Abstract
The goal of this work is to provide (non-specialist) users with the means to seamlessly setup and monitor a Wireless Sensor-Actuator Network (WSN) without writing any code or performing subtle hardware configurations. Towards this goal, we present an architecture that allows the seamless configuration, deployment and management of applications over WSN. We explore the fact that most deployments have a common modus operandi: (a) simple data readers running on the nodes periodically gather and send data to sinks, and; (b) sinks process incoming data and, accordingly, issue actuation commands to the nodes. We argue that, given the knowledge of a platform's capabilities, its sensors and actuators and their respective programming interfaces, it is possible to fully automate the process of configuring, building, and deploying an application over a WSN. Similarly, monitoring and managing the deployment can be vastly simplified by using a middleware that supports user defined tasks that process data from the nodes, divide the WSN into regions, defined by simple boolean predicates over data, and eventually issue actuation commands on regions.

Abstract

Abstract

Abstract
Tabling is a commonly used technique in logic programming for avoiding cyclic behavior of logic programs and enabling more declarative program definitions. Furthermore, tabling often improves computational performance. Rational term are terms with one or more infinite sub-terms but with a finite representation. Rational terms can be generated in Prolog by omitting the occurs check when unifying two terms. Applications of rational terms include definite clause grammars, constraint handling systems, and coinduction. In this paper, we report our extension of YAP's Prolog tabling mechanism to support rational terms. We describe the internal representation of rational terms within the table space and prove its correctness. We then use this extension to implement a tabling based approach to coinduction. We compare our approach with current coinductive transformations and describe the implementation. In addition, we present an algorithm that ensures a canonical representation for rational terms.

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Abstract
Interest in the MapReduce programming model has been rekindled by Google in the past 10 years; its popularity is mostly due to the convenient abstraction for parallelization details this framework provides. State-of-the-art systems such as Google's, Hadoop or SAGA often provide added features like a distributed file system, fault tolerance mechanisms, data redundancy and portability to the basic MapReduce framework. However, these features pose an additional overhead in terms of system performance. In this work, we present a MapReduce design for Prolog which can potentially take advantage of hybrid parallel environments; this combination allies the easy declarative syntax of logic programming with its suitability to represent and handle multi-relational data due to its first order logic basis. MapReduce for Prolog addresses efficiency issues by performing load balancing on data with different granularity and allowing for parallelization in shared memory, as well as across machines. In an era where multicore processors have become common, taking advantage of a cluster's full capabilities requires the hybrid use of parallelism.