AboutThe Aalto Systems and Services Engineering Analytics (AaltoSEA) Group concentrates and consolidates research activities, resources, results, and collaborations on foundational principles, concepts and techniques for service engineering analytics of distributed software systems and services, big data applications, machine learning systems and IoT. We are a member of The Department of Computer Science, Aalto University. We also contribute to the newly established Aalto Center for Autonomous Systems. At Aalto we teach students to study topics in Big Data Platforms and Systems for Big Data/Machine Learning.
Engineering AnalyticsEngineering analytics is concerned with the techniques and tools for designing, monitoring, analyzing, explaining 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 and their interplay(IoT, Cloud and Edge Systems), Software (Machine Learning, Middleware, Protocols, and Tools) , Data (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. See some high-level ideas in this presentation.
we focus on emerging complex distributed systems covering cloud systems, IoT, edge 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 IoT, cloud, edge, enterprise, cyber-physical systems, and machine learning systems. Application domains are smart agriculture, smart city, e-science, and industrial internet. Some recent tools are IoT management, Uncertainty Testing for CPS, and IoT Cloud uncertainties
We focus on IoT/big data data models and 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, Quality of Analytics, and incidents in big 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 machine learning services
Data concerns Modeling and Evaluation
Uncertainties, performance and reliability evaluation
Elasticity Engineering and Analytics
Blockchain and Services
Blockchain-based IoT middleware and services, including interaction designs, performance, monitoring and DevOps.
Service-based Hybrid Computing Systems
Open position: Full time funded PhD position, 2021
Master Thesis Students
- Eero Silfverberg: IoT in marine industry (with Oceanvolt)
- Minh Nguyen: Smart Contract for Flextimers (Aalto School of Business)
- Yoselin Garcia Salinas: Benchmarking of in-memory Big Data technologies (with FA Solutions)
- Kimmo Mikkola: IoT Sensors in 3GPP MCS Environments (with Airbus Defense and Space Oy)
Adrian Orive Oneca (IKERLAN), Kyle Chard (University of Chicago), Liang Zhang (Fudan University), The-Vu Tran (Da Nang University), Huu-Hung Huynh (Da Nang University), Nam Huynh (HoChiMinh City University of Technology)
Postdocs: Dr. Bunjamin Memishi, Dr. Luca Berardinelli, Dr. Georgiana Copil, Dr. Daniel Moldovan,
Research assistants/Master Students: Minh-Duc Ta, Strasdosky Otewa, Oscar Henriksson, Kreics Krists, Filip Rydzi, Michael Hammerer, Lingfan Gao, Juraj Cik, Florin Balint, Manfred Halper, Matthias Karan, Peter Klein
Our GitHub prototypes: https://github.com/rdsea
Our Docker Hub: https://hub.docker.com/u/rdsea/
- ChainML - practical applications in the intersection of blockchain and machine learning
- 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
- IoTCloudSamples: IoT Cloud Units for Research and Development
- KALBI- Blockchain knowledge, artefacts, benchmarks and utilities
- 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
- Data and Service Contracts
See our full list here. Three recent publications:
- Hong-Linh Truong, Minh-Tri Nguyen, QoA4ML – A Framework for Supporting Contracts in Machine Learning Services, (Accepted PDF), 2021 IEEE International Conference on Web Services (ICWS), September 5-10, 2021.
- Rohit Raj, Hong-Linh Truong, On Analysis of Security and Elasticity Dependency in IIoT Platform Services, (Accepted PDF), 2021 IEEE International Conference on Services Computing (SCC), September 5-10, 2021.
- Hong-Linh Truong, "Using IoTCloudSamples as a Software Framework for Simulations of Edge Computing Scenarios", Internet of Things: Engineering Cyber Physical Human Systems, 2021
- See the full list of publications