Abstract
Traditional centralized and rigid control structures are becoming inflexible to face the requirements of reconfigurability, responsiveness and robustness, imposed by customer demands in the current global economy. The Ho Ionic Manufacturing Systems (HMS) paradigm, which was pointed out as a suitable solution to face these requirements, translates the concepts inherited from social organizations and biology to the manufacturing world. It offers an alternative way of designing adaptive systems where the traditional centralized control is replaced by decentralization over distributed and autonomous entities organized in hierarchical structures formed by intermediate stable forms. In spite of its enormous potential, methods regarding the self-adaptation and self-organization of complex systems are still missing. This paper discusses how the insights from biology in connection with new fields of computer science can be useful to enhance the holonic design aiming to achieve more self-adaptive and evolvable systems. Special attention is devoted to the discussion of emergent behavior and self-organization concepts, and the way they can be combined with the holonic rationale.

Abstract
Intensive research and development has been conducted in the design and creation of groupware systems for distributed users. While for some activities, these groupware tools are widely used, for other activities the impact in the groupware community has been smaller and can be improved. One reason for this fact is that the mostly common used applications do not support collaborative features and users are reluctant to change to a different application. In this paper we discuss how available file system mechanisms can help to address this problem. In this context, we present Forby, a system that allows to provide groupware features to distributed users by combining filesystem monitoring and distributed event dissemination. To demonstrate our solution, we present three systems that rely on Forby for providing groupware features to users running unmodified applications.

Abstract
The most challenging applications of knowledge discovery involve dynamic environments where data continuous flow at high-speed and exhibit non-stationary properties. In this chapter we discuss the main challenges and issues when learning from data streams. In this work, we discuss the most relevant issues in knowledge discovery from data streams: incremental learning, cost-performance management, change detection, and novelty detection. We present illustrative algorithms for these learning tasks, and a real-world application illustrating the advantages of stream processing. The chapter ends with some open issues that emerge from this new research area. © 2009 Springer-Verlag Berlin Heidelberg.

Abstract
Service-oriented Multi-Agent Systems (SoMAS) is art approach to combine the fundamental characteristics of service-oriented and multi-agent methods into a new platform for industrial automation. Several research works already targeted the connection of these technologies, presenting different perspectives in how and why to join them. This research focuses on available efforts and solutions in the area of SoMAS and explains the idea behind the service-oriented agents in industrial automation. A SoMAS system is mainly composed by shared resources in form of services and their providing/requesting agents. The paper also discusses the required engineering aspects of these systems, from the internal anatomy to the interaction patterns. Parameters of flexibility, reconfiguration, autonomy and reduced development efforts were considered and they should be the trademark of SoMAS. Aiming to illustrate the proposed approach, an example of service-oriented automation agents is given.

Abstract
Data aggregation plays an important role in the design of scalable systems, allowing the determination of meaningful system-wide properties to direct the execution of distributed applications. In the particular case of wireless sensor networks, data collection is often only practicable if aggregation is performed. Several aggregation algorithms have been proposed in the last few years, exhibiting different properties in terms of accuracy, speed and communication tradeoffs. Nonetheless, existing approaches are found lacking in terms of fault tolerance. In this paper, we introduce a novel fault-tolerant averaging based data aggregation algorithm. It tolerates substantial message loss (link failures), while competing algorithms in the same class can be affected by a Single lost message. The algorithm is based on manipulating flows (in the graph theoretical sense), that are updated using idempotent messages, providing it with unique robustness capabilities. Furthermore, evaluation results obtained by comparing it with other averaging approaches have revealed that it outperforms them in terms of time and message complexity.

Abstract
This paper presents the logic programming concept of thread-based competitive or-parallelism, which combines the original idea of competitive or-parallelism with committed-choice nondeterminism and speculative threading. In thread-based competitive or-parallelism, an explicit; disjunction of subgoals is interpreted as a set of concurrent alternatives, each running in its own thread. The individual subgoals usually correspond to predicates implementing different procedures that, depending on the problem specifics, are expected to either fail or succeed with different performance levels. The subgoals compete for providing an answer and the first successful subgoal leads to the termination of the remaining ones. We discuss the implementation of thread-based competitive or-parallelism in the context of Logtalk, an object-oriented logic programming language, and present experimental results.