Multimedia big data computing for trend detection

The Big data analysis has becoming increasingly more relevant for the enterprises because the efficient handling of information represents a unique competitive advantage, being its application so diverse as the nature of the data. Ejm. Fraud detection, advertising strategies, web traffic m onitoring, etc. Apache Spark is a engine for large - scale data processing, intended to be a drop in replacement for Hadoop MapReduce providing the benefit of improved performance; the main goal of this project is proof the capabilities of this system, throu gh the development and implementation of a distributed pipeline for processing and indexing at high speed and real - time multimedia data streams generated by social networks and detect trends in these, using for this purpose the Spark related projects and l ibraries: Spark Streaming and Spark MLlib. To verify the effectiveness of the algorithm, different benchmarks (with different configurations) will be performed, these results will be analyzed

Generalised Decision Level Ensemble Method for Classifying Multi-media Data

In recent decades, multimedia data have been commonly generated and used in various domains, such as in healthcare and social media due to their ability of capturing rich information. But as they are unstructured and separated, how to fuse and integrate multimedia datasets and then learn from them eectively have been a main challenge to machine learning. We present a novel generalised decision level ensemble method (GDLEM) that combines the multimedia datasets at decision level. After extracting features from each of multimedia datasets separately, the method trains models independently on each media dataset and then employs a generalised selection function to choose the appropriate models to construct a heterogeneous ensemble. The selection function is dened as a weighted combination of two criteria: the accuracy of individual models and the diversity among the models. The framework is tested on multimedia data and compared with other heterogeneous ensembles. The results show that the GDLEM is more exible and eective