1DATA MINING REPORT Introduction The alignment of the data mining process is helpful for managing the utilization of the improved functional development. The utilization of the functions would be aligned for easing the functions of using the implication management. The integration would be implied for easing the listing the implication management for easing the utilization management. The activities would be implied for easing the alignment development. The usefulness of the operations and listing of the successful management development can be implied for easing the utilization management. The formation development is implied for aligning the deployment and formation. The Non-Obvious Relationship Awareness (NORA) would be aligned for easing the implication and development of formation management. The implication management would be effectively formed using the cohesive management. Background The comprehensive use of the development model can be assisted by the significant alignment model. The data mining in NORA is helpful for successfully forming the use of data collectively and easing the flow of information processing. The use of the successfully implied management process can be effectively used for easing the significance of developing faster and effective management process. The usefulness of the data mining can be aligned cohesively for easingtheimplicationmanagement.Thealignmentwouldbeimpliedformanagingthe successful alignment model. The new process would involve the implication of the activities of data mining using innovative technology.
2DATA MINING REPORT Assumptions Some assumptions have to be made for ensuring the completion of the work and alignment of the improved communication management. The alignment would also take care of the information management of the data mining process. The automation of the data mining would include the development of the operations for aligning the inclusion of the effective data collection and mining process.
3DATA MINING REPORT As-Is Process Diagram The following is the As-Is diagram for the data mining of NORA, The diagram has been developed for the existing functionalities of the operations and alignment of the improved functional analysis model. The influence of the functions has bene helpful for defining the simplification of the functions to manual process of data generation, business understanding, data publishing, data preparation, modelling, report and decision making. The complete data collection and mining process is supported by the use of these processes. To-Be Process Diagram The following is the to-be diagram for the data mining of the NORA,
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5DATA MINING REPORT The proposed method of data mining would be supported by the utilization of the automatic process. NORA would get the benefit of easy work process and aligning it with the completion of the successful project completion. The implication of the improved management would be achieved by the use of the processes of data generation, business understanding, data preparation process apply PPDP, data publishing, data preparation, data privacy preserving process, modelling, report, legal measure, get report, evaluation/validating the results, and report and decision making. The ease of dividing the work into phases of data collection and data mining is helpful for achievement of the successful completion of the data modelling. Cost Analysis Task Document TypeProcess Time (Elapsed)Process Time (Working)RateCost CurrentProposedCurrentProposedCurrentPropose dCurrentProposedCurrentProposed Data generationManualAutomatic3015200100$15.00$10.00$3,450.00$1,150.00 Business UnderstandingManualAutomatic1055020$10.00$15.00$600.00$375.00 Data Preparation Process Apply PPDPNAAutomatic010050$0.00$10.00$0.00$600.00 Data PublishingManualAutomatic151010050$20.00$20.00$2,300.00$1,200.00 Data PreparationManualAutomatic501035050$15.00$10.00$6,000.00$600.00 Data Privacy Preserving ProcessNAAutomatic0200100$0.00$15.00$0.00$1,800.00 ModelingManualAutomatic201220060$15.00$10.00$3,300.00$720.00 ReportManualAutomatic10710020$20.00$20.00$2,200.00$540.00 Legal MeasureNAAutomatic04010$0.00$10.00$0.00$140.00 Get ReportNAAutomatic05030$0.00$15.00$0.00$525.00 Evaluation/Validating the ResultsNAAutomatic06040$0.00$10.00$0.00$460.00 Report and Decision MakingManualAutomatic25624060$25.00$20.00$6,625.00$1,320.00 Total$24,475.00$9,430.00
6DATA MINING REPORT Conclusions The alignment of the data mining process was helpful for managing the utilization of the improved functional development. The utilization of the functions had been aligned for easing the functions of using the implication management. The integration had been implied for easing the listing the implication management for easing the utilization management. The activities had been implied for easing the alignment development. The usefulness of the operations and listing of the successful management development was implied for easing the utilization management. The formation development was implied for aligning the deployment and formation. The Non- Obvious Relationship Awareness (NORA) had been aligned for easing the implication and development of formation management. The implication management had been effectively formed using the cohesive management.
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7DATA MINING REPORT Bibliography Chen, D and Zhao, H., 2012, March, “Data security and privacy protection issues in cloud computing”,inComputerScienceandElectronicsEngineering(ICCSEE),2012 International Conference on(Vol.1, pp. 647-651). Chen, H.; Chiang, R. and Storey, V. 2012. "Business Intelligence and Analytics: From Big Data to Big Impact,"MIS Quarterly, 36(4) pp.1165-1188. Cormode, G., Procopiuc, C, Srivastava, D., Shen, E., Yu, T., 2013, "Empirical privacy and empirical utility of anonymized data", IEEE 29th International Conference on Data Engineering Workshops (ICDEW),pp. 77-82. Lyon, D 2014, “Surveillance, Snowden, and big data: Capacities, consequences, critique”,Big Data & Society, 1(2), pp.1-13. Magdy, W., Darwish, K., and Weber, I. (2015), “# FailedRevolutions: Using Twitter to study the antecedents of ISIS support”,arXiv preprint arXiv:1503.02401 Inthasone S., Pasquier N., Tettamanzi A., da Costa Pereira C. (2014) “The BioKET Biodiversity Data Warehouse: Data and Knowledge Integration and Extraction” In: Blockeel H., van Leeuwen M., Vinciotti V. (eds)Advances in Intelligent Data AnalysisXIII. IDA 2014. Lecture Notes in Computer Science, vol 8819. Springer. Stein, S., Hamilton, B., Peterson, T., Guyer, C., 2016,Develop using Always Encrypted with .NETFramework Data Provider. [Online] Available at:
8DATA MINING REPORT Xu, L., Jiang, C., Wang, J., Yuan, J., and Ren, Y., 2014, “Information Security in Big Data: Privacy and Data Mining”,IEEE Access, vol. 2, pp.1149-1176.