[PragmaticWeb] Semantic Reactive Microservices
Sebastian Samaruga
ssamarug at gmail.com
Wed Oct 17 14:29:25 CEST 2018
Sorry for cross-posting.
Online at:
https://exampledotorg.blogspot.com/2018/10/request-for-comments-ii.html
Draft: could a framework of patterns leverage SW / ML adoption for end to
end business applications / BI / user centered use cases?
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
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://lists.spline.inf.fu-berlin.de/pipermail/pragmaticweb/attachments/20181017/28446067/attachment-0001.html>
-------------- next part --------------
A non-text attachment was scrubbed...
Name: Schema.pdf
Type: application/pdf
Size: 75764 bytes
Desc: not available
URL: <http://lists.spline.inf.fu-berlin.de/pipermail/pragmaticweb/attachments/20181017/28446067/attachment-0001.pdf>
More information about the PragmaticWeb
mailing list