Abstract
The rapid growth in information science and technology in general and the complexity and volume of data in particular have introduced new challenges for the research community.Many sources produce data continuously. Examples include sensor networks, wireless networks, radio frequency identification (RFID), customer click streams, telephone records, multimedia data, scientific data, sets of retail chain transactions etc. These sources are called data streams. A data stream is an ordered sequence of instances that can be read only once or a small number of times using limited computing and storage capabilities. These sources of data are characterized by being open-ended, flowing at high-speed, and generated by non stationary distributions in dynamic environments. What distinguishes current data from earlier one is automatic data feeds. We do not just have people who are entering information into a computer. Instead, we have computers entering data into each other [25]. Nowadays there are applications in which the data are modeled best as transient data streams instead of as persistent tables. Examples of applications include network monitoring, user modeling in web applications, sensor networks in electrical networks, telecommunications data management, prediction in stock markets, monitoring radio frequency identification etc. In these applications it is not feasible to load the arriving data into a traditional data base management system (DBMS) and traditional DBMS are not designed to directly support the continuous queries required by these applications [3]. Carney et al. [6] pointed out the significant differences between data bases that are passive repositories of data and data bases that actually monitor applications and alert humans when abnormal activity is detected. In the former, only the current state of the data is relevant for analysis. Humans initiate queries, usually one-time, predefined queries. In the latter, data come from external sources (e.g., sensors), and require processing historic data. For example, in monitoring activity, queries should run continuously. The answer to a continuous query is produced over time, reflecting the data seen so far. Moreover, if the process is not strictly stationary (as most of real-world applications), the target concept could gradually change over time. For example, the type of abnormal activity (e.g., attacks in TCP/IP networks, frauds in credit card transactions etc.) changes over time. Organizations use decision support systems to identify potential useful patterns in data. Data analysis is complex, interactive, and exploratory over very large volumes of historic data, eventually stored in distributed environments. Traditional pattern discovery process requires online ad-hoc queries, not previously defined, that are successively refined. Nowadays, given the current trends in decision support and data analysis, the computer plays a much more active role, by searching hypotheses, evaluating and suggesting patterns. Due to the exploratory nature of these queries, an exact answer may not be required. A user may prefer a fast approximate answer. Range queries and selectivity estimation (the proportion of tuples that satisfy a query) are two illustrative examples where fast but approximate answers are more useful than slow and exact ones. Sensor networks are distributed environments producing multiple streams of data. We can consider the network as a distributed database we are interested in querying and mining. In this chapter we review the main techniques used for query and mining data streams that are of potential use in sensor networks. In Sect. 3.2 we refer to the data stream models and identify its main research challenges. Section 3.3 presents basic stream models. Section 3.4 present basic stream algorithms for maintaining synopsis over data streams. Section 3.5 concludes the chapter and points out future directions for research. © 2007 Springer-Verlag Berlin Heidelberg.

Abstract
In the recent evolution of production control systems, the emergence of decentralized systems capable of dealing with the rapid changes in the production environment better than the traditional centralized architectures has been one of the most significant developments. The agent-based and holonic paradigms symbolize this approach, and ADACOR holonic control architecture is a successful example of such a system. In this paper, authors discusses the current challenges and the way to go in the direction of new, reconfigurable, evolvable and ubiquitous systems, able to respond to current production environment demands and variability. © 2007 IFAC.

Abstract
The growth of the Internet and its associated technologies did open space for a new type of human interaction: virtual, social interaction environments. Nowadays, these virtual places are spread all over the Internet and are accessible to almost everyone. However, in these environments interaction is still mostly ad hoc, which is a drawback that, as pointed out by some authors, may lead to their future extinction. Therefore, the introduction of regulated interaction in these virtual interaction spaces may be a solution towards their organization and inherent increased credibility. In this paper we propose a model for interaction regulation and control for virtual, social interaction spaces, called Social Theatres. Social Theatres stand for the application of the theatrical metaphor to social virtual environments, intended to virtually reproduce some of the common useful people interaction contexts. Inside these environments, users become actors, playing previously well defined roles within a well known, commonly established virtual interaction scenario. The interaction between users becomes regulated and has to obey and follow very well established rules, flows and conversation protocols. This paper discusses the advantages of regulated interaction, presents the Social Theatre metaphor and proposes a software architecture for the implementation of these interaction spaces. A small case study of a regulated virtual interaction environment is also presented.

Abstract
In industrial environments, disturbance handling is a major issue in reconfigurable manufacturing control systems, supporting the fast, effective and efficient response to the occurrence of unexpected disturbances. Those disturbances usually degrade the performance of the system, causing the loss of productivity and business opportunities, which are crucial roles to achieve competitiveness. This paper proposes an agent-based disturbance handling architecture that distributes the disturbance handling functions by several autonomous control units and considers the main types of shop floor disturbances that have impact at planning and scheduling level. The proposed architecture also integrates a prediction component, transforming the traditional "fail and recover" practices into "predict and prevent" practices. © 2007 IFAC.

Abstract
We present a new class of digital audio effects which can automatically relate parameter values to the tempo of a musical input in real-time. Using a beat tracking system as the front end, we demonstrate a tempo-dependent delay effect and a set of beat-synchronous low frequency oscillator (LFO) effects including auto-wah, tremolo and vibrato. The effects show better performance than might be expected as they are blind to certain beat tracker errors. All effects are implemented as VST plug-ins which operate in real-time, enabling their use both in live musical performance and the off-line modification of studio recordings.

Abstract
In this paper we present an extension of the Nelder and Mead simplex algorithm for non-linear programming, which makes it suitable for both unconstrained and constrained optimisation.(1) We then explore several extensions of the method for escaping local optima, which make it a simple, yet powerful tool for optimisation of nonlinear functions with many local optima. A strategy which proved to be extremely robust was random start local search, with a correct, though unusual, setup. Actually, for some of the benchmarks, this simple metaheuristic remained the most effective one. The idea is to use a very large simplex at the begin; the initial movements of this simplex are very large, and therefore act as a kind of filter, which naturally drives the search into good areas. We propose two more mechanisms for escaping local optima, which, still being very simple to implement, provide better results for some difficult problems.