Augmented Understanding and Automated Adaptation of Curation Rules

TL;DR

This paper introduces techniques and systems to enhance data curation processes by automating tasks, adapting curation rules autonomously, and assisting analysts in large-scale data environments, thereby reducing errors and effort.

Contribution

It presents novel algorithms and systems for automating data curation, autonomously adapting curation rules, and supporting user preferences in large-scale data environments.

Findings

Automated feature-based data curation technique.

Autonomic approach for adapting curation rules.

APIs for automating key curation tasks.

Abstract

Over the past years, there has been many efforts to curate and increase the added value of the raw data. Data curation has been defined as activities and processes an analyst undertakes to transform the raw data into contextualized data and knowledge. Data curation enables decision-makers and data analyst to extract value and derive insight from the raw data. However, to curate the raw data, an analyst needs to carry out various curation tasks including, extraction linking, classification, and indexing, which are error-prone, tedious and challenging. Besides, deriving insight require analysts to spend a long period of time to scan and analyze the curation environments. This problem is exacerbated when the curation environment is large, and the analyst needs to curate a varied and comprehensive list of data. To address these challenges, in this dissertation, we present techniques, algorithms and systems for augmenting analysts in curation tasks. We propose: ~(1) a feature-based and automated technique for curating the raw data. ~(2) We propose an autonomic approach for adapting data curation rules. ~(3) We provide a solution to augment users in formulating their preferences while curating data in large scale information spaces. ~(4) We implement a set of APIs for automating the basic curation tasks, including Named Entity extraction, POS tags, classification, and etc.