Graduate School and Research Center in Digital Sciences


Eurecom - Multimedia Communications 
Cifre Doctoral student ( 2012 - 2015)


Enabling Self-Service Data Provisioning Through Semantic Enrichment of Data


Enterprises use a wide range of heterogeneous information systems in their business activities such as Enterprise Resource Planning (ERP), Customer Relationships Management (CRM) and Supply Chain Management (SCM) systems. In addition to the large amounts of heterogeneous data produced by those systems, external data is an important resource that can be leveraged to take quick and rational business decisions. Classic Business Intelligence (BI) focus much of their selling features on attractive and unique visualizations. Preparing data for those visualizations still remains the far most challenging task in most BI projects, large and small. Self-service data provisioning aims at tackling this problem by providing intuitive datasets discovery, data acquisition and integration techniques to the end user.
The goal of this thesis is to provide a framework that enables self-service data provisioning in the enterprise. This framework, called ROOMBA, empowers business users to search, inspect and reuse data through semantically enriched datasets profiles. We propose a mechanism to automatically attach meta information to data objects by leveraging knowledge bases like DBpedia and Freebase which facilitates data search and acquisition for business users. We also propose a mechanism to select what properties should be used when augmenting extra columns into an existing dataset or annotating instances with semantic tags.
Data portals, which are datasets' access points, offer metadata represented in different and heterogeneous models. We first propose a harmonized dataset model based on a systematic literature survey that enables complete metadata coverage to enable data discovery, exploration and reuse by business users. Second, rich metadata information is currently very limited to a few data portals where they are usually provided manually, thus being often incomplete and inconsistent in terms of quality. We propose a scalable automatic approach for extracting, validating, correcting and generating descriptive linked dataset profiles. We further present an extensible quality measurement tool implementing this framework that helps on one hand data owners to rate the quality of their datasets and get some hints on possible improvements, and on the other hand, data consumers to choose their data sources from a ranked set. Finally, we propose the SNARC service that brings relevant, live and archived information shared on social networks to the business user. The key advantage is an instantaneous access to complementary information without the need to search for it. Information appears when it is relevant enabling the user to focus on what is really important.