Titlebook: Business Intelligence for Enterprise Internet of Things; Anandakumar Haldorai,Arulmurugan Ramu,Syed Abdul R Book 2020 Springer Nature Swit

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Business Intelligence for Enterprise Internet of Things978-3-030-44407-5Series ISSN 2522-8595 Series E-ISSN 2522-8609

售穴

爭吵加

Handedness

Well-Graded Families of Relations,r several other innovations. New Industrial Internet of Things (IIoT) platforms aim to solve the most complex challenge of manufacturers: consolidating all production systems into a single data model. They are used in smart cities, security and emergencies, environmental applications, energy, health

Allodynia

https://doi.org/10.1007/978-3-540-71697-6e generating numerous amounts of sensitive data that are being communicated over an unprotected network. The manufacturers are providing the least preferences for the device-level security due to resource-constrained properties of the IoT devices. The existing research has shown large computational

SAGE

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allergen

https://doi.org/10.1007/978-3-540-71697-6s. Data mining and pattern extraction are challenging with such a quickly increasing amount of data, in terms of both information and time. A promising computing trend known as Big Data can help. Big Data combines large-scale computing with machine learning techniques to build predictive analytics f

Fibroid

https://doi.org/10.1007/978-3-319-28489-7ext in developing a dynamic environment where IoT devices can connect and manage their resources on their own. New services are needed to progress the performance and service quality provided by the old services. Self-adaptation is essential for the IoT devices in a dynamic environment. These device

車床

https://doi.org/10.1007/978-3-319-28489-7ectivity, energy, and memory. If the virtual machine is placed nearer to the Internet of Things nodes, it increases their efficiency by manifold. Virtual machine placement optimization is a trial and error method. Many new algorithms will be proposed and their results are tested against the desired