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Network management, whether for malfunction analysis, failure prediction, performance monitoring and improvement, generally involves large amounts of data from different sources. To effectively integrate and manage these sources, automatically finding semantic matches among their schemas or ontologies is crucial. Existing approaches on database matching mainly fall into two categories. One focuses on the schema-level matching based on schema properties such as field names, data types, constraints and schema structures. Network management databases contain massive tables (e.g., network products, incidents, security alert and logs) from different departments and groups with nonuniform field names and schema characteristics. It is not reliable to match them by those schema properties. The other category is based on the instance-level matching using general string similarity techniques, which are not applicable for the matching of large network management databases. In this paper, we develop a matching technique for large NEtwork MAnagement databases (NEMA) deploying instance-level matching for effective data integration and connection. We design matching metrics and scores for both numerical and non-numerical fields and propose algorithms for matching these fields. The effectiveness and efficiency of NEMA are evaluated by conducting experiments based on ground truth field pairs in large network management databases. Our measurement on large databases with 1,458 fields, each of which contains over 10 million records, reveals that the accuracies of NEMA are up to 95%. It achieves 2%-10% higher accuracy and 5x-14x speedup over baseline methods., Comment: 14 pages, 13 Figures, 7 tables

Head-Related Transfer Function (HRTF) is the most important factor to achieve Auditory Space Modeling(ASM). HRTF has many applications in the areas of room acoustic modeling, spatial hearing and multimedia. For the reason that HRTF is related to the location and frequency of sound source and the physiological structures of the listener, customization of HRTF becomes the bottle-neck problem of the research and application of ASM. This paper, based on the measured data offers the personalization method of the HRTF based on the multi-dimensional physiological parameters, and the method is verified through the spectral distortion and the subjective auditory localization experiments. The results show that database matching can obtain the personalized HRTF efficiently and in the spatial hearing experiment the personalized HRTF can reduce the front-back confusion of auditory localization and improve the accuracy.

The Automated Self-Administered 24-Hour Dietary Assessment Tool (ASA24) is a free dietary recall system that outputs fewer nutrients than the Nutrition Data System for Research (NDSR). NDSR uses the Nutrition Coordinating Center (NCC) Food and Nutrient Database, both of which require a license. Manual lookup of ASA24 foods into NDSR is time-consuming but currently the only way to acquire NCC-exclusive nutrients. Using lactose as an example, we evaluated machine learning and database matching methods to estimate this NCC-exclusive nutrient from ASA24 reports. ASA24-reported foods were manually looked up into NDSR to obtain lactose estimates and split into training (n = 378) and test (n = 189) datasets. Nine machine learning models were developed to predict lactose from the nutrients common between ASA24 and the NCC database. Database matching algorithms were developed to match NCC foods to an ASA24 food using only nutrients (&ldquo, Nutrient-Only&rdquo, ) or the nutrient and food descriptions (&ldquo, Nutrient + Text&rdquo, ). For both methods, the lactose values were compared to the manual curation. Among machine learning models, the XGB-Regressor model performed best on held-out test data (R2 = 0.33). For the database matching method, Nutrient + Text matching yielded the best lactose estimates (R2 = 0.76), a vast improvement over the status quo of no estimate. These results suggest that computational methods can successfully estimate an NCC-exclusive nutrient for foods reported in ASA24.

Tactile sensors, because of their intrinsic insensitivity to lighting conditions and water turbidity, provide promising opportunities for augmenting the capabilities of vision sensors in applications involving object recognition and localization. This paper presents two approaches for haptic object recognition and localization for ground and underwater environments. The first approach called Batch Ransac and Iterative Closest Point augmented Particle Filter (BRICPPF) is based on an innovative combination of particle filters, Iterative-Closest-Point algorithm, and a feature-based Random Sampling and Consensus (RANSAC) algorithm for database matching. It can handle a large database of 3D-objects of complex shapes and performs a complete six-degree-of-freedom localization of static objects. The algorithms are validated by experimentation in ground and underwater environments using real hardware. To our knowledge this is the first instance of haptic object recognition and localization in underwater environments. The second approach is biologically inspired, and provides a close integration between exploration and recognition. An edge following exploration strategy is developed that receives feedback from the current state of recognition. A recognition by parts approach is developed which uses the BRICPPF for object sub-part recognition. Object exploration is either directed to explore a part until it is successfully recognized, or is directed towards new parts to endorse the current recognition belief. This approach is validated by simulation experiments.

The administrative information about top officials and civil servants’ activities in Spain is still kept under important restrictions. Such limitation is particularly severe on personal data of any type related to these politicians, in accordance with the Spanish preference for protection to privacy over the right to public interest information. Even so, since 2009 some regulations openness has taken place in relation to Government and Parliament members’ declarations about their assets, their personal economic interests and their business ties. The new regulation supposes a tiny advance towards a democratic transparency of general interest administrative documentation. Through the access to the specific data bases, precision journalism –a newsmedia strategy specialized on computer cross-checking and matching of big data collections-, contributes to a real democratic transparency by means of the disclosure of unveil irregularities, institutional unbalances or just basic data about public servants’ activities which can improve the control of the public institutions responsiveness by the citizens. Across an accurate data petition and the access to the specific datafiles recently unlocked to the public scrutiny in Spain, this article shows the results of an application of precision journalism to the case of the main directors of the executive power regarding their personal assets, complementary incomes and business connections. Nevertheless, the poor data specification showed in the disclosure records of the Spanish Administration seriously restricts the goal of a full watchdogging of the political representatives. La información administrativa sobre la actividad de altos cargos y funcionarios continúa en España bajo importantes restricciones en cuanto pueda afectar a datos personales de cualquier índole, conforme a la prevalente protección en nuestro país de la privacidad sobre el derecho a la información de interés público. Aun así, desde el año 2009 se ha producido alguna apertura reglamentaria en materia de declaración de patrimonio e intereses socioeconómicos de miembros del Gobierno y diputados, lo que constituye un tímido avance hacia la transparencia democrática de la documentación administrativa de interés general. Mediante el acceso a las correspondientes bases de datos, el periodismo de precisión como especialidad dedicada al cruce o contraste sistemático de grandes conjuntos de datos objetivados sobre asuntos de interés público, obtiene así una oportunidad de contribuir a la transparencia democrática en cuestiones que pudieran afectar al desvelamiento de irregularidades, desequilibrios institucionales o simple conocimiento ciudadano de los datos contrastados de la actividad de los servidores públicos. A través de una petición concreta de datos y de acceso a los correspondientes ficheros de reciente liberación, este artículo muestra los resultados de una aplicación del periodismo de precisión al conocimiento de la situación económica e intereses de los principales miembros del poder Ejecutivo, al tiempo que pone de manifiesto la debilidad e insuficiencia de alcance que las citadas modificaciones legales suponen aún para una fiscalización plena de la responsabilidad de los representantes políticos.

La información administrativa sobre la actividad de altos cargos y funcionarios continúa en España bajo importantes restricciones en cuanto pueda afectar a datos personales de cualquier índole, conforme a la prevalente protección en nuestro país de la privacidad sobre el derecho a la información de interés público. Aun así, desde el año 2009 se ha producido alguna apertura reglamentaria en materia de declaración de patrimonio e intereses socioeconómicos de miembros del Gobierno y diputados, lo que constituye un tímido avance hacia la transparencia democrática de la documentación administrativa de interés general. Mediante el acceso a las correspondientes bases de datos, el periodismo de precisión como especialidad dedicada al cruce o contraste sistemático de grandes conjuntos de datos objetivados sobre asuntos de interés público, obtiene así una oportunidad de contribuir a la transparencia democrática en cuestiones que pudieran afectar al desvelamiento de irregularidades, desequilibrios institucionales o simple conocimiento ciudadano de los datos contrastados de la actividad de los servidores públicos. A través de una petición concreta de datos y de acceso a los correspondientes ficheros de reciente liberación, este artículo muestra los resultados de una aplicación del periodismo de precisión al conocimiento de la situación económica e intereses de los principales miembros del poder Ejecutivo, al tiempo que pone de manifiesto la debilidad e insuficiencia de alcance que las citadas modificaciones legales suponen aún para una fiscalización plena de la responsabilidad de los representantes políticos. The administrative information about top officials and civil servants’ activities in Spain is still kept under important restrictions. Such limitation is particularly severe on personal data of any type related to these politicians, in accordance with the Spanish preference for protection to privacy over the right to public interest information. Even so, since 2009 some regulations openness has taken place in relation to Government and Parliament members’ declarations about their assets, their personal economic interests and their business ties. The new regulation supposes a tiny advance towards a democratic transparency of general interest administrative documentation. Through the access to the specific data bases, precision journalism –a newsmedia strategy specialized on computer cross-checking and matching of big data collections-, contributes to a real democratic transparency by means of the disclosure of unveil irregularities, institutional unbalances or just basic data about public servants’ activities which can improve the control of the public institutions responsiveness by the citizens. Across an accurate data petition and the access to the specific datafiles recently unlocked to the public scrutiny in Spain, this article shows the results of an application of precision journalism to the case of the main directors of the executive power regarding their personal assets, complementary incomes and business connections. Nevertheless, the poor data specification showed in the disclosure records of the Spanish Administration seriously restricts the goal of a full watchdogging of the political representatives.

International audience; This paper participates in the challenging data science opportunity offered by the growing number of databases made available to public institutions. It presents an innovative method to match household-scale databases using address information. The developed algorithm authorizes different matching qualities, depending on the reliability of the link between the paired elements. This work was carried out in collaboration with the French DSO Enedis, which provided valuable customer information that was matched with a national database describing dwellings. The matching algorithm performances are analyzed, and adjustments are proposed to improve the matching quality in urban, suburban and rural contexts. Lastly, two basic characterization analyses were made to highlight the potential of these consolidated databases.

International audience; This paper presents a method that permits to match customer information from the French DSO Enedis and housing information from the French population census institute INSEE. Our method allows having a list of housings linked to each customer in order to add household and building information to customers. We show with our method improvements in predictions of aggregated load curve indicators compared to the traditional method that averages socio demographic indicators from housing information of the zone covered by measurements. Our results indicate that the proposed algorithm is able to capture efficiently the information of housings in some feeders. This permits to combine the databases of the DSO with external databases that exist from census or other processes. Enriching the information at the level of clients through the proposed automated way is a cost effective approach given the number of customers served by a DSO. This enhanced information can be then the basis to model, analyse and simulate demand in a bottom up approach which can be useful for planning purposes of the distribution networks.