Related papers: A Unified System for Data Analytics and In Situ Qu…
In recent IoT (Internet of Things) and Web 2.0 technologies, a critical problem arises with respect to storing and processing the large amount of collected data. In this paper we develop and evaluate distributed infrastructures for storing…
The vision of $\Upsilon$-DB introduces deterministic scientific hypotheses as a kind of uncertain and probabilistic data, and opens some key technical challenges for enabling data-driven hypothesis management and analytics. The…
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…
Data science tasks involving tabular data present complex challenges that require sophisticated problem-solving approaches. We propose AutoKaggle, a powerful and user-centric framework that assists data scientists in completing daily data…
Current operating systems are complex systems that were designed before today's computing environments. This makes it difficult for them to meet the scalability, heterogeneity, availability, and security challenges in current cloud and…
Relational databases have limited support for data collaboration, where teams collaboratively curate and analyze large datasets. Inspired by software version control systems like git, we propose (a) a dataset version control system, giving…
We present DashQL, a language that describes complete analysis workflows in self-contained scripts. DashQL combines SQL, the grammar of relational database systems, with a grammar of graphics in a grammar of analytics. It supports preparing…
HRDBMS is a novel distributed relational database that uses a hybrid model combining the best of traditional distributed relational databases and Big Data analytics platforms such as Hive. This allows HRDBMS to leverage years worth of…
Analyzing the increasingly large volumes of data that are available today, possibly including the application of custom machine learning models, requires the utilization of distributed frameworks. This can result in serious productivity…
Database system is an indispensable part of software projects. It plays an important role in data organization and storage. Its performance and efficiency are directly related to the performance of software. Nowadays, we have many general…
This paper describes a prototype of extended XDB. XDB is an open-source and extensible database architecture developed by National Aeronautics and Space Administration (NASA) to provide integration of heterogeneous and distributed…
Data lakes have emerged as a flexible and scalable solution for storing and analyzing large volumes of heterogeneous data, including structured, semi-structured, and unstructured formats. Despite their growing adoption in both industry and…
The rate at which data is generated has been increasing rapidly, raising challenges related to its management. Traditional database management systems suffer from scalability and are usually inefficient when dealing with large-scale and…
This paper tries to reduce the effort of learning, deploying, and integrating several frameworks for the development of e-Science applications that combine simulations with High-Performance Data Analytics (HPDA). We propose a way to extend…
A number of popular systems, most notably Google's TensorFlow, have been implemented from the ground up to support machine learning tasks. We consider how to make a very small set of changes to a modern relational database management system…
In the last few years, the field of data science has been growing rapidly as various businesses have adopted statistical and machine learning techniques to empower their decision making and applications. Scaling data analysis, possibly…
The FoundationDB Record Layer is an open source library that provides a record-oriented data store with semantics similar to a relational database implemented on top of FoundationDB, an ordered, transactional key-value store. The Record…
This article presents the implementation process of a Data Warehouse and a multidimensional analysis of business data for a holding company in the financial sector. The goal is to create a business intelligence system that, in a simple,…
As the amount of scientific data continues to grow at ever faster rates, the research community is increasingly in need of flexible computational infrastructure that can support the entirety of the data science lifecycle, including…
Scientific applications produce a huge amount of data, which imposes serious management and analysis challenges. In particular, limitations in current database management systems prevent their adoption in simulation applications, in which…