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Semantic Reactive Microservices

From: Sebastian Samaruga <ssamarug@gmail.com>
Date: Mon, 15 Oct 2018 16:47:04 -0300
Message-ID: <CAOLUXBtp2=6xN=c9q1xTG65fUrkwTwSw_D38PJXjWxuVCkN5Qg@mail.gmail.com>
To: W3C Semantic Web IG <semantic-web@w3.org>, public-lod <public-lod@w3.org>, public-rww <public-rww@w3.org>, public-aikr@w3.org
Draft: could a framework of patterns leverage SW / ML adoption for end to
end business applications / BI use cases: (sending this again because
download link in blog is not working)

Semantic Web / RDF as the 'glue' of / for ML dataflow encoded input
'features' / output 'tensors' and ontology aligments. Mappings for
knowledge input / augmented (learning) output rows (RDBMS example)
processed by ML models in semantic alignments.

SW contexts encodes 'meaning' into translated input features / obtained
learning output tensors via RDF CSPO quads Resource ID creation /
assignment algorithm. Tensor shapes rendered as algorithmically Resource ID
enabled (ANNs activation functions) operating over and preserving Resource
IDs / Statements integrity (validation).

Dataflow semantic 'forms' application language: encode code and data
functionally as Context RDF quad statements. Context activations performs
functional intra / inter Context transforms across layers statements.
Encoded 'form' statement resolves to getters / setters applications by
means of algorithm to obtain resulting inferred 'forms' (templates: system
resource encoded quads).

Tools:

*TensorFlow / ML:*

I/O: Tensors (features / classes, discrete values in 'shapes').

Learning:
Classification: class / instance identification. "Messi: Player : 10"

Learning:
Clustering: similarity (common attributes / links resolution). "Messi
player of Barcelona".

Learning:
Regression (discrete value in function of input features, roles in
contexts: value / event for x when y was z in w). "Messi captain of
Barcelona in last tournament".

Semantic Microservices (proposed component):

I/O: RDF encoded features / outputs CSPO quad statements (reactive stream
events bus). Resource ID creation / assignation algorithm (Semantic IDs:
operable, tensor embeddings).

Augmentation:
Aggregations: data, schema, behavior statement layers dimensional
aggregation. Type inference by attributes / values aggregation.

Alignments:
ID resolution: class / instance identity discovery (ontology / schema
matching) ML models.
Attributes / links resolution: clustering ML models
Roles in contexts resolution: regression ML models

*Distribution / Dataflow:*

Integration / Discovery / Activations.
Contexts / Layers: Dimensional upper ontology layers alignments between
Contexts (data, domain, application levels).

Reactive Extensions (RX). Dataflow 'forms' enabled 'templates' inter
context levels.

Activation: Resources Context's streams as observers / observables (RX) of
Context / upper layers events. Event ('form') fires node augmentation
(learning) / resolves to nodes emmiting knowledge 'forms' events related to
their knowledge of the source event.

*Use Cases:*

Semantic Microservices Adapters (endpoints, integration / transforms).

GraphQL: adapters schema / tenmplate transforms. Forms functional language
translation (I/O: integration).

Adapters: Workflows / API Rendering (OData, REST: Spring HATEOAS / HAL).

Refine / ETL (Adapters I/O).

Big Data Deployments (Adapters I/O).

BI / Dashboards (Adapters I/O).

Declarative Business Applications Framework (Adapters I/O).

*Links:*

OData: https://www.odata.org
Spring HATEOAS: https://spring.io/projects/spring-hateoas
HAL: http://stateless.co/hal_specification.html
http://openrefine.org/
http://www.opencalais.com
https://solid.mit.edu

Sebastian Samaruga.
http://exampledotorg.blogspot.com

Received on Monday, 15 October 2018 19:49:19 UTC

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