Self-Sustainable Model Based on Big Data Flows

A Generic Platform explored below shows the main components to support heterogeneous applications, for example in a Smart City design.

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The value chain and different stakeholders:

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Representation of the data flows and rela ers in the corporate open data services framework:

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Source: Ignasi Vilajosana et.al., Bootstrapping Smart Cities through a Self-Sustainable Model Based on Big Data Flows, IEEE Communications Magazine, June 2013.

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Evolutionary Architecture for Smart City

Smart cities services use components of both the ICT industry and mobile telecommunications industries. This article describes the architectural evolution required to ensure that the rollout and deployment of smart city technologies is smooth through acknowledging and integrating the strengths of both the system architectures proposed.

Here’s the pragmatic framework:

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Links to context information sources via mobile access:

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Some aspects of smart city design, and the technical impacts result from them:

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Source: Catherine E. A. Mulligan & Magnus Olsson, Architectural Implications of Smart City Business Models: An Evolutionary Perspective, IEEE Communications Magazine, July 2013.

Vertical & Horizontal Architecture for Smart City

Smart cities encompass services in diverse business and technological domains. Presently, most of these services are delivered through domain-specific, tightly coupled systems, which entail limited scalability and extensibility. Such structure is supposed to be transformed into a horizontal architecture through service-delivery platform.

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The platform is a new type of platform-as-a-service (PaaS) offering that integrates Internet of Things infrastructure and provides services for application providers to employ IoT and cloud resources on demand.

The service-delivery workflows are presented below. The work flows result in three service-delivery models: virtual verticals, third-party applications, and Internet of Things infrastructure as a service (IoT IaaS).

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Source: Fei Li et al, Web-Scale Service Delivery for Smart Cities, IEEE Internet Computing, July/Aug 2013.

Seamless Intelligence

Seamless intelligence is a configuration where everything is connected through ubiquitous networks, interfaces, and so on. While similar to previous pervasive and ubiquitous computing scenarios, seamless intelligence has deep roots in technology advancement that didn’t exist in the near past.

With pervasive computing and communication capabilities, information technology will increasingly enhance and augment human knowledge, intelligence, and connectivity. We project that by 2022 society will advance so that intelligence becomes seamless and ubiquitous for those who can afford and use state-of- the-art information technology. We expect this new reality to result from the confluence of multiple information and communication technologies. Computing devices—ranging from wearables, subdermal chips, and the computers inside our mobile devices, laptops, desktops, home servers, TV sets, and refrigerators to the computing cloud we reach via the Internet—will interconnect via different communication and networking technologies. Together, these will form an intelligent mesh: a computing and communication ecosystem that augments reality with information and intelligence gathered from our fingertips, eyes, ears, noses, and brain waves. See Figure 4 for a visual interpretation of this seamless integration.
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Source: Hasan Alkhatib et al, What Will 2022 Look Like? The IEEE CS 2022 Report, IEEE Computer Magazine, March 2015.

23 Disruptive Technology Area

Over the last two years, IEEE Computer Society technology leaders collaborated to identify important industry advances that promise to change the world by 2022. The 23 technologies provide new insights into the emergence of seamless intelligence.

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To validate the premises and conclusions made by the CS 2022 Report team, we surveyed more than 5,000 IEEE CS senior members.

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Source: Hasan Alkhatib et al, What Will 2022 Look Like? The IEEE CS 2022 Report, IEEE Computer Magazine, March 2015.

 

Enterprise SOA for Big Data Service

Introduction to ESARC:

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ESARC Business & Information Reference architecture:

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ESARC Business & Information Reference architecture metamodel:

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ESARC Information Systems Reference architecture:

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Source: Alfred Zimmermann et al, Towards Service-oriented Enterprise Architectures for Big Data Applications in the Cloud, IEEE International Enterprise Distributed Object Computing Conference Workshops, 2013.

Cloud-Based Enterprise IT

Trends:

  • Cloud
  • IT commercialisation
  • Cross-enterprise collaboration

IT as Enterprise Integrator

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Service-centric IT Ecosystem

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Hybrid IT Portfolio

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Five-phase approach (adapting ITIL methodology):

  1. Strategy: Our approach starts by identifying business needs and then defines the service capabilities and parameters necessary to support the business.
  2. Design: The enterprise identifies the necessary services and selects potential service providers. This process includes conventionally provided in-house services, but mainly focuses on external service providers.
  3. Implementation: In service-based IT, implementation includes negotiating and contracting for services, followed by assembly (or integration) of those services.
  4. Operation: The ongoing maintenance of business and technical relationships with service providers helps account for changes in the business and technology landscapes.
  5. Continuous improvement: The final phase is an ongoing effort to manage relationships with service providers to maximize business benefit. Efforts in this phase include assessing risks and devising mitigation strategies to protect against service disruptions or failures.

Source: Jamie Erbes et al, The Future of Enterprise IT in the Cloud, IEEE Computer, May 2012.