DIAS, the Dynamic Intelligent Aggregation Service is a decentralized, privacy-preserving, networked information system that performs lightweight real-time data analytics such as computation of aggregation functions, for instance, summation, average, maximum, minimum, top-k and other. A DIAS node is actually a server, a cloud computing node or even a personal computer running a piece of software that shares data to other interconnected DIAS nodes and receives back collective information about all shared data in the network. DIAS is designed for deployment over crowdsourced computational resources to open up the democratization of data analytics and their transformation into a public good.
Examples of DIAS applications include the following:
- Collective sensing of residential energy demand to enable sustainable energy usage and crowdsourcing of power grid resilience.
- Privacy-preserving social sensing via smart phones, such as participatory sharing of traffic information, crowd mobility, crises response, and other.
- Crowdsourced environmental monitoring such climate change and pollution monitoring.
- Political participation, engagement and collective decision-making such as trustworthy and crowdsourced referendums.
Existing data analytics systems usually perform centralized computations or they are centrally managed, in contrast to DIAS that is designed to operated in a fully decentralized fashion. Common implications of centralized data analytics methodologies are the low privacy-preservation, the enabling of surveillance or discriminatory actions and the undermining of citizens’ autonomy. On the other hand, DIAS allows self-determination of information sharing, exchange and storage of hashed or encrypted data, decentralized computations and collective bottom-up self-management of the computational resources in which data analytics are performed.
Some of the novel and innovative features of DIAS include its fully decentralized design, its privacy preservation and the computations of several aggregation functions without changes in the main system operation. Moreover, DIAS is intelligent and efficient as it can adjust its computational and communication load based on application requirements and available resources. DIAS is capable of ensuring highly accurate estimations of aggregation function even under rapid changes in the input data.
- Evangelos Pournaras, Edward Gaere, Renato Kunz, Atif Nabi Ghulam, Democratizing Data Analytics: Crowd-sourcing Decentralized Collective Measurements, in the proceedings of the 13th International Conference on Self-adaptive and Self-organizing Systems-SASO 2019, Umea, Sweeden, June 2019 © IEEE [Poster] (Best Demo Award)
- Jovan Nikolic, Marcel Schoengens and Evangelos Pournaras, Train Global, Test Local: Privacy-preserving Learning of Cost-effectiveness in Decentralized Systems, in the Proceedings of the 10th International Conference on Intelligent Networking and Collaborative Systems-INCoS-2018, Bratislava, Slovakia, September 2019 © Springer
- Evangelos Pournaras and Jovan Nikolic, On-demand Self-adaptive Data Analytics in Large-scale Decentralized Networks, in the Proceedings of the 16th IEEE International Symposium on Network Computing and Applications-NCA-2017, Cambridge, USA, November 2017 © IEEE [Presentation]
- Evangelos Pournaras, Jovan Nikolic, Self-corrective Dynamic Networks via Decentralized Reverse Computations, in the Proceedings of the 14th International Conference on Autonomic Computing-ICAC-2017, Columbus, Ohio, July 2017 © IEEE [Presentation]
- Evangelos Pournaras, Jovan Nikolic, Alex Omerzel, Dirk Helbing, Engineering Democratization in Internet of Things Data Analytics, in the proceedings of the 31st IEEE International Conference on Advanced Information Networking and Applications-AINA-2017, Taiwan, March 2017 © IEEE [Presentation]
- Evangelos Pournaras, Martijn Warnier and Frances M.T. Brazier, Peer-to-peer Aggregation for Dynamic Adjustments in Power Demand, Peer-to-Peer Networking and Applications, Vol. 8, Nr. 2, pp. 189-202, 2015 © Springer
- Evangelos Pournaras, Martijn Warnier and Frances M.T. Brazier, A Generic and Adaptive Aggregation Service for Large-scale Decentralized Networks, Complex Adaptive Systems Modeling, 1:19, 2013 © SpringerOpen
- Evangelos Pournaras, Multi-level Reconfigurable Self-organization in Overlay Services, PhD Thesis, Delft University of Technology, March 2013 (Chapter 5)