Cloud Thinking. Simplifying Big Data Processing. Rui L. Aguiar, Diogo Gomes Universidade de Aveiro - Portugal

Transcription

1 Cloud Thinking Simplifying Big Data Processing Rui L. Aguiar, Diogo Gomes Universidade de Aveiro - Portugal

2 Problem A connected world of Information Systems and Electronic Devices produces terabytes of information per day. Most of this information is nonetheless put away unused. Researchers in ICT have created systems capable of processing these amounts of information. Most experts in non ICT areas have difficulties using these systems.

3 Data Collection Data Processing Models Cloud Thinking Effective Tools

4 Objectives Develop solutions for multiple application areas, relying on a common approach to complexity management. Develop a set of tools for application to complex problem solving, exploring these tools in multiple application areas, Enhance Cloud Computing with strong capabilities of data gathering. Extend the Cloud to the real world through sensor networks Key application areas considered: Health Robotics

5 Cloud Thinking Organization

6 Cloud Thinking The same way our nervous system is a distributed network, capable of collecting information We intend to create a distributed pervasive sensing network capable of acquiring terabytes of information from the most heterogeneous sources - Using low power wireless sensor networks - Developing efficient M2M solutions - Efficiently using Next Generation Networks to transport and aggregate information in a distributed way - Allocating Computational resources accordingly to proximity to data and to information needs - Federating multiple Databases and Information Sources

7 Cloud Seeds secure cloud infrastructure to support the project activities; capture, retrieval and storage of data big data handling; seamless federation of cloud computing providers; semantic-enabled repositories; Cloud based real time and near real time applications; standard cloud management interfaces; Cloud Thinking PaaS.

8 Cloud Gears cloud based tools and methods methods for multivariate data smoothing; data models and ontology mapping methods; data mining and knowledge extraction methods; efficient algorithms for large scale problems conceptual methods to be applied in the cloud thinking environment preliminary explorations of Cloud Thinking tools and methods to the field of nanosciences; Evaluate the performance of Cloud Thinking

9 archiving and distribution of medical imaging modalities and vital signals repositories over the Cloud; establishment of shared image banks of interesting cases for scientific and medical applications, available for evaluation and interpretation in the context of a multitude of research and development targets; inter-institutional collaborative environments based on shared repositories and telematics platforms; Content Based Image Retrieval over large medical repositories; ehealth Use-Case High Performance Cloud Computing model; Security and trust model oriented to Cloud-based medical applications that will grant the privacy of three main entities: medical institutions, physicians and patients; Health Cloud API

10 System View G U I Processing Infrastructure Knowledge Model Cloud Data Storage Transport and Access Network

11 hardware/software architecture for using cloud computing in robotics applications Mobile Robotics Use-Case cloud-based learning / knowledge extraction capabilities for robotics, supporting in particular behavior learning, object recognition and user modeling cloud-based information integration and sharing capabilities for robot teams

12 Final Remarks Multi-disciplinary Project Started January 2013 Cloud based approach to deal with: data acquisition, processing, administration Information retrieval, management knowledge extraction, discovery, sharing Identity and Privacy management Ultimate goal: Knowledge as a Service (KaaS) Two use-cases ehealth Mobile Robotics

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