Abstract
We consider a methodology for flexible software design, runtime programming, defined by recurrent, incremental software modifications to a program at runtime, called runtime patches. The principles we consider for runtime programming are model preservation and scalability. Model preservation means that a runtime patch preserves the programming model in place for programs - in terms of syntax, semantics, and correctness properties - as opposed to an "ad-hoc", disruptive operation, or one that requires an extra level of abstraction. Scalability means that, for practicality and performance, the effort in program compilation required by a runtime patch should ideally scale in proportion to the change induced by it. We formulate runtime programming over an abstract model for component-based concurrent programs, defined by a modular relation between the syntax and semantics of programs, plus built-in notions of initialization and quiescence. The notion of a runtime patch is defined over these assumptions, as a model-preserving transition between two programs and respective states. Additionally, we propose an incremental compilation framework for scalability in patch compilation. The formulation is put in perspective through a case-study instantiation over a language for distributed hard real-time systems, the Hierarchical Timing Language (HTL). © 2011 IEEE.

Abstract
Nowadays applications produce infinite streams of data distributed across wide sensor networks. In this work we study the problem of continuously maintain a cluster structure over the data points generated by the entire network. Usual techniques operate by forwarding and concentrating the entire data in a central server, processing it as a multivariate stream. In this paper, we propose DGClust, a new distributed algorithm which reduces both the dimensionality and the communication burdens, by allowing each local sensor to keep an online discretization of its data stream, which operates with constant update time and (almost) fixed space. Each new data point triggers a cell in this univariate grid, reflecting the current state of the data stream at the local site. Whenever a local site changes its state, it notifies the central server about the new state it is in. This way, at each point in time, the central site has the global multivariate state of the entire network. To avoid monitoring all possible states, which is exponential in the number of sensors, the central site keeps a small list of counters of the most frequent global states. Finally, a simple adaptive partitional clustering algorithm is applied to the frequent states central points in order to provide an anytime definition of the clusters centers. The approach is evaluated in the context of distributed sensor networks, focusing on three outcomes: loss to real centroids, communication prevention, and processing reduction. The experimental work on synthetic data supports our proposal, presenting robustness to a high number of sensors, and the application to real data from physiological sensors exposes the aforementioned advantages of the system.

Abstract
In ubiquitous streaming data sources, such as sensor networks, clustering nodes by the data they produce is an important problem that gives insights on the phenomenon being monitored by such networks. However, if these techniques require data to be gathered centrally, communication and storage requirements are often unbounded. The goal of this paper is to assess the feasibility of computing local clustering at each node, using only neighbors' centroids, as an approximation of the global clustering computed by a centralized process. A local algorithm is proposed to perform clustering of sensors based on the moving average of each node's data over time: the moving average of each node is approximated using memory-less fading average; clustering is based on the furthest point algorithm applied to the centroids computed by the node's direct neighbors. The algorithm was evaluated on a state-of-the-art sensor network simulator, measuring the agreement between local and global clustering. Experimental work on synthetic data with spherical Gaussian clusters is consistently analyzed for different network size, number of clusters and cluster overlapping. Results show a high level of agreement between each node's clustering definitions and the global clustering definition, with special emphasis on separability agreement. Overall, local approaches are able to keep a good approximation of the global clustering, improving privacy among nodes, and decreasing communication and computation load in the network. Hence, the basic requirements for distributed clustering of streaming data sensors recommend that clustering on these settings should be performed locally. © 2011 ACM.

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Abstract
We describe and compare design choices for meta-predicate semantics, as found in representative Prolog predicate-based module systems and in Logtalk. We look at the consequences of these design choices from a pragmatic perspective, discussing explicit qualification semantics, computational reflection support, expressiveness of meta-predicate directives, meta-predicate definitions safety, portability of meta-predicate definitions, and meta-predicate performance. We also describe how to extend the usefulness of meta-predicate definitions. Our aim is to provide useful insights to discuss meta-predicate semantics and portability issues based on actual implementations and common usage patterns. © 2012 Springer-Verlag.

Abstract