Related papers: Comparative analysis of large data processing in A…

Existing profilers for scripting languages (a.k.a. "glue" languages) like Python suffer from numerous problems that drastically limit their usefulness. They impose order-of-magnitude overheads, report information at too coarse a…

The lexical and syntactic disparities among different programming languages (e.g., Java and Python) pose significant challenges for multi-language software engineering tasks such as cross-language code clone detection and code retrieval,…

Hadoop is emerging as the primary data hub in enterprises, and SQL represents the de facto language for data analysis. This combination has led to the development of a variety of SQL-on-Hadoop systems in use today. While the various…

XSLT is an increasingly popular language for processing XML data. It is widely supported by application platform software. However, little optimization effort has been made inside the current XSLT processing engines. Evaluating a very…

This work explores the use of big data technologies deployed in the cloud for processing of astronomical data. We have applied Hadoop and Spark to the task of co-adding astronomical images. We compared the overhead and execution time of…

Apache SAMOA (Scalable Advanced Massive Online Analysis) is an open-source platform for mining big data streams. Big data is defined as datasets whose size is beyond the ability of typical software tools to capture, store, manage, and…

Context. The rise of Large Language Models (LLMs) has led to their widespread adoption in development pipelines. Goal. We empirically assess the energy efficiency of Python code generated by LLMs against human-written code and code…

Non-linear spectral dimensionality reduction methods, such as Isomap, remain important technique for learning manifolds. However, due to computational complexity, exact manifold learning using Isomap is currently impossible from large-scale…

Static analysis approximates the results of a program by examining only its syntax. For example, control-flow analysis (CFA) determines which syntactic lambdas (for functional languages) or (for object-oriented) methods may be invoked at…

Data of the order of terabytes, petabytes, or beyond is known as Big Data. This data cannot be processed using the traditional database software, and hence there comes the need for Big Data Platforms. By combining the capabilities and…

Models of parallel processing systems typically assume that one has $l$ workers and jobs are split into an equal number of $k=l$ tasks. Splitting jobs into $k > l$ smaller tasks, i.e. using ``tiny tasks'', can yield performance and…

Profiling techniques are used extensively at different parts of the computing stack to achieve many goals. One major goal is to make a piece of software execute more efficiently on a specific hardware platform, where efficiency spans…

Distributed data processing ecosystems are widespread and their components are highly specialized, such that efficient interoperability is urgent. Recently, Apache Arrow was chosen by the community to serve as a format mediator, providing…

Spark NLP is a Natural Language Processing (NLP) library built on top of Apache Spark ML. It provides simple, performant and accurate NLP annotations for machine learning pipelines that can scale easily in a distributed environment. Spark…

With the emergence of the big data age, the issue of how to obtain valuable knowledge from a dataset efficiently and accurately has attracted increasingly attention from both academia and industry. This paper presents a Parallel Random…

This paper introduces Sparklen, a statistical learning toolkit for Hawkes processes in Python, designed to bring together efficiency and ease of use. The purpose of this package is to provide the Python community with a complete suite of…

The most commonly used open-source process mining software tools today are ProM and PM4Py, written in Java and Python, respectively. Such high-level, often interpreted, programming languages trade off performance with memory safety and…

Execution logs are a crucial medium as they record runtime information of software systems. Although extensive logs are helpful to provide valuable details to identify the root cause in postmortem analysis in case of a failure, this may…

Comparison of programming languages is a common topic of discussion among software engineers. Multiple programming languages are designed, specified, and implemented every year in order to keep up with the changing programming paradigms,…

Code large language models mark a pivotal breakthrough in artificial intelligence. They are specifically crafted to understand and generate programming languages, significantly boosting the efficiency of coding development workflows. In…