During the federal election of 2013 there was huge loss of ballot papers. This type of loss was not expected by the federal election commission. Due to this issue the election commission started investigation about the incident of the ballot paper loss. This investigation led to the fact that how the system of voting can be improved. From the prior research of the on the topic of the voting system it has been assessed that implementing new technologies will reduce the chances of occurring this type of scenario in the near future (Avgerou 2013). The research indicated that one of this technology was the electronic voting system or the e-voting system.
This e-voting system is a typical system which uses enterprise information architecture. To implement the e-voting system first it is very much important to convince the federal government of Australia. For this reason, in this paper the benefits of the Enterprise Information Architecture Reference Architecture for developing the e-voting system will be discussed. Following that, nature of the system within the government, volume of the unstructured and the structured data and the sensitivity of those data will be evaluated. In the further section some recommendation will be shared to implement the e-voting system properly in the election system of the federal election.
Enterprise Information Architecture Reference Architecture:
The Enterprise Information Architecture is one of the component of the enterprise architecture. This information architecture is used for providing a common framework that is very much useful for the cost-effective sharing of the data (Abdulhamid et al. 2013). This type of data is shared among different units of the organization which take care of the privacy and security of some specific information. The enterprise information architecture includes three type of sub components. These three are the business architecture, organization architecture and lastly the technical architecture. In this case the EIA is chosen here for the development of the electronic voting system. The main reason behind choosing the Enterprise Information Architecture is the main benefits which is provided by this system.
Considering the benefits, there are many benefits which are provided by the Enterprise Information Architecture for developing the electronic voting system. The first benefit which is provided by the Enterprise Information Architecture is that it provides the common understanding which is very much essential for designing a system. It assists to create a model which are created from the perspective of enterprise-wide. This provides the corporate data model to evaluate the existing structure of the data and the re-engineering planning which are essential for maintaining the integrated structure set. The second benefit provided by the Enterprise Information Architecture is that the existing corporate data can be easily integrated to an existing system. This integration can be easily done by the data mapping model rather than using the database model only. The third benefit provided by the Enterprise Information Architecture for designing the electronic voting system is that it provides some quick orientation. This quick orientation is very much useful for the new type of resources as this is able to quickly access the chosen parts of the necessary models of the Enterprise Information Architecture.
For developing the e-voting system the above mentioned benefits can be very much useful. The main concern with an e-voting system is the design. On the design and the mechanism it depends how it will be working. Using the Enterprise Information Architecture, a suitable design for the e-voting system can be achieved. The Enterprise Information Architecture helps in the common understandings of the hardware which is used by the e-voting system. In the e-voting process the data evaluation is very much important. In this case the data refers to the stored voting results. Corporate data model of the enterprise Information Architecture helps the e-voting system to evaluate the data quickly. This is extremely necessary for the e-voting system to evaluate the voting data quickly (Al-Ameen and Talab 2013). In the case of the e-voting, quality of the data must be maintained for the further evaluation of the data. It is very much essential that the gathered data must not be tampered. As tampered data can raise unethical problems. The EIA helps to improve the consistency and the accuracy of the information which will overall improve the e-voting system.
The management of the information is also very important for the e-voting system. By the implementation of the EIA the information of the e-voting system can be managed very efficiently. The effectiveness of the voting data is achieved by the full cycle view of the stored data. In this information management, the EIA delivers framework for the e-voting system (Kaliyamurthie et al. 2013). In the environment of the e-voting system this framework accommodates easy data access by the users by the various levels of sophistication which actually helps in using the device for the voters.
The overall process of the e-voting system becomes more efficient by the use of the EIA. The consolidated data architecture of the EIA is heavily optimised for the delivery of the information. It eliminates the time of obtaining the data from the e-voting system. Thus the data efficiency is achieved. Also the self-service tools of this reduce the technical staff dependency for the access of the data and eliminates the duplicate. By eliminating the data of the duplicate personal data, only authenticated data will be retrieved form the e-voting system which is a must have function of the e-voting system.
Security is one of the main important aspects of the e-voting system. Without proper security, the e-voting system will be useless. The EIA helps to handle the released information with privacy and regulatory requirements by some standardized guidelines (Haynes and Healey 2014). Also, the audit trails of the EIA is very much helpful for consistent representation of the e-voting data. Thus enough security is provided to the EIA in the e-voting system.
Another benefit is the agility, which is provided by the EIA to the e-voting systems. This agility is achieved by implementing the enterprise data model of the EIA. This enterprise data model forms foundation of all data accommodates and initiatives which are based on the future requirement of the e-voting system (Goodman 2014). The EIA also provides an information framework which is capable to provide consistency and stability to the current data stored by the e-voting system while the standardising process of the EIA quickly helps to include the future requirements by the e-voting system. The EIA also provides the support for the unstructured data, which is capable to enable more complete system of the knowledge management for the e-voting system.
(Figure 1: Diagram of E-voting system)
(Source: Created by the author)
One of the major challenges in the data integration process is the setting of the realistic expectations. The term data integration denotes to a perfect cooperation of the differentiated software, databases, personnel, and the equipment in a smooth alliance of functioning, free of all the persistent troubles. Some of the challenges to the integration of data includes:
This challenge is most commonly seen in the e-voting as the integration of the data includes the synchronisation of huge quantity of heterogeneous data, and variable from the legacy systems that are working internally and they vary in the format of data. The legacy systems might have been created around the flat file, hierarchical databases or the network, unlike the current generations of the databases that includes the relational data. The data in the various formats from several external sources is continuously added to the databases of legacy for improving the information value (Springall et al. 2014). Each product, generation and the systems that are home grown has some distinct requirements that has to be fulfilled for storing or extracting the data. Therefore, the integration of the data can include some variety of strategies for the copying with the heterogeneity.
Mitigation strategies:
A comprehensive analysis of characteristics and the uses for the data in essential for the mitigation of the issues with the heterogeneous data. Initially, the selection of the model is done which can be data warehouse or a federated environment that fulfils the requirements of the applications of the business and the other potential utilisation of the data. Then the developer of the database would need to ensure the several applications that can utilise this format.
The quality of the data is the major concern in the strategy of data integration. The legacy data should be properly cleaned before the integration and the conversion, or any agency would almost most certainly face some serious problems of data (Olaniyi et al. 2013). The impurities of the legacy data have extensive effect and it has a tendency of concentrating around the users who are using high volume data.
If the information is corrupt, then the decisions and the results of using the data would be corrupt too. It is not uncommon for the undiscovered problems of data quality for emerging in the information cleaning process for the use by the system that is integrated (Romero and Vernadat 2016). The data of the voting would have adverse effect on the results if in any case the data is corrupt or the data has altered. This could lead to some serious negative decisions and the ultimate result would be significantly affected.
Mitigation strategies:
This issue with the bad data only exists all through the life period of the data integration system. It is commonly practised to establish the responsibilities and the practices from the beginning and create provisions for each of these.
The most suitable processes leads when the developers and the users work mutually on the determination of the controls of quality that can be implemented in both phase of development and the prevailing uses of the systems.
The unpredicted requirement of the extra performance and the capacity is considered to be one of the major threats in the data integration and to the data, and particularly in the warehousing of the data of e-voting. The requirements that are related to the two storage commonly grow in play scalability and extensibility (Kumar and Singh 2013). Predicting the growth level in any environment in which the requirement for the storage can grow exponentially once the system is started, creates the fears of the cost of storage which will exceed with the benefit of the integration of data. Introduction of such huge data quantity can extend the limits of the software and the hardware (Niu Da Xu and Bi 2013). This might force the developers in instigating the fixes that can be costly if any architecture for the processing huge data amount should be retrofitted in the planned system.
Mitigations strategies:
The approach of implementing the alternative storage is growing to be the best practise for the data warehouses that are predicted to increase in size. The planning of such options assists in the expansion of the affordable databases.
The costs of the integration of data are commonly fuelled by the items that are significantly difficult in the quantification and therefore prediction. These can include:
It is essential to streamline the maintenance, realities of any completely functioning system of data integration can demand the increased maintenance than the predicted cost.
The unrealistic estimating might be driven by any extensively optimistic budget, specifically in the budget shortfall times. The increased users, increased analysis requirements and the more complicated needs can drive the problems of capacity and performance. The limited resources can create the extension of the timeliness. The unpredicted issues or any new issues can call for the costly help of consulting.
Mitigation strategies:
The approach for the estimation of the costs of projects should be realistic and far-sighted. This needs an extensive investment in the experienced analysts, and cooperation, wherever it is possible.
Particular efforts are required for the identification of the items that might seem doubtful but still it can dramatically affect the total cost of the project.
A viable approach of data integration should identify the better integration of the data suits the users, the increased basic it will grow to be the processes of business. The usage level should be supported with the constant maintenance. Often it is observed that the most suitable processes and the systems tends in thriving on routine support and care for the well trained personnel.
The user groups within any agency might have created the databases by their own but sometimes independently from the staff of information systems that are commonly responsive to the specific needs of the users (Panetto and Cecil 2013). The staff of any organisation might not want the data to be collected and tracked transparently visible to the headquarters staff deprived of the opportunity to addressing the issues of the data that might be revealed.
Conclusion:
The e-voting system is now capable enough to conduct the voting system where the normal ballot paper voting system faces a prior risk. The main reason behind this much efficiency of the e-voting system is the Enterprise Information Architecture which is used for the development of the e-voting system. The EIA or the Enterprise Information Architecture helped the mechanism of the e-voting system by the providing various benefits which includes the data quality, management of the information, increasing the efficiency of the data process, improved security and by providing improved agility to the e-voting system. All of this benefits are discussed briefly in this paper. Though the e-voting system is having various types of benefits due to the EIA, it still faces many problems and challenges also. The main challenge faced by the e-voting system is the management of the data. The voting system consists some confidential information which must not be disclosed. In many of the cases it has been seen that the data of the e-voting system is compromised due to the challenges of the data integration and the management. Brief description of this challenges has been given in this paper and strategies of overcoming this problems has been discussed thoroughly in this paper.
Some recommendation can be given based on the evolution of the above scenario iof the implementation of the e-voting system.
References:
Abdulhamid, S.Í.M., Adebayo, O.S., Ugiomoh, D.O. and AbdulMalik, M.D., 2013. The Design and Development of Real-Time E-Voting System in Nigeria with Emphasis on Security and Result Veracity. International Journal of Computer Network & Information Security, 5(5).
Al-Ameen, A. and Talab, S.A., 2013. The technical feasibility and security of e-voting. Int. Arab J. Inf. Technol., 10(4), pp.397-404.
Avgerou, C., 2013. Explaining Trust in IT-Mediated Elections: A Case Study of E-Voting in Brazil. Journal of the Association for Information Systems, 14(8).
Goodman, N.J., 2014. Internet voting in a local election in Canada. In The Internet and Democracy in Global Perspective(pp. 7-24). Springer, Cham.
Haynes, P. and Healey, J., 2014. Online Voting, Rewards and Risks. Atlantic Council Brent Scowcroft Center on International Security.
Kaliyamurthie, K.P., Udayakumar, R., Parameswari, D. and Mugunthan, S.N., 2013. Highly secured online voting system over network. Indian Journal of Science and Technology, 6(6), pp.4831-4836.
Kumar, S. and Singh, M., 2013. Design a secure electronic voting system using fingerprint technique. International Journal of Computer Science Issues (IJCSI), 10(4), p.192.
Niu, N., Da Xu, L. and Bi, Z., 2013. Enterprise information systems architecture—Analysis and evaluation. IEEE Transactions on Industrial Informatics, 9(4), pp.2147-2154.
Olaniyi, O.M., Arulogun, O.T., Omidiora, E.O. and Oludotun, A., 2013. Design of secure electronic voting system using multifactor authentication and cryptographic Hash Functions.
Panetto, H. and Cecil, J., 2013. Information systems for enterprise integration, interoperability and networking: theory and applications.
Romero, D. and Vernadat, F., 2016. Enterprise information systems state of the art: Past, present and future trends. Computers in Industry, 79, pp.3-13.
Springall, D., Finkenauer, T., Durumeric, Z., Kitcat, J., Hursti, H., MacAlpine, M. and Halderman, J.A., 2014, November. Security analysis of the Estonian internet voting system. In Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security (pp. 703-715). ACM.
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