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. . 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 (Data Marketplace, Service Models, APIs, and Configuration). We apply our engineering analytics techniques to various applications, including smart cities, smart agriculture, enterprises, e-science, logistics, robotics, and e-health.
We also work on Service Engineering Analytics of Hybrid Computing Systems in which humans play a crucial role in machine-human computation loops.
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.
we focus on emerging complex distributed systems covering cloud systems, IoT, edge/fog systems, cyber-physical systems and social-cyber-physical systems. Some of our novel concepts are elastic systems, IoT Cloud systems and the ensembles of IoT, Cloud and Networks
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. Some recent tools are IoT management, Uncertainty Testing for CPS
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. Some our novel concepts are elastic data analytics
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. Some of our novel conconcepts are Data-as-a-Service, data contracts for data marketplaces, human sensing data marketplace, and IoT data marketplaces
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
- INTER-HINC, IoT Interoperability, Part of Inter-IoT, H2020, May-2017-Oct-2018
- 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, 2016-2917
- 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
- T4UME - Tool for Uncertainty Modeling and Evaluation for IoT Cloud Systems
- 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
See our full list here. Recent publications:
- Hong-Linh Truong, Aitor Murguzur, Erica Yang, "Challenges in Enabling Quality of Analytics in the Cloud", (Pre-print PDF), Journal of Data and Information Quality, ACM, 2017
- Hong-Linh Truong, Luca Berardinelli, Ivan Pavkovic and Georgiana Copil, Modeling and Provisioning IoT Cloud Systems for Testing Uncertainties, (Pre-print PDF), 14th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous 2017), November 7–10, 2017,Melbourne, Australia. To appear.
- Hong-Linh Truong, Luca Berardinelli, Testing Uncertainty of Cyber-physical Systems in IoT Cloud Infrastructures – Combining Model-Driven Engineering and Elastic Execution, Workshop on Testing Embedded and Cyber-Physical Systems, ISSTA 2017
- Phu Phung, Hong-Linh Truong, Divya Teja Yasoju, P4SINC - An Execution Policy Framework for IoT Services in the Edge , the 2nd IEEE International Congress on Internet of Things (IEEE ICIOT 2017), June 25 - June 30, 2017, Honolulu, Hawaii, USA
- 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
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Events & News
19-20 Aug 2016
Luca Berardinelli gives a talk about Modeling, Provisioning, Deployment of IoT/Cloud-based CPS at Fudan University, Shanghai, China Aug 19-20 2016.Read More