Abstracts & Bios

---

On demand access to Big Data through Semantic Technologies

Abstract

Dealing with Big Data involves a number of different challenges, including increasing volume (amount of data), velocity (speed of data), and variety (range of data types, sources). Most Big Data solutions today focus on volume, in particular supporting vertical scalability. Yet the Big Data problem is not fully solved by vertical scale technologies alone.

A huge problem is that of horizontal scale. Consider the wealth of data that is published in open data initiatives: We are faced with a massive number of data sources, with a high degree of variety and heterogeneity in coverage, data models, and structure. Solving these problems and enabling users to tap into this wealth of data for on demand analytics bears enormous potentials and economic opportunities.

In this talk we present building blocks for solutions that enable on demand access to heterogeneous, distributed Big Data, in particular applying semantic technologies and the Linked Data paradigm. We demonstrate the use of these technologies in the Information Workbench, a platform for self-service analytics. Following a simple self-service process, the platform supports end users in 1) the discovery of relevant data sources, tapping into the Linked Open Data cloud and other open data sources, 2) the automated integration and interlinking of sources, and 3) on demand and interactive exploration and analysis of data.

---