Abstract
Sales promotions (SP) are short-term instruments usually designed to yield an immediate sales effect. Previous research has suggested that SP can be seen as detrimental to a brand's consumer franchise/equity as, in the long term, SP deteriorates brand value. In this paper, we theoretically broaden the scope of SP research relation to the following topics: strategy concept, marketing strategy, the Integrated Marketing Communication (IMC) concept, the specific nature of each SP instruments and the underlying processes associated with consumer uptake of SP. We present findings that illustrate managers' perceptions of the positioning of SP instruments. We argue that the strategic nature of SP needs to be incorporated into marketers' research agendas.

Abstract
A process of Knowledge Discovery in Databases (KDD) involving large amounts of data requires a considerable amount of computational power. The process may be done on a dedicated and expensive machinery or, for some tasks, one can use distributed computing techniques on a network of affordable machines. In either approach it is usual the user to specify the workflow of the sub-tasks composing the whole KDD process before execution starts. In this paper we propose a technique that we call Distributed Generative Data Mining. The generative feature of the technique is due to its capability of generating new sub-tasks of the Data Mining analysis process at execution time. The workflow of sub-tasks of the DM is, therefore, dynamic. To deploy the proposed technique we extended the Distributed Data Mining system HARVARD and adapted an Inductive Logic Programming system (IndLog) used in a Relational Data Ming task. As a proof-of-concept, the extended system was used to analyse an artificial dataset of a credit scoring problem with eighty million records.

Abstract
The HARVARD system is a general purpose system adequate for Knowledge Discover in Databases (KDD) running in general purpose PCs and based on distributed computing over a connected network of PCs. In this paper we discuss the extension of HARVARD to interact with a Grid Computing setting. This extension, called HARVARD-g, enable the HARVARD system to schedule task to the Grid and therefore largely increase its available computational power.

Abstract
We address the single-source uncapacitated minimum cost network flow problem with general concave cost functions. Exact methods to solve this class of problems in their full generality are only able to address small to medium size instances, since this class of problems is known to be NP-Hard. Therefore, approximate methods are more suitable. In this work, we present a hybrid approach combining a genetic algorithm with a local search. Randomly generated test problems have been used to test the computational performance of the algorithm. The results obtained for these test problems are compared to optimal solutions obtained by a dynamic programming method for the smaller problem instances and to upper bounds obtained by a local search method for the larger problem instances. From the results reported it can be shown that the hybrid methodology improves upon previous approaches in terms of efficiency and also on the pure genetic algorithm, i.e., without using the local search procedure. (C) 2007 Wiley Periodicals, Inc.

Abstract

Abstract