About

Service Engineering Analytics - Research and Development (RDSEA) concentrates and consolidates research activities, resources, results, and collaborations on Systems, Software and Data Service Engineering Analytics carried out under the lead of Hong-Linh Truong at The Distributed Group Systems, TU Wien.

Service Engineering Analytics

Engineering analytics is concerned with the techniques and tools for desiging, monitoring, analyzing, and optimizing functions, performance, data quality, elasticity, and uncertainties associated with systems, software, data and services. Some challenges, directions and results were outlined in this habilitation thesis. In our work, we focus on engineering analytics for: Systems (IoT, Cloud, and Edge Systems), Software (Middleware, Protocols, and Tools) , Data (Processing Models and Analytics), Services (Service Models, APIs, and Configuration).
We also work on Service Engineering Analytics of Hybrid Computing Systems.
Many results of our research are reflected in our Advanced Services Engineering course for PhD/Master students at TU Wien and the Distributed Systems Technologies course for master studies.

Systems

we focus on emerging complex distributed systems covering cloud systems, IoT, edge/fog systems, cyber-physical systems and social-cyber-physical systems.

Software

We focus on system software, middleware, tools and applications in the IoT, cloud, edge/fog, cyber-physical systems, and social-cyber-physical systems. Application domains are smart agriculture, smart city, e-science, and industrial internet

Data

We focus on data models and data analytics, including various types of data in complex distributed systems that are gathered, processed and provisioned under different services

Services

Services offer functions built on capabilities of the above-mentioned systems, software and data. We focus on novel technical service models (data-as-a-service, IoT services, e-science services, and several application-specific services), service API and execution management, and dynamic business models (e.g. pay-per-use) for consumers

Postdocs

PhD Students

Master Students

  • Ivan Pavkovic: Data Uncertainty Testing in Cyber-Physical Systems
  • Manfred Halper: Identification and Analysis of Incidents in Cloud Services
  • Peter Klein: working on IoT software units and execution management
  • Christian Proinger: Resource slice monitoring
  • Daniel Hopfer: Data lake for cloud forensics

Past members

Postdocs: Dr. Georgiana Copil, Dr. Daniel Moldovan,
Master Students: Juraj Cik, Florin Balint

Results

Concepts & Prototypes

Events & News

13 June 2017

Ericsson R & D Bangalore

We talk about Resource Slices of IoT, network functions, and cloud and their uncertainties and testing

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11 October 2016

ICSOC 2016

We presented and discussed our extensive experiences in service analytics for IoT cloud systems

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4 May 2016

Talk in ICCCRI 2016@CloudAsia 2016

We present the SINC conceptual framework

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