Towards a model-driven engineering approach of data mining

Nowadays, data mining is based on low-level specications of the employed techniques typically bounded to a specic analysis platform. Therefore, data mining lacks a modelling architecture that allows analysts to consider it as a truly software-engineering process. Here, we propose a model-driven approach based on (i) a conceptual modelling framework for data mining, and (ii) a set of model transformations to automatically generate both the data under analysis (via data-warehousing technology) and the analysis models for data mining (tailored to a specic platform). Thus, analysts can concentrate on the analysis problem via conceptual data-mining models instead of low-level programming tasks related to the underlying-platform technical details. These tasks are now entrusted to the model-transformations scaffolding.

Integrating the development of data mining and data warehouses via model-driven engineering

Data mining is one of the most important analysis techniques to automatically extract knowledge from large amount of data. Nowadays, data mining is based on low-level specifications of the employed techniques typically bounded to a specific analysis platform. Therefore, data mining lacks a modelling architecture that allows analysts to consider it as a truly software-engineering process. Bearing in mind this situation, we propose a model-driven approach which is based on (i) a conceptual modelling framework for data mining, and (ii) a set of model transformations to automatically generate both the data under analysis (that is deployed via data-warehousing technology) and the analysis models for data mining (tailored to a specific platform). Thus, analysts can concentrate on understanding the analysis problem via conceptual data-mining models instead of wasting efforts on low-level programming tasks related to the underlying-platform technical details. These time consuming tasks are now entrusted to the model-transformations scaffolding. The feasibility of our approach is shown by means of a hypothetical data-mining scenario where a time series analysis is required.

A data driven study of relationships between relief and farmland abandonment in a Mediterranean region

The environmental, cultural and socio-economic causes and consequences of farmland abandonment are issues of increasing concern for researchers and policy makers. In previous studies, we proposed a new methodology for selecting the driving factors in farmland abandonment processes. Using Data Mining and GIS, it is possible to select those variables which are more significantly related to abandonment. The aim of this study is to investigate the application of the above mentioned methodology for finding relationships between relief and farmland abandonment in a Mediterranean region (SE Spain).We have taken into account up to 28 different variables in a single analysis, some of them commonly considered in land use change studies (slope, altitude, TWI, etc), but also other novel variables have been evaluated (sky view factor, terrain view factor, etc). The variable selection process provides results in line with the previous knowledge of the study area, describing some processes that are region specific (e.g. abandonment versus intensification of the agricultural activities). The European INSPIRE Directive (2007/2/EC) establishes that the digital elevation models for land surfaces should be available in all member countries, this means that the research described in this work can be extrapolated to any European country to determine whether these variables (slope, altitude, etc) are important in the process of abandonment.

3D Visual Data-Driven Spatiotemporal Deformations for Non-Rigid Object Grasping Using Robot Hands

Sensing techniques are important for solving problems of uncertainty inherent to intelligent grasping tasks. The main goal here is to present a visual sensing system based on range imaging technology for robot manipulation of non-rigid objects. Our proposal provides a suitable visual perception system of complex grasping tasks to support a robot controller when other sensor systems, such as tactile and force, are not able to obtain useful data relevant to the grasping manipulation task. In particular, a new visual approach based on RGBD data was implemented to help a robot controller carry out intelligent manipulation tasks with flexible objects. The proposed method supervises the interaction between the grasped object and the robot hand in order to avoid poor contact between the fingertips and an object when there is neither force nor pressure data. This new approach is also used to measure changes to the shape of an object’s surfaces and so allows us to find deformations caused by inappropriate pressure being applied by the hand’s fingers. Test was carried out for grasping tasks involving several flexible household objects with a multi-fingered robot hand working in real time. Our approach generates pulses from the deformation detection method and sends an event message to the robot controller when surface deformation is detected. In comparison with other methods, the obtained results reveal that our visual pipeline does not use deformations models of objects and materials, as well as the approach works well both planar and 3D household objects in real time. In addition, our method does not depend on the pose of the robot hand because the location of the reference system is computed from a recognition process of a pattern located place at the robot forearm. The presented experiments demonstrate that the proposed method accomplishes a good monitoring of grasping task with several objects and different grasping configurations in indoor environments.

Motorcycle accident cause factors and identification of countermeasures. Volume II: appendix/supplemental data. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

Status report of accident investigation data, motorcycle accident cause factors and identification of countermeasures. Preliminary report.

National Highway Traffic Safety Administration, Washington, D.C.

A model for the identification of high risk occupational groups using RTECS and NOHS data /

Accompanying microfiches (issued in pocket inside back cover) have title: NIOSH risk identification model.

Targeted watershed approach : a data driven prioritization /

"Protecting, enhancing, and restoring Illinois water resources utilizing a watershed approach."--Cover.

Identification of dislocated workers utilizing unemployment insurance administrative data : results from a five state analysis /

"April 1984."

The preparation, properties, chemical behavior and identification of organic chlorine compounds; tables of data on selected compounds of order III.

"Memorandum of certain 1946-1947 references not incorporated in regular text": p. xvii-xxv.

Data-driven fuzzy analysis in quantitative mineral resource assessment

The integration of geo-information from multiple sources and of diverse nature in developing mineral favourability indexes (MFIs) is a well-known problem in mineral exploration and mineral resource assessment. Fuzzy set theory provides a convenient framework to combine and analyse qualitative and quantitative data independently of their source or characteristics. A novel, data-driven formulation for calculating MFIs based on fuzzy analysis is developed in this paper. Different geo-variables are considered fuzzy sets and their appropriate membership functions are defined and modelled. A new weighted average-type aggregation operator is then introduced to generate a new fuzzy set representing mineral favourability. The membership grades of the new fuzzy set are considered as the MFI. The weights for the aggregation operation combine the individual membership functions of the geo-variables, and are derived using information from training areas and L, regression. The technique is demonstrated in a case study of skarn tin deposits and is used to integrate geological, geochemical and magnetic data. The study area covers a total of 22.5 km(2) and is divided into 349 cells, which include nine control cells. Nine geo-variables are considered in this study. Depending on the nature of the various geo-variables, four different types of membership functions are used to model the fuzzy membership of the geo-variables involved. (C) 2002 Elsevier Science Ltd. All rights reserved.

Data driven ontology evaluation

The evaluation of ontologies is vital for the growth of the Semantic Web. We consider a number of problems in evaluating a knowledge artifact like an ontology. We propose in this paper that one approach to ontology evaluation should be corpus or data driven. A corpus is the most accessible form of knowledge and its use allows a measure to be derived of the ‘fit’ between an ontology and a domain of knowledge. We consider a number of methods for measuring this ‘fit’ and propose a measure to evaluate structural fit, and a probabilistic approach to identifying the best ontology.

Learning and teaching through context: a data-driven approach

A word may have many potential meanings, but its actual meaning in any authentic written or spoken text is determined by its context: its collocations, structural patterns, and pragmatic functions. Large language corpora offer access to words in a wide range of natural contexts, which can improve and enrich both language learning and teaching.