Clustering-based Query Routing in Cooperative Semi-structured Peer to Peer Networks

We consider the problem of resource selection in clustered Peer-to-Peer Information Retrieval (P2P IR) networks with cooperative peers. The clustered P2P IR framework presents a significant departure from general P2P IR architectures by employing clustering to ensure content coherence between resources at the resource selection layer, without disturbing document allocation. We propose that such a property could be leveraged in resource selection by adapting well-studied and popular inverted lists for centralized document retrieval. Accordingly, we propose the Inverted PeerCluster Index (IPI), an approach that adapts the inverted lists, in a straightforward manner, for resource selection in clustered P2P IR. IPI also encompasses a strikingly simple peer-specific scoring mechanism that exploits the said index for resource selection. Through an extensive empirical analysis on P2P IR testbeds, we establish that IPI competes well with the sophisticated state-of-the-art methods in virtually every parameter of interest for the resource selection task, in the context of clustered P2P IR.

Designing an interactive laboratory experiment to investigate buoyancy

Laboratory classes provide a visual and practical way of supplementing traditional teaching through lectures and tutorial classes. A criticism of laboratories in our School is that they are largely based on demonstration with insufficient participation by students. This provided the motivation to create a new laboratory experiment which would be interactive, encourage student enthusiasm with the subject and improve the quality of student learning.

The topic of the laboratory is buoyancy. While this is a key topic in the first-year fluids module, the laboratory has been designed in such a way that prior knowledge of the topic is unnecessary and therefore it would be accessible by secondary school pupils. The laboratory climaxes in a design challenge. However, it begins with a simple task involving students identifying some theoretical background information using given websites. They then have to apply their knowledge by developing some equations. Next, given some materials (a sheet of tinfoil, card and blu-tack), they have to design a vessel to carry the greatest mass without sinking. Thus, they are given an open-ended problem and have to provide a mathematical justification for their design. Students are expected to declare the maximum mass for their boat in advance of it being tested to create a sense of competition and fun. Overall, the laboratory involves tasks which begin at a low level and progressively get harder, incorporating understanding, applying, evaluating and designing (with reference to Bloom’s taxonomy).

The experiment has been tested in a modern laboratory with wall-mounted screens and access to the internet. Students enjoyed the hands-on aspect and thought the format helped their learning.

The use of cheap materials which are readily available means that many students can be involved at one time. Support documentation has been produced, both for the student participants and the facilitator. The latter is given advice on how to guide the students (without simply giving them the answer) and given some warning about potential problems the students might have.

The authors believe that the laboratory can be adapted for use by secondary school pupils and hope that it will be used to promote engineering in an engaging and enthusing way to a wider audience. To this end, contact has already been made with the Widening Participation Unit at the University to gain advice on possible next steps.

Evaluating Document Retrieval Methods for Resource Selection in Clustered P2P IR

Resource Selection (or Query Routing) is an important step in P2P IR. Though analogous to document retrieval in the sense of choosing a relevant subset of resources, resource selection methods have evolved independently from those for document retrieval. Among the reasons for such divergence is that document retrieval targets scenarios where underlying resources are semantically homogeneous, whereas peers would manage diverse content. We observe that semantic heterogeneity is mitigated in the clustered 2-tier P2P IR architecture resource selection layer by way of usage of clustering, and posit that this necessitates a re-look at the applicability of document retrieval methods for resource selection within such a framework. This paper empirically benchmarks document retrieval models against the state-of-the-art resource selection models for the problem of resource selection in the clustered P2P IR architecture, using classical IR evaluation metrics. Our benchmarking study illustrates that document retrieval models significantly outperform other methods for the task of resource selection in the clustered P2P IR architecture. This indicates that clustered P2P IR framework can exploit advancements in document retrieval methods to deliver corresponding improvements in resource selection, indicating potential convergence of these fields for the clustered P2P IR architecture.