Log In Sign Up

TADOC: Text Analytics Directly on Compression

by   Feng Zhang, et al.

This article provides a comprehensive description of Text Analytics Directly on Compression (TADOC), which enables direct document analytics on compressed textual data. The article explains the concept of TADOC and the challenges to its effective realizations. Additionally, a series of guidelines and technical solutions that effectively address those challenges, including the adoption of a hierarchical compression method and a set of novel algorithms and data structure designs, are presented. Experiments on six data analytics tasks of various complexities show that TADOC can save 90.8 memory usage, while halving data processing times.


page 1

page 2

page 3

page 4


G-TADOC: Enabling Efficient GPU-Based Text Analytics without Decompression

Text analytics directly on compression (TADOC) has proven to be a promis...

Simple-ML: Towards a Framework for Semantic Data Analytics Workflows

In this paper we present the Simple-ML framework that we develop to supp...

Towards Understanding Analytics in Software Startups

Analytics plays a crucial role in the data-informed decision-making proc...

MAIA: A Microservices-based Architecture for Industrial Data Analytics

In recent decades, it has become a significant tendency for industrial m...

Loss Data Analytics

Loss Data Analytics is an interactive, online, freely available text. Th...

An Algebraic Approach for High-level Text Analytics

Text analytical tasks like word embedding, phrase mining, and topic mode...

Slim Graph: Practical Lossy Graph Compression for Approximate Graph Processing, Storage, and Analytics

We propose Slim Graph: the first programming model and framework for pra...