Understanding the origin of the stress and strain distribution is crucial to increasing the durability of components under dynamic load.
Numerical simulations based on finite element (FE) models help with this understanding but must be validated by real component data.
It comprises a set of hardware and software components enabling a PSV-500-3D Scanning Vibrometer – a precision tool for full-field optical vibration mapping – to measure and analyze the dynamic strain and stress distribution on surfaces with high resolution and low noise.The Scanning Vibrometer makes a series of non-contact deflection measurements on a predefined grid using the laser probe to characterize strain at each point instead of attaching individual strain gauges.Thus, the test set-up is fast and very repeatable with no mass loading from an attached transducer.In big data applications, data privacy is one of the most concerned issues because processing large-scale privacy-sensitive data sets often requires computation resources provisioned by public cloud services.Sub-tree data anonymization is a widely adopted scheme to anonymize data sets for privacy preservation.
Top–Down Specialization (TDS) and Bottom–Up Generalization (BUG) are two ways to fulfill sub-tree anonymization.
However, existing approaches for sub-tree anonymization fall short of parallelization capability, thereby lacking scalability in handling big data in cloud.
Still, either TDS or BUG individually suffers from poor performance for certain valuing of -anonymity parameter.
In this paper, we propose a hybrid approach that combines TDS and BUG together for efficient sub-tree anonymization over big data.
Further, we design Map Reduce algorithms for the two components (TDS and BUG) to gain high scalability.
Experiment evaluation demonstrates that the hybrid approach significantly improves the scalability and efficiency of sub-tree anonymization scheme over existing approaches.