Fabiana Soares Santana

A reference business process for ecological niche modelling

Ecological niche modelling combines species occurrence points with environmental raster layers in order to obtain models for describing the probabilistic distribution of species. The process to generate an ecological niche model is complex. It requires dealing with a large amount of data, use of different software packages for data conversion, for model generation and for different types of processing and analyses, among other functionalities. A software platform that integrates all requirements under a single and seamless interface would be very helpful for users. Furthermore, since biodiversity modelling is constantly evolving, new requirements are constantly being added in terms of functions, algorithms and data formats. This evolution must be accompanied by any software intended to be used in this area. In this scenario, a Service-Oriented Architecture (SOA) is an appropriate choice for designing such systems. According to SOA best practices and methodologies, the design of a reference business process must be performed prior to the architecture definition. The purpose is to understand the complexities of the process (business process in this context refers to the ecological niche modelling problem) and to design an architecture able to offer a comprehensive solution, called a reference architecture, that can be further detailed when implementing specific systems. This paper presents a reference business process for ecological niche modelling, as part of a major work focused on the definition of a reference architecture based on SOA concepts that will be used to evolve the openModeller software package for species modelling. The basic steps that are performed while developing a model are described, highlighting important aspects, based on the knowledge of modelling experts. In order to illustrate the steps defined for the process, an experiment was developed, modelling the distribution of Ouratea spectabilis (Mart.) Engl. (Ochnaceae) using openModeller. As a consequence of the knowledge gained with this work, many desirable improvements on the modelling software packages have been identified and are presented. Also, a discussion on the potential for large-scale experimentation in ecological niche modelling is provided, highlighting opportunities for research. The results obtained are very important for those involved in the development of modelling tools and systems, for requirement analysis and to provide insight on new features and trends for this category of systems. They can also be very helpful for beginners in modelling research, who can use the process and the experiment example as a guide to this complex activity.

A reference process for automating bee species identification based on wing images and digital image processing

Pollinators play a key role in biodiversity conservation, since they provide vital services to both natural ecosystems and agriculture. In particular, bees are excellent pollinators; therefore, their mapping, classification, and preservation help to promote biodiversity conservation. However, these tasks are difficult and time consuming since there is a lack of classification keys, sampling efforts and trained taxonomists. The development of tools for automating and assisting the identification of bee species represents an important contribution to biodiversity conservation. Several studies have shown that features extracted from patterns of bee wings are good discriminatory elements to differentiate among species, and some have devoted efforts to automate this process. However, the automated identification of bee species is a particularly hard problem, because (i) individuals of a given species may vary hugely in morphology, and (ii) closely related species may be extremely similar to one another. This paper proposes a reference process for bee classification based on wing images to provide a complete understanding of the problem from the experts point of view, and a foundation to software systems development and integration using Internet services. The results can be extended to other species identification and taxonomic classification, as long as similar criteria are applicable. The reference process may also be helpful for beginners in this research field, as they can use the process and the experiments presented here as a guide to this complex activity.

A Reference Process to Design Information Systems for Sustainable Design Based on LCA, PSS, Social and Economic Aspects

The purpose of Sustainable Design, SD, is to satisfy customer needs while reducing environmental impacts. The main challenge is to integrate Life Cycle Assessment, Product Service Systems, social and economic aspects while considering the tensions and trade-offs of each activity in depth. SD requires data from many sources in addition to many software tools to perform each analysis. In order to provide information systems for SD, the adoption of a Service-Oriented Architecture, SOA, is appropriate because of its integration requirements. SOA best practices recommend the design of a reference process prior to architectural definitions, so as to identify the complexities and provide a comprehensive solution to the problem. A reference process is presented here as the first step for building information systems for SD. In addition, the reference process presents a list of activities to be performed during the design stage and is very helpful as a guide for SD beginners.

Evaluation of Adaptive Genetic Algorithm to Environmental Modeling of Peponapis and Cucurbita

The environmental modeling could be performed combining environmental data and species occurrence points in order to create distribution models, and this requires non trivial algorithms. The objective of this work is comparing GARP, one of the most used algorithms, with ADAPTGARP, that implements adaptive tools. The experiments considered some species of Peponapis and Cucurbita, respectively, pollinator insects and their plants. The maps with potential species distribution resulting from comparative experiments are very similar. The variations of AUC values, an accepted measure of accuracy on modeling, are also within the tolerance limits. The experiment shows that the algorithms are equivalent and the adaptive tools are adequate. Future works could be developed using adaptivity, such as including generic characteristics on the genetic algorithm and new types of treatment, looking for more accurate models.

Species Distribution Modeling with Scalability: The Case Study of P-GARP, a Parallel Genetic Algorithm for Rule-Set Production

Species distribution modeling (SDM) calculates a species’ probabilistic distribution by combining Environmental raster layers with species datasets. Such models can help to answer complex questions in Ecology/Biology/Health, e.g., by calculating impacts of climate changes in Biodiversity, or the potential for a disease spread (vectors’ modeling). Machine learning is largely applied in SDM, being the Genetic Algorithm for Rule-set Production (GARP) one of the most reliable solutions. However, GARP’s convergence needs to speedup under certain conditions (high resolution or number of layers), for which this paper proposes P-GARP, a parallel, scalable implementation of GARP. P-GARP was implemented onto a SGI Altix XE 1300 cluster with 2 quad-core processors/node. Preliminary results show an expressive 3.2/node speedup. Premature convergence is not observed in PGARP and its accuracy is very similar to GARP´s. Effective solutions to improve this speedup in even larger scale are proposed, along with a discussion about P-GARP correctness and efficiency.

FCFilter: Feature selection based on clustering and genetic algorithms

The search for patterns in big amounts of textual data, or text mining, can be at once rewarding and challenging. The patterns can reveal tendencies, similarities and predictions, but the information is usually implicit and difficult to be validated. Classification is one of the most relevant research areas in text mining, and it usually consists of predicting the class of a textual document based on a set of documents previously organized into different classes, such as author or topic. Choosing the words to compose the feature set is crucial to a proper classification. A well selected feature set can improve the performance of the classification method and enlighten the interpretation of the classification model adjusted to the data. This paper introduces the Feature Cluster Filter (FCFilter) method for feature selection. FCFilter eliminates the need to input or optimize the number of clusters by grouping the words in a sufficiently high number of clusters. Genetic algorithms are applied to optimize the combination of groups that will provide the final feature set. The method is based on the selection of features that are good predictors for text classification by clustering features and selecting only the suitable clusters. Experiments performed to evaluate the FCFilter with the Reuters-21578, SCY-Genes and SCY-Clusters datasets showed a significant reduction in the feature-value table dimensionality with slight improvements in the classification accuracy when compared to the baselines. The results are very promising, indicating potential improvements in the research on feature selection for text mining.

Evaluation of an ICP Based Algorithm for Simultaneous Localization and Mapping Using a 3D Simulated P3DX Robot

Autonomous mobile robots can be applied to perform activities that should not, or cannot, be performed by humans due to inhospitable conditions or high level of danger. An autonomous mobile robot must be able to navigate safely in unfamiliar environments by reconstructing information from its sensors so as to plan and execute routes. Simultaneous Localization And Mapping, SLAM, technique allows the gradual creation of a map using data obtained from sensors while estimating the robot localization, and the Iterative Closest Point, ICP, algorithm is one of the approaches adopted for SLAM. This work proposes and evaluates an ICP-based algorithm for simultaneous localization and mapping of a robot. The algorithm was implemented in a simulated environment using Microsoft Robotics Developer Studio, MRDS. Experimental results show that, in the evaluated trajectory, the method presented in this work has a better performance than the one obtained by the original ICP algorithm.

A complete RM-ODP case-study to integrate geospatial services and ecological niche modeling systems

Conservation and sustainable use of natural resources are relevant research areas for many different purposes, including biodiversity maintenance, global warming studies and sustainable development. An ecological niche model presents the geographic distribution of a species, considering the spatial, ecological and evolutionary perspectives. It also allows the definition of present, past and future scenarios for species distribution. The study of species distribution can be determinant for their management, so ecological niche modeling is a major current research trend. However, providing the resources for ecological niche modeling is a highly complex computational problem because it demands solutions with many integration, distribution and interoperability features. The Reference Model of Open Distributed Processing provided by the International Organization for Standardization, ISO RM-ODP, establishes five viewpoints to design a system: Enterprise, Computational, Information, Engineering, and Technology. Each viewpoint treats specifics constraints in order to describe a complex architectural solution, as the ones required for ecological niche modeling. The Open Geospatial Consortium, OGC, defined the OGC Reference Model, a guideline to provide the features for geospatial services, another major software requirement for ecological niche modeling. This work proposes an architectural solution for ecological niche modeling integrating the OGC and RM-ODP specifications in order to address all the constraints of the problem by the usage of viewpoints. The presented solution is already implemented and available on the Internet. Besides solving the problem itself, the solution is in the form of a complete RM-ODP case-study and may also be used as reference for further works in the ecological niche modeling research area.

Modelling the Effects of Genetic Changes in Tumour Progression

Tumours can be considered a set of cells that accumulate genetic and epigenetic alterations. According to the Multi-stage Hit theory, the transformation of a normal into a tumour cell involves a number of limiting events that occur in a number of discrete stages (driver mutations). However, not all mutations that occur in the cell are directly involved in the development of cancer and some probably do not contribute in any way (passenger mutations). Moreover, the process of tumour evolution is punctuated by selection of advantageous mutations and clonal expansions. Actually, it is not known how many limiting-events, i.e., how many driver mutations are necessary or sufficient to promote a carcinogenic process. This conjecture should be explored and tested - mathematically and statistically, with the availability of genomic data on databanks. In this work, we explore the model proposed by Bozic and collaborators (2010) that describes the evolution of the tumour according to a Galton-Watson process. Besides, the model gives the relation between the numbers of passenger mutations giving a specific number of driver mutations. We intend to explore some of the model parameters and test some premises about the number of drive mutations and selective advantage, comparing the simulation results with genomic data from colorectal cancer patients. The genomic data was obtained from the DBMutation (http://www.bioinformatics-brazil.org/dbmutation/), a comprehensive database for genomic mutations in cancer. We expect that correlations between driver mutations and the time evolution of tumour process will facilitate the interpretation of genomic information, to make them useful and applicable to clinical oncology.

SOC & SOA in Ecological Niche Modelling and Agribusiness: Discussion and Case Studies

Sustainable development demands the conservation of biodiversity while requires maintaining or increasing economic development. To study the biodiversity so as to obtain adequate approaches for sustainable development is a great challenge. There is much information supplied by several sources which must be treated and integrated before supporting any decision. The nature of the problem, however, requires systems with strong integration skills. Thus, biodiversity research communities are not completely integrated to each other and this lack must be treated by IT systems. SOC, Service-Oriented Computing, and their respective architectural paradigm, SOA, Service-Oriented Architecture, are recommended for such problems. This paper discusses the application of SOA & SOC in ecological niche modelling and precision agriculture systems. In order to clarify the discussion, case studies are presented for both problems, including a SOA-based solution and a SOC implementation for the specific process. This modelling technique is highly important for biodiversity studies because it allows mapping species distribution based on simple information, such as species occurrence points and environmental data. From the computing viewpoint, it requires many data from different providers, so case studies are very illustrative. Results presented here are important not only for biodiversity systems but they may be extended to agribusiness, as shown in the work. In addition, other systems with similar requirements may use this paper as a reference to develop their own solutions applying SOC & SOA concepts.