[PragmaticWeb] Semantics for EAI

[Sebastian Samaruga]

Yes, sure. And it's not only ERM & CRM but any transactional system /
contents repository an organization could have deployed inside (BI, BPM /
Workflow / any source of domains "linkeable" and augmentable knowledge).

My efforts are towards an unified "client" layer, in a given protocol
(SPARQL / OData for example) for whatever "domain connectors" could be
deployed / implemented (datasources). Then enable operations over this
aggregated layer performing augmentations: aggregation (hierarchical
regression) / alignment (attributes / links clustering) / activation (type
/ ontology matching).

Although abstracting everything into (a layered architecture of organized)
URIs and reactive and event driven models will help a lot, I lack the
knowledge for develop / deploy many of the tasks needed for having what I'd
like to be implemented.

Any type of collaborations / comments / schema / backends or language
recommendations / sample ontologies are welcome. Regards,

Sebastián
http://snxama.blogspot.com


On Fri, Jun 21, 2019, 8:13 AM Amirouche Boubekki <
amirouche.boubekki at gmail.com> wrote:

> Getting together a vocabulary to allow ERP and CRM to interop is a very
> difficult task.
>
> Le ven. 14 juin 2019 à 03:12, Sebastian Samaruga <ssamarug at gmail.com> a
> écrit :
>
>> I've been very enthusiastic lately because, around the stuff I've been
>> reading and researching, I see great chances of a solution being
>> implemented that, despite looking a lot of work, has the same underlying
>> backbone in all of it's functionality. Let me state first that I'm
>> convinced of the vast role of semantics into the EAI niche.
>>
>> The basic attempt is to achieve a "semantic overlay" over ERP, CRM and
>> application domains of organizations allowing for enhancements and
>> extensions in the field of the extended knowledge that such applications
>> could "learn" in base of the information contained in their data and
>> adjacent knowledge integrated from various origins.
>>
>> In the beginning I'd use an "abstraction layer" approach, reuse backends
>> and existing applications with semantic "enhancements" and, also, leverage
>> integration with existing facilities with frameworks and techniques in use
>> today (ORM with Hibernate / Spring, for example, implementing custom JDBC
>> driver). This leveraging extension for new requirements or use cases as
>> also for existing schema "enrichment" with inferences and learning
>> "semantic" in the more transparent manner.
>>
>> As a begging there exists Tryton (and its derivatives: GNU Health) that
>> in principle seems like an excellent departure point for an example ERP /
>> CRP integration example, highly configurable and customizable. It could be
>> the main "external" component considering the backbone of a proof of
>> concept for the integration of applications knowledge.
>>
>> The components to be developed also will leverage being consumer /
>> producer of data streams for frameworks such as Apache Metamodel, JBoss
>> Teiid y JBoss Drools / JBPM. It could also leverage standards and
>> frameworks such as OData and R2RQ for consumption and production of
>> "enriched" information and knowledge. This will enhance capabilities of
>> implementing connectors to consume and provide services and backends for
>> later integration.
>>
>> In respect to an Enterprise Java ecosystem (Spring) an MVC / DCI IoC
>> pattern implementation is to be facilitated by the existence of a JDBC
>> driver who procures ORM / OGM (Object Graph Mapper). JCA (Java Connector
>> Architecture) / Activation JAF (Java Activation Framework) facilitate
>> descriptive and declarative implementation of process flows and state
>> exposed as services.
>>
>> Features:
>>
>> Declarative hypermedia:
>> REST / HAL / HATEOAS.
>> SOAP / WSDL.
>> Services (endpoints) "learnt" from systems and they integrations.
>>
>> CMS / Wiki (API / Protocol / DAV). Docs. Forms (Docs Flows).
>>
>> Facets: BI Views. Functional, Dimensional, Semiotic. BI APIs.
>>
>> Distributed Persistency. Meta Model, Facets / Levels, Encoding:
>> semiotics, relations, categories, groups, sets, lists. Signatures /
>> inferences (routes / mappings).
>>
>> Semantic Backend: upper ontologies. Model Alignment, inference services
>> (Model Resource encoded).
>>
>> Learning Backend:  Model Augmentation services (Model Resource encoded).
>>
>> Reactive Dataflow Backbone. Nodes: Protocol (dialog) Message flows
>> (Augmentation) services (Model Resource encoded)
>>
>> This repository and attachments are fuzzy "scrapbook" notes:
>> http://github.com/snxama/scrapbook
>>
>> Best,
>> Sebastián Samaruga.
>>
>>
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