The world's largest oil and gas hydrocarbon deposits: ROSA database and GIS project development

This article proposes the use of Big Data principles to support the future extraction of hydrocarbon resources. It starts out by assessing the possible energy-system transformations in order to shed some light on the future need for hydrocarbon resource extraction and corresponding drilling needs. The core contribution of this work is the development of a new database and the corresponding GIS (geographic information system) visualization project as basis for an analytical study of worldwide hydrocarbon occurrences and development of extraction methods. The historical period for the analytical study is from 1900 to 2000. A number of tasks had to be implemented to develop the database and include information about data collection, processing, and development of geospatial data on hydrocarbon deposits. Collecting relevant information made it possible to compile a list of hydrocarbon fields, which have served as the basis for the attribute database tables and its further filling. To develop an attribute table, the authors took into account that all accumulated data features on hydrocarbon deposits and divided them into two types: static and dynamic. Static data included the deposit parameters that do not change over time. On the other hand, dynamic data are constantly changing. Creation of a web service with advanced functionality based on the Esri Geoportal Server software platform included search by parameter presets, viewing and filtering of selected data layers using online mapping application, sorting of metadata, corresponding bibliographic information for each field and keywords accordingly. The collected and processed information by ROSA database and GIS visualization project includes more than 100 hydrocarbon fields across different countries

Investigation of Optical Spectroscopy Techniques for On-Line Materials Accountability in the Solvent Extraction Process

The goal of this project is to evaluate the application of these analytical techniques to the on-line, real-time measurement of the actinide elements in the process streams of a solvent extraction process, with particular attention to the UREX+ and PUREX processes. Based on the experience gained through this effort, engineers will have the information necessary to decide if these technologies should be advanced to the prototype stage and tested at the pilot plant level. Through the experimental work planned as part of this effort, researchers will also develop a better understanding of the chemical interactions of the actinide elements, providing additional data for the development of first-principles based models of the solvent extraction process. The information gathered through these experiments will also add to the database on the UREX+ solvent extraction process, particularly in the off-normal operating regimes

An Answer Explanation Model for Probabilistic Database Queries

Following the availability of huge amounts of uncertain data, coming from diverse ranges of applications such as sensors, machine learning or mining approaches, information extraction and integration, etc. in recent years, we have seen a revival of interests in probabilistic databases. Queries over these databases result in probabilistic answers. As the process of arriving at these answers is based on the underlying stored uncertain data, we argue that from the standpoint of an end user, it is helpful for such a system to give an explanation on how it arrives at an answer and on which uncertainty assumptions the derived answer is based. In this way, the user with his/her own knowledge can decide how much confidence to place in this probabilistic answer. \ud The aim of this paper is to design such an answer explanation model for probabilistic database queries. We report our design principles and show the methods to compute the answer explanations. One of the main contributions of our model is that it fills the gap between giving only the answer probability, and giving the full derivation. Furthermore, we show how to balance verifiability and influence of explanation components through the concept of verifiable views. The behavior of the model and its computational efficiency are demonstrated through an extensive performance study

Handling uncertainty in information extraction

This position paper proposes an interactive approach for developing information extractors based on the ontology definition process with knowledge about possible (in)correctness of annotations. We discuss the problem of managing and manipulating probabilistic dependencies

A unified view of data-intensive flows in business intelligence systems : a survey

Data-intensive flows are central processes in today’s business intelligence (BI) systems, deploying different technologies to deliver data, from a multitude of data sources, in user-preferred and analysis-ready formats. To meet complex requirements of next generation BI systems, we often need an effective combination of the traditionally batched extract-transform-load (ETL) processes that populate a data warehouse (DW) from integrated data sources, and more real-time and operational data flows that integrate source data at runtime. Both academia and industry thus must have a clear understanding of the foundations of data-intensive flows and the challenges of moving towards next generation BI environments. In this paper we present a survey of today’s research on data-intensive flows and the related fundamental fields of database theory. The study is based on a proposed set of dimensions describing the important challenges of data-intensive flows in the next generation BI setting. As a result of this survey, we envision an architecture of a system for managing the lifecycle of data-intensive flows. The results further provide a comprehensive understanding of data-intensive flows, recognizing challenges that still are to be addressed, and how the current solutions can be applied for addressing these challenges.Peer ReviewedPostprint (author's final draft

Template Mining for Information Extraction from Digital Documents

published or submitted for publicatio

Safe to Be Open: Study on the Protection of Research Data and Recommendations for Access and Usage

Openness has become a common concept in a growing number of scientific and academic fields. Expressions such as Open Access (OA) or Open Content (OC) are often employed for publications of papers and research results, or are contained as conditions in tenders issued by a number of funding agencies. More recently the concept of Open Data (OD) is of growing interest in some fields, particularly those that produce large amounts of data – which are not usually protected by standard legal tools such as copyright. However, a thorough understanding of the meaning of Openness – especially its legal implications – is usually lacking. Open Access, Public Access, Open Content, Open Data, Public Domain. All these terms are often employed to indicate that a given paper, repository or database does not fall under the traditional “closed” scheme of default copyright rules. However, the differences between all these terms are often largely ignored or misrepresented, especially when the scientist in question is not familiar with the law generally and copyright in particular – a very common situation in all scientific fields. On 17 July 2012 the European Commission published its Communication to the European Parliament and the Council entitled “Towards better access to scientific information: Boosting the benefits of public investments in research”. As the Commission observes, “discussions of the scientific dissemination system have traditionally focused on access to scientific publications – journals and monographs. However, it is becoming increasingly important to improve access to research data (experimental results, observations and computer-generated information), which forms the basis for the quantitative analysis underpinning many scientific publications”. The Commission believes that through more complete and wider access to scientific publications and data, the pace of innovation will accelerate and researchers will collaborate so that duplication of efforts will be avoided. Moreover, open research data will allow other researchers to build on previous research results, as it will allow involvement of citizens and society in the scientific process. In the Communication the Commission makes explicit reference to open access models of publications and dissemination of research results, and the reference is not only to access and use but most significantly to reuse of publications as well as research data. The Communication marks an official new step on the road to open access to publicly funded research results in science and the humanities in Europe. Scientific publications are no longer the only elements of its open access policy: research data upon which publications are based should now also be made available to the public. As noble as the open access goal is, however, the expansion of the open access policy to publicly funded research data raises a number of legal and policy issues that are often distinct from those concerning the publication of scientific articles and monographs. Since open access to research data – rather than publications – is a relatively new policy objective, less attention has been paid to the specific features of research data. An analysis of the legal status of such data, and on how to make it available under the correct licence terms, is therefore the subject of the following sections