This paper proposes a Big Data approach to automatically identify and extract buildings from a digital surface model created from aerial laser scanning data. The approach consists of two steps. The first step is a MapReduce process where neighboring points in a digital surface model are mapped into cubes. The second step uses a non-MapReduce algorithm first to remove trees and other obstructions and then to extract adjacent cubes.According to this approach, all adjacent cubes belong to the same object and an object is a set of adjacent cubes that belong to one or more adjacent buildings. Finally, an evaluation study is presented for a section of Dublin, Ireland to demonstrate the applicability of the approach resulting in a 92% quality level for the extraction of 106 buildings over 1 km 2 including buildings that had more than 10 adjacent components of different heights and complicated roof geometries. The proposed approach is notable not only for its Big Data context but its usage of vector data.

: This paper proposes an approach to classify, localize, and extract automatically urban objects such as buildings and the ground surface from a digital surface model created from aerial laser scanning data. To achieve that, the approach involves three steps: 1) dividing the original data into smaller, more manageable pieces using a method based on MapReduce gridding for subspace partitioning; 2) applying the DBSCAN algorithm to identify interesting subspaces depending on point density; and 3) grouping of identified subspace to form potential objects.Validation of the method was achieved using an architecturally dense and complex portion of Dublin, Ireland. The best results were achieved with a 1 m 3 sized clustering cube, for which the number of classified clusters equaled that which was derived manually and that amongst those there the following scores: correctness = 84.91%, completeness = 84.39%, and quality = 84.65%.

One of the most important types of applications currently being used to share knowledge across the Internet are social networks. In addition to their use in social, professional and organizational spheres, social networks are also frequently utilized by researchers in the social sciences, particularly in anthropology and social psychology. In order to obtain information related to a particular social network, analytical techniques are employed to represent the network as a graph, where each node is a distinct member of the network and each edge is a particular type of relationship between members including, for example, kinship or friendship. This article presents a proposal for the efficient solution to one of the most frequently requested services on social networks; namely, taking different types of relationships into account in order to locate a particular member of the network. The solution is based on a biologically-inspired modification of the ant colony optimization algorithm.

Dendrimer peptides are promising vaccine candidates against the foot-and-mouth disease virus (FMDV). Several B-cell epitope (B2T) dendrimers, harboring a major FMDV antigenic B-cell site in VP1 protein, are covalently linked to heterotypic T-cell epitopes from 3A and/or 3D proteins, and elicited consistent levels of neutralizing antibodies and IFN-γ-producing cells in pigs. To address the contribution of the highly polymorphic nature of the porcine MHC (SLA, swine leukocyte antigen) on the immunogenicity of B2T dendrimers, low-resolution (Lr) haplotyping was performed. We looked for possible correlations between particular Lr haplotypes with neutralizing antibody and T-cell responses induced by B2T peptides. In this study, 63 pigs immunized with B2T dendrimers and 10 non-immunized (control) animals are analyzed. The results reveal a robust significant correlation between SLA class-II Lr haplotypes and the T-cell response. Similar correlations of T-cell response with SLA class-I Lr haplotypes, and between B-cell antibody response and SLA class-I and SLA class-II Lr haplotypes, were only found when the sample was reduced to animals with Lr haplotypes represented more than once. These results support the contribution of SLA class-II restricted T-cells to the magnitude of the T-cell response and to the antibody response evoked by the B2T dendrimers, being of potential value for peptide vaccine design against FMDV.

Database Design discipline involves so different aspects as conceptual and logical modelling knowledge or domain understanding. That implies a great effort to carry out the real world abstraction task and represent it through a data model. CASE tools emerge in order to automating the database development process. These platforms try to help to the database designer in different database design phases. Nevertheless, this tools are frequently mere diagrammers and do not carry completely out the design methodology that they are supposed to support; furthermore, they do not offer intelligent methodological advice to novice designers. This paper introduces the PANDORA tool (acronym of Platform for Database Development and Learning via Internet) that is being developed in a research project which tries to mitigate some of the deficiencies observed in several CASE tools, defining methods and techniques for database development which are useful for students and practitioners. Specifically, this work is focused on two PANDORA components: Conceptual Modelling and Learning Support subsystems.