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About 10,000 papers for “data merging”

Merging data

Jean-François Grandin · Cairn.info · 2026

Cited by 0

Microarray data merging.

Jin Yang (86490), Zongxing Yang (381694), Hangjun Lv (381695) et al. · Figshare · 2015

<p>All the effects, including batch effects, profile effects, interaction between batch and profile effects, and residual effects, were estimated for their contribution to the overall variation by PVCA. (<b>A</b>) Data before batch adjustment. (<b>B</b>) Data processed by ComBat as batch adjustment tool/model. (<b>C</b>) Hierarchical clustering of the data before batch adjustment. (<b>D</b>) Hierarchical clustering of the data after batch adjustment.</p>

Cited by 0Open access

Merging Microsatellite Data

Angela P. Presson, Eric M. Sobel, Kenneth Lange et al. · Journal of Computational Biology · 2006

Genotype calling procedures vary from laboratory to laboratory for many microsatellite markers. Even within the same laboratory, application of different experimental protocols often leads to ambiguities. The impact of these ambiguities ranges from irksome to devastating. Resolving the ambiguities can increase effective sample size and preserve evidence in favor of disease-marker associations. Because different data sets may contain different numbers of alleles, merging is unfortunately not a simple process of matching alleles one to one. Merging data sets manually is difficult, time-consuming, and error-prone due to differences in genotyping hardware, binning methods, molecular weight standards, and curve fitting algorithms. Merging is particularly difficult if few or no samples occur in common, or if samples are drawn from ethnic groups with widely varying allele frequencies. It is dangerous to align alleles simply by adding a constant number of base pairs to the alleles of one of the data sets. To address these issues, we have developed a Bayesian model and a Markov chain Monte Carlo (MCMC) algorithm for sampling the posterior distribution under the model. Our computer program, MicroMerge, implements the algorithm and almost always accurately and efficiently finds the most likely correct alignment. Common allele frequencies across laboratories in the same ethnic group are the single most important cue in the model. MicroMerge computes the allelic alignments with the greatest posterior probabilities under several merging options. It also reports when data sets cannot be confidently merged. These features are emphasized in our analysis of simulated and real data.

Cited by 16

Agent data merging

Е.В. Антонов, Екатерина Сергеевна Лопатина, Kristina Ionkina et al. · Procedia Computer Science · 2020

The present article deals with data collection in a given field using the agent-based technologies from various information sources of the Internet with the aim to ob-tain reliable and up-to-date data. The agent-based approach is illustrated by the data collection on the nuclear power plants operating all over the world. Three open information sources have been selected for data extraction. The information sources concerned have been analyzed and the features of data provision structure identified. In the course of the present work the following tools for the develop-ment of the software agents have been described: the browser control for human behavior simulation, HTML markup analysis using the XPath query language and data extraction from PDF-documents using regular expressions. Above all, the article considers the software architecture and the database scheme. In the re-sult of the software operation, data regarding 789 nuclear power plants has been obtained.

Cited by 3Open access

The data merging process.

Magnhild Oust Torske (4183060), Natalie Steen (18272116), Jonil Tau Ursin (18272119) et al. · Figshare · 2024

<div><p>Stockmanship is an important determinant for good animal welfare and health. The goal of the FarmMERGE project is to investigate the associations between farmer health and work environment, and the health, productivity and welfare of their livestock. We merged several livestock industry databases with a major total population-based health study in Norway (The Trøndelag Health Study 2017–2019 (HUNT4)). This paper describes the project’s collection and merging of data, and the cohort of farmers and farms that were identified as a result of our registry merge. There were 56,042 participants of HUNT4 (Nord-Trøndelag County participants only, participation rate: 54.0%). We merged a list of HUNT4 participants whose self-reported main occupation was “farmer” (n = 2,407) with agricultural databases containing production and health data from sheep, swine, dairy and beef cattle from 2017–2020. The Central Coordinating Register for Legal Entities was used as an intermediary step to achieve a link between the farmer and farming enterprise data. We identified 816 farmers (89.5% male, mean age 51.3 years) who had roles in 771 farming enterprises with documented animal production. The cohort included 675 unique farmer-farm combinations in cattle production, 139 in sheep, and 125 in swine. We linked at least one HUNT4 participant to approximately 63% of the dairy farms, 53% of the beef cattle farms, 30% of the sheep farms, and 38% of the swine farms in Nord-Trøndelag County in the 2017–2019 period. Using existing databases may be an efficient way of collecting large amounts of data for research, and using total population-based human health surveys may decrease response bias. However, the quality of the resulting research data will depend on the quality of the databases used, and thorough knowledge of the databases is required.</p></div>

Cited by 0Open access

Merging Route Data and Cartographic Data

Hanna Stigmar · Lund University Publications (Lund University) · 2004

Routing is an interesting application for Location Based Services. In order to provide this application on the National Mapping Agencies’ cartographic data the routes must be provided by an external source. The integration of cartographic data and route data is somewhat problematic, why the problems and possible solutions are discussed.

Cited by 2

3D data merging using Holoimage

Song Zhang, Shing‐Tung Yau · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2007

Three-dimensional data merging is critical for full-field 3-D shape measurement. 3-D range data patches, acquired either from different sensors or from the same sensor in different viewing angles, have to be merged into a single piece to facilitate future data analysis. In this research, we propose a novel method for 3-D data merging using Holoimage. Similar to the 3-D shape measurement system using a phase-shifting method, Holoimage is a phase-shifting-based computer synthesized fringe image. The virtual projector projects the phase-shifted fringe pattern onto the object, the reflected fringe images are rendered on the screen, and the Holoimage is generated by recording the screen. The 3-D information is retrieved from the Holoimage using a phase-shifting method. If two patches of 3-D data with overlapping areas are rendered by OpenGL, the overlapping areas are resolved by the graphics pipeline, i.e., only the front geometry can been visualized. Therefore, the merging is done if the front geometry information can be obtained. Holoimage is to obtain the front geometry by projecting the fringe patterns onto the rendered scene. Unlike real world, the virtual camera and projector can be used as orthogonal projective devices, and the setup of the system can be controlled accurately and easily. Both simulation and experiments demonstrated the success of the proposed method.

Cited by 0

Utilization: Merging Data and Process

George Tillmann · Apress eBooks · 2017

Our biggest cost is not power, or servers, or people. It’s lack of utilization. It dominates all other costs.

Cited by 0

Data Fusion: Merging Classification Streams

Jeffrey A. Cardille, Rylan Boothman, Mary Villamor et al. · 2023

Abstract As the ability to rapidly produce classifications of satellite images grows, it will be increasingly important to have algorithms that can shift through them to separate the signal from inevitable classification noise. The purpose of this chapter is to explore how to update classification time series by blending information from multiple classifications made from a wide variety of data sources. In this lab, we will explore how to update the classification time series of the Roosevelt River found in Fortin et al. (Remote Sens Environ 238, 2020). That time series began with the 1972 launch of Landsat 1, blending evidence from 10 sensors and more than 140 images to show the evolution of the area until 2016. How has it changed since 2016? What new tools and data streams might we tap to understand the land surface through time?

Cited by 0Open access

Merging customer relationship management data

Lönnqvist Gustafsson, Fredrik · Lund University Publications Student Papers (Lund University) · 2016

Working distributed is increasingly important today with technology being more and more portable while connectivity is still lacking in some areas. Cellphones and laptops are increasingly in use outside the office with connectivity to internal office services being unreliable, either due to lacking speed or inability to have a connection. Different solutions exist. One is a client-server setup where the data traffic can be minimized. Another is an internet service where the client is in the web browser. A third is to use a distributed system where each system is has full functionality by its own but needs synchronizing with other copies of the system. Distributed work requires duplication of data and therefore, also a need to merge data. Great efforts have been made to be able to merge program code in text files and this kind of merge solutions are pretty mature. However, for merging other types of data the methods are still young and unproved. For a CRM system, the data is highly structured and different data fields are known in advance. This gives good opportunities to merge diverged data. However the current systems on the market are lacking support for merge algorithm knowing about the data format. Even if the data is structured it doesn't mean that a merge is trivial. Structured data might have dependencies between different data fields, so that one field depends on the value of another field. That means that a merge tool needs to be aware of these dependencies. If also taking the history of changes since the diverging point into account the possibilities for detecting merge problems is greatly increased. Different CRM systems have used different approaches to merging data, however, all found approaches have been without actually merging any data, but instead either identify a difference or guessing the most correct version without merging. That means that a result will be either one of two conflicting changes but not a combination of the two. This paper has studied different use cases for altering CRM data and how three different types of CRM programs solve these cases. A custom merge algorithm that solves all studied cases has been designed and implemented as a proof of concept. The proof of concept implementation works satisfying. It uses the history and the structure of the data, combined with information about dependencies between different data fields to identify conflicts and perform a merge. Merge is only done when it can be done without any risk of loosing information. The conclusion is that current CRM software handles these problems poorly and that it is possible to merge this data and/or detect conflicts in a good way.

Cited by 0Open access

Merging data in metabolic diagnosis

Gabriel Correia Brito, Rui Fonseca-Pinto, Maria P. Guarino et al. · Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) · 2018

The metabolic syndrome is a set of risk factors associated with increased cardiovascular risk. These changes to the standard metabolic functions are associated with increased blood insulin and with insulin resistance, which is the common feature of disease pathophysiology. Although with admissible genetic inheritance, the metabolic syndrome symptoms increase with age, sedentarism, weight gain, tobacco, and poor dietetic habits. Due to their characteristics, clinical manifestations of metabolic diseases are perceived by the patient at advanced stages of metabolic dysfunction, when the risk of an acute cardiovascular event is high. Early detection of disturbed glucose homeostatic mechanisms, by recording efferent responses to stimuli like meal ingestion, is, therefore, a methodology with diagnostic potential acting as a predictive measure of metabolic dysfunction. This work presents a novel software (CBView) that analyses the records of physiological responses mediated by the carotid bodies to provocation tests, obtained by a new medical device (CBMeter) aiming to the early tracking of changes in autonomic responses that control metabolism, and thus providing quantitative metrics to assess metabolic dysfunction.

Cited by 0Open access

ACE-Merging: Data-Free Model Merging with Adaptive Covariance Estimation

Bo Xu, Haotian Wu, Hehai Lin et al. · arXiv (Cornell University) · 2026

Model merging aims to combine multiple task-specific expert models into a single model while preserving generalization across diverse tasks. However, interference among experts, especially when they are trained on different objectives, often leads to significant performance degradation. Despite recent progress, resolving this interference without data access, retraining, or architectural modification remains a fundamental challenge. This paper provides a theoretical analysis demonstrating that the input covariance of each task, which is a key factor for optimal merging, can be implicitly estimated from the parameter differences of its fine-tuned model, even in a fully data-free setting. Building on this insight, we introduce \acem, an Adaptive Covariance Estimation framework that effectively mitigates inter-task interference. Our approach features a principled, closed-form solution that contrasts with prior iterative or heuristic methods. Extensive experiments on both vision and language benchmarks demonstrate that \acem sets a new state-of-the-art among data-free methods. It consistently outperforms existing baselines; for example, \acem achieves an average absolute improvement of 4\% over the previous methods across seven tasks on GPT-2. Owing to its efficient closed-form formulation, \acem delivers superior performance with a modest computational cost, providing a practical and theoretically grounded solution for model merging.

Cited by 0Open access

Merging Smarter, Generalizing Better: Enhancing Model Merging on OOD Data

Bingjie Zhang, Hongkang Li, Chengyong Shi et al. · ArXiv.org · 2025

Multi-task learning (MTL) concurrently trains a model on diverse task datasets to exploit common features, thereby improving overall performance across the tasks. Recent studies have dedicated efforts to merging multiple independent model parameters into a unified model for MTL, thus circumventing the need for training data and expanding the scope of applicable scenarios of MTL. However, current approaches to model merging predominantly concentrate on enhancing performance within in-domain (ID) datasets, often overlooking their efficacy on out-of-domain (OOD) datasets. In this work, we proposed LwPTV (Layer-wise Pruning Task Vector) by building a saliency score, measuring the redundancy of parameters in task vectors. Designed in this way ours can achieve mask vector for each task and thus perform layer-wise pruning on the task vectors, only keeping the pre-trained model parameters at the corresponding layer in merged model. Owing to its flexibility, our method can be seamlessly integrated with most of existing model merging methods to improve their performance on OOD tasks. Extensive experiments demonstrate that the application of our method results in substantial enhancements in OOD performance while preserving the ability on ID tasks.

Cited by 0Open access