Big Data Analytics Platforms for Real-Time Applications in IoT
By Yogesh L. Simmhan, Srinath Perera. 2016
17 citations recorded by the source corpus.
Big data platforms have predominantly focused on the volume aspects of large-scale data management. The growing pervasiveness of Internet of Things (IoT) applications, along with their associated ability to collect data from physical and virtual sensors continuously, highlights the importance of managing the velocity dimension of big data too. In this chapter, we motivate the analytics requirements of IoT applications using several practical use cases, characterize the trade-offs between processing latency and data volume capacity of contemporary big data platforms, and discuss the critical role that Distributed Stream Processing and Complex Event Processing systems play in addressing the analytics needs of IoT applications.
Subfields: Artificial Intelligence, Computer Networks and Communications, Information Systems
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Ala Al-Fuqaha
- Mohsen Guizani
- Sameh Sorour
- M. Mohammadi
- Ekram Hossain
- Sanjay Chawla
- Raghavendra Chalapathy
- Syed Ali Hassan
In Computer Networks and Communications
- Mohsen Guizani
- Ala Al-Fuqaha
- M. Mohammadi
- Sameh Sorour
- Umair Mohammad
- Ekram Hossain
- Syed Ali Hassan
- Rasheed Hussain
In Information Systems
Citations in the corpus
- Deep Learning for IoT Big Data and Streaming Analytics: A Survey (2017, methodology): “• DL model and task distribution: Partitioning the execution of DL models and tasks among the fog nodes, and optimally distributing of the data stream among the available nodes are critical for time-sensitive applications [166].”