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Dynamic Aggregate: An Elastic Framework for
QoS-Aware Distributed Processing of RFID
Data on Enterprise Hierarchy
ABSTRACT:
Enterprise RFID data management is highly challenging not only because
of the huge volume of data from distributed sources, but particularly
because of the dynamic nature of the reader inputs. With user-designated
quality of service (QoS) requirements, the data management system must
be able to dynamically detect the status changes of the RFID inputs and
adjust the processing strategies for continuously maintaining the desired
level of QoS. We propose a QoS-aware framework for modelling the
enterprise data service problem, and for on-line adaptive processing of
distributed RFID data streams. The data processing structure is modelled
as a hierarchy of aggregation nodes in accordance with the structure of an
organization. Leaf nodes correspondto the RFID streaming inputs. A set of
aggregation/disaggregation operations is devised to adjust the processing
granularity level based on QoS dynamics. A QoS constrained query issued
at any aggregation node is parsed into an aggregation sub tree rooted at
that node. For QoS-aware processing of the query, several algorithms are
designed to dynamically apply proper aggregation/disaggregation
operations on selected nodes for raising or lowering the granularity levels
or changing the aggregationmethods. The goal is to continuously maintain
the desired level of QoS under constant variation of the streaming data
volume. Prototype development and extensive simulation show that our

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framework and can handle highly varied RFID streaming inputs and
continuously satisfy the QoS constraints.
EXISTING SYSTEM:
We propose a QoS-aware framework for modelling the enterprise data
service problem, and for on-line adaptive processing of distributed RFID
data streams. The data processing structure is modelled as a hierarchy of
aggregation nodes in accordance with the structure of an organization.
Leaf nodes correspond to the RFID streaming inputs. A set of
aggregation/disaggregation operations is devised to adjust the processing
granularity level based on QoS dynamics. A QoS constrained query issued
at any aggregation node is parsed into an aggregation sub tree rooted at
that node. For QoS-aware processing of the query, several algorithms are
designed to dynamically apply proper aggregation/deaggregation
operations on selected nodes for raising or lowering the granularity levels
or changing the aggregation methods.
PROPOSED SYSTEM:
Proposed a middleware framework for QoS specification, negotiation and
data transmission. However, no detail algorithm was given to dynamically
adjust the RFID data processing for QoS fulfillment. The set of techniques
proposed in the next section are exactly designed to support QoS-aware
adaptive processing of continuous queries over distributed RFID data
streams .we proposed a QoS-aware framework for hierarchical modelling
of the enterprise data service problem, identification and formulation of
the dynamic aggregation operators, as well as on-line adaptive processing

3. ECRUITMENTSOLUTIONS (0)9751442511, 9750610101
#1, Ist
Cross, Ist
Main Road, Elango Nagar,Pondicherry-605 011. tech@ecruitments.com
www.ecruitments.com
of distributed RFID data. Three algorithms are proposedto employ the DA
operators and adjust the processing strategies according to the dynamics in
data volume and QoS. Prototype implementation and extensive simulation
demonstrate that our framework and techniques can handle highly varied
RFID streaming inputs and continuously satisfy the QoS constraints.
CONCLUSION:
We proposed a QoS-aware framework for hierarchical modelling of the
enterprise data service problem, identification and formulation of the
dynamic aggregation operators, as well as on-line adaptive processing of
distributed RFID data. Three algorithms are proposed to employ the DA
operators and adjust the processing strategies according to the dynamics in
data volume and QoS. Prototype implementation and extensive simulation
demonstrate that our framework and techniques can handle highly varied
RFID streaming inputs and continuously satisfy the QoS constraints.
Extending the framework to cover other distributed environments such as
supply-chain or mobile RFID data management is also on top of our list.
The DA operators for such cases must be designedwith care since they may
often need to process data across administrative boundaries.