at
work) or on the Internet or Web, you ultimately are delving into a
mishmash of disparate computer systems, applications, service (Web
or SOA), and databases (of the RDBMS variety in a majority of
cases) associated with a plethora of disparate schemas. Yes, but
even today "rip and replace" is still the norm pushed by most
vendors; pitting one mono culture against another as exemplified by
irrelevances such as: FOSS/LAMP vs Commercial or Web vs.
Enterprise, when none of this matters if the data access and
integration issues are recognized let alone addressed (see:
Applications are Like Fish and Data Like
Wine).
Like the current credit-crunch, exponential growth of data
originating from disparate application databases and associated
schemas, within shrinking processing time frames, has triggered a
rethinking of what defines data access and data management value
today en route to an inevitable RDBMS downgrade within the value
pyramid.
Technology
There have been many attempts to address real-world modeling
requirements across the broader DBMS community from Object
Databases to Object-Relational Databases, and more recently the
emergence of simple Entity-Attribute-Value model DBMS engines. In
all cases failure has come down to the existence of one or more of
the following deficiencies, across each potential alternative:
- Query language standardization - nothing close to SQL
standardization
- Data Access API standardization - nothing close to ODBC, JDBC,
OLE-DB, or ADO.NET
- Wire protocol standardization - nothing close to HTTP
- Distributed Identity infrastructure - nothing close to the
non-repudiatable digital Identity that foaf+ssl accords
- Use of Identifiers as network based pointers to data sources -
nothing close to RDF based Linked Data
- Negotiable data representation - nothing close to Mime and HTTP
based Content Negotiation
- Scalability especially in the era of Internet & Web
scale.
Entity-Attribute-Value with Classes & Relationships
(EAV/CR) data models
A common characteristic shared by all post-relational DBMS
management systems (from Object Relational to pure Object) is an
orientation towards variations of EAV/CR based data models.
Unfortunately, all efforts in the EAV/CR realm have typically
suffered from at least one of the deficiencies listed above. In
addition, the same "one DBMS model fits all" approach that lies at
the heart of the RDBMS downgrade also exists in the EAV/CR
realm.
What Comes Next?
The RDBMS is not going away (ever), but its era of primacy -- by
virtue of its placement at the apex of the data access and data
management value pyramid -- is over! I make this bold claim for the
following reasons:
- The Internet aided "Global Village" has brought "Open World" vs "Closed World" assumption issues to the fore
e.g., the current global economic crisis remains centered on the
inability to connect dots across "Open World" and "Closed World"
data frontiers
- Entity-Attribute-Value with Classes & Relationships
(EAV/CR) based DBMS models are more effective when dealing with
disparate data associated with disparate schemas, across disparate
DBMS engines, host operating systems, and networks.
Based on the above, it is crystal clear that a different kind of
DBMS -- one with higher AVF relative to the RDBMS -- needs to sit
atop today's data access and data management value pyramid. The
characteristics of this DBMS must include the following:
- Every item of data (Datum/Entity/Object/Resource) has
Identity
- Identity is achieved via Identifiers that aren't locked at the
DBMS, OS, Network, or Application levels
- Object Identifiers and Object values are independent
(extricably linked by association)
- Object values should be de-referencable via Object
Identifier
- Representation of de-referenced value graph (entity,
attributes, and values mesh) must be negotiable (i.e. content
negotiation)
- Structured query language must provide mechanism for Creation,
Deletion, Updates, and Querying of data objects
- Performance & Scalability across "Closed World"
(enterprise) and "Open World" (Internet & Web) realms.
Quick recap, I am not saying that RDBMS engine technology is
dead or obsolete. I am simply stating that the era of RDBMS primacy
within the data access and data management value pyramid is
over.
The problem domain (conceptual model views over heterogeneous
data sources) at the apex of the aforementioned pyramid has simply
evolved beyond the natural capabilities of the RDBMS which is
rooted in "Closed World" assumptions re., data definition, access,
and management. The need to maintain domain based conceptual
interaction with data is now palpable at every echelon within our
"Global Village" - Internet, Web, Enterprise, Government etc.
It is my personal view that an EAV/CR model based DBMS, with
support for the seven items enumerated above, can trigger the long
anticipated RDBMS downgrade. Such a DBMS would be inherently
multi-model because you would need to the best of RDBMS and EAV/CR
model engines in a single product, with in-built support for HTTP
and other Internet protocols in order to effectively address data
representation and serialization issues.
EAV/CR Oriented Data Access & Management Technology
Examples of contemporary EAV/CR frameworks that provide concrete
conceptual layers for data access and data management currently
include:
The frameworks above provide the basis for a revised AVF
pyramid, as depicted below, that reflects today's data access and
management realities i.e., an Internet & Web driven global
village comprised of interlinked distributed data objects,
compatible with "Open World" assumptions.
See: New EAV/CR Primacy Diagram.
Related
01/27/2009 19:19 GMT-0500 |
Modified: 03/17/2009 11:50
GMT-0500 |
The Time for RDBMS Primacy Downgrade is Nigh!
As the world works it way through a "once in a generation"
economic crisis, the long overdue downgrade of the RDBMS, from its pivotal position at the
apex of the data access and data management pyramid is
nigh.
What is the Data Access, and Data Management Value
Pyramid?
As depicted below, a top-down view of the data access and data
management value chain. The term: apex, simply indicates value
primacy, which takes the form of a data access API based entry
point into a DBMS realm -- aligned to an underlying data model.
Examples of data access APIs include: Native Call Level Interfaces
(CLIs), ODBC, JDBC, ADO.NET, OLE-DB, XMLA, and Web Services.
The degree to which ad-hoc views of data managed by a DBMS can
be produced and dispatched to relevant data consumers (e.g.
people), without compromising concurrency, data durability, and
security, collectively determine the "Agility Value Factor" (AVF)
of a given DBMS. Remember, agility as the cornerstone of
environmental adaptation is as old as the concept of evolution, and
intrinsic to all pursuits of primacy.
In simpler business oriented terms, look at AVF as the degree to
which DBMS technology affects the ability to effectively implement
"Market Leadership Discipline" along the following pathways:
innovation, operation excellence, or customer intimacy.
Why has RDBMS Primacy has Endured?
Historically, at least since the late '80s, the RDBMS genre of
DBMS has consistently offered the highest AVF relative to other
DBMS genres en route to primacy within the value pyramid. The
desire to improve on paper reports and spreadsheets is basically
what DBMS technology has fundamentally addressed to date, even
though conceptual level interaction with data has never been its
forte.
For more then 10 years -- at the very least -- limitations of
the traditional RDBMS in the realm of conceptual level interaction
with data across diverse data sources and schemas (enterprise, Web,
and Internet) has been crystal clear to many
RDBMS technology practitioners, as indicated by some of the quotes
excerpted below:
"Future of Database Research is excellent, but what is the
future of data?"
"..it is hard for me to disagree with the conclusions in this
report. It captures exactly the right thoughts, and should be a
must read for everyone involved in the area of databases and
database research in particular."
-- Dr. Anant Jingran, CTO, IBM Information Management Systems, commenting on
the 2007 RDBMS technology retreat attended by a
number of key DBMS technology pioneers and researchers.
"One size fits all: A concept whose time has come
and gone
-
They are direct descendants of System R and Ingres and were architected more than 25
years ago
-
They are advocating "one size fits all"; i.e. a single
engine that solves all DBMS needs.
-- Prof. Michael Stonebreaker, one of the founding
fathers of the RDBMS industry.
Until this point in time, the requisite confluence of
"circumstantial pain" and "open standards" based technology
required to enable an objective "compare and contrast" of RDBMS
engine virtues and viable alternatives hasn't occurred. Thus, the
RDBMS has endured it position of primacy albeit on a "one size fits
all basis".
Circumstantial Pain
As mentioned earlier, we are in the midst of an economic crisis
that is ultimately about a consistent inability to connect dots
across a substrate of interlinked data sources that transcend
traditional data access boundaries with high doses of schematic
heterogeneity. Ironically, in a era of the dot-com, we haven't been
able to make meaningful connections between relevant "real-world
things" that extend beyond primitive data hosted database tables
and content management style document containers; we've struggled
to achieve this in the most basic sense, let alone evolve our
ability to connect inline with the exponential rate at which the Internet & Web
are spawning "universes of discourse" (data spaces) that emanate
from user activity (within the enterprise and across the
Internet & Web). In a nutshell, we haven't been able to upgrade
our interaction with data such that "conceptual models" and
resulting "context lenses" (or facets) become concrete;
by this I mean: real-world entity interaction making its way into the
computer realm as opposed to the impedance we all suffer today when
we transition from conceptual model interaction (real-world) to
logical model interaction (when dealing with RDBMS based data
access and data management).
Here are some simple examples of what I can only best describe
as: "critical dots unconnected", resulting from an inability to
interact with data conceptually:
Government (Globally) -
Financial regulatory bodies couldn't effectively discern that a
Credit Default Swap is an Insurance policy in
all but literal name. And in not doing so the cost of an
unregulated insurance policy laid the foundation for
exacerbating the toxicity of fatally flawed mortgage backed
securities. Put simply: a flawed insurance policy was the fallback
on a toxic security that financiers found exotic based on
superficial packaging.
Enterprises -
Banks still don't understand that capital really does exists in
tangible and intangible forms; with the intangible being the
variant that is inherently dynamic. For example, a tech companies
intellectual capital far exceeds the value of fixture, fittings,
and buildings, but you be amazed to find that in most cases this
vital asset has not significant value when banks get down to the
nitty gritty of debt collateral; instead, a buffer of flawed
securitization has occurred atop a borderline static asset class
covering the aforementioned buildings, fixtures, and fittings.
In the general enterprise arena, IT executives continued to "rip
and replace" existing technology without ever effectively
addressing the timeless inability to connect data across disparate
data silos generated by internal enterprise applications, let alone
the broader need to mesh data from the inside with external data
sources. No correlations made between the growth of buzzwords and
the compounding nature of data integration challenges. It's 2009
and only a miniscule number of executives dare fantasize about
being anywhere within distance of the: relevant information at your
fingertips vision.
Looking more holistically at data interaction in general,
whether you interact with data in the enterprise space (i.e., at
work) or on the Internet or Web, you ultimately are delving into a
mishmash of disparate computer systems, applications, service (Web
or SOA), and databases (of the RDBMS variety in a majority of
cases) associated with a plethora of disparate schemas. Yes, but
even today "rip and replace" is still the norm pushed by most
vendors; pitting one mono culture against another as exemplified by
irrelevances such as: FOSS/LAMP vs Commercial or Web vs.
Enterprise, when none of this matters if the data access and
integration issues are recognized let alone addressed (see:
Applications are Like Fish and Data Like
Wine).
Like the current credit-crunch, exponential growth of data
originating from disparate application databases and associated
schemas, within shrinking processing time frames, has triggered a
rethinking of what defines data access and data management value
today en route to an inevitable RDBMS downgrade within the value
pyramid.
Technology
There have been many attempts to address real-world modeling
requirements across the broader DBMS community from Object
Databases to Object-Relational Databases, and more recently the
emergence of simple Entity-Attribute-Value model DBMS engines. In
all cases failure has come down to the existence of one or more of
the following deficiencies, across each potential alternative:
- Query language standardization - nothing close to SQL
standardization
- Data Access API standardization - nothing close to ODBC, JDBC,
OLE-DB, or ADO.NET
- Wire protocol standardization - nothing close to HTTP
- Distributed Identity infrastructure - nothing close to the
non-repudiatable digital Identity that foaf+ssl accords
- Use of Identifiers as network based pointers to data sources -
nothing close to RDF based Linked Data
- Negotiable data representation - nothing close to Mime and HTTP
based Content Negotiation
- Scalability especially in the era of Internet & Web
scale.
Entity-Attribute-Value with Classes & Relationships
(EAV/CR) data models
A common characteristic shared by all post-relational DBMS
management systems (from Object Relational to pure Object) is an
orientation towards variations of EAV/CR based data models.
Unfortunately, all efforts in the EAV/CR realm have typically
suffered from at least one of the deficiencies listed above. In
addition, the same "one DBMS model fits all" approach that lies at
the heart of the RDBMS downgrade also exists in the EAV/CR
realm.
What Comes Next?
The RDBMS is not going away (ever), but its era of primacy -- by
virtue of its placement at the apex of the data access and data
management value pyramid -- is over! I make this bold claim for the
following reasons:
- The Internet aided "Global Village" has brought "Open World" vs "Closed World" assumption issues to the fore
e.g., the current global economic crisis remains centered on the
inability to connect dots across "Open World" and "Closed World"
data frontiers
- Entity-Attribute-Value with Classes & Relationships
(EAV/CR) based DBMS models are more effective when dealing with
disparate data associated with disparate schemas, across disparate
DBMS engines, host operating systems, and networks.
Based on the above, it is crystal clear that a different kind of
DBMS -- one with higher AVF relative to the RDBMS -- needs to sit
atop today's data access and data management value pyramid. The
characteristics of this DBMS must include the following:
- Every item of data (Datum/Entity/Object/Resource) has
Identity
- Identity is achieved via Identifiers that aren't locked at the
DBMS, OS, Network, or Application levels
- Object Identifiers and Object values are independent
(extricably linked by association)
- Object values should be de-referencable via Object
Identifier
- Representation of de-referenced value graph (entity,
attributes, and values mesh) must be negotiable (i.e. content
negotiation)
- Structured query language must provide mechanism for Creation,
Deletion, Updates, and Querying of data objects
- Performance & Scalability across "Closed World"
(enterprise) and "Open World" (Internet & Web) realms.
Quick recap, I am not saying that RDBMS engine technology is
dead or obsolete. I am simply stating that the era of RDBMS primacy
within the data access and data management value pyramid is
over.
The problem domain (conceptual model views over heterogeneous
data sources) at the apex of the aforementioned pyramid has simply
evolved beyond the natural capabilities of the RDBMS which is
rooted in "Closed World" assumptions re., data definition, access,
and management. The need to maintain domain based conceptual
interaction with data is now palpable at every echelon within our
"Global Village" - Internet, Web, Enterprise, Government etc.
It is my personal view that an EAV/CR model based DBMS, with
support for the seven items enumerated above, can trigger the long
anticipated RDBMS downgrade. Such a DBMS would be inherently
multi-model because you would need to the best of RDBMS and EAV/CR
model engines in a single product, with in-built support for HTTP
and other Internet protocols in order to effectively address data
representation and serialization issues.
EAV/CR Oriented Data Access & Management Technology
Examples of contemporary EAV/CR frameworks that provide concrete
conceptual layers for data access and data management currently
include:
The frameworks above provide the basis for a revised AVF
pyramid, as depicted below, that reflects today's data access and
management realities i.e., an Internet & Web driven global
village comprised of interlinked distributed data objects,
compatible with "Open World" assumptions.
Related
01/24/2009 20:04 GMT-0500 |
Modified: 06/03/2009 18:09
GMT-0500 |
|
|