Data pre-processing is a fundamental component in any data-driven application. With the increasing complexity of data processing operations and volume of data, Cylon, a distributed dataframe system, is developed to facilitate data processing both as a standalone application and as a library, especially for Python applications. While Cylon shows promising performance results, we experienced difficulties trying to integrate with frameworks incompatible with the traditional Message Passing Interface (MPI). While MPI implementations encompass scalable and efficient c ommunication routines, their process launching mechanisms work well with mainstream HPC systems but are incompatible with some environments that adopt their own resource management systems. In this work, we alleviated this issue by directly integrating the Unified Communication X (UCX) framework, which supports a variety of classic HPC and non-HPC process-bootstrapping mechanisms as our communication framework. While we experimented with our methodology on Cylon, the same technique can be used to bring MPI communication to other applications that do not employ MPI’s built-in process management approach.

In the current era of Big Data, data engineering has transformed into an essential field of study across many branches of science. Advancements in Artificial Intelligence (AI) have broadened the scope of data engineering and opened up new applications in both enterprise and research communities. Aggregations (also termed reduce in functional programming) are an integral functionality in these applications. They are traditionally aimed at generating meaningful information on large data-sets, and today, they are being used for engineering more effective features for complex AI models. Aggregations are usually carried out on top of data abstractions such as tables/ arrays and are combined with other operations such as grouping of values. There are frameworks that excel in the said domains individually. But, we believe that there is an essential requirement for a data analytics tool that can universally integrate with existing frameworks, and thereby increase the productivity and efficiency of the entire data analytics pipeline. Cylon endeavors to fulfill this void. In this paper, we present Cylon's fast and scalable aggregation operations implemented on top of a distributed in-memory table structure that universally integrates with existing frameworks.

Designing low latency applications that can process large volumes data with higher efficiency is a challenging problem. With the limited time to process data, usage of online algorithms are becoming important in the big-data applications. Stream processing is a well-known area that has been studied for a long time. In this research, our objective is to use state of the art big-data analytic engines to implement online algorithms and compare the strengths and weaknesses in each system. We use a streaming version of Support Vector Machines (SVM) and KMeans to do the analysis. Apache Flink, Apache Storm and Twister2 streaming frameworks are used to implement these algorithms. Our study focuses on the efficiency of online training of these algorithms and the results show higher performance in Twister2 framework for these algorithms.

The growing use of Big Data frameworks on large machines highlights the importance of performance issues and the value of High Performance Computing (HPC) technology. This paper looks carefully at three major frameworks Spark, Flink and Message Passing Interface (MPI) both in scaling across nodes and internally over the many cores inside modern nodes. We focus on the special challenges of the Java Virtual Machine (JVM) using an Intel Haswell HPC cluster with 24 cores per node. Two parallel machine learning algorithms, K-Means clustering and Multidimensional Scaling (MDS) are used in our performance studies. We identify three major issues - thread models, affinity patterns, and communication mechanisms - as factors affecting performance by large factors and show how to optimize them so that Java can match the performance of traditional HPC languages like C. Further we suggest approaches that preserve the user interface and elegant dataflow approach of Flink and Spark but modify the runtime so that these Big Data frameworks can achieve excellent performance and realize the goals of HPC-Big Data convergence.

Large volumes of high dimensional time series data are increasingly becoming commonplace, and the ability to project such data into three dimensional space to visually inspect them is an important capability for scientific exploration. Algorithms such as Multidimensional Scaling (MDS) and Principal Component Analysis (PCA) can be used to reduce high dimensional data into a lower dimensional space. The time sensitive nature of such data requires continuous processing in time windows and visualizations to be shown as moving plots. In this paper we present: 1. an MDS-based approach to project high dimensional time series data to 3D with automatic transformation to align successive data segments; 2. an open source commodity visualization of three-dimensional time series in web browser based on Three.js; and 3. An example based on stock market data. The paper discusses various options available when producing the visualizations and how one optimizes the heuristic methods based on experimental results.

Enterprise Service Bus facilitates message-based communication between modular software components in a SOA application. In addition to message routing, the ESB communication abstraction includes filtering and transformation capabilities that enable composing separate software modules quickly, with minimal code changes. For this reason, this richer communication capability is often termed "mediation".