A survey of real-time approximate nearest neighbor query over streaming data for fog computing

2018 ◽  
Vol 116 ◽  
pp. 50-62 ◽  
Author(s):  
Xiaohui Jiang ◽  
Peng Hu ◽  
Yanchao Li ◽  
Chi Yuan ◽  
Isma Masood ◽  
...  
Author(s):  
Samson Paul K ◽  
Ateeq Ahmed H ◽  
Emmanuel Raju A

Choosing the right database platform(s) for IoT solutions is daunting. First, IoT solutions can be distributed across geographical regions. As opposed to a centralized cloud-based solution, more solutions are adopting a combination of fog computing at the edge and cloud computing. As such, your database platforms must offer you the flexibility to process the data at the edge and synchronize between the edge servers and the cloud. Second, depending on your IoT use cases, the capabilities you want in your database could range from real-time data streaming, data filtering and aggregation, near-zero latency read operations, instant analytics, high availability, geo distribution, schema flexibility and so on. This article walks you through the four steps in choosing the right database platforms for your IoT solutions. The amount of data stored in IoT databases increases as the IoT applications extend throughout smart city appliances, industry and agriculture. Contemporary database systems must process huge amounts of sensory and actuator data in real-time or interactively. Facing this first wave of IoT revolution, database vendors struggle day-by-day in order to gain more market share, develop new capabilities and attempt to overcome the disadvantages of previous releases, while providing features for the IoT.


Author(s):  
Biji Nair ◽  
S. Mary Saira Bhanu

Real-time streaming applications (RTSAs) generate huge volumes of temporally ordered, infinite, continuous, high speed data streams demanding both real-time and long-term data analytics. Fog computing is a reliable solution for processing and analyzing real-time streaming data as it offers low latency, location-aware, geographically distributed service at fog node and provides long-term services at the cloud data center (DC). This chapter addresses the challenge of coordinating the fog nodes and cloud for efficient processing of real-time streaming data in motion and at rest. The fog-cloud collaboration framework proposed in this chapter employs data stream management system (DSMS) schema at the fog node for real-time stream data processing and response generation. The data representation in micro-clusters at fog node and macro-clusters at DC facilitates accurate data analytics. The coordination between fog node and DC is through local ontology and global ontology respectively.


2019 ◽  
Vol 23 (1) ◽  
pp. 346-357
Author(s):  
Vithya G ◽  
Naren J ◽  
Varun V

2010 ◽  
Vol 30 (7) ◽  
pp. 1947-1949
Author(s):  
Bao-wen WANG ◽  
Jing-jing HAN ◽  
Zi-jun CHEN ◽  
Wen-yuan LIU

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