Дослідження продуктивності кластера Apache Spark на платформі Azure для методів машинного навчання

Getting Started with Apache Spark

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...

Вполне интегрируемые на Zd+ потенциалы для электромагнитного оператора Шрёдингера: лучевые асимптотики и задача рассеянияВполне интегрируемые на Zd+ потенциалы для электромагнитного оператора Шрёдингера: лучевые асимптотики и задача рассеяния

MLlib: Machine Learning in Apache Spark

Apache Spark is a popular open-source platform for large-scale data processing that is well-suited for iterative machine learning tasks. In this paper we present MLlib, Spark’s open-source distributed machine learning library. MLlib provides efficient functionality for a wide range of learning settings and includes several underlying statistical, optimization, and linear algebra primitives. Shi...

Modeling and Simulating Apache Spark Streaming Applications

Stream processing systems are used to analyze big data streams with low latency. The performance in terms of response time and throughput is crucial to ensure all arriving data are processed in time. This depends on various factors such as the complexity of used algorithms and configurations of such distributed systems and applications. To ensure a desired system behavior, performance evaluatio...