Video2Flink: real-time video partitioning in Apache Flink and the cloud

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...

State Management in Apache Flink®: Consistent Stateful Distributed Stream Processing

Stream processors are emerging in industry as an apparatus that drives analytical but also mission critical services handling the core of persistent application logic. Thus, apart from scalability and low-latency, a rising system need is first-class support for application state together with strong consistency guarantees, and adaptivity to cluster reconfigurations, software patches and partial...

SFRDF+: Join Plans for SPARQL Processing in Apache Flink

Reproducible Experiments for Comparing Apache Flink and Apache Spark on Public Clouds

Big data processing is a hot topic in today’s computer science world. There is a significant demand for analysing big data to satisfy many requirements of many industries. Emergence of the Kappa architecture created a strong requirement for a highly capable and efficient data processing engine. Therefore data processing engines such as Apache Flink and Apache Spark emerged in open source world ...

Development of a News Recommender System based on Apache Flink

The amount of data on the web is constantly growing. The separation of relevant from less important information is a challenging task. Due to the huge amount of data available in the World Wide Web, the processing cannot be done manually. Software components are needed that learn the user preferences and support users in finding the relevant information. In this work we present our recommender ...