ABSTRACT. In this paper, based on the study of the specificity of historical printed books, we first explain the main error sources in classical methods used for page layout analysis. We show that each method (bottom-up and top-down)

Highlights• This article renews knowledge about distributed software deployment.• It provides up-to-date de nitions and terminology.• It proposes a framework to analyze the state of the art of automatic deployment.• It reviews the recent research works and synthesizes the results.• Actual methods barely satisfy heterogeneity, scalability and dynamics requirements. AbstractDeployment of software systems is a complex post-production process that consists in making software available for use and then keeping it operational. It must deal with constraints concerning both the system and the target machine(s), in particular their distribution, heterogeneity and dynamics, and satisfy requirements from different stakeholders. In the context of mobility and openness, deployment must react to the instability of the network of machines (failures, connections, disconnections, variations in the quality of the resources, etc.). Thus, deployment should be an uninterrupted process which also works when software is running and requires adaptiveness in order to continually satisfy the constraints and the requirements. Originally managed "by hand", software deployment demands an increasing level of automation and autonomy. This article first provides up-to-date terminology and definitions related to software deployment. Then, it proposes an analytical framework and reviews recent research works on automatic deployment with reference to this framework, and synthesizes the results. The review shows that existing solutions are incomplete, and possibly inefficient or unusable, when distribution, heterogeneity, scalability, dynamics and openness are primary concerns. In particular, they barely support dynamic reactions to unforeseeable events. Additionally, abstraction level and expressiveness at design time are rather limited regarding deployment complexity.

Nowadays, context management solutions in ambient networks are well-known. However, with the IoT paradigm, ambient information is not anymore the only source of context. Context management solutions able to address multiple network scales ranging from ambient networks to the Internet of Things (IoT) are required. We present the INCOME project whose goal is to provide generic software and middleware components to ease the design and development of mass market context-aware applications built above the Internet of Things. By revisiting ambient intelligence (AmI) context management solutions for extending them to the IoT, INCOME allows to bridge the gap between these two very active research domains. In this landscape paper, we identify how INCOME plans to advance the state of the art and we briefly describe its scientific program which consists of three main tasks: (i) multi-scale context management, (ii) management of extrafunctional concerns (quality of context and privacy), and (iii) autonomous deployment of context management entities.