Location of Things (LoT) is an Internet of Things paradigm for mobility analytics. In LoT, massive mobility data is being gathered, processed and transmitted among heterogeneous data nodes in a decentralized architecture. Thus, managing data quality for LoT has become a prominent challenge as traditional techniques cannot cope with the aforementioned characteristics of LoT. In our project MALOT, we aim at designing a set of new techniques to manage data quality for LoT effectively and efficiently. Specifically, MALOT includes 1) a core model for assessing mobility data quality at individual LoT nodes; 2) effective data enhancement algorithms based on the quality model for resolving data heterogeneity and inconsistency; 3) a task scheduling mechanism for improving overall efficiency of data quality management in LoT.
The Location of Things (LoT) entails the collection of massive amounts of mobility data, which are then processed and transmitted among heterogeneous data nodes in a decentralised architecture. Since traditional centralised data quality management techniques can't cope with such LoT processes, data quality management for the LoT remains a challenge. The EU-funded MALOT project intends to design a set of new techniques that can adapt to the decentralised and heterogeneous LoT architecture. This will involve developing a core model for assessing mobility data quality at decentralised and dynamic data nodes, effective quality-aware data enhancement algorithms and a mechanism for the optimal scheduling of quality management tasks among relevant nodes. The project’s work will contribute to the innovation of Europe’s Internet of Things and expand its applications.
|Effective start/end date||15/04/2020 → 14/04/2022|
- Data Quality Modeling
- Data Enhancement
- Decentralized Data Management, Task Planning
- Data Integration
- Graph Theory
- Internet of Things