Abstract
In many scenarios, such as the ones related to Data Warehousing Extract-Transform-Load (ETL) processes, logging techniques are usually applied for capturing event metrics across system levels for system auditing and system recovery. The diversity of strategies and architectures of the toolset used to support the ETL implementation introduces another layer of complexity, both for system development and audit. Although a valuable system diagnosis resource for the development team, logging is generally underestimated, being used only when the system reveals unexpected behaviours and not to drive the ETL system evolution. We believe that the use of logs for steering ETL development and maintenance can improve significantly global system quality. However, this approach is only effective if flexible and efficient logging systems exist. In this paper, we describe a Log Pattern used in a pattern-oriented approach for ETL systems development, which provides a configurable and flexible component for using to drive ETL development and maintenance phases.

Abstract
In this paper, we address the Capacitated Facility Location Problem (CFLP) in which the assignment of facilities to customers must ensure enough facility capacity and all the customers must be served. We propose both sequential and parallel Relaxation Adaptive Memory Programming approaches for the CFLP, combining a Lagrangean subgradient search with an improvement method to explore primal-dual relationships to create advanced memory structures that integrate information from both primal and dual solution spaces. Computational experiments of the effectiveness of this approach are presented and discussed.

Abstract
In this paper, we address the Single Source Capacitated Facility Location Problem (SSCFLP) which considers a set of possible locations for opening facilities and a set of clients whose demand must be satisfied. The objective is to minimize the cost of assigning the clients to the facilities, ensuring that all clients are served by only one facility without exceeding the capacity of the facilities. We propose a Relaxation Adaptive Memory Programming (RAMP) heuristic for solving the SSCFLP to efficiently explore the relation between the primal and the dual sides of this combinatorial optimisation problem. Computational experiments demonstrated that the proposed heuristic is very effective in terms of solution quality with reasonable computing times.

Abstract
Facility Location embodies a class of problems concerned with locating a set of facilities to serve a geographically distributed population of customers at minimum cost. We address the classical Capacitated Facility Location Problem (CFLP) in which the assignment of facilities to customers must ensure enough facility capacity and all the customers must be served. This is a well-known NP-hard problem in combinatorial optimization that has been extensively studied in the literature. Due to the difficulty of the problem, significant research efforts have been devoted to developing advanced heuristic methods aimed at finding high-quality solutions in reasonable computational times. We propose a Relaxation AdaptiveMemory Programming (RAMP) approach for the CFLP. Our method combines lagrangean subgradient search with an improvement method to explore primal-dual relationships to create advanced memory structures that integrate information from both primal and dual solution spaces. The algorithm was tested on the standard ORLIB dataset and on other very large-scale instances for the CFLP. Our approach efficiently found the optimal solution for all ORLIB instances and very competitive results for the large-scale ones. Comparisons with current best-performing algorithms for the CFLP show that our RAMP algorithm exhibits excellent results.

Abstract
We propose a Relaxation Adaptive Memory Programming (RAMP) algorithm for the solution of the Single Source Capacitated Facility Location Problem (SSCFLP). This problem considers a set of possible locations for opening facilities and a set of clients whose demand must be satisfied. The objective is to minimize the cost of assigning the clients to the facilities, ensuring that all clients are served by only one facility without exceeding the capacity of the facilities. The RAMP framework efficiently explores the relation between the primal and the dual sides of combinatorial optimization problems. In our approach, the dual problem, obtained through a lagrangean relaxation, is solved by subgradient optimization. Computational experiments of the effectiveness of this approach are presented and discussed.

Abstract
We consider the three-dimensional Bin Packing Problem in which a set of boxes must be packed into the minimum number of identical bins. We present a heuristic that iteratively creates new sequences of boxes that defines the packing order used to generate a new solution. The sequences are generated retaining, adaptively, characteristics of previous sequences for search intensification and diversification. Computational experiments of the effectiveness of this approach are presented and discussed.