On-Demand Service-Based Big Data Integration: Optimized for Research Collaboration

Today I presented my paper "Obidos" at the VLDB DMAH workshop in Munich. The abstract and the presentation of the paper are given below:

Abstract: Biomedical research requires distributed access, analysis, and sharing of data from various disperse sources in the Internet scale. Due to the volume and variety of big data, materialized data integration is often infeasible or too expensive including the costs of bandwidth, storage, maintenance, and management. Óbidos (On-demand Big Data Integration, Distribution, and Orchestration System) provides a novel on-demand integration approach for heterogeneous distributed data. Instead of integrating data from the data sources to build a complete data warehouse as the initial step, Óbidos employs a hybrid approach of virtual and materialized data integrations. By allocating unique identifiers as pointers to virtually integrated data sets, Óbidos supports efficient data sharing among data consumers. We design Óbidos as a generic service-based data integration system, and implement and evaluate a prototype for multimodal medical data.

Please find the full text of the paper here and the presentation below:

On-Demand Service-Based Big Data Integration: Optimized for Research Collaboration from Pradeeban Kathiravelu
 

I mostly worked on this paper while I was doing my internship at Emory University. This is also my first paper to get accepted from UCLouvain/Belgium, under the supervision of Prof. Van Roy. In this presentation, I have also included "A tale of Ana, Abdul, Viktoria, Pereira, Chen, and Raj", a subtle message I wanted to include in my presentation for quite some time.