Global Proceedings Repository - American Research Foundation, ICCIIDT London - UK

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The Impact of Big Data on Building the Knowledge Base at Makkah Techno Valley (MTV): An Exploratory Study
Nasser Juwaber AlKhudairi

Last modified: 2016-12-19


Saudi Vision 2030 is seeking to support the national industry for creativity, innovation and technology localization. The expanding in the utilization of Techno Valleys that are distributed around many universities in Saudi Arabia would help to achieve this vision. Those techno valleys could be utilized to convert the outcomes researches from universities and research centers into products to build up the knowledge base in the society. To do so, Vision 2030 would need to rely on modern and advanced Internet protocols to align with major developments in the field of digital evolution, such as the Internet of Things (IoT) and Big Data. Since the importance and relationship between Techno Valleys, building knowledge base and Big Data analysis are not well acknowledged in Vision 2030, this paper aim to study this relation and address the impact of applying Big Data analysis to build the knowledge base in Makkah Techno Valley (MTV). Basically, the study is focused to answer the following research question: what is the impact of applying big data to enhance the knowledge base building in MTV? Also, the challenges and opportunities of applying big data in MTV will be discussed. To achieve the above objective, an exploratory research has been conducted, where three previous studies were selected and analyzed using the SWOT analysis method, to determine the strength, weaknesses, opportunities and threats. Preliminary result shows that MTV should start narrowing the gap and put the necessary mechanisms in place to make use of big data in building the knowledge base. The study found that the main opportunities of applying big data in MTV, is the potential for making faster advances in many scientific disciplines and improving the profitability and success of many enterprises. However, many technical challenges must be addressed before this potential can be realized fully. The challenges include not just the obvious issues of scale, but also heterogeneity, lack of structure, error handling, privacy, timeliness, provenance, and visualization, at all stages of the analysis pipeline from data acquisition to result interpretation.

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