By Nik Bessis, Ciprian Dobre

ISBN-10: 3319050281

ISBN-13: 9783319050287

ISBN-10: 331905029X

ISBN-13: 9783319050294

This e-book offers present growth on demanding situations relating to substantial information administration via concentrating on the actual demanding situations linked to context-aware data-intensive purposes and companies. The e-book is a cutting-edge reference discussing development made, in addition to prompting destiny instructions at the theories, practices, criteria and techniques which are on the topic of the rising computational applied sciences and their organization with assisting the net of items complex functioning for organizational settings together with either enterprise and e-science. except inter-operable and inter-cooperative features, the publication bargains with a awesome chance particularly, the present pattern during which a jointly shared and generated content material is emerged from net end-users. particularly, the booklet offers advances on coping with and exploiting the great dimension of information generated from in the shrewdpermanent setting (i.e. shrewdpermanent towns) in the direction of an built-in, collective intelligence strategy. The e-book additionally provides equipment and practices to enhance huge garage infrastructures in keeping with expanding calls for of the information in depth purposes. The publication comprises 19 self-contained chapters that have been very conscientiously chosen in keeping with peer evaluation through not less than professional and self reliant reviewers and is equipped into the 3 sections reflecting the overall subject matters of curiosity to the IoT and large information communities:

Section I: Foundations and ideas

Section II: complex types and Architectures

Section III: complicated purposes and destiny Trends

The booklet is meant for researchers drawn to becoming a member of interdisciplinary and transdisciplinary works within the parts of shrewdpermanent Environments, web of items and diverse computational applied sciences for the aim of an built-in collective computational intelligence process into the large information period.

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Additional resources for Big Data and Internet of Things: A Roadmap for Smart Environments

Example text

To have a suitable representation of users’ social behavior, an approach that is similar to the caveman model is used, that has a community structure which assumes that users are grouped into home communities, while at the same time having relationships in acquainted communities. For content analysis nodes compute a utility value for each data object. The utility is a weighted sum of one component for each community its user has relationships with. The utility component of a data object for a community is the product of the object’s access probability from the community members, by its cost (which is a function of the object’s availability in the community), divided by the object’s size.

The smaller the data unit, the bigger is the chance of a transmission to successfully finish, even in opportunistic networks where contact durations are very small. Therefore, one of the best approaches is the one employed by Ad Hoc Podcasting, where data is split into episodes and chunks. The type of content organization that best suits opportunistic networks is the publish/subscribe pattern. The reason for this is that participants are decoupled from time, space and flow. Interested users simply subscribe to certain channels and receive data whenever the publishers post it.

Not all dissemination algorithms analyze the data from other nodes: some simply fetch as much data as they can, until their cache is full, like Epidemic routing, while others only verify if they do not already contain the data or if they have not contained it recently. More advanced content analysis can be accomplished by assigning priorities (or utilities) to each content object from a neighboring node. In this way, considering the amount of free cache memory, a node can decide what and how many content objects it can fetch from another node.

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Big Data and Internet of Things: A Roadmap for Smart Environments by Nik Bessis, Ciprian Dobre


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