Litigation in court is still the main dispute resolution mode. However, given the amount and characteristics of the new disputes, mostly arising out of electronic contracting, courts are becoming slower and outdated. Online Dispute Resolution (ODR) recently emerged as a set of tools and techniques, supported by technology, aimed at facilitating conflict resolution. In this paper we present a critical evaluation on the use of Artificial Intelligence (AI) based techniques in ODR. In order to fulfill this goal, we analyze a set of commercial providers (in this case twenty four) and some research projects (in this circumstance six). Supported by the results so far achieved, a new approach to deal with the problem of ODR is proposed, in which we take on some of the problems identified in the current state of the art in linking ODR and AI.

Sophisticated data analytical methods such as data mining, where the focus is upon exploration and developing new insights, are becoming increasingly useful tools in analysing elite sports performance data and supporting decision making that is crucial to gaining success. In this article, we investigate the different data mining demands of elite sports with respect to a number of features that describe sport competitions. The aim is to more structurally connect the sports and data mining domains through: (a) describing a framework for categorizing elite sports, and (b) understanding the analytical demands of different performance analysis problems. Therefore, we review different aspects such as sport categories and performance analysis requirements that influence each stage in sports data mining. We also present a model bringing together performance analysis requirements, data mining methods, data mining techniques, and technique characteristics. This will assist both data scientists and sport professionals to more effectively collaborate and contribute to success in elite sport events.

Alternative dispute resolution has now supplanted litigation as the principal method of dispute resolution. This overview of dispute resolution addresses practical developments in areas such as family law, plea bargaining, industrial relations and torts. The authors elaborate on the necessary legal safeguards that should be taken into account when developing technology-enhanced dispute resolution and explore a wide range of potential applications for new information technologies in dispute resolution.

Performance in triathlon is dependent upon factors that include somatotype, physiological capacity, technical proficiency and race strategy. Given the multidisciplinary nature of triathlon and the interaction between each of the three race components, the identification of target split times that can be used to inform the design of training plans and race pacing strategies is a complex task. The present study uses machine learning techniques to analyse a large database of performances in Olympic distance triathlons (2008-2012). The analysis reveals patterns of performance in five components of triathlon (three race "legs" and two transitions) and the complex relationships between performance in each component and overall performance in a race. The results provide three perspectives on the relationship between performance in each component of triathlon and the final placing in a race. These perspectives allow the identification of target split times that are required to achieve a certain final place in a race and the opportunity to make evidence-based decisions about race tactics in order to optimise performance.