Version 13 (modified by karianne, 12 years ago)

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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.

Peter Haase


Scaling-out Semantic Data - Management and Processing

Abstract

Growing data amount creates new type of problems with data management and analysis. Many current solutions simply cannot scale and provide functionality they are supposed to deliver. During this talk we will take a look at some problem areas, try to understand their origin and see what approaches can be taken in the presence of large amount of data. We will also take a short look how some of those areas are tackled in current research projects at CIPSI.

Dr Tomasz Wiktor Wlodarczyk

Postdoctoral Research Fellow at CIPSI - Center for IP-base Service Innovation at University of Stavanger, Norway
His work focuses on analysis and management of Big Data. His interests include: data-intensive analysis and mining, knowledge modeling and complex event processing. He is currently working on those areas in several research projects including: SEEDS – Self-learning Energy Efficient Buildings, Safer@Home – Smart System to support Safer Independent Living and Social Interaction for Elderly at Home, and SCC-Computing – Strategic collaboration with China on super-computing based on Tianhe-1A. He is also the Program Committee Chair of IEEE CloudCom 2012 – International Conference on Cloud Computing Technology and Science.


Building Earth Observatories using Semantic Web and Scientific Database Technologies

Abstract

Earth Observation data has been constantly increasing in size in the last few years (now reaching multiple petabyte sizes), and have become a valuable source of information for many scientific and application domains (environment, oceanography, geology, archaeology, security etc.). TELEIOS is a recent European project that addresses the need for scalable access to petabytes of Earth Observation data and the discovery of knowledge that can be used in applications. To achieve this, TELEIOS builds on Semantic Web and scientific database technologies technologies. In this talk we outline the vision of TELEIOS (now in its second year), present its software architecture and give a detailed example of a fire monitoring application that we have completed.

Prof. Manolis Koubarakis

Dept. of Informatics and Telecommunications, National and Kapodistrian University of Athens.
He has published more than 100 papers that have been widely cited in the areas of Artificial Intelligence (especially Knowledge Representation), Databases, Semantic Web and P2P Computing. His research has been financially supported by the European Commission, the Greek General Secretariat for Research and Technology and industry sources.


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