A comprehensive model for management and validation of federal big data analytical systems. Issue 1 (December 2017)

• Record Type: Journal Article
• Authors: Batarseh, Feras; Yang, Ruixin; Deng, Lin
• Is Part Of: Big data analytics. Volume 2:Issue 1(2017)
• Page Start: 1
• Page End: 22
• Publication Date: 2017-12
• Subjects: Data management -- Big data analytics -- Validation -- Unstructured data -- Federal agency; Big data -- Periodicals; Biology -- Data processing -- Periodicals; 570.28557
• DOI: 10.1186/s41044-016-0017-x
• Languages: English
• ISSNs: 2058-6345

Abstract

Abstract Background In this era of data science, many software vendors are rushing towards providing better solutions for data management, analytics, validation and security. The government, being one of the most important customers, is riding the wave of data and business intelligence. However, federal agencies have certain requirements and bureaucracies for data-related processes, certain rules and specific regulations that would entail special models for building and managing data analytical systems. In this paper, and based on work done at the US government, a model for data management and validation is introduced: Federal Model for Data Management and Validation (FedDMV). FedDMV is 4-step model that has a set of best practices, databases, software tools and analytics. Automated procedures are used to develop the system and maintain it, and association rules are used for improving its quality. Results After working with multiple engineers and analysts at the federal agency, there is a general consent that FedDMV is easy to follow (please refer to the experimental survey). However, to quantify that satisfaction, three experimental studies were performed. One is a comparison to other state-of-the-art development models at the government, the second one is a survey that was collected at the government to quantify the level of satisfaction regarding FedDMV and its tool; and finally, a data validation study was performed through detailed testing of the federal system (using an Association Rules algorithm). Conclusions To develop a safe and sound federal data analytical system, a tested and rigorous model is required. There is a lack of government-specific models in industry and research. FedDMV aims to provide solutions and guided steps to facilitate the development of data analytics systems given the governmental constraints. FedDMV deals with unstructured data that streams from multiple sources, automates steps that are usually manual, validates the data and maximizes its security. The results of the experimental work are recorded and reported in this manuscript.