This document proposes a metadata-driven and rule-based system for validating data across multiple data sources for business continuity planning purposes. The system has three main components - a rules handler to manage validation rules, a communication handler to distribute rules and collect results, and a data sources handler where validation is performed. The conceptual architecture is distributed with rules objects sent to each data source and results returned to a central dispatcher. The system was tested on real data sets and proved to be adaptable, faster than centralized validation, scalable, and protects sensitive data.

1. Data Validation
for
Business Continuity Planning
Prepared by:
Ms. Pooja Mehta
ITSNS Branch,
GTU-CDAC-BISAG ME Program,
Gandhinagar
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2. 3 March 2016By: Pooja Mehta
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Content
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 Introduction
 Conceptual Architecture
 Rules
 Experiments
 Conclusion
 References

3. Introduction
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 Important activity for any services delivery
organization
 Important activities in BCP are:
 Impact statement &
 Develop resumption plan
 Data validation is an important step for any
organization to verify and validate the sanctity of the
data.

4. 4
Conceptual Architecture
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5. Cont..
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 At a broad level the system has three components.
 The overall system has two physical components:
 user machine and
 data source host
 A virtual component - Communication Layer
handles the data exchange and overlaps with the
physical component.

6. Cont..
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7. Cont..
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A. Rules Handler
 This component provides the functionality to edit,
store and transform the rules with the use of meta
model.
a) User Interface:
 The interfaces provides the user with the capability of
specifying the rules.
b) Rules Validation:
 The function of this module is to validate if all the
rules present in file are consistent with the meta model.

8. Cont..
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B. Communication Handler
c) Dispatcher:
 Dispatcher takes the validated rules object and
generate separate object for each data source such that
each object will have rules only for a corresponding
data source.
d) Listener:
 Listener runs on each data source machine as a daemon
process in the background. It takes the validation
object send by dispatcher and execute the rules and
revert back the response to the dispatcher.

9. Cont..
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C. Data Sources Handler
 These are all the different data sources hosts in the
organization.
 Each host may contain single or multiple databases.

10. 10
Rules
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11. Cont..
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12. Cont..
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Fig. 5 Example of a rule for multi data sources

13. Experiments
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14. 14
Cont..
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15. Conclusion
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 In this paper, they have proposed a Metadata driven
rule-based data validation system, which is domain
independent, distributed and can easily accommodate
changes in business requirements.
 As proof-of-concept, they have applied their system on
real data sets.
 Experimental results illustrated that their system is easy
to use, very adaptable for changes in business
requirements, faster then traditional Centralized
validation systems, scalable and does not expose the
sensitive data.
3 March 2016By: Pooja Mehta

16. Reference
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 http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnum
ber=6273507
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Thank
you...!