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. Informed by requirements of flexibility and scalability of federated services, the analysis focuses on the identification and assessment of model invariants. In particular, it abstracts over application domains, services, and protocol implementations. The analytical evidence produced shows that the harvesting model offers stronger guarantees of satisfying the identified requirements. In addition, it suggests a first characterisation of services based on their suitability to either model and thus indicates how they could be integrated in the context of a single federated digital library

CloudBooks: An infrastructure for reading on multiple devices

The use of light, portable devices such as iPads whose reading angle is readily changed is radically different to reading on a desktop or laptop. However, it would be naive to view this as mere evolution. Rather, such devices permit reading activity to more closely mirror paper. A light, keyboardless device can be used in many different locations and orientations. This paper reports an infrastructure for supporting reading on multiple slate devices using a single cloud-based system to provide for numerous configurations

Report on the 11th bibliometric-enhanced information retrieval workshop (BIR 2021)

Algorithms and the Foundations of Software technolog

Annotation Search: the FAST Way

Περιέχει το πλήρες κείμενοThis paper discusses how annotations can be exploited to develop information access and retrieval algorithms that take them into account. The paper proposes a general framework for developing such algorithms that specifically deals with the problem of accessing and retrieving topical information from annotations and annotated documents

線形応答理論の変分原理 : 時の流れを見る尺度

この論文は国立情報学研究所の電子図書館事業により電子化されました

Reinforcement learning-driven information seeking: A quantum probabilistic approach

Understanding an information forager’s actions during interaction is very important for the study of interactive information retrieval. Although information spread in an uncertain information space is substantially complex due to the high entanglement of users interacting with information objects (text, image, etc.). However, an information forager, in general, accompanies a piece of information (information diet) while searching (or foraging) alternative contents, typically subject to decisive uncertainty. Such types of uncertainty are analogous to measurements in quantum mechanics which follow the uncertainty principle. In this paper, we discuss information seeking as a reinforcement learning task. We then present a reinforcement learning-based framework to model the foragers exploration that treats the information forager as an agent to guide their behaviour. Also, our framework incorporates the inherent uncertainty of the foragers’ action using the mathematical formalism of quantum mechanics

BIRDS-Bridging the Gap between Information Science, Information Retrieval and Data Science

The BIRDS workshop aimed to foster the cross-fertilization of Information Science (IS), Information Retrieval (IR) and Data Science (DS). Recognising the commonalities and differences between these communities, the proposed full-day workshop brought together experts and researchers in IS, IR and DS to discuss how they can learn from each other to provide more user-driven data and infor-mation exploration and retrieval solutions. Therefore, the papers aimed to convey ideas on how to utilise, for instance, IS concepts and theories in DS and IR or DS approaches to support users in data and information exploration