About
The 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. Recently, we have also started to study what does it mean software and services analytics in the age of quantum computing through the collaboration in The Finnish Quantum Institute. At Aalto we teach students to study topics in Big Data Platforms and Systems for Big Data/Machine Learning and supervise research theses in, for example, cloud computing, big data, IoT, edge computing, and ML.Engineering Analytics
Engineering 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. See some high-level ideas in this presentation.Systems
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
Software
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
Data
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
Services
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
Research Directions
Machine Learning Observability and Optimization
Data concerns Modeling and Evaluation
Data Concerns, Data Quality Evaluation and Data Contracts for Data Services
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
Engineering Analytics of service-based hybrid computing systems
Team
We are looking for motivated PhD students in IoT/edge computing/ML/Big Data in Edge-Cloud Continuum. Contact us for open positions!
Past members
Postdocs: Dr. Thanh-Phuong Pham, Dr. Bunjamin Memishi, Dr. Luca Berardinelli, Dr. Georgiana Copil, Dr. Daniel
Moldovan,
Research assistants/Master Students: My-Linh Nguyen, Rohit Raj, 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
Past visitors
Jingyu Liang (China University of Petroleum (Beijing)), 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)
Results
Our GitHub prototypes: https://github.com/rdsea
Our Docker Hub: https://hub.docker.com/u/rdsea/
Projects
- 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
- QoA4ML - Quality of Analytics for ML
- CAMLE - Context-aware ML Explainability
- ZETA - Zero Trust Elasticity
- 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
Publications & Theses
See our full list here. Three recent publications:
- Nguyen Ngoc Nhu Trang, Hong-Linh Truong Context-aware, Composable Anomaly Detection in Large-scale Mobile Networks (PDF), The 2023 IEEE International Conference on Computers, Software & Applications (COMPSAC 2023)
- Hong-Linh Truong, Nguyen Ngoc Nhu Trang, HAIVAN: a Holistic ML Analytics Infrastructure for a Variety of Radio Access Networks (PDF), 2022 IEEE International Conference on Big Data (IEEE BigData 2022)
- Tien-Dung Cao, Hong-Linh Truong, Tram Truong-Huu, Minh-Tri Nguyen, Enabling Awareness of Quality of Training and Costs in Federated Machine Learning Marketplaces (PDF), 2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC), 2022.
- See the full list of publications