Semantic features for context organization

In recent years the technological world has grown by incorporating billions of small sensing devices, collecting and sharing real-world information. As the number of such devices grows, it becomes increasingly difficult to manage all these new information sources. There is no uniform way to share, process and understand context information. In previous publications we discussed efficient ways to organize context information that is independent of structure and representation. However, our previous solution suffers from semantic sensitivity. In this paper we review semantic methods that can be used to minimize this issue, and propose an unsupervised semantic similarity solution that combines distributional profiles with public web services. Our solution was evaluated against Miller-Charles dataset, achieving a correlation of 0.6.

Mediator framework for inserting xDRs into hadoop

During the last decades, we assisted to what is called “information explosion”. With the advent of the new technologies and new contexts, the volume, velocity and variety of data has increased exponentially, becoming what is known today as big data. Among them, we emphasize telecommunications operators, which gather, using network monitoring equipment, millions of network event records, the Call Detail Records (CDRs) and the Event Detail Records (EDRs), commonly known as xDRs. These records are stored and later processed to compute network performance and quality of service metrics. With the ever increasing number of collected xDRs, its generated volume needing to be stored has increased exponentially, making the current solutions based on relational databases not suited anymore. To tackle this problem, the relational data store can be replaced by Hadoop File System (HDFS). However, HDFS is simply a distributed file system, this way not supporting any aspect of the relational paradigm. To overcome this difficulty, this paper presents a framework that enables the current systems inserting data into relational databases, to keep doing it transparently when migrating to Hadoop. As proof of concept, the developed platform was integrated with the Altaia - a performance and QoS management of telecommunications networks and services.

The suppliers selection problem: a case study

The effective supplier evaluation and purchasing processes are of vital importance to business organizations, making the suppliers selection problem a fundamental key issue to their success. We consider a complex supplier selection problem with multiple products where minimum package quantities, minimum order values related to delivery costs, and discounted pricing schemes are taken into account. Our main contribution is to present a mixed integer linear programming (MILP) model for this supplier selection problem. The model is used to solve several examples including three real case studies from an electronic equipment assembly company.

Airport ground movement

Worldwide air traffic tends to increase and for many airports it is no longer an op-tion to expand terminals and runways, so airports are trying to maximize their op-erational efficiency. Many airports already operate near their maximal capacity. Peak hours imply operational bottlenecks and cause chained delays across flights impacting passengers, airlines and airports. Therefore there is a need for the opti-mization of the ground movements at the airports. The ground movement prob-lem consists of routing the departing planes from the gate to the runway for take-off, and the arriving planes from the runway to the gate, and to schedule their movements. The main goal is to minimize the time spent by the planes during their ground movements while respecting all the rules established by the Ad-vanced Surface Movement, Guidance and Control Systems of the International Civil Aviation. Each aircraft event (arrival or departing authorization) generates a new environment and therefore a new instance of the Ground Movement Prob-lem. The optimization approach proposed is based on an Iterated Local Search and provides a fast heuristic solution for each real-time event generated instance granting all safety regulations. Preliminary computational results are reported for real data comparing the heuristic solutions with the solutions obtained using a mixed-integer programming approach.