SpaRC: scalable sequence clustering using Apache Spark

Scalable SDE Filtering and Inference with Apache Spark

In this paper, we consider the problem of Bayesian filtering and inference for time series data modeled as noisy, discrete-time observations of a stochastic differential equation (SDE) with undetermined parameters. We develop a Metropolis algorithm to sample from the high-dimensional joint posterior density of all SDE parameters and state time series. Our approach relies on an innovative densit...

Fast Data Clustering and Outlier Detection Using K-means Clustering on Apache Spark

The components forming the information society nowadays are seen in all areas of our lives. As computers have a great deal of importance in our lives, the amount of information has begun to gather meaningful and specific qualities. Not only the amount of information is increased, but also the speed of access to information has increased. Large data is the transformed form of all data recovered ...

Real-time News Recommendations using Apache Spark

Recommending news articles is a challenging task due to the continuous changes in the set of available news articles and the contextdependent preferences of users. Traditional recommender approaches are optimized for analyzing static data sets. In news recommendation scenarios, characterized by continuous changes, high volume of messages, and tight time constraints, alternative approaches are n...

Parallel Maritime Traffic Clustering Based on Apache Spark

Maritime traffic patterns extraction is an essential part for maritime security and surveillance and DBSCANSD is a density based clustering algorithm extracting the arbitrary shapes of the normal lanes from AIS data. This paper presents a parallel DBSCANSD algorithm on top of Apache Spark. The project is an experimental research work and the results shown in this paper is preliminary. The exper...

Getting Started with Apache Spark