Table2Vec: Automated Universal Representation Learning to Encode All-round Data DNA for Benchmarkable and Explainable Enterprise Data Science release_jolg4eiidvekbhbo2s4brkac5e

by Longbing Cao, Chengzhang Zhu

Released as a article .

2021  

Abstract

Enterprise data typically involves multiple heterogeneous data sources and external data that respectively record business activities, transactions, customer demographics, status, behaviors, interactions and communications with the enterprise, and the consumption and feedback of its products, services, production, marketing, operations, and management, etc. A critical challenge in enterprise data science is to enable an effective whole-of-enterprise data understanding and data-driven discovery and decision-making on all-round enterprise DNA. We introduce a neural encoder Table2Vec for automated universal representation learning of entities such as customers from all-round enterprise DNA with automated data characteristics analysis and data quality augmentation. The learned universal representations serve as representative and benchmarkable enterprise data genomes and can be used for enterprise-wide and domain-specific learning tasks. Table2Vec integrates automated universal representation learning on low-quality enterprise data and downstream learning tasks. We illustrate Table2Vec in characterizing all-round customer data DNA in an enterprise on complex heterogeneous multi-relational big tables to build universal customer vector representations. The learned universal representation of each customer is all-round, representative and benchmarkable to support both enterprise-wide and domain-specific learning goals and tasks in enterprise data science. Table2Vec significantly outperforms the existing shallow, boosting and deep learning methods typically used for enterprise analytics. We further discuss the research opportunities, directions and applications of automated universal enterprise representation and learning and the learned enterprise data DNA for automated, all-purpose, whole-of-enterprise and ethical machine learning and data science.
In text/plain format

Archived Files and Locations

application/pdf  1.8 MB
file_sf6jtaswgvb5hbeun2ozfik2jm
arxiv.org (repository)
web.archive.org (webarchive)
Read Archived PDF
Preserved and Accessible
Type  article
Stage   submitted
Date   2021-12-03
Version   v1
Language   en ?
arXiv  2112.01830v1
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: a347ac88-1001-4f60-bd12-062db0657ba2
API URL: JSON