Search results for "Data modeling"
showing 10 items of 112 documents
A heuristic for problem formalization in agent based simulation studies
2015
Agent Based Modeling and Simulation (ABMS) is considered an effective approach for conducting simulation studies in many fields. In order to develop high quality simulation models, methodological approaches are demanded. In such direction we are moving by proposing a heuristic for the formalization of agent based simulation problems. The proposed heuristic is based on some guidelines developed for identifying the main elements of the problem domain description by analysing verbs and their common taxonomy in grammar.
A Decentralized Ontology Versioning Model Designed for Inter-operability and Multi-organizational Data Exchange
2021
Current information systems operated in a multi-organization context use centralized or hierarchical data models proven inefficient as the network’s scale rapidly increases. The introduction of blockchain and decentralized specific mechanisms stimulate the use of ontology-based data models. While the adoption of blockchain as a communication medium facilitates the communication and data exchange in a more reliably and securely fashion, this opens new challenges regarding the evolution and versioning of the ontology. This paper proposes a versioning mechanism tailored to the decentralized environment. It provides the conceptual building blocks allowing an ontology to evolve under the supervi…
Physics-Aware Machine Learning For Geosciences And Remote Sensing
2021
Machine learning models alone are excellent approximators, but very often do not respect the most elementary laws of physics, like mass or energy conservation, so consistency and confidence are compromised. In this paper we describe the main challenges ahead in the field, and introduce several ways to live in the Physics and machine learning interplay: encoding differential equations from data, constraining data-driven models with physics-priors and dependence constraints, improving parameterizations, emulating physical models, and blending data-driven and process-based models. This is a collective long-term AI agenda towards developing and applying algorithms capable of discovering knowled…
<title>Spectral/spatial integration effects on information extraction from multispectral data: multiresolution approaches</title>
1995
New techniques for information extraction from multispectral data require physical modeling to understand the energy transfer at the atmosphere/surface interface and to develop appropriate inversion procedures, in combination with advanced processing techniques. A multi-step procedure is proposed in this work: the first step implies a binary decision about the second step to be applied in each case. If the pixel is considered as being a `pure' pixel, through a spectral/spatial classification procedure based on multiresolution techniques, then numerical inversion techniques, based on a multiple-scattering reflectance model, are used to extract parameters representing specific surface propert…
Modeling recurrent distributions in streams using possible worlds
2015
Discovering changes in the data distribution of streams and discovering recurrent data distributions are challenging problems in data mining and machine learning. Both have received a lot of attention in the context of classification. With the ever increasing growth of data, however, there is a high demand of compact and universal representations of data streams that enable the user to analyze current as well as historic data without having access to the raw data. To make a first step towards this direction, we propose a condensed representation that captures the various — possibly recurrent — data distributions of the stream by extending the notion of possible worlds. The representation en…
Data Modelling for Dynamic Monitoring of Vital Signs: Challenges and Perspectives
2017
The use-case described in this paper covers data acquisition and real-time analysis of the gathered medical data from wearable sensor system. Accumulated data is essential for monitoring vital signs and tracking the dynamics of the treatment process of disabled patients or patients undergoing the recovery after traumatic knee joint injury (e.g. post-operative rehabilitation). The main goal of employing the wearable sensor system is to conduct rehabilitation process more effectively and increase the rate of successful rehabilitation. The results of data analysis of patient’s vital signs and feedback allow a physiotherapist to adjust the rehabilitation scenario on the fly. In this paper, we f…
XML Integration and Toolkit for B2B Applications
2003
This paper presents a Web-based data integration methodology and tool framework, called X-TIME, for the development of business-to-business (B2B) design environments and applications. X-TIME provides a data model translator toolkit based on an extensible metamodel and XML. It allows the creation of adaptable semantics oriented metamodels to facilitate the design of wrappers or reconciliators (mediators) by taking into account several characteristics of interoperable information systems such as extensibility and composability. X-TIME defines a set of meta-types for representing meta-level semantic descriptors of data models found in the Web. The meta-types are organized in a generalization h…
Near-Real-Time Estimation of Water Vapor Column From MSG-SEVIRI Thermal Infrared Bands: Implications for Land Surface Temperature Retrieval
2015
The Meteosat Second Generation-Spinning Enhanced Visible and Infrared Imager (MSG-SEVIRI) instrument provides observations of half the globe every 15 min, at low spatial resolution. These data are an invaluable tool to observe daily to yearly cycle of land surface temperature (LST), as well as for various early warning systems. However, advanced algorithms for LST estimation requires a previous estimation of the water vapor (WV) column above the observed pixel, for which no instantaneous retrieval methods are yet available, and therefore hinders their implementation in a near-real-time processing chain for MSG-SEVIRI data. This work analyzes three different formulations for such WV retrieva…
Quality Improvement Based on Big Data Analysis
2016
Big data analysis has become an important trend in computer science. Quality improvement is a constant in current industry trends. In this paper, we present an idea of quality improvement based on big data analysis with the aid of linked data and ontologies in order to implement it in the case of automotive parts production. We consider defective automotive products and try to find the best refurbishment solution for them considering their characteristics. Moreover, we propose to develop a recommender system that is able to give recommendations in order to prevent or to alleviate defects and to provide insights for possible causes that led to these defective parts. This study intends to hel…
Compartmental analysis of dynamic nuclear medicine data: Models and identifiability
2016
Compartmental models based on tracer mass balance are extensively used in clinical and pre-clinical nuclear medicine in order to obtain quantitative information on tracer metabolism in the biological tissue. This paper is the first of a series of two that deal with the problem of tracer coefficient estimation via compartmental modelling in an inverse problem framework. Specifically, here we discuss the identifiability problem for a general n-dimension compartmental system and provide uniqueness results in the case of two-compartment and three-compartment compartmental models. The second paper will utilize this framework in order to show how non-linear regularization schemes can be applied t…