A customizable multi-agent system for distributed data mining

We present a general Multi-Agent System framework for distributed data mining based on a Peer-to-Peer model. Agent protocols are implemented through message-based asynchronous communication. The framework adopts a dynamic load balancing policy that is particularly suitable for irregular search algorithms. A modular design allows a separation of the general-purpose system protocols and software components from the specific data mining algorithm. The experimental evaluation has been carried out on a parallel frequent subgraph mining algorithm, which has shown good scalability performances.

Engaging in European spatial planning: a central and Eastern European perspective on the territorial cohesion debate

The following paper sets out to determine the differential extent of the engagement of Central and Eastern European (CEE) member states with the European spatial planning (ESP) debate over territorial cohesion. It focuses on the written statements submitted in response to the European Commission “Green Paper on Territorial Cohesion” consultation in 2009. The geographical distribution of the respondents is analysed, before CEE member states’ responses are examined in detail, to explore the diverse interpretations of the concept of “territorial cohesion” among CEE actors. While the data collection for this paper has been restricted empirically to the consultation process, it reflects its findings in consideration of member states’ engagement with the debate as it manifested before and after the Green Paper. The debate over the exact conceptual and operational “framing” of territorial cohesion, launched by the European Commission's Green Paper, constitutes one of the many arenas through which ESP currently evolves, together with the transnational initiatives developed in the framework of the European Territorial Cooperation objective, the European Observation Network for Territorial Development and Cohesion (ESPON) and the process that recently led to the publication of the Territorial Agenda of the European Union 2020. The paper concludes that the overall level of engagement of CEE actors in ESP is proportionally lower in comparison with that of their northwestern European counterparts. The increasing involvement of some CEE member states within the ESPON 2013 Programme, as well as the activities undertaken by the Hungarian and Polish EU Presidencies in 2011, suggests that this level of CEE engagement is growing albeit differentially.

The engagement of territorial knowledge communities with European spatial planning and the territorial cohesion debate: a Baltic perspective

Recent, dramatic spatial development trends have contributed to the consolidation of a unique territorial governance landscape in the Baltic States. The paper examines the transformation of this evolving institutional landscape for planning practice and knowledge, which has been marked by the disintegration of Soviet institutions and networks, the transition to a market-based economy and the process of accession to the EU. It explores the evolution of territorial knowledge channels in the Baltic States, and the extent and nature of the engagement of actors' communities with the main knowledge arenas and resources of European spatial planning (ESP). The paper concludes that recent shifts in the evolution of these channels suggest the engagement of ESP has concentrated among epistemic communities at State and trans-national levels of territorial governance. The limited policy coordination across a broader spectrum of diverse actors is compounded by institutionally weak and fragmented professional communities of practice, fragmented government structures and marginalized advocacy coalitions.

Policy ‘framing’ and evidence-based planning: ‘epistemic communities’ in the multi-jurisdictional environment of an enlarged Europe

The following paper builds on ongoing discussions over the spatial and territorial turns in planning, as it relates to the dynamics of evidence-based planning and knowledge production in the policy process. It brings this knowledge perspective to the organizational and institutional dynamics of transformational challenges implicit in the recent enlargement of the EU. Thus it explores the development of new spatial ideas and planning approaches, and their potential to shape or ‘frame’ spatial policy through the formulation of new institutional arrangements and the de-institutionalization of others. That is, how knowledge is created, contested, mobilized and controlled across governance architectures or territorial knowledge channels. In so doing, the paper elaborates and discusses a theoretical framework through which the interplay of knowledge and policymaking can be conceptualized and analyzed.

Epidemic K-Means clustering

The K-Means algorithm for cluster analysis is one of the most influential and popular data mining methods. Its straightforward parallel formulation is well suited for distributed memory systems with reliable interconnection networks. However, in large-scale geographically distributed systems the straightforward parallel algorithm can be rendered useless by a single communication failure or high latency in communication paths. This work proposes a fully decentralised algorithm (Epidemic K-Means) which does not require global communication and is intrinsically fault tolerant. The proposed distributed K-Means algorithm provides a clustering solution which can approximate the solution of an ideal centralised algorithm over the aggregated data as closely as desired. A comparative performance analysis is carried out against the state of the art distributed K-Means algorithms based on sampling methods. The experimental analysis confirms that the proposed algorithm is a practical and accurate distributed K-Means implementation for networked systems of very large and extreme scale.

Symmetric Push-Sum Protocol for decentralised aggregation

Gossip (or Epidemic) protocols have emerged as a communication and computation paradigm for large-scale networked systems. These protocols are based on randomised communication, which provides probabilistic guarantees on convergence speed and accuracy. They also provide robustness, scalability, computational and communication efficiency and high stability under disruption. This work presents a novel Gossip protocol named Symmetric Push-Sum Protocol for the computation of global aggregates (e.g., average) in decentralised and asynchronous systems. The proposed approach combines the simplicity of the push-based approach and the efficiency of the push-pull schemes. The push-pull schemes cannot be directly employed in asynchronous systems as they require synchronous paired communication operations to guarantee their accuracy. Although push schemes guarantee accuracy even with asynchronous communication, they suffer from a slower and unstable convergence. Symmetric Push- Sum Protocol does not require synchronous communication and achieves a convergence speed similar to the push-pull schemes, while keeping the accuracy stability of the push scheme. In the experimental analysis, we focus on computing the global average as an important class of node aggregation problems. The results have confirmed that the proposed method inherits the advantages of both other schemes and outperforms well-known state of the art protocols for decentralized Gossip-based aggregation.

Fault tolerant decentralised K-Means clustering for asynchronous large-scale networks

The K-Means algorithm for cluster analysis is one of the most influential and popular data mining methods. Its straightforward parallel formulation is well suited for distributed memory systems with reliable interconnection networks, such as massively parallel processors and clusters of workstations. However, in large-scale geographically distributed systems the straightforward parallel algorithm can be rendered useless by a single communication failure or high latency in communication paths. The lack of scalable and fault tolerant global communication and synchronisation methods in large-scale systems has hindered the adoption of the K-Means algorithm for applications in large networked systems such as wireless sensor networks, peer-to-peer systems and mobile ad hoc networks. This work proposes a fully distributed K-Means algorithm (EpidemicK-Means) which does not require global communication and is intrinsically fault tolerant. The proposed distributed K-Means algorithm provides a clustering solution which can approximate the solution of an ideal centralised algorithm over the aggregated data as closely as desired. A comparative performance analysis is carried out against the state of the art sampling methods and shows that the proposed method overcomes the limitations of the sampling-based approaches for skewed clusters distributions. The experimental analysis confirms that the proposed algorithm is very accurate and fault tolerant under unreliable network conditions (message loss and node failures) and is suitable for asynchronous networks of very large and extreme scale.

BodyCloud: a SaaS approach for community body sensor networks

Body Sensor Networks (BSNs) have been recently introduced for the remote monitoring of human activities in a broad range of application domains, such as health care, emergency management, fitness and behaviour surveillance. BSNs can be deployed in a community of people and can generate large amounts of contextual data that require a scalable approach for storage, processing and analysis. Cloud computing can provide a flexible storage and processing infrastructure to perform both online and offline analysis of data streams generated in BSNs. This paper proposes BodyCloud, a SaaS approach for community BSNs that supports the development and deployment of Cloud-assisted BSN applications. BodyCloud is a multi-tier application-level architecture that integrates a Cloud computing platform and BSN data streams middleware. BodyCloud provides programming abstractions that allow the rapid development of community BSN applications. This work describes the general architecture of the proposed approach and presents a case study for the real-time monitoring and analysis of cardiac data streams of many individuals.