Data Validation for Business Continuity Planning
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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.
Data Validation for Business Continuity Planning
- 1. Data Validation for Business Continuity Planning Prepared by: Ms. Pooja Mehta ITSNS Branch, GTU-CDAC-BISAG ME Program, Gandhinagar 1
- 2. 3 March 2016By: Pooja Mehta 2 Content 2 Introduction Conceptual Architecture Rules Experiments Conclusion References
- 3. Introduction 3 March 2016By: Pooja Mehta 3 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 3 March 2016By: Pooja Mehta
- 5. Cont.. 3 March 2016By: Pooja Mehta 5 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.. 3 March 2016By: Pooja Mehta 6
- 7. Cont.. 3 March 2016By: Pooja Mehta 7 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.. 3 March 2016By: Pooja Mehta 8 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.. 3 March 2016By: Pooja Mehta 9 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 3 March 2016By: Pooja Mehta
- 11. Cont.. 3 March 2016By: Pooja Mehta 11
- 12. Cont.. 3 March 2016By: Pooja Mehta 12 Fig. 5 Example of a rule for multi data sources
- 13. Experiments 3 March 2016By: Pooja Mehta 13
- 14. 14 Cont.. 3 March 2016By: Pooja Mehta
- 15. Conclusion 15 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 16 http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnum ber=6273507 3 March 2016By: Pooja Mehta
- 17. 17 Thank you...!