Abstract
Inductive logic programming (ILP) is a sub-field of machine learning that provides an excellent framework for multi-relational data mining applications. The advantages of ILP have been successfully demonstrated in complex and relevant industrial and scientific problems. However, to produce valuable models, ILP systems often require long running times and large amounts of memory. In this paper we address fundamental issues that have direct impact on the efficiency of ILP systems. Namely, we discuss how improvements in the indexing mechanisms of an underlying logic programming system benefit ILP performance. Furthermore, we propose novel data structures to reduce memory requirements and we suggest a new lazy evaluation technique to search the hypothesis space more efficiently. These proposals have been implemented in the April ILP system and evaluated using several well-known data sets. The results observed show significant improvements in running time without compromising the accuracy of the models generated. Indeed, the combined techniques achieve several order of magnitudes speedup in some data sets. Moreover, memory requirements are reduced in nearly half of the data sets. Copyright (C) 2008 John Wiley & Sons, Ltd.

Abstract

Abstract
The changes in the communication paradigm envisioned for future networks, with peer-to-peer/symmetric attachments gaining momentum and two IP (Internet Protocol) versions coexisting, will pose new challenges to mobile communication networks. Traditional IP auto-configuration mechanisms will not work properly, since they were designed mostly having in mind a client-server/asymmetric attachment model, they assume a single IP version paradigm, and they target the auto-configuration of devices only. The IST Ambient Networks project has introduced a new concept - the Ambient Network - that enables handling every communication entity, either a single device or an entire network, as an Ambient Network (AN). This paper describes a new efficient mechanism, named Basic Connectivity (BC) mechanism, for auto-configuring IP connectivity between attaching ANs. A proof-of-concept prototype, experimental results, and theoretical analysis show that BC suites the future networking paradigm and represents a solution more efficient than the current trial-and-error mechanism for auto-configuring IP connectivity.

Abstract

Abstract
The growth of machine-generated relational databases, both in the sciences and in industry, is rapidly outpacing our ability to extract useful information from them by manual means. This has brought into focus machine learning techniques like Inductive Logic Programming (ILP) that are able to extract human-comprehensible models for complex relational data. The price to pay is that ILP techniques are not efficient: they can be seen as performing a form of discrete optimisation, which is known to be computationally hard; and the complexity is usually some super-linear function of the number of examples. While little can be done to alter the theoretical bounds on the worst-case complexity of ILP systems, some practical gains may follow from the use of multiple processors. In this paper we survey the state-of-the-art on parallel ILP. We implement several parallel algorithms and study their performance using some standard benchmarks. The principal findings of interest are these: (1) of the techniques investigated, one that simply constructs models in parallel on each processor using a subset of data and then combines the models into a single one, yields the best results; and (2) sequential (approximate) ILP algorithms based on randomized searches have lower execution times than (exact) parallel algorithms, without sacrificing the quality of the solutions found.

Abstract
In beyond 3G networks the user will not be aware of the access network technology used to provide a telecommunications service. Heterogeneous network technologies will be seamlessly integrated in one "common" access network, enabling users to move around and continuously receive their subscribed services. In a commercial environment, this network evolution requires that a telecommunications operator jointly manages its networks resources to improve the service offered to the users and, at the same time, to increase its revenue. Starting from the UMTS and WLAN interconnection architecture defined by 3GPP, this paper analyzes the performance of a new joint radio resource management strategy, comparing it with two well-known strategies used in scenarios where both networks, the UMTS and the WLAN, are interconnected. The new strategy presented in the paper bases its decisions on criteria related to user mobility characteristics and the application characteristics. The strategy also introduces the possibility of renegotiating new calls and reallocating running calls from one access network to another. The performance analysis considers two traffic scenarios. One where only real-time applications are running and other which also introduces TCP applications. The comparison studies show the proposed strategy outperforms the other strategies in what concerns call blocking probability and applications QoS support. Besides, the proposed strategy tends to reduce the handoffs between networks.