Regis Pires Magalhães

Graphast: an extensible framework for building applications on time-dependent networks

Graphast is a framework tool that allows developers to compose a number of network models, data importing/exporting services as well as query services, in order to quickly build applications on time-dependent networks. The main goal is to allow developers to implement solutions to different types of problems on time-dependent networks using spatial queries, such as nearest neighbor queries, optimal sequenced routes, etc. Graphast allows the combination of facilities provided by the framework via a public API and/or the building of new facilities, e.g., a new query processing algorithm, and incorporate those into Graphast for others to use them as well. In this paper, we discuss Graphasts architectural components and how one can create/store instances of those components in order to build an application. The steps necessary for building a real world application are also presented.

A semantic integration approach to publish and retrieve ecological data

Purpose – The purpose of this paper is to present a four-level architecture that aims at integrating, publishing and retrieving ecological data making use of linked data (LD). It allows scientists to explore taxonomical, spatial and temporal ecological information, access trophic chain relations between species and complement this information with other data sets published on the Web of data. The development of ecological information repositories is a crucial step to organize and catalog natural reserves. However, they present some challenges regarding their effectiveness to provide a shared and global view of biodiversity data, such as data heterogeneity, lack of metadata standardization and data interoperability. LD rose as an interesting technology to solve some of these challenges. Design/methodology/approach – Ecological data, which is produced and collected from different media resources, is stored in distinct relational databases and published as RDF triples, using a relational-Resource Description...

Towards an Efficient and Distributed DBSCAN Algorithm Using MapReduce

Clustering is a major data mining technique that groups a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups. Among several types of clustering, density-based clustering algorithms are more efficient in detecting clusters with varied density and different shapes. One of the most important density-based clustering algorithms is DBSCAN. Due to the huge size of generated data by the widespread diffusion of wireless technologies and the complexity of big data analysis, new scalable algorithms for efficiently processing such data are needed. In this chapter we are particularly interested in using traffic data for finding congested areas in a city. For this purpose, we developed a new distributed and efficient strategy of DBSCAN algorithm that uses MapReduce to detect dense areas based on the input parameters. We conducted experiments using real traffic data of a brazilian city, Fortaleza, and compared our approach with the centralized and the MapReduce-based approaches. Our preliminary results confirmed that our approach is scalable and more efficient than the other ones. We also present an incremental version of DBSCAN considering the MapReduce version of it.