Cost Effective Road Traffic Prediction Model using Apache Spark

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...

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...

Efficient iterative virtual screening with Apache Spark and conformal prediction

BACKGROUND Docking and scoring large libraries of ligands against target proteins forms the basis of structure-based virtual screening. The problem is trivially parallelizable, and calculations are generally carried out on computer clusters or on large workstations in a brute force manner, by docking and scoring all available ligands. CONTRIBUTION In this study we propose a strategy that is b...

Getting Started with Apache Spark

Estimating the Cost of Road Traffic Accidents in Iran using Human Capital Method

Road traffic accidents and the effects they subsequently incur are increasing dramatically, and estimating the cost of road traffic crashes could be a vital step in improving the recognition of this widespread problem. The main objective of this paper is to estimate the cost of road traffic crashes in Iran using the Human Capital (HC) Method. Results of this study indicate that the cost of road...