Fabio Simeoni

Deploying general-purpose virtual research environments for humanities research

Several virtual research environments (VREs) have been developed to address specific tasks or application domains. Building on the experiences and use cases coming out of these projects, this paper addresses the creation of more general-purpose VREs for the humanities, which move beyond specific, focused tasks, and instead provide services and environments that support more general-purpose humanities research activities. Specifically, we are investigating use cases related to the organization and integration of the dispersed and heterogeneous information on which such research is based. These use cases are highly interactive, interpretative and researcher centric, addressing topics such as annotation environments and support for ‘active-reading’ processes and scholarly dialogues. We present the background to our work and the technical approach taken, and report the results obtained so far.

A bounded distance metric for comparing tree structure

Comparing tree-structured data for structural similarity is a recurring theme and one on which much effort has been spent. Most approaches so far are grounded, implicitly or explicitly, in algorithmic information theory, being approximations to an information distance derived from Kolmogorov complexity. In this paper we propose a novel complexity metric, also grounded in information theory, but calculated via Shannons entropy equations. This is used to formulate a directly and efficiently computable metric for the structural difference between unordered trees. The paper explains the derivation of the metric in terms of information theory, and proves the essential property that it is a distance metric. The property of boundedness means that the metric can be used in contexts such as clustering, where second-order comparisons are required. The distance metric property means that the metric can be used in the context of similarity search and metric spaces in general, allowing trees to be indexed and stored within this domain. We are not aware of any other tree similarity metric with these properties.

Hybrid applications over XML: integrating the procedural and declarative approaches

We discuss the design of a quasi-statically typed language for XML in which data may be associated with different structures and different algebras in different scopes, whilst preserving identity. In declarative scopes, data are trees and may be queried with the full flexibility associated with XML query algebras. In procedural scopes, data have more conventional structures, such as records and sets, and can be manipulated with the constructs normally found in mainstream languages.For its original form of structural polymorphism, the language offers integrated support for the development of hybrid applications over XML, where data change form to reflect programming expectations and enable their enforcement.

An Approach to high-level language bindings to XML

Abstract Values of existing typed programming languages are increasingly generated and manipulated outside the language jurisdiction. Instead, they often occur as fragments of XML documents, where they are uniformly interpreted as labelled trees in spite of their domain-specific semantics. In particular, the values are divorced from the high-level type with which they are conveniently, safely, and efficiently manipulated within the language. We propose language-specific mechanisms which extract language values from arbitrary XML documents and inject them in the language. In particular, we provide a general framework for the formal interpretation of extraction mechanisms and then instantiate it to the definition of a mechanism for a sample language core L . We prove that such mechanism can be built by giving a sound and complete algorithm that implements it. The values, types, and type semantics of L are sufficiently general to show that extraction mechanisms can be defined for many existing typed languages, including object-oriented languages. In fact, extraction mechanisms for a large class of existing languages can be directly derived from L s. As a proof of this, we introduce the SNAQue prototype system, which transforms XML fragments into CORBA objects and exposes them across the ORB framework to any CORBA-compliant language.

Servicing the federation: the case for metadata harvesting

The paper presents a comparative analysis of data harvesting and distributed computing as complementary models of service delivery within large-scale federated digital libraries.

Functional adaptivity for digital library services in e-infrastructures: the gCube approach

We consider the problem of e-Infrastructures that wish to reconcile the generality of their services with the bespoke requirements of diverse user communities. We motivate the requirement of functional adaptivity in the context of gCube, a service-based system that integrates Grid and Digital Library technologies to deploy, operate, and monitor Virtual Research Environments defined over infrastructural resources. We argue that adaptivity requires mapping service interfaces onto multiple implementations, truly alternative interpretations of the same functionality. We then analyse two design solutions in which the alternative implementations are, respectively, full-fledged services and local components of a single service. We associate the latter with lower development costs and increased binding flexibility, and outline a strategy to deploy them dynamically as the payload of service plugins. The result is an infrastructure in which services exhibit multiple behaviours, know how to select the most appropriate behaviour, and can seamlessly learn new behaviours.

Towards a universal information distance for structured data

The similarity of objects is one of the most fundamental concepts in any collection of complex information; similarity, along with techniques for storing and indexing sets of values based on it, is a concept of ever increasing importance as inherently unordered data sets become ever more common. Examples of such datasets include collections of images, multimedia, and semi-structured data. There are however two, largely separate, classes of related research. On the one hand, techniques such as clustering and similarity search give general treatments over sets of data. Results are domain-independent, typically relying only on the existence of an anonymous distance metric over the set in question. On the other hand, results in the domain of similarity measurement are often limited to the context of pairwise comparison over individual objects, and are not typically set in a wider context. Published algorithms are scattered over various demand-led subject areas, including for example bioinformatics, library sciences, and crime detection. Few, if any, of the published algorithms have the distance metric properties. We have identified a distance metric, Ensemble Distance, which we believe can help to bridge this gap. Ensemble Distance is a non-Euclidean distance metric which we believe can be used in the treatment of many classes of structured data. For any complex type where a useful characterisation exists in the form of an ensemble, we can produce a distance metric for that type. This will in turn allow use of the complex type within off-the-shelf clustering and similarity search algorithms; this would be a major result in the management of complex data sets.

Structural Entropic Difference: A Bounded Distance Metric for Unordered Trees

We show a new metric for comparing unordered, tree-structured data. While such data is increasingly important in its own right, the methodology underlying the construction of the metric is generic and may be reused for other classes of ordered and partially ordered data. The metric is based on the information content of the two values under consideration, which is measured using Shannons entropy equations. In essence, the more commonality the values possess, the closer they are. As values in this domain may have no commonality, a good metric should be bounded to represent this. This property has been achieved, but is in tension with triangle inequality.

Safer typing of complex API usage through Java generics

When several incompatible implementations of a single API are in use in a Java program, the danger exists that instances from different implementations may inadvertently be mixed, leading to errors. In this paper we show how to use generics to prevent such mixing. The core idea of the approach is to add a type parameter to the interfaces of the API, and tie the classes that make up an implementation to a unique choice of type parameter. In this way methods of the API can only be invoked with arguments that belong to the same implementation. We show that the presence of a type parameter in the interfaces does not violate the principle of interface-based programming: clients can still completely abstract over the choice of implementation. In addition, we demonstrate how code can be reused between different implementations, how implementations can be defined as extensions of other implementations, and how different implementations may be mixed in a controlled and safe manner. To explore the feasibility of the approach, gauge its usability, and identify any issues that may crop up in practical usage, we have refactored a fairly large existing API-based application suite, and we report on the experience gained in the process.

Matchmaking for covariant hierarchies

We describe a model of matchmaking suitable for the implementation of services, rather than for their discovery and composition. In the model, processing requirements are modelled by client requests and computational resources are software processors that compete for request processing as the covariant implementations of an open service interface. Matchmaking then relies on type analysis to rank processors against requests in support of a wide range of dispatch strategies. We relate the model to the autonomicity of service provision and briefly report on its deployment within a production-level infrastructure for scientific computing.