| 1. |
- Osman, Ahmed M. Shahat, et al.
(författare)
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Big Data Analytics and Smart Cities : A Loose or Tight Couple?
- 2017
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Ingår i: Proceedings of the International Conference on ICT, Society and Human Beings 2017. - : IADIS. - 9789898533678 ; , s. 157-168
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Konferensbidrag (refereegranskat)abstract
- Smart City (SC) is an emerging concept aiming at mitigating the challenges raised due to the continuous urbanization development. To face these challenges, government decision makers sponsor SC projects targeting sustainable economic growth and better quality of life for inhabitants and visitors. Information and Communication Technologies (ICT) is the enabling technology for smartening. These technologies yield massive volumes of data known as Big Data (BD). If spawned BD are integrated and analyzed, both city decision makers and citizens can benefit from valuable insights and information services. The process of extracting information and insights from BD is known as Big Data Analytics (BDA). Although BDA involves non-trivial challenges, it attracted academician and industrialist. Surveying the literature reveals the novelty and increasing interest in addressing BD applications in SCs. Although literature is replete with abundant number of articles about SCs applications harnessing BD, comprehensive discussion on BDA frameworks fitting SCs requirements is still needed. This paper attempts to fill this gap. It is a systematic literature review on BDA frameworks in SCs. In this review, we will try to answer the following research questions: what are the big data analytics frameworks applied in smart cities? what are the functional gaps in the current available frameworks? what are the conceptual guidelines of designing integrated scalable big data analytics frameworks for smart cities purposes? The paper concludes with a proposal for a novel conceptual analytics framework to serve SCs requirements. Additionally, open issues and further research directions are presented.
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| 2. |
- Rizk, Aya, et al.
(författare)
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Digital Service Innovation Enabled by Big Data Analytics : A Review and the Way Forward
- 2017
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Ingår i: Proceedings of the 50th Hawaii International Conference on System Sciences 2017. - : University of Hawai'i at Manoa. - 9780998133102 ; , s. 1247-1256
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Konferensbidrag (refereegranskat)abstract
- Service innovation is attracting attention with the expanding service industries and economies. Accompanied by major developments in ICT and sensory and digital technologies, the interest in digital service innovation (DSI), both from academia and industry, is increasing. Digitization and the accompanying technological advancements are leading to phenomena that call for extensive research in relation to service innovation; one of which is big data analytics (BDA). In this paper, we review the DSI literature and explore how BDA can contribute along the different dimensions of DSI. The ex post literature suffers from the lack of such studies. Accordingly, we suggest a research agenda for BDA-enabled DSI, motivated by emerging research gaps, as well as opportunities and guiding research questions. It is expected that such research agenda will contribute to shape an ex ante research efforts in an attempt to advance the state-of-the-art in BDA-enabled DSI.
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| 3. |
- Rizk, Aya, 1988-, et al.
(författare)
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Towards A Taxonomy of Data-driven Digital Services
- 2018
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Konferensbidrag (refereegranskat)abstract
- Digitization is transforming every domain nowadays, leading to a growing body of knowledge on digital service innovation. Coupled with the generation and collection of big data, data-driven digital services are becoming of great importance to business, economy and society. This paper aims to classify the different types of data-driven digital services, as a first step to understand their characteristics and dynamics. A taxonomy is developed and the emerging characteristics include data acquisition mechanisms, data exploitation, insights utilization, and service interaction characteristics. The examined services fall into 15 distinct types and are further clustered into 3 classes of types: distributed analytics intermediaries, visual data-driven services, and analytics-embedded services. Such contribution enables service designers and providers to understand the key aspects in utilizing data and analytics in the design and delivery of their services. The taxonomy is set out to shape the direction and scope of scholarly discourse around digital service innovation research and practice.
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