Architecture of Environmental Risk Modelling: for a faster and more robust response to natural disasters

Demands on the disaster response capacity of the European Union are likely to increase, as the impacts of disasters continue to grow both in size and frequency. This has resulted in intensive research on issues concerning spatially-explicit information and modelling and their multiple sources of uncertainty. Geospatial support is one of the forms of assistance frequently required by emergency response centres along with hazard forecast and event management assessment. Robust modelling of natural hazards requires dynamic simulations under an array of multiple inputs from different sources. Uncertainty is associated with meteorological forecast and calibration of the model parameters. Software uncertainty also derives from the data transformation models (D-TM) needed for predicting hazard behaviour and its consequences. On the other hand, social contributions have recently been recognized as valuable in raw-data collection and mapping efforts traditionally dominated by professional organizations. Here an architecture overview is proposed for adaptive and robust modelling of natural hazards, following the Semantic Array Programming paradigm to also include the distributed array of social contributors called Citizen Sensor in a semantically-enhanced strategy for D-TM modelling. The modelling architecture proposes a multicriteria approach for assessing the array of potential impacts with qualitative rapid assessment methods based on a Partial Open Loop Feedback Control (POLFC) schema and complementing more traditional and accurate a-posteriori assessment. We discuss the computational aspect of environmental risk modelling using array-based parallel paradigms on High Performance Computing (HPC) platforms, in order for the implications of urgency to be introduced into the systems (Urgent-HPC).Comment: 12 pages, 1 figure, 1 text box, presented at the 3rd Conference of Computational Interdisciplinary Sciences (CCIS 2014), Asuncion, Paragua

A unified view of data-intensive flows in business intelligence systems : a survey

Data-intensive flows are central processes in today’s business intelligence (BI) systems, deploying different technologies to deliver data, from a multitude of data sources, in user-preferred and analysis-ready formats. To meet complex requirements of next generation BI systems, we often need an effective combination of the traditionally batched extract-transform-load (ETL) processes that populate a data warehouse (DW) from integrated data sources, and more real-time and operational data flows that integrate source data at runtime. Both academia and industry thus must have a clear understanding of the foundations of data-intensive flows and the challenges of moving towards next generation BI environments. In this paper we present a survey of today’s research on data-intensive flows and the related fundamental fields of database theory. The study is based on a proposed set of dimensions describing the important challenges of data-intensive flows in the next generation BI setting. As a result of this survey, we envision an architecture of a system for managing the lifecycle of data-intensive flows. The results further provide a comprehensive understanding of data-intensive flows, recognizing challenges that still are to be addressed, and how the current solutions can be applied for addressing these challenges.Peer ReviewedPostprint (author's final draft

Self-organising agent communities for autonomic resource management

The autonomic computing paradigm addresses the operational challenges presented by increasingly complex software systems by proposing that they be composed of many autonomous components, each responsible for the run-time reconfiguration of its own dedicated hardware and software components. Consequently, regulation of the whole software system becomes an emergent property of local adaptation and learning carried out by these autonomous system elements. Designing appropriate local adaptation policies for the components of such systems remains a major challenge. This is particularly true where the system’s scale and dynamism compromise the efficiency of a central executive and/or prevent components from pooling information to achieve a shared, accurate evidence base for their negotiations and decisions.In this paper, we investigate how a self-regulatory system response may arise spontaneously from local interactions between autonomic system elements tasked with adaptively consuming/providing computational resources or services when the demand for such resources is continually changing. We demonstrate that system performance is not maximised when all system components are able to freely share information with one another. Rather, maximum efficiency is achieved when individual components have only limited knowledge of their peers. Under these conditions, the system self-organises into appropriate community structures. By maintaining information flow at the level of communities, the system is able to remain stable enough to efficiently satisfy service demand in resource-limited environments, and thus minimise any unnecessary reconfiguration whilst remaining sufficiently adaptive to be able to reconfigure when service demand changes

Improving the Scalability of DPWS-Based Networked Infrastructures

The Devices Profile for Web Services (DPWS) specification enables seamless discovery, configuration, and interoperability of networked devices in various settings, ranging from home automation and multimedia to manufacturing equipment and data centers. Unfortunately, the sheer simplicity of event notification mechanisms that makes it fit for resource-constrained devices, makes it hard to scale to large infrastructures with more stringent dependability requirements, ironically, where self-configuration would be most useful. In this report, we address this challenge with a proposal to integrate gossip-based dissemination in DPWS, thus maintaining compatibility with original assumptions of the specification, and avoiding a centralized configuration server or custom black-box middleware components. In detail, we show how our approach provides an evolutionary and non-intrusive solution to the scalability limitations of DPWS and experimentally evaluate it with an implementation based on the the Web Services for Devices (WS4D) Java Multi Edition DPWS Stack (JMEDS).Comment: 28 pages, Technical Repor

An active, ontology-driven network service for Internet collaboration

Web portals have emerged as an important means of collaboration on the WWW, and the integration of ontologies promises to make them more accurate in how they serve users’ collaboration and information location requirements. However, web portals are essentially a centralised architecture resulting in difficulties supporting seamless roaming between portals and collaboration between groups supported on different portals. This paper proposes an alternative approach to collaboration over the web using ontologies that is de-centralised and exploits content-based networking. We argue that this approach promises a user-centric, timely, secure and location-independent mechanism, which is potentially more scaleable and universal than existing centralised portals

Entity Identification Problem in Big and Open Data

Big and Open Data provide great opportunities to businesses to enhance their competitive advantages if utilized properly. However, during past few years’ research in Big and Open Data process, we have encountered big challenge in entity identification reconciliation, when trying to establish accurate relationships between entities from different data sources. In this paper, we present our innovative Intelligent Reconciliation Platform and Virtual Graphs solution that addresses this issue. With this solution, we are able to efficiently extract Big and Open Data from heterogeneous source, and integrate them into a common analysable format. Further enhanced with the Virtual Graphs technology, entity identification reconciliation is processed dynamically to produce more accurate result at system runtime. Moreover, we believe that our technology can be applied to a wide diversity of entity identification problems in several domains, e.g., e- Health, cultural heritage, and company identities in financial world.Ministerio de Ciencia e Innovación TIN2013-46928-C3-3-

SDN/NFV-enabled satellite communications networks: opportunities, scenarios and challenges

In the context of next generation 5G networks, the satellite industry is clearly committed to revisit and revamp the role of satellite communications. As major drivers in the evolution of (terrestrial) fixed and mobile networks, Software Defined Networking (SDN) and Network Function Virtualisation (NFV) technologies are also being positioned as central technology enablers towards improved and more flexible integration of satellite and terrestrial segments, providing satellite network further service innovation and business agility by advanced network resources management techniques. Through the analysis of scenarios and use cases, this paper provides a description of the benefits that SDN/NFV technologies can bring into satellite communications towards 5G. Three scenarios are presented and analysed to delineate different potential improvement areas pursued through the introduction of SDN/NFV technologies in the satellite ground segment domain. Within each scenario, a number of use cases are developed to gain further insight into specific capabilities and to identify the technical challenges stemming from them.Peer ReviewedPostprint (author's final draft