Parallelizing an experiment to decide shellability on bipartite graphs using Apache Spark

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...

An Apache Spark Implementation for Sentiment Analysis on Twitter Data

Sentiment Analysis on Twitter Data is a challenging problem due to the nature, diversity and volume of the data. In this work, we implement a system on Apache Spark, an open-source framework for programming with Big Data. The sentiment analysis tool is based on Machine Learning methodologies alongside with Natural Language Processing techniques and utilizes Apache Spark’s Machine learning libra...

Getting Started with Apache Spark

Scaling Collaborative Filtering to Large-Scale Bipartite Rating Graphs Using Lenskit and Spark

Popular social networking applications such as Facebook, Twitter, Friendster, etc. generate very large graphs with different characteristics. These social networks are huge, comprising millions of nodes and edges that push existing graph mining algorithms and architectures to their limits. In product-rating graphs, users connect with each other and rate items in tandem. In such bipartite graphs...

An Experiment in Parallelizing an Application Using Formal Methods

Scientists and engineers have successfully developed much useful sequential code using classical programming languages, such as Fortran, and highly performant computers can help them to model problems whilst improving their results of simulation. However, great skill and care are required to design correct parallel code from sequential code. We focus our work on 3 aspects of producing correct p...