Peer - Mediated Distributed Knowledge Management

Distributed Knowledge Management is an approach to knowledge management based on the principle that the multiplicity (and heterogeneity) of perspectives within complex organizations is not be viewed as an obstacle to knowledge exploitation, but rather as an opportunity that can foster innovation and creativity. Despite a wide agreement on this principle, most current KM systems are based on the idea that all perspectival aspects of knowledge should be eliminated in favor of an objective and general representation of knowledge. In this paper we propose a peer-to-peer architecture (called KEx), which embodies the principle above in a quite straightforward way: (i) each peer (called a K-peer) provides all the services needed to create and organize "local" knowledge from an individual's or a group's perspective, and (ii) social structures and protocols of meaning negotiation are introduced to achieve semantic coordination among autonomous peers (e.g., when searching documents from other K-peers). A first version of the system, called KEx, is imple-mented as a knowledge exchange level on top of JXTA

Epidemic Information Diffusion: A Simple Solution to Support Community-based Recommendations in P2P Overlays

Epidemic protocols proved to be very efficient solutions for supporting dynamic and complex information diffusion in highly dis- tributed computing infrastructures, like P2P environments. They are useful bricks for building and maintaining virtual network topologies, in the form of overlay networks as well as to support pervasive diffusion of information when it is injected into the network. This paper proposes a simple architecture exploiting the features of epidemic approaches to foster a collaborative percolation of information between computing nodes belonging to the network aimed at building a system that groups similar users and spread useful information among them.Comment: 8 pages, 2 figure

A Peer-to-Peer Architecture for Distributed Knowledge Management.

Most of the knowledge management systems of complex organizations are based on technological architectures that are in contradiction with the social processes of knowledge creation. In particular, centralized architectures are adopted to manage a process that is intrinsically distributed. In this paper, assuming a Distributed approach to Knowledge Management (DKM), is proposed that technological and social architectures must be reciprocally consistent. Moreover, in the domain of Knowledge Management, technological architectures should be designed in order to support the interplay between two qualitatively different processes: the autonomous management of knowledge of individuals and groups - here called Knowledge Nodes (KNs) -, and the coordination required in order to exchange knowledge among them. Finally a peer to peer architecture to support knowledge exchange across distributed and autonomous KNs is presented

A Self-organizing and Self-configuration Algorithm for Resource Management in Service-oriented Systems

With the ever increasing deployment of service-oriented distributed systems in large-scale and heterogeneous computing environments, clustering and communication overlay topology design has become more and more important to address several challenging issues and conflicting requirements, such as efficient scheduling and distribution of services among computing resources, reducing communication cost between services, high performance service and resource discovery while considering both inter-service and inter-node properties and also increasing the load distribution and the load balance. In this paper, a four-stage hierarchical clustering algorithm is proposed which automates the process of the optimally composing communicating groups in a dynamic way while preserving the proximity of the nodes. The simulation results show the performance of the algorithm with respect to load balance, scalability and efficiency

Dynamic data placement and discovery in wide-area networks

The workloads of online services and applications such as social networks, sensor data platforms and web search engines have become increasingly global and dynamic, setting new challenges to providing users with low latency access to data. To achieve this, these services typically leverage a multi-site wide-area networked infrastructure. Data access latency in such an infrastructure depends on the network paths between users and data, which is determined by the data placement and discovery strategies. Current strategies are static, which offer low latencies upon deployment but worse performance under a dynamic workload. We propose dynamic data placement and discovery strategies for wide-area networked infrastructures, which adapt to the data access workload. We achieve this with data activity correlation (DAC), an application-agnostic approach for determining the correlations between data items based on access pattern similarities. By dynamically clustering data according to DAC, network traffic in clusters is kept local. We utilise DAC as a key component in reducing access latencies for two application scenarios, emphasising different aspects of the problem: The first scenario assumes the fixed placement of data at sites, and thus focusses on data discovery. This is the case for a global sensor discovery platform, which aims to provide low latency discovery of sensor metadata. We present a self-organising hierarchical infrastructure consisting of multiple DAC clusters, maintained with an online and distributed split-and-merge algorithm. This reduces the number of sites visited, and thus latency, during discovery for a variety of workloads. The second scenario focusses on data placement. This is the case for global online services that leverage a multi-data centre deployment to provide users with low latency access to data. We present a geo-dynamic partitioning middleware, which maintains DAC clusters with an online elastic partition algorithm. It supports the geo-aware placement of partitions across data centres according to the workload. This provides globally distributed users with low latency access to data for static and dynamic workloads.Open Acces

Fast, linked, and open – the future of taxonomic publishing for plants: launching the journal PhytoKeys

The paper describes the focus, scope and the rationale of PhytoKeys, a newly established, peer-reviewed, open-access journal in plant systematics. PhytoKeys is launched to respond to four main challenges of our time: (1) Appearance of electronic publications as amendments or even alternatives to paper publications; (2) Open Access (OA) as a new publishing model; (3) Linkage of electronic registers, indices and aggregators that summarize information on biological species through taxonomic names or their persistent identifiers (Globally Unique Identifiers or GUIDs; currently Life Science Identifiers or LSIDs); (4) Web 2.0 technologies that permit the semantic markup of, and semantic enhancements to, published biological texts. The journal will pursue cutting-edge technologies in publication and dissemination of biodiversity information while strictly following the requirements of the current International Code of Botanical Nomenclature (ICBN)