Scaling Distributed Machine Learning with the Parameter Server

TLDR

Big data introduces challenges across theory, architecture, frameworks, algorithms, and domain tools, characterized by inexact, incremental, and inductive processing, creating new research opportunities. The study seeks to determine whether a new theoretical framework exists for big data and how to operationally manage its computing algorithms. The report outlines identified challenges and illustrates application scenarios, including micro‑blog analysis and next‑generation search engine data processing.

Abstract

Big data may contain big values, but also brings lots of challenges to the computing theory, architecture, framework, knowledge discovery algorithms, and domain specific tools and applications. Beyond the 4-V or 5-V characters of big datasets, the data processing shows the features like inexact, incremental, and inductive manner. This brings new research opportunities to research community across theory, systems, algorithms, and applications. Is there some new "theory" for the big data? How to handle the data computing algorithms in an operatable manner? This report shares some view on new challenges identified, and covers some of the application scenarios such as micro-blog data analysis and data processing in building next generation search engines.