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Matchmaker
Query
Handler
Input
Stream
Handler
Status
Monitor
RSP
Engine 1
Output
Result
Controller
Input
Stream
RSP
engine n
Application
Output ResultRequest
Output Rate
Status report
Input
sream
AdaptiveLayerRSPEngines
Negotiator
Context
Query
+Parameters
Register/ remove Query
Engine’s status
Output result
stream
Input rate
RSP
Engine 2
RSP
Engine 3
On the need for applications aware
adaptive middleware in real-time
RDF data analytics
Zia Ush Shamszaman, National University of Ireland Galway
This project has been funded by CISCO Galway and SFI
Motivation
•  Different RSP Engines e.g. CQELS, C-
SPARQL, SPARQLstream, EPSPARQL,
ETALIS, SPARKWAVE differ on various
aspects,
•  Query language
•  Input data model
•  Execution strategy
•  Output data Model, and more.
•  Performance of existing RSP engines are
affected by the different query sets and data
sets in different benchmarks.
Hypothesis
•  An adaptive approach to RSP can
improve efficiency and correctness by
serving a broader category of application
requirements.
•  The adaptive approach is capable to
a d a p t t o d y n a m i c a p p l i c a t i o n
requirements and properties of data
streams at run-time with improved
scalability.
Event 1: Unauthorized access in the house.
-  This notification must be triggered immediately i.e. instantly when
the data arrives.
Event 2: Children activities
- their location and accompanied by whom at every 10 minutes
If there is an emergency then it shall be an instant notification.
Event 3: Emergency and disaster monitoring
- One notification per day if there is no emergency during the
emergency it has to be immediately after the incident it has to be
hourly for 2 next weeks.
Architecture of Adaptive RSPScenario

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On the need for applications aware adaptive middleware in real-time RDF data analytics

  • 1. Matchmaker Query Handler Input Stream Handler Status Monitor RSP Engine 1 Output Result Controller Input Stream RSP engine n Application Output ResultRequest Output Rate Status report Input sream AdaptiveLayerRSPEngines Negotiator Context Query +Parameters Register/ remove Query Engine’s status Output result stream Input rate RSP Engine 2 RSP Engine 3 On the need for applications aware adaptive middleware in real-time RDF data analytics Zia Ush Shamszaman, National University of Ireland Galway This project has been funded by CISCO Galway and SFI Motivation •  Different RSP Engines e.g. CQELS, C- SPARQL, SPARQLstream, EPSPARQL, ETALIS, SPARKWAVE differ on various aspects, •  Query language •  Input data model •  Execution strategy •  Output data Model, and more. •  Performance of existing RSP engines are affected by the different query sets and data sets in different benchmarks. Hypothesis •  An adaptive approach to RSP can improve efficiency and correctness by serving a broader category of application requirements. •  The adaptive approach is capable to a d a p t t o d y n a m i c a p p l i c a t i o n requirements and properties of data streams at run-time with improved scalability. Event 1: Unauthorized access in the house. -  This notification must be triggered immediately i.e. instantly when the data arrives. Event 2: Children activities - their location and accompanied by whom at every 10 minutes If there is an emergency then it shall be an instant notification. Event 3: Emergency and disaster monitoring - One notification per day if there is no emergency during the emergency it has to be immediately after the incident it has to be hourly for 2 next weeks. Architecture of Adaptive RSPScenario