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 generated continuously from a 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 the astoundingly large amounts of sensor data over the Internet to 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

Systemintegratoren
,
Techniker

Gesamtklassifikation

Dissertation | PhD
,
Studie|Konzept

Technologie

Big Data
,
Cloud Service
,
R
,
Sensorik

Lizenz

CC-BY

Projektergebnisse

Zwischenbericht CC-BY

Stipendium-Zwischenbericht (midterm report) May 2019

Paper

Der Konferenzbeitrag für die ECSA 2019 mit dem Titel "Architecturing Elastic Edge Storage Services for Data-Driven Decision Making" ist unter Springer-Copyright in Lecture Notes in Computer Science (LNCS, volume 11681) Buchreihe veröffentlicht. Zusätzlich dazu findet ihr hier die aktualisierte Konferenz-Präsentation.

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