Abstract. Many scientific experiments deal with data-intensive applications and the orchestration of computational workflow activities. These can benefit from data parallelism exploited in parallel systems to minimize execution time. Due to its complexity, robustness and efficiency to exploit data parallelism, grid infrastructures are widely used in some e-Science areas like bioinformatics. Workflow techniques are very important to in-silico bioinformatics experiments, allowing the e-scientist to describe and enact experimental process in a structured, repeatable and verifiable way. The main purpose of this paper is to describe our experience with Tavena Workbench and PeDRo, which are part of my Grid project. Taverna is provided with a workflow toolset and enactor, allowing the specification of processing units, data transfer and execution constraints. As a data entry tool, PeDRo provides a model, a controlled vocabulary and field validations for Web Services descriptions, leveraging the knowledge associated to the workflows. The main contribution of this work is a summary of some considerations drawn by our experience with the use of these tools, emphasizing its advantages and negative aspects, together with proposals for some future improvements.
Abstract. Cost parameters and database statistics are the basis of query optimization techniques. However, in distributed and heterogeneous database systems, acquiring and treating information in order to help the optimization process are often tasks of a global query processor, which adapts its functionalities to a specific system architecture. Moreover, this acquisition process involves a large number of parameters and requires customized methods to retrieve data from specific sources. DIG (Distributed Information Gatherer) is a provider of data statistics and query costs that, through an independent and flexible service, aims to support global query optimization processing in distributed and heterogeneous database systems over autonomous data sources. We have developed a DIG prototype and experimented it with specific wrappers for a query middleware on both semi-structured data sources and an object DBMS.
Environmental, Social, and Governance (ESG) factors are critical for investors and financing institutions like the Brazilian Development Bank (BNDES). Such institutions are currently working on setting up a framework to assess companies' ESG factors in their financing evaluation. In this study, we identify an opportunity to use Natural Language Processing (NLP) to improve the framework. This opportunity stems from the fact that the key documents for ESG analysis, such as the company's activity report (RAA), Environmental Impact Study (EIA), and Environmental Impact Report (RIMA), undergo manual screening and decomposition whilst being analyzed by specialists. By incorporating NLP, we aim to automate the classification of text passages from these reports and enhance the efficiency of the analysis process.
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