SPIDERS
Semantic P2p data Interchange with Distributed
agEnts among netwoRk management Systems
TELEFÓNICA I+D
Date: 25th October 2007
Sergio Garcí á Gómez (sergg@tid.es)
© 2007 Telefónica Investigación y Desarrollo, S.A. Unipersonal
W3C Workshop on
RDF Access to Relational Databases
25-26 October, 2007 — Cambridge, MA, USA
Index
01
Objectives
- Motivation
- Vision
- Ingredients
- Goals
02
Today’s approach
- IU Screenshots
03
Requirements
04
Conclusions
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01 SPIDERS motivation

Operations Support Systems (OSS) and Business Support Systems
(BSS) in an incumbent operator compose a complex ecosystem of
legacy systems.

Each system supports different functionalities and manages and
shares with other systems large amounts of data.

Data is propagated by remote invocations, message passing
systems, ad-hoc interfaces and also by manual data loads. These are
error prone procedures.

Problems in the quality of data may cause delays and failures in
network management processes and therefore, higher operation costs
(OPEX) and delays in the time-to-market.
—
It also hampers the automation of management processes and the
development of autonomic communications systems and elements.
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01 SPIDERS motivation
Sistemas
Supervisión
Planta Exterior
Sist. Medidas,
Prestaciones
y Contabilidad
MAPA
RPE
Red
Energía
Sistemas
Supervisión
Gestión
Actividad
Sistemas de Soporte al Negocio (BSS)
GESCAL
GAOM
PORTICO
PLC
MCP
GEDEÓN
SERA
OVSA
SPCI
SIAAM
SPRA
SIGA
SONIX
SCRABA
SEMTA
ATLAS
SIGA
CARMELA
• Red GigADSL
• Red ATM
• Red RIMA
• Red Imagenio
• Red NIMBA
• Red Transporte
(JDS, WDM, JDP)
• Red Ibermic
Otras ...
MCE
GRI
Sistemas
Supervisión
Sist. Medidas,
Prestaciones
y Contabilidad
• Red GigADSL
• Red ATM
• Red RIMA
• Red Imagenio
• Red NIMBA
SERA
SIGA-HD
Aplicaciones
Prueba
Sist. Medidas,
Prestaciones
y Contabilidad
• RTC
• Red Señ. Nº 7
• Red FITL
SERES
CAT
GRILLO
ARI
Plan 1000 Otras ...
Orders
EECC
SAMGRED
SGT
• RTC
• Red Señ. Nº 7
• Red FITL
E-DICRE
e-agora
APO
Customer
Sistemas
Supervisión
Sistemas
Supervisión
SERA
Trouble tickets
Needs
SSP
SCAR
ENRUTA
HADA
• Red GigADSL
• Red ATM
• Red RIMA
• Red Imagenio
• Red NIMBA
TELESAP
DITRA
• Red Transporte
(JDS, WDM, JDP)
• Red Ibermic
EBR
SSP
Gestión
Actividad
Aplicaciones
Prueba
DIPE_VP
GEOPLANTA
(4 provincias)
VIPE
SINPLEX
SIRP FO.interUrb
Planta Exterior
CARPE
GAM
CIOCO
Sistemas de Gestión de Averías
VPN-IP
EECC
Red
Energía
e-agora
e-agora
e-agora
Sist. Operación Planta
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INTEGRA
• Red Transporte
(JDS, WDM, JDP)
• Red Ibermic
Sist. Operación Planta
• Red GigADSL
• Red ATM
Sist. Operación Planta
• Red RIMA
• Red Imagenio
• Red NIMBA
4
• Red Telefónica Conmutada
• Red Señalización Nº 7
• Red Acceso FITL
EECC
01 Vision
OSS 1
Local Ontology
To create a semantic layer which makes
up a federated and consolidated view of
information throughout the network and
service management ecosystem.
Relational BD
OSS 2
OSS 3
Local Ontology
P2P Network
Local Ontology
Relational BD
Relational BD
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01 Ingredients (some buzzwords)

Semantic technology: ontology (OWL) based data models and
SPARQL queries.

P2P: each legacy OSS is a peer node in a network.

Distributed Agents: every peer system

Network Management Systems: systems are, in fact, Operations
Support Systems (OSS)

NGOSS: New Generation Operations Systems and Software.

SID: NGOSS Shared Information and Data.
SPIDERS: Semantic P2p data Interchange with Distributed
agEnts among netwoRk management Systems
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01 Goals

To model the SID as an ontology and to extend the model to the needs of
information to each OSS.

To provide the mechanisms to create a federated view of all the SID-based
ontologies.

To create a P2P distributed network of OSS-attached agents which are able
to:
—
translate relational information into semantic information through SPARQL queries.
—
insert or update the relational data with information from the SPARQL query results.

To provide the mechanisms to support the needs from real systems:
subscriptions to data changes, on demand queries, massive loads or
validation of data integrity.

The system must be as less intrusive as possible with the existing legacy
OSS's.
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02 Today’s approach
OSS 1
OSS 2
SPIDERS Operation:
1
•Variables
3
•SPARQL Queries
P2P Network
•Assignments
SPARQL Query
SPIDERS
Agent
6
Local
Ontology
2
SPIDERS
Agent
SPARQL
ResultSet to
DB
(assignment)
SPARQL Query
Federated Ontology
7
4
D2R Mapping
8
Relational BD
Relational BD
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02 UI Screenshot (I)
Based on Protègè and
OntoSphere plugin
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02 UI Screenshot (II)
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02 UI Screenshot (&III)
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03 Requirements (I)


SPARQL based access
—
Efficient access.
—
The data will remain relational.
Local SPARQL endpoint
—
To offer a common SPARQL endpoint for multiple distributed data sources
and to enable the resolution of queries joining results from all of them.
OSS 3
OSS 2
Query
P2P Network
Who
answers
?
OSS 1
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03 Requirements (II)

OSS 3
Bidirectional translation
—
—
—
It is required to insert, update or
delete data in the relational
schemas from RDF data or
SPARQL results, based on an
semantic to relational mapping.
Ontology
The same mapping approach for
both directions would be desirable.
Complex problems:
generation/work out of primary and
foreign keys, identifying properties,
data types compatibilities,
fulfillment of constraints, data
formats, etc.
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Relational Data to
RDF / SPARQL
results
OWL Schema
Mapping
RDF / SPARQL
results to
Relational Data
Relational BD
03 Requirements (&III)



Support for morphologic transformations
—
Origin and destiny data must follow coherent formats (i.e., capital letters).
—
It must be provided mechanisms to define and transform morphologic
properties.
Propagation of data changes
—
Data is dynamic.
—
There are different interaction mechanisms: push, poll, subscription…
—
From the semantic condition to the relational database trigger.
Security
—
There must be a control over which systems access every piece of data,
and under which conditions.
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04 Conclusions and future work

The objectives of the project have created great expectation on
Telefonica’s OSS domain.

But there is a long road to go:

—
Bidirectional mapping.
—
Standardization of mappings.
—
Federated data model based on ontologies alignment/merging.
At the end, we will be enabled to:
—
Automate data acquisition processes.
—
Automate network management processes based on stored
information.
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