Data Management Strategies for Near Real-Time Edge Analytics
Developing an Edge Data Management framework (EDMframe)
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Ivan Lujic

Data Management Strategies for Near Real-Time Edge Analytics

Förderjahr 2018 / Stipendien Call #13 / Stipendien ID: 3793

Internet of things (IoT) sensors are used in wide set of applications,such as smart cities, eHealth monitoring or intelligent traffic management systems. As a consequence, massive amounts of data are constantly generated from growing number of IoT sensors. Decision making processes depend on information obtained through the analysis of collected data. Traditionally, managing such systems includes data processing in the cloud. However, performing data analytics in cloud data centers brings serious challenges including the transfer of astoundingly large amount of sensor data over the Internet strongto the cloud and strict latency and accuracy requirements of IoT applications. To overcome aforementioned challenges, we investigate strategies of adaptive data management for near real-time data analytics performed on edge nodes that are closer to the source of data.

Uni | FH [Universität]

Technische Universität Wien

Themengebiet

Big Data
,
IoT

Zielgruppe

Techniker

Gesamtklassifikation

Cloud Service
,
Datensammlung
,
Dissertation | PhD

Technologie

Big Data
,
Cloud Service
,
Datenbank
,
R
,
Sensorik

Lizenz

CC-BY