Abstract
The increasing requirement for ubiquitous access of the users, enable the seamless support of different networks, with different technologies, and also with different types, such as moving networks and ad-hoc networks. This paper describes the Ad-hoc network integration architecture being developed inside the IST project Daidalos II. The main purpose of this architecture is to seamlessly support the movement of nodes between ad-hoc and infrastructure networks, maintaining in the ad-hoc networks all the features being supported in the infrastructure, such as, efficient routing for unicast and multicast flows, distributed QoS mechanisms' security, and seamless mobility, including multihoming support.

Abstract
The amount of data collected and stored in databases is growing considerably for almost all areas of human activity. Processing this amount of data is very expensive, both humanly and computationally. This justifies the increased interest both on the automatic discovery of useful knowledge from databases, and on using parallel processing for this task. Multi Relational Data Mining (MRDM) techniques, such as Inductive Logic Programming (ILP), can learn rules from relational databases consisting of multiple tables. However current ILP systems are designed to run in main memory and can have long running times. We propose a pipelined data-parallel algorithm for ILP. The algorithm was implemented and evaluated on a commodity PC cluster with 8 processors. The results show that our algorithm yields excellent speedups, while preserving the quality of learning.

Abstract
Inductive Logic Programming (ILP) is a Machine Learning research field that has been quite successful in knowledge discovery in relational domains. ILP systems use a set of pre-classified examples (positive and negative) and prior knowledge to learn a theory in which positive examples succeed and the negative examples fail. In this paper we present a novel ILP system called April, capable of exploring several parallel strategies in distributed and shared memory machines.

Abstract

Abstract
We present a service-oriented scripting language for programming mobile agents in distributed systems. The main novelty of the language we call MOB, is the integration of the service-oriented and mobile agent paradigms. MOB is also encoded onto a process calculus with a well studied semantics. The encoding provides a specification for the front-end of the language compiler and allows us to use, for the back-end and for the run-time system, a compiler and a virtual machine previously developed for the process calculus.

Abstract
In this paper we present MOB, a service-oriented scripting language for programming mobile agents in distributed systems. The main feature of the language is the integration of the service-oriented and the mobile agent paradigms. MOB is encoded onto a process calculus with a well studied semantics which provides us with a tool to prove the soundness of the language relative to the underlying calculus. Copyright 2006 ACM.