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"The new source of power is not money in the hands of a few, but information in the hands of many." The aforementioned quote from John Naisbitt seems to be even more relevant in the world of finance at this very moment. Many financial decisions come from watching the information stream, selecting relevant data, analyzing it and acting accordingly. With the increasing global competition, the need for swift data analysis, high accuracy and quality becomes a must. XBRL (Extensible Business Reporting Language)XBRL: http://www.xbrl.org/ standard was proposed to improve efficiency of data exchange in the financial domain. However; it is still struggling with interoperability problems, not to mention comparability of data or multisource data integration. This paper presents the FLORA intelligent platform: an approach for dealing with current financial information shortcomings and achieving more effective way of processing financial data based on the Linked Data principles. The article also explains the process of data extraction and semantic modeling which are the cornerstones of efficient financial data analysis. As a result, the FLORA architecture facilitates effective, data-driven, financial analyses and Web-scale integration between financial applications and platforms.

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Nowadays, solar radiation information is provided from sensors installed in different geographic locations and platforms of meteorological agencies. However, common formats such as PDF files and HTML documents to provide solar radiation information do not offer semantics in their content, and they may pose problems to integrate and fuse data from multiple resources. One of the challenges of sensors Web is the unification of data from multiple sources, although this type of information facilitates interoperability with other sensor Web systems. This research proposes architecture SREQP (Solar Radiation Extraction and Query Platform) to extract solar radiation data from multiple external sources and merge them on a single and unique platform. SREQP makes use of Linked Data to generate a set of triples containing information about extracted data, which allows final users to query data through a SPARQL endpoint. The conceptual model was developed by using known vocabularies, such as SSN or WGS84. Moreover, an Analytic Hierarchy Process was carried out for the evaluation of SREQP in order to identify and evaluate the main features of Linked-Sensor-Data and the sensor Web systems. Results from the evaluation indicated that SREQP contained most of the features considered essential in Linked-Sensor-Data and sensor Web systems.

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