Apache spark, famously known for big data handling ability, is a distributed open-source framework that utilizes the idea of memory to process data. As performance spark mostly being affected by predominant configuration parameters, it challenging achieve optimal result from spark. The current practice tuning parameters ineffective, as performed manually. Manual large space and complex interact...

For Big Data processing, Apache Spark has been widely accepted. However, when dealing with events or any other spatio-temporal data sets, Spark becomes very inefficient as it does not include any spatial or temporal data types and operators. In this paper we demonstrate our STARK project that adds the required data types and operators, such as spatio-temporal filter and join with various predic...

One of the main goals Big Data research, is to find new data mining methods that are able process large amounts in acceptable times. In classification, as traditional class imbalance a common problem must be addressed, case also looking for solution can applied an execution time. this paper we present Approx-SMOTE, parallel implementation SMOTE algorithm Apache Spark framework. The key differen...

The need for modern data analytics to combine relational, procedural, and map-reduce-style functional processing is widely recognized. State-of-the-art systems like Spark have added SQL front-ends and relational query optimization, which promise an increase in expressiveness and performance. But how good are these extensions at extracting high performance from modern hardware platforms? While S...