DCCLOSER 2015 Abstracts


Short Papers
Paper Nr: 3
Title:

Insights for Manage Geospatial Big Data in Ecosystem Monitoring using Processing Chains and High Performance Computing

Authors:

Fabián Santos and Gunther Menz

Abstract: Manage Geospatial Big Data is a challenging task and each time a more frequently task in ecosystem monitoring, due the accelerated increase and accessibility of geographical technologies and archives. For this reason, this research focus in the design and development of two reproducible processing chains in open source software, using the High Performance Computing approach for manage the volume, variety and velocity dimensions of two cases of Geospatial Big Data. The first one, constitutes a large collection of images of the Landsat satellites, which should be sequentially processed, in order to prepare a time-series analysis of the regeneration process of disturbed tropical forests in Ecuador. The second case constitutes a unique complex database of different sources and types of Geospatial data, which should be organized and harmonized to allow an exploratory statistical analysis and pattern extraction of the drivers that influence the restoration process of disturbed tropical forests in Ecuador. For this purpose, the design of the processing chains are based in parallel computing for divide and distribute small pieces of data between the processing units available. Therefore, the design implemented allows the possibility to scale-up the computing resources, if they are available. Our first results, applied to a multi-core computer, showed that the design of the processing chain applied to the large collection of images of the Landsat satellites is the only way to manage the volume and velocity dimensions of Geospatial Big Data.

Paper Nr: 4
Title:

Sporadic Cloud Computing over a Virtualization Layer - A New Paradigm to Support Mobile Multi-hop Ad-hoc Networks

Authors:

Esteban F. Ordóñez-Morales, Yolanda Blanco-Fernández and Martín López-Nores

Abstract: In our doctoral proposal we deploy Sporadic Ad-hoc Networks (SANs) over the devices of a group of always-on users who happen to meet in a place. The goal is to develop tailor-made services that exploit the possible similarities among the preferences of the users and the technological capabilities of their terminals to establish direct and hop-by-hop ad-hoc communications. In order to overcome the intrinsic limitations of mobile devices, we explore the new concept of Sporadic Cloud Computing (SCC) that is aimed at providing each terminal with additional resources by exploiting the (computational, networking, storing...) capabilities of the rest of devices connected to the SAN. In order to abstract the complexity stemmed from the mobility scenarios, SCC works with an enhanced Virtualization Layer that deals with a few static virtual nodes instead of a higher number of mobile real nodes. This allows to turn our SANs into reliable and stable communication environments to promote interactions among potentially like-minded strangers in a great diversity of mobility scenarios, involving both pedestrians and cars in vehicular environments.

Paper Nr: 6
Title:

Towards Domain Model Optimized Deployment and Execution of Scientific Applications in Cloud Environments

Authors:

Fabian Glaser

Abstract: Existing solutions for automatic scaling of applications in the cloud focus on the requirements of web services. A number of application servers is deployed, a load balancer is utilized to distribute the requests to these application servers, and new application servers are launched and configured when the requests exceed a certain capacity. However, the requirements for scaling scientific applications in a cloud are different. Often, these applications are used by a single scientist and the computational load is defined by the complexity of the model to be computed rather than by the number of users. In this paper, we present an alternative approach to scale scientific applications in the cloud. Hereby, the deployment scaling is driven by a domain model defined by the scientist.

Paper Nr: 7
Title:

Secure Data Integration Systems

Authors:

Fatimah Y. Akeel, Gary B. Wills and Andrew M. Gravell

Abstract: With the web witnessing an immense shift towards publishing data, integrating data from diverse sources that have heterogeneous security and privacy levels and varying in trust becomes even more challenging. In a Data Integration System (DIS) that integrates confidential data in critical domains to contain a problem and make faster and reliable decisions, there is a need to integrate multiple data sources while maintaining the security levels and privacy requirements of each data source before and during the integration. This situation becomes even more challenging when using cloud services and third parties in achieving any part of the integration. Therefore, such systems face a threat of data leakage that compromises data confidentiality and privacy. The lack of literature addressing security in DIS encourages this research to provide a data leakage prevention framework that focuses on the level prior to the actual data integration, which is the analysis and early design of the system. As a result, we constructed SecureDIS, an architectural framework that consists of several components containing guidelines to build secure DIS. The framework was confirmed by 16 experts in the field and it is currently being prepared to be applied on a real-life data integration context such as the cloud context.