Mobile government implementation in Malaysia is still in its very early stages -indeed a comprehensive m-Government has not been shaped yet. In this regard, we need to investigate the potential needs of users of m-Government services. In addition, there is a need to describe the factors that lead to the integration of the services provided with users' everyday practices. In this research, we examined different groups of citizens with varying needs and practices in the available technologies. Focus groups method is used to collect data. Results show that although awareness of mobile government services is reasonably high, only a small number of Malaysians actually use mobile government services. However, citizens acknowledged that mobile government services can be useful, easy to use and convenient. Moreover, both the discussion groups and the questionnaire addressed the problems, limitations, and improvement needed in mGovernment services which include information content, information presentation, system structure, search alternatives, and navigation logic.

Big data is facing many challenges in different aspects, which appear in characteristics such as: Velocity, Volume, Value and Veracity. Processing and analysis of big data are challenging issues to acquire quality information in order to support accurate medical drug practice. The quality of data taxonomy is indicated by three basic elements: are meaningful, predication and decision-making. These elements have been encouraged in previous work that focused on the same challenges of big data. Consequently, the proposed approach preserves the quality of medical drug data toward meaningful data lake by clustering. It consists of four components. Data collection and pre-processing represent the first component in the data lake. Profile data is treated with semi-structured data to clean it up. The second component is extracting data through enforcing rules on whole data to produce different groups and generate weight based on constraints within groups. In component three, data is organized and clustering. This component complies with schema profiling referring to component two in the data lake. Weight outputs of component three are inputs for component four, where K-Mean clustering is applied to obtain different clusters. Each cluster presents an alternative drug to achieve meaningful drug data that is consistent with component three in the data lake.This paper addressed two main challenges; the first challenge is extracting meaningful data from big data; whereas the second challenge is using big data technique with K-Mean clustering algorithm. An experimental approach was followed through using Food and Drug Administration (FDA) data and symptoms in R framework. ANOVA statistical test was carried out to calculate sum of square error, P- Value and F-Valuefor the evaluation of variances between clusters and variances within clusters. The results showed the efficiency of the proposed approach.

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Independent studies have shown that mobile government (M-Government) can have an important influence on business and society in the future. Hence, network designers, service providers, government and application developers must carefully take the needs and considerations of various users into account to provide better services and attract them to M-Government Consequently, identifying the M-Government user requirements and their significance becomes an essential and crucial process for the standardization and improvement of associated systems. On this line, the objective of this paper is to propose a requirements model as a result of quantitative and qualitative methods, this model provide practitioners a more effective and efficient model for prioritizing M-Government user requirements.

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