  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.
