Anomaly Detection and Repair for Accurate Predictions in Geo-distributed Big Data

The increasing presence of geo-distributed sensor networks implies the generation of huge volumes of data from multiple geographical locations at an increasing rate. This raises important issues which become more challenging when the final goal is that of the analysis of the data for forecasting purposes or, more generally, for predictive tasks. This paper proposes a framework which supports predictive modeling tasks from streaming data coming from multiple geo-referenced sensors. In particular, we propose a distance-based anomaly detection strategy which considers objects described by embedding features learned via a stacked auto-encoder. We then devise a repair strategy which repairs the data detected as anomalous exploiting non-anomalous data measured by sensors in nearby spatial locations. Subsequently, we adopt Gradient Boosted Trees (GBTs)to predict/forecast values assumed by a target variable of interest for the repaired newly arriving (unlabeled)data, using the original feature representation or the embedding feature representation learned via the stacked auto-encoder. The workflow is implemented with distributed Apache Spark programming primitives and tested on a cluster environment. We perform experiments to assess the performance of each module, separately and in a combined manner, considering the predictive modeling of one-day-ahead energy production, for multiple renewable energy sites. Accuracy results show that the proposed framework allows reducing the error up to 13.56%. Moreover, scalability results demonstrate the efficiency of the proposed framework in terms of speedup, scaleup and execution time under a stress test.