Harnessing multimodal data integration to advance precision oncology
Advances in quantitative biomarker development have accelerated new forms of data-driven
insights for patients with cancer. However, most approaches are limited to a single mode of …
insights for patients with cancer. However, most approaches are limited to a single mode of …
Mlops-definitions, tools and challenges
This paper is an concentrated overview of the Machine Learning Operations (MLOps) area.
Our aim is to define the operation and the components of such systems by highlighting the …
Our aim is to define the operation and the components of such systems by highlighting the …
Amazon Redshift re-invented
In 2013, AmazonWeb Services revolutionized the data warehousing industry by launching
Amazon Redshift, the first fully-managed, petabyte-scale, enterprise-grade cloud data …
Amazon Redshift, the first fully-managed, petabyte-scale, enterprise-grade cloud data …
Multi-tenant cloud data services: state-of-the-art, challenges and opportunities
Enterprises are moving their business-critical workloads to public clouds at an accelerating
pace. Multi-tenancy is a crucial tenet for cloud data service providers allowing them to …
pace. Multi-tenancy is a crucial tenet for cloud data service providers allowing them to …
Data warehouse systems
A Vaisman, E Zimányi - Data-Centric Systems and Applications, 2014 - Springer
Since the late 1970s, relational database technology has been adopted by most
organizations to store their essential data. However, nowadays, the needs of these …
organizations to store their essential data. However, nowadays, the needs of these …
End-to-end optimization of machine learning prediction queries
Prediction queries are widely used across industries to perform advanced analytics and
draw insights from data. They include a data processing part (eg, for joining, filtering …
draw insights from data. They include a data processing part (eg, for joining, filtering …
What can data-centric AI learn from data and ML engineering?
Data-centric AI is a new and exciting research topic in the AI community, but many
organizations already build and maintain various" data-centric" applications whose goal is to …
organizations already build and maintain various" data-centric" applications whose goal is to …
Optimizing Data Warehousing Performance through Machine Learning Algorithms in the Cloud
S Ahmadi - International Journal of Science and Research (IJSR), 2023 - papers.ssrn.com
This comprehensive overview explores the integration of machine learning (ML) in data
warehousing, focusing on optimization challenges, methodologies, results, and future …
warehousing, focusing on optimization challenges, methodologies, results, and future …
Cloud-native databases: A survey
Cloud databases have been widely accepted and deployed due to their unique advantages,
such as high elasticity, high availability, and low cost. Many new techniques, such as …
such as high elasticity, high availability, and low cost. Many new techniques, such as …
Data lakes: A survey of functions and systems
Data lakes are becoming increasingly prevalent for Big Data management and data
analytics. In contrast to traditional 'schema-on-write'approaches such as data warehouses …
analytics. In contrast to traditional 'schema-on-write'approaches such as data warehouses …