Search results for "Data model"
showing 10 items of 162 documents
Domain-Specific Characteristics of Data Quality
2017
The research discusses the issue how to describe data quality and what should be taken into account when developing an universal data quality management solution. The proposed approach is to create quality specifications for each kind of data objects and to make them executable. The specification can be executed step-by-step according to business process descriptions, ensuring the gradual accumulation of data in the database and data quality checking according to the specific use case. The described approach can be applied to check the completeness, accuracy, timeliness and consistency of accumulated data.
Managing Multi-center Flow Cytometry Data for Immune Monitoring.
2014
With the recent results of promising cancer vaccines and immunotherapy 1 – 5 , immune monitoring has become increasingly relevant for measuring treatment-induced effects on T cells, and an essential tool for shedding light on the mechanisms responsible for a successful treatment. Flow cytometry is the canonical multi-parameter assay for the fine characterization of single cells in solution, and is ubiquitously used in pre-clinical tumor immunology and in cancer immunotherapy trials. Current state-of-the-art polychromatic flow cytometry involves multi-step, multi-reagent assays followed by sample acquisition on sophisticated instruments capable of capturing up to 20 parameters per cell at a…
Improvement of Temperature Based ANN Models for ETo Prediction in Coastal Locations by Means of Preliminary Models and Exogenous Data
2008
This paper reports the application of artificial neural networks for estimating reference evapotranspiration (ETo) as a function of local maximum and minimum air temperatures and exogenous relative humidity and evapotranspiration in twelve coastal locations of the autonomous Valencia region, Spain. The Penman-Monteith model for ETo prediction, as been proposed by the Food and Agriculture Organization of the United Nations (FAO) as the standard method for ETo forecast, has been used to provide the ANN targets. The number of stations where reliable climatic data are available for the application of the Penman-Monteith equation is limited. Thus, the development of more precise predicting tools…
Hierarchical modeling for rare event detection and cell subset alignment across flow cytometry samples.
2013
Flow cytometry is the prototypical assay for multi-parameter single cell analysis, and is essential in vaccine and biomarker research for the enumeration of antigen-specific lymphocytes that are often found in extremely low frequencies (0.1% or less). Standard analysis of flow cytometry data relies on visual identification of cell subsets by experts, a process that is subjective and often difficult to reproduce. An alternative and more objective approach is the use of statistical models to identify cell subsets of interest in an automated fashion. Two specific challenges for automated analysis are to detect extremely low frequency event subsets without biasing the estimate by pre-processing…
Querying and reasoning over large scale building data sets
2016
International audience; The architectural design and construction domains work on a daily basis with massive amounts of data. Properly managing, exchanging and exploiting these data is an ever ongoing challenge in this domain. This has resulted in large semantic RDF graphs that are to be combined with a significant number of other data sets (building product catalogues, regulation data, geometric point cloud data, simulation data, sensor data), thus making an already huge dataset even larger. Making these big data available at high performance rates and speeds and into the correct (intuitive) formats is therefore an incredibly high challenge in this domain. Yet, hardly any benchmark is avai…
Executable Data Quality Models
2017
The paper discusses an external solution for data quality management in information systems. In contradiction to traditional data quality assurance methods, the proposed approach provides the usage of a domain specific language (DSL) for description data quality models. Data quality models consists of graphical diagrams, which elements contain requirements for data object's values and procedures for data object's analysis. The DSL interpreter makes the data quality model executable therefore ensuring measurement and improving of data quality. The described approach can be applied: (1) to check the completeness, accuracy and consistency of accumulated data; (2) to support data migration in c…
Advancing Deep Learning for Earth Sciences: From Hybrid Modeling to Interpretability
2020
Machine learning and deep learning in particular have made a huge impact in many fields of science and engineering. In the last decade, advanced deep learning methods have been developed and applied to remote sensing and geoscientific data problems extensively. Applications on classification and parameter retrieval are making a difference: methods are very accurate, can handle large amounts of data, and can deal with spatial and temporal data structures efficiently. Nevertheless, several important challenges need still to be addressed. First, current standard deep architectures cannot deal with long-range dependencies so distant driving processes (in space or time) are not captured, and the…
Measuring the agreement between brain connectivity networks.
2016
Investigating the level of similarity between two brain networks, resulting from measures of effective connectivity in the brain, can be of interest from many respects. In this study, we propose and test the idea to borrow measures of association used in machine learning to provide a measure of similarity between the structure of (un-weighted) brain connectivity networks. The measures here explored are the accuracy, Cohen's Kappa (K) and Area Under Curve (AUC). We implemented two simulation studies, reproducing two contexts of application that can be particularly interesting for practical applications, namely: i) in methodological studies, performed on surrogate data, aiming at comparing th…
Deep Learning-Based Real-Time Object Detection in Inland Navigation
2019
International audience; Semi-autonomous and fully-autonomous systems must have knowledge about the objects in their environment to ensure a safe navigation. Modern approaches implement deep learning techniques to train a neural network for object detection. This project will study the effectiveness of using several promising algorithms such as Faster R-CNN, SSD, and different versions of YOLO, to detect, classify, and track objects in near real-time fluvial domain. Since no dataset is available for this purpose in literature, we first started by annotating a dataset of 2488 images with almost 35 400 annotations for training the convolutional neural network architectures. We made this data s…
Zur Identifikation von Strukturanalogien in Datenmodellen
2005
On the one hand, data models decrease the complexity of information system development. On the other hand, data models causes additional complexity. Recently structural analogies are discussed as instruments reducing the complexity of data models. This piece of research presents a procedure to identify structural analogies in data models and demonstrates its performance by analyzing Scheer’s reference model for industrial enterprises (Y-CIM-model). The proposed procedure is based on formalizing data models within set theory and uses a quantitative similarity measure. The obtained results show both identical and very similar information structures within the Y-CIM-model. Furthermore, ways of…