AboutService 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 AnalyticsEngineering 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).
Many results of our research are reflected in our Advanced Services Engineering course for PhD/Master students at TU Wien.
we focus on emerging complex distributed systems covering cloud systems, IoT, edge/fog systems, cyber-physical systems and social-cyber-physical systems.
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
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 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
Research DirectionIn our research, we focus on different aspects of engineering analytics.
Data concerns Modeling and Evaluation
Elasticity Engineering and Analytics
Lead: Priv.Doz. Dr. Hong-Linh Truong
- 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
- BADALING - Basic Abstractions for Developing Advanced Models of IoT Network in the Edge, Oct, 2016 - Sep, 2017
- AlPS - Analytics, Privacy, and Security for IoT and Big Data, Partially funded by ASEA-UNINET, 2016
- U-Test - Testing Cyber-Physical Systems under Uncertainty: Systematic, Extensible, and Configurable Model-based and Search-based Testing Methodologies, H2020 project, EU funded, Jan 2015-Dec 2017
- HAIVAN: Strengthening Critical IoT Software Development and Training in Highly Volatile and Unreliable Environments, partially funded by ASEA-UNINET, 2016.
Concepts & Prototypes
- SINC - Slicing IoT, Network functions, and Clouds
- HINC - Harnomizing IoT, Network Functions, and Clouds
- COMOT4U - Control, Monitoring, and Testing under and for Uncertainties, including Runtime Health Verification of CPS
- iCOMOT for Control, Monitoring, Governance and Configuration of IoT Cloud Systems
- MARSA - A Marketplace for Realtime Human-Sensing Data
- SALSA - A Framework for Dynamic Configuration of IoT Cloud Systems
- SOD1 - Service-Oriented Data and Computation Fusion
- Florin Balint: Negotiating, Monitoring and Recommending Data Contracts in IoT Dataspaces, Master thesis, TU Wien, 2017
- Florin-Bogdan Balint, Hong-Linh Truong, On Support ing Contract-aware IoT Dataspace Services, the 5th IEEE International Conference on Mobile Cloud Computing, Services, and Engineering (MobileCloud 2017), 6-8 April 2017 in San Francisco, USA To appear
- Hong-Linh Truong, Aitor Murguzur, Erica Yang, Challenges in Enabling Quality of Analytics in the Cloud, September 2016, Working paper
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