Generation of a Social Network Graph by Using Apache Spark

Balanced Graph Partitioning with Apache Spark

A significant part of the data produced every day by online services is structured as a graph. Therefore, there is the need for efficient processing and analysis solutions for large scale graphs. Among the others, the balanced graph partitioning is a well known NP-complete problem with a wide range of applications. Several solutions have been proposed so far, however most of the existing state-...

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...

Getting Started with Apache Spark

assessment of the efficiency of s.p.g.c refineries using network dea

data envelopment analysis (dea) is a powerful tool for measuring relative efficiency of organizational units referred to as decision making units (dmus). in most cases dmus have network structures with internal linking activities. traditional dea models, however, consider dmus as black boxes with no regard to their linking activities and therefore do not provide decision makers with the reasons...

Approximate Stream Analytics in Apache Flink and Apache Spark Streaming

Approximate computing aims for efficient execution of workflows where an approximate output is sufficient instead of the exact output. The idea behind approximate computing is to compute over a representative sample instead of the entire input dataset. Thus, approximate computing — based on the chosen sample size — can make a systematic trade-off between the output accuracy and computation effi...