Peer-to-Peer (P2P) networking is aimed at exploiting the potential of widely distributed information pools and its effortless access and retrieval irrespectively of underlying networking protocols, operating systems or devices. However, prohibiting limitations have been identified and perhaps the most important one is the successful location of relevant information sources and the efficient query routing in large, highly distributed P2P networks. In this paper, a novel, cluster-based architecture for IR over P2P networks is presented and its evaluation is focused on retrieval effectiveness. We reason in favour of using clustering for P2P IR, by considering two fundamental hypotheses drawn from current P2P file-sharing systems. We also study the potential usefulness of a simplified version of Dempster-Shafer (D-S) theory of evidence combination for results fusion in the network. We simulated the IR behaviour of the system by using the TREC 6 and 7 ad-hoc track. The proposed architecture bears very promising results in terms of precision and recall.

We present dispel4py a versatile data-intensive kit presented as a standard Python library. It empowers scientists to experiment and test ideas using their familiar rapid-prototyping environment. It delivers mappings to diverse computing infrastructures, including cloud technologies, HPC architectures and specialised data-intensive machines, to move seamlessly into production with large-scale data loads. The mappings are fully automated, so that the encoded data analyses and data handling are completely unchanged. The underpinning model is lightweight composition of fine-grained operations on data, coupled together by data streams that use the lowest cost technology available. These fine-grained workflows are locally interpreted during development and mapped to multiple nodes and systems such as MPI and Storm for production.We explain why such an approach is becoming more essential in order that data-driven research can innovate rapidly and exploit the growing wealth of data while adapting to current technical trends. We show how provenance management is provided to improve understanding and reproducibility, and how a registry supports consistency and sharing. Three application domains are reported and measurements on multiple infrastructures show the optimisations achieved. Finally we present the next steps to achieve scalability and performance.

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Abstract. Peer-to-peer (P 2 P) networking continuously gains popularity among computing science researchers. The problem of information retrieval (IR) over P 2 P networks is being addressed by researchers attempting to provide valuable insight as well as solutions for its successful deployment. All published studies have, so far, been evaluated by simulation means, using well-known document collections (usually acquired from TREC). Researchers test their systems using divided collections whose documents have been previously distributed to a number of simulated peers. This practice leads to two problems: First, there is little justification in favour of the document distributions used by relevant studies and second, since different studies use different experimental testbeds, there is no common ground for comparing the solutions proposed. In this work, we contribute a number of different document testbeds for evaluating P 2 P IR systems. Each of these has been deduced from TREC's WT10g collection and corresponds to different potential P 2 P IR application scenarios. We analyse each methodology and testbed with respect to the document distributions achieved as well as to the location of relevant items within each setting. This work marks the beginning of an effort to provide more realistic evaluation environments for P 2 P IR systems as well as to create a common ground for comparisons of existing and future architectures.

This paper presents dispel4py, a new Python framework for describing abstract stream-based workflows for distributed data-intensive applications. These combine the familiarity of Python programming with the scalability of workflows. Data streaming is used to gain performance, rapid prototyping and applicability to live observations. dispel4py enables scientists to focus on their scientific goals, avoiding distracting details and retaining flexibility over the computing infrastructure they use. The implementation, therefore, has to map dispel4py abstract workflows optimally onto target platforms chosen dynamically. We present four dispel4py mappings: Apache Storm, MPI, multi-threading and sequential, showing two major benefits: a) smooth transitions from local development on a laptop to scalable execution for production work, and b) scalable enactment on significantly different distributed computing infrastructures. Three application domains are reported and measurements on multiple infrastructures show the optimisations achieved; they have provided demanding real applications and helped us develop effective training. The dispel4py.org is an open-source project to which we invite participation. The effective mapping of dispel4py onto multiple target infrastructures demonstrates exploitation of data-intensive and HPC architectures and consistent scalability.