Relevant bibliographies by topics / Multiple data sources

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Multiple data sources'

Author: Grafiati
Published: 4 June 2021
Last updated: 16 June 2025

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Journal articles on the topic "Multiple data sources"

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JAMES, Daniel, Raymond LEADBETTER, James LEE, Brendan BURKETT, and David THIEL. "B23 Integration of multiple data sources for swimming biomechanics." Proceedings of the Symposium on sports and human dynamics 2011 (2011): 364–66. http://dx.doi.org/10.1299/jsmeshd.2011.364.

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COSTAGLIOLA, GENNARO, and ERLAND JUNGERT. "Towards Querying Multiple Data Sources." Journal of Visual Languages & Computing 12, no. 1 (2001): 1–2. http://dx.doi.org/10.1006/jvlc.2000.0196.

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Saunders, Robert C., and Craig Anne Heflinger. "Integrating Data from Multiple Public Sources." Evaluation 10, no. 3 (2004): 349–65. http://dx.doi.org/10.1177/1356389004048282.

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Wang, Ruili, Wanting Ji, Mingzhe Liu, et al. "Review on mining data from multiple data sources." Pattern Recognition Letters 109 (July 2018): 120–28. http://dx.doi.org/10.1016/j.patrec.2018.01.013.

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Gross, Richard D., and Bruce A. Barton. "P74 Insuring data completeness from multiple data sources." Controlled Clinical Trials 16, no. 3 (1995): 114S. http://dx.doi.org/10.1016/0197-2456(95)90554-i.

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Sridevi, P. "Informative Knowledge Discovery using Multiple Data Sources, Multiple Features and Multiple Data Mining Techniques." IOSR Journal of Engineering 3, no. 01 (2013): 20–25. http://dx.doi.org/10.9790/3021-03142025.

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Nguyen, Khanh, and Jinli Cao. "Top-K data source selection for keyword queries over multiple XML data sources." Journal of Information Science 38, no. 2 (2012): 156–75. http://dx.doi.org/10.1177/0165551511435875.

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Saegusa, Takumi. "Semiparametric inference for merged data from multiple data sources." Journal of Statistical Planning and Inference 216 (January 2022): 1–14. http://dx.doi.org/10.1016/j.jspi.2021.05.002.

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Luaces, Miguel R., Jesús A. Fisteus, Luis Sánchez-Fernández, et al. "Accessible Routes Integrating Data from Multiple Sources." ISPRS International Journal of Geo-Information 10, no. 1 (2020): 7. http://dx.doi.org/10.3390/ijgi10010007.

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Providing citizens with the ability to move around in an accessible way is a requirement for all cities today. However, modeling city infrastructures so that accessible routes can be computed is a challenge because it involves collecting information from multiple, large-scale and heterogeneous data sources. In this paper, we propose and validate the architecture of an information system that creates an accessibility data model for cities by ingesting data from different types of sources and provides an application that can be used by people with different abilities to compute accessible routes. The article describes the processes that allow building a network of pedestrian infrastructures from the OpenStreetMap information (i.e., sidewalks and pedestrian crossings), improving the network with information extracted obtained from mobile-sensed LiDAR data (i.e., ramps, steps, and pedestrian crossings), detecting obstacles using volunteered information collected from the hardware sensors of the mobile devices of the citizens (i.e., ramps and steps), and detecting accessibility problems with software sensors in social networks (i.e., Twitter). The information system is validated through its application in a case study in the city of Vigo (Spain).
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Sperhac, Arlene M., and Laura D. Goodwin. "Using multiple data sources for curriculum revision." Journal of Pediatric Health Care 17, no. 4 (2003): 169–75. http://dx.doi.org/10.1067/mph.2003.21.

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Dissertations / Theses on the topic "Multiple data sources"

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Zhang, Ping. "Learning from Multiple Knowledge Sources." Diss., Temple University Libraries, 2013. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/214795.

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Computer and Information Science<br>Ph.D.<br>In supervised learning, it is usually assumed that true labels are readily available from a single annotator or source. However, recent advances in corroborative technology have given rise to situations where the true label of the target is unknown. In such problems, multiple sources or annotators are often available that provide noisy labels of the targets. In these multi-annotator problems, building a classifier in the traditional single-annotator manner, without regard for the annotator properties may not be effective in general. In recent years, how to make the best use of the labeling information provided by multiple annotators to approximate the hidden true concept has drawn the attention of researchers in machine learning and data mining. In our previous work, a probabilistic method (i.e., MAP-ML algorithm) of iteratively evaluating the different annotators and giving an estimate of the hidden true labels is developed. However, the method assumes the error rate of each annotator is consistent across all the input data. This is an impractical assumption in many cases since annotator knowledge can fluctuate considerably depending on the groups of input instances. In this dissertation, one of our proposed methods, GMM-MAPML algorithm, follows MAP-ML but relaxes the data-independent assumption, i.e., we assume an annotator may not be consistently accurate across the entire feature space. GMM-MAPML uses a Gaussian mixture model (GMM) and Bayesian information criterion (BIC) to find the fittest model to approximate the distribution of the instances. Then the maximum a posterior (MAP) estimation of the hidden true labels and the maximum-likelihood (ML) estimation of quality of multiple annotators at each Gaussian component are provided alternately. Recent studies show that it is not the case that employing more annotators regardless of their expertise will result in improved highest aggregating performance. In this dissertation, we also propose a novel algorithm to integrate multiple annotators by Aggregating Experts and Filtering Novices, which we call AEFN. AEFN iteratively evaluates annotators, filters the low-quality annotators, and re-estimates the labels based only on information obtained from the good annotators. The noisy annotations we integrate are from any combination of human and previously existing machine-based classifiers, and thus AEFN can be applied to many real-world problems. Emotional speech classification, CASP9 protein disorder prediction, and biomedical text annotation experiments show a significant performance improvement of the proposed methods (i.e., GMM-MAPML and AEFN) as compared to the majority voting baseline and the previous data-independent MAP-ML method. Recent experiments include predicting novel drug indications (i.e., drug repositioning) for both approved drugs and new molecules by integrating multiple chemical, biological or phenotypic data sources.<br>Temple University--Theses
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Brizzi, Francesco. "Estimating HIV incidence from multiple sources of data." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/273803.

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This thesis develops novel statistical methodology for estimating the incidence and the prevalence of Human Immunodeficiency Virus (HIV) using routinely collected surveillance data. The robust estimation of HIV incidence and prevalence is crucial to correctly evaluate the effectiveness of targeted public health interventions and to accurately predict the HIV- related burden imposed on healthcare services. Bayesian CD4-based multi-state back-calculation methods are a key tool for monitoring the HIV epidemic, providing estimates of HIV incidence and diagnosis rates by disentangling their competing contribution to the observed surveillance data. Improving the effectiveness of public health interventions, requires targeting specific age-groups at high risk of infection; however, existing methods are limited in that they do not allow for such subgroups to be identified. Therefore the methodological focus of this thesis lies in developing a rigorous statistical framework for age-dependent back-calculation in order to achieve the joint estimation of age-and-time dependent HIV incidence and diagnosis rates. Key challenges we specifically addressed include ensuring the computational feasibility of proposed methods, an issue that has previously hindered extensions of back-calculation, and achieving the joint modelling of time-and-age specific incidence. The suitability of non-parametric bivariate smoothing methods for modelling the age-and-time specific incidence has been investigated in detail within comprehensive simulation studies. Furthermore, in order to enhance the generalisability of the proposed model, we developed back-calculation that can admit surveillance data less rich in detail; these handle surveillance data collected from an intermediate point of the epidemic, or only available on a coarse scale, and concern both age-dependent and age-independent back-calculation. The applicability of the proposed methods is illustrated using routinely collected surveillance data from England and Wales, for the HIV epidemic among men who have sex with men (MSM).
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Yeang, Chen-Hsiang 1969. "Inferring regulatory networks from multiple sources of genomic data." Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/28731.

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Thesis (Sc. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.<br>Includes bibliographical references (p. 279-299).<br>(cont.) algorithm to identify the regulatory models from protein-DNA binding and gene expression data. These models to a large extent agree with the knowledge of gene regulation pertaining to the corresponding regulators. The three works in this thesis provide a framework of modeling gene regulatory networks.<br>This thesis addresses the problems of modeling the gene regulatory system from multiple sources of large-scale datasets. In the first part, we develop a computational framework of building and validating simple, mechanistic models of gene regulation from multiple sources of data. These models, which we call physical network models, annotate the network of molecular interactions with several types of attributes (variables). We associate model attributes with physical interaction and knock-out gene expression data according to the confidence measures of data and the hypothesis that gene regulation is achieved via molecular interaction cascades. By applying standard model inference algorithms, we are able to obtain the configurations of model attributes which optimally fit the data. Because existing datasets do not provide sufficient constraints to the models, there are many optimal configurations which fit the data equally well. In the second part, we develop an information theoretic score to measure the expected capacity of new knock-out experiments in terms of reducing the model uncertainty. We collaborate with biologists to perform suggested knock-out experiments and analyze the data. The results indicate that we can reduce model uncertainty by incorporating new data. The first two parts focus on the regulatory effects along single pathways. In the third part, we consider the combinatorial effects of multiple transcription factors on transcription control. We simplify the problem by characterizing a combinatorial function of multiple regulators in terms of the properties of single regulators: the function of a regulator and its direction of effectiveness. With this characterization, we develop an incremental<br>by Chen-Hsiang Yeang.<br>Sc.D.
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Liao, Zhining. "Query processing for data integration from multiple data sources over the Internet." Thesis, University of Ulster, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.422192.

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Dembowski, John S. "dbUNiFier: A Framework for Automated Unification of Textual Data in Multiple Remote Data Sources." UNF Digital Commons, 2003. http://digitalcommons.unf.edu/etd/366.

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Over time, advances in database technology and utilization have resulted in a rapid increase in the number and types of data sources. Simultaneously, numerous methods of unifying these various data sources have emerged. Research has shown that a more comprehensive set of data attribute matches between multiple schemas can be detected by combining a number of the unification methodologies as opposed to using a single method. In this research project, a unification framework, dbUNiFier, has been proposed as an approach to allow for easy integration of both existing and future unification methods and data sources.
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Gupta, Sunil Kumar. "Unsupervised modeling of multiple data sources : a latent shared subspace approach." Thesis, Curtin University, 2011. http://hdl.handle.net/20.500.11937/2583.

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The growing number of information sources has given rise to joint analysis. While the research community has mainly focused on analyzing data from a single source, there has been relatively few attempts on jointly analyzing multiple data sources exploiting their statistical sharing strengths. In general, the data from these sources emerge without labeling information and thus it is imperative to perform the joint analysis in an unsupervised manner.This thesis addresses the above problem and presents a general shared subspace learning framework for jointly modeling multiple related data sources. Since the data sources are related, there exist common structures across these sources, which can be captured through a shared subspace. However, each source also has some individual structures, which can be captured through an individual subspace. Incorporating these concepts in nonnegative matrix factorization (NMF) based subspace learning, we develop a nonnegative shared subspace learning model for two data sources and demonstrate its application to tag based social media retrieval. Extending this model, we impose additional regularization constraints of mutual orthogonality on the shared and individual subspaces and show that, compared to its unregularized counterpart, the new regularized model effectively deals with the problem of negative knowledge transfer – a key issue faced by transfer learning methods. The effectiveness of the regularized model is demonstrated through retrieval and clustering applications for a variety of data sets. To take advantage from more than one auxiliary source, we extend above models generalizing two sources to multiple sources with an added flexibility of allowing sources having arbitrary sharing configurations. The usefulness of this model is demonstrated through improved performance, achieved with multiple auxiliary sources. In addition, this model is used to relate the items from disparate media types allowing us to perform cross-media retrieval using tags.Departing from the nonnegative models, we use a linear-Gaussian framework and develop Bayesian shared subspace learning, which not only models the mixed-sign data but also learns probabilistic subspaces. Learning the subspace dimensionalities for the shared subspace models has an important role in optimum knowledge transfer but requires model selection – a task that is computationally intensive and time consuming. To this end, we xii propose a nonparametric Bayesian joint factor analysis model that circumvents the problem of model selection by using a hierarchical beta process prior, inferring subspace dimensionalities automatically from the data. The effectiveness of this model is shown on both synthetic and real data sets. For synthetic data set, successful recovery of both shared and individual subspace dimensionalities is demonstrated, whilst for real data set, the model outperforms recent state-of-the-art techniques for text modeling and image retrieval.
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Argudo, Medrano Oscar. "Realistic reconstruction and rendering of detailed 3D scenarios from multiple data sources." Doctoral thesis, Universitat Politècnica de Catalunya, 2018. http://hdl.handle.net/10803/620733.

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During the last years, we have witnessed significant improvements in digital terrain modeling, mainly through photogrammetric techniques based on satellite and aerial photography, as well as laser scanning. These techniques allow the creation of Digital Elevation Models (DEM) and Digital Surface Models (DSM) that can be streamed over the network and explored through virtual globe applications like Google Earth or NASA WorldWind. The resolution of these 3D scenes has improved noticeably in the last years, reaching in some urban areas resolutions up to 1m or less for DEM and buildings, and less than 10 cm per pixel in the associated aerial imagery. However, in rural, forest or mountainous areas, the typical resolution for elevation datasets ranges between 5 and 30 meters, and typical resolution of corresponding aerial photographs ranges between 25 cm to 1 m. This current level of detail is only sufficient for aerial points of view, but as the viewpoint approaches the surface the terrain loses its realistic appearance. One approach to augment the detail on top of currently available datasets is adding synthetic details in a plausible manner, i.e. including elements that match the features perceived in the aerial view. By combining the real dataset with the instancing of models on the terrain and other procedural detail techniques, the effective resolution can potentially become arbitrary. There are several applications that do not need an exact reproduction of the real elements but would greatly benefit from plausibly enhanced terrain models: videogames and entertainment applications, visual impact assessment (e.g. how a new ski resort would look), virtual tourism, simulations, etc. In this thesis we propose new methods and tools to help the reconstruction and synthesis of high-resolution terrain scenes from currently available data sources, in order to achieve realistically looking ground-level views. In particular, we decided to focus on rural scenarios, mountains and forest areas. Our main goal is the combination of plausible synthetic elements and procedural detail with publicly available real data to create detailed 3D scenes from existing locations. Our research has focused on the following contributions: - An efficient pipeline for aerial imagery segmentation - Plausible terrain enhancement from high-resolution examples - Super-resolution of DEM by transferring details from the aerial photograph - Synthesis of arbitrary tree picture variations from a reduced set of photographs - Reconstruction of 3D tree models from a single image - A compact and efficient tree representation for real-time rendering of forest landscapes<br>Durant els darrers anys, hem presenciat avenços significatius en el modelat digital de terrenys, principalment gràcies a tècniques fotogramètriques, basades en fotografia aèria o satèl·lit, i a escàners làser. Aquestes tècniques permeten crear Models Digitals d'Elevacions (DEM) i Models Digitals de Superfícies (DSM) que es poden retransmetre per la xarxa i ser explorats mitjançant aplicacions de globus virtuals com ara Google Earth o NASA WorldWind. La resolució d'aquestes escenes 3D ha millorat considerablement durant els darrers anys, arribant a algunes àrees urbanes a resolucions d'un metre o menys per al DEM i edificis, i fins a menys de 10 cm per píxel a les fotografies aèries associades. No obstant, en entorns rurals, boscos i zones muntanyoses, la resolució típica per a dades d'elevació es troba entre 5 i 30 metres, i per a les corresponents fotografies aèries varia entre 25 cm i 1m. Aquest nivell de detall només és suficient per a punts de vista aeris, però a mesura que ens apropem a la superfície el terreny perd tot el realisme. Una manera d'augmentar el detall dels conjunts de dades actuals és afegint a l'escena detalls sintètics de manera plausible, és a dir, incloure elements que encaixin amb les característiques que es perceben a la vista aèria. Així, combinant les dades reals amb instàncies de models sobre el terreny i altres tècniques de detall procedural, la resolució efectiva del model pot arribar a ser arbitrària. Hi ha diverses aplicacions per a les quals no cal una reproducció exacta dels elements reals, però que es beneficiarien de models de terreny augmentats de manera plausible: videojocs i aplicacions d'entreteniment, avaluació de l'impacte visual (per exemple, com es veuria una nova estació d'esquí), turisme virtual, simulacions, etc. En aquesta tesi, proposem nous mètodes i eines per ajudar a la reconstrucció i síntesi de terrenys en alta resolució partint de conjunts de dades disponibles públicament, per tal d'aconseguir vistes a nivell de terra realistes. En particular, hem decidit centrar-nos en escenes rurals, muntanyes i àrees boscoses. El nostre principal objectiu és la combinació d'elements sintètics plausibles i detall procedural amb dades reals disponibles públicament per tal de generar escenes 3D d'ubicacions existents. La nostra recerca s'ha centrat en les següents contribucions: - Un pipeline eficient per a segmentació d'imatges aèries - Millora plausible de models de terreny a partir d'exemples d’alta resolució - Super-resolució de models d'elevacions transferint-hi detalls de la fotografia aèria - Síntesis d'un nombre arbitrari de variacions d’imatges d’arbres a partir d'un conjunt reduït de fotografies - Reconstrucció de models 3D d'arbres a partir d'una única fotografia - Una representació compacta i eficient d'arbres per a navegació en temps real d'escenes
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Tremblay, Monica Chiarini. "Uncertainty in the information supply chain : integrating multiple health care data sources." [Tampa, Fla.] : University of South Florida, 2007. http://purl.fcla.edu/usf/dc/et/SFE0002086.

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Endress, William. "Merging Multiple Telemetry Files from Widely Separated Sources for Improved Data Integrity." International Foundation for Telemetering, 2012. http://hdl.handle.net/10150/581824.

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Merging telemetry data from multiple data sources into a single file, provides the ability to fill in gaps in the data and reduce noise by taking advantage of the multiple sources. This is desirable when analyzing the data as there is only one file to work from. Also, the analysts will spend less time trying to explain away gaps and spikes in data that are attributable to dropped and noisy telemetry frames, leading to more accurate reports. This paper discusses the issues and solutions for doing the merge.
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Cooke, Payton, and Payton Cooke. "Comparative Analysis of Multiple Data Sources for Travel Time and Delay Measurement." Thesis, The University of Arizona, 2016. http://hdl.handle.net/10150/622847.

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Arterial performance measurement is an essential tool for both researchers and practitioners, guiding decisions on traffic management, future improvements, and public information. Link travel time and intersection control delay are two primary performance measures that are used to evaluate arterial level of service. Despite recent technological advancements, collecting travel time and intersection delay data can be a time-consuming and complicated process. Limited budgets, numerous available technologies, a rapidly changing field, and other challenges make performance measurement and comparison of data sources difficult. Three common data collection sources (probe vehicles, Bluetooth media access control readers, and manual queue length counts) are often used for performance measurement and validation of new data methods. Comparing these and other data sources is important as agencies and researchers collect arterial performance data. This study provides a methodology for comparing data sources, using statistical tests and linear correlation to compare methods and identify strengths and weaknesses. Additionally, this study examines data normality as an issue that is seldom considered, yet can affect the performance of statistical tests. These comparisons can provide insight into the selection of a particular data source for use in the field or for research. Data collected along Grant Road in Tucson, Arizona, was used as a case study to evaluate the methodology and the data sources. For evaluating travel time, GPS probe vehicle and Bluetooth sources produced similar results. Bluetooth can provide a greater volume of data more easily in addition to samples large enough for more rigorous statistical evaluation, but probe vehicles are more versatile and provide higher resolution data. For evaluating intersection delay, probe vehicle and queue count methods did not always produce similar results.
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Books on the topic "Multiple data sources"

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Bock, Mary Ellen, and Nancy J. Kirkendall, eds. Improving Crop Estimates by Integrating Multiple Data Sources. National Academies Press, 2017. http://dx.doi.org/10.17226/24892.

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Groves, Robert M., and Brian A. Harris-Kojetin, eds. Federal Statistics, Multiple Data Sources, and Privacy Protection. National Academies Press, 2017. http://dx.doi.org/10.17226/24893.

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Chong, Sau. Grounded Theory Analysis: Synthesising Multiple Data Sources in Investigating What Counts as School Literacies. SAGE Publications, Ltd., 2019. http://dx.doi.org/10.4135/9781526469496.

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Associates, Marvin Shaffer and, Canada Forestry Canada, Canada-British Columbia Partnership Agreement on Forest Resource Development: FRDA II., and British Columbia. Ministry of Forests., eds. Evaluation methodology and data sources for social and economic impact assessment of forest land management options: Background reports. Forestry Canada, 1992.

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Long, Vicky. A Mixed-Methods Design: Triangulating Multiple Data Sources to Understand Intellectual Property Management in the Digital Era. SAGE Publications Ltd, 2025. https://doi.org/10.4135/9781036217327.

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Melamid, Elan. What works?: Integrating multiple data sources and policy research methods in assessing need and evaluating outcomes in community-based child and family service systems. RAND, 2002.

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Dutwin, David, and Trent Buskirk. Triangulation, Modeling, and Adjustment: Correcting Online Surveys for Coverage Biases Among Non-Internet Households Using Machine Learning Methods Trained on Multiple Data Sources Spanning Several Domains. SAGE Publications Ltd, 2025. https://doi.org/10.4135/9781036215590.

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Turner, D. Bruce. TUPOS: A multiple source Gaussian dispersion algorithm using on-site turbulence data. U.S. Environmental Protection Agency, Atmospheric Sciences Research Laboratory, 1986.

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Turner, D. Bruce. TUPOS: A multiple source Gaussian dispersion algorithm using on-site turbulence data. U.S. Environmental Protection Agency, Atmospheric Sciences Research Laboratory, 1986.

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Cathcart, Adam, Christopher Green, and Steven Denney, eds. Decoding the Sino-North Korean Borderlands. Amsterdam University Press, 2021. http://dx.doi.org/10.5117/9789462987562.

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Since the 1990s, the Chinese-North Korean border region has undergone a gradual transformation into a site of intensified cooperation, competition, and intrigue. These changes have prompted a significant volume of critical scholarship and media commentary across multiple languages and disciplines. Drawing on existing studies and new data, Decoding the Sino-North Korean Borderlands brings much of this literature into concert by pulling together a wide range of insight on the region's economics, security, social cohesion, and information flows. Drawing from multilingual sources and transnational scholarship, this volume is enhanced by the extensive fieldwork undertaken by the editors and contributors in their quests to decode the borderland. In doing so, the volume emphasizes the link between theory, methodology, and practice in the field of Area Studies and social science more broadly.
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Book chapters on the topic "Multiple data sources"

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Nuckolls, Glen, Chip Martel, and Stuart G. Stubblebine. "Certifying Data from Multiple Sources." In Data and Applications Security XVII. Springer US, 2004. http://dx.doi.org/10.1007/1-4020-8070-0_4.

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McCollam, Ronald. "Working with Multiple Data Sources." In Getting Started with Grafana. Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-8309-7_8.

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Talbott, Elizabeth, and Andres De Los Reyes. "Making Sense of Multiple Data Sources." In Handbook of Special Education Research, Volume I. Routledge, 2022. http://dx.doi.org/10.4324/9781003156857-19.

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Dong, Xin Luna, Laure Berti-Equille, and Divesh Srivastava. "Data Fusion: Resolving Conflicts from Multiple Sources." In Handbook of Data Quality. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36257-6_13.

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Hu, Shaofeng, Jiangtao Ren, Changshui Zhang, and Chaogui Zhang. "Domain Transfer via Multiple Sources Regularization." In Advances in Knowledge Discovery and Data Mining. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-06605-9_43.

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Zhong, Ning. "Mining Interesting Patterns in Multiple Data Sources." In Information Fusion in Data Mining. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-36519-8_5.

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Xie, Zizhe, Qizhi Liu, and Zhifeng Bao. "Sifting Truths from Multiple Low-Quality Data Sources." In Web and Big Data. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63579-8_7.

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Dong, Xin Luna, Laure Berti-Equille, and Divesh Srivastava. "Data Fusion: Resolving Conflicts from Multiple Sources." In Web-Age Information Management. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38562-9_7.

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Baliga, Ganesh, Sanjay Jain, and Arun Sharma. "Learning from multiple sources of inaccurate data." In Analogical and Inductive Inference. Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/3-540-56004-1_8.

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Acharya, Seema, and Subhashini Chellappan. "Working with Single and Multiple Data Sources." In Pro Tableau. Apress, 2016. http://dx.doi.org/10.1007/978-1-4842-2352-9_2.

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Conference papers on the topic "Multiple data sources"

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Liu, Zehua, Yanqin Wang, Yifan Liu, and Hongying Miao. "Power User Profiling Techniques for Fusing Heterogeneous Data from Multiple Sources." In 2024 5th International Conference on Smart Grid and Energy Engineering (SGEE). IEEE, 2024. https://doi.org/10.1109/sgee64306.2024.10865857.

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Purba, Rini Apriyanti, and Luca Bedogni. "Assessing Benefits and Limitations of Multiple Data Sources for Environmental Monitoring." In 2025 IEEE 22nd Consumer Communications & Networking Conference (CCNC). IEEE, 2025. https://doi.org/10.1109/ccnc54725.2025.10975904.

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Zhang, Guanghua, E. Thirion, and Andrew M. Wallace. "Model matching using multiple data sources." In IS&T/SPIE's Symposium on Electronic Imaging: Science and Technology, edited by Frederick Y. Wu and Benjamin M. Dawson. SPIE, 1993. http://dx.doi.org/10.1117/12.144800.

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Gao, S., W. Y. Zou, Y. Y. Liu, et al. "Integrating multiple sources of genomic data by multiplex network reconstruction." In 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2015. http://dx.doi.org/10.1109/bibm.2015.7359926.

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Lu, Zhengdong, Berkant Savas, Wei Tang, and Inderjit S. Dhillon. "Supervised Link Prediction Using Multiple Sources." In 2010 IEEE 10th International Conference on Data Mining (ICDM). IEEE, 2010. http://dx.doi.org/10.1109/icdm.2010.112.

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Zhang, Fan, Li Yu, Xiangrui Cai, Ying Zhang, and Haiwei Zhang. "Truth Finding from Multiple Data Sources by Source Confidence Estimation." In 2015 12th Web Information System and Application Conference (WISA). IEEE, 2015. http://dx.doi.org/10.1109/wisa.2015.45.

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Subbian, Karthik, Arindam Banerjee, and Sugato Basu. "PLUMS: Predicting Links Using Multiple Sources." In Proceedings of the 2015 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, 2015. http://dx.doi.org/10.1137/1.9781611974010.42.

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Le, Trung, Quan Hoang, Hung Vu, Tu Dinh Nguyen, Hung Bui, and Dinh Phung. "Learning Generative Adversarial Networks from Multiple Data Sources." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/391.

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Generative Adversarial Networks (GANs) are a powerful class of deep generative models. In this paper, we extend GAN to the problem of generating data that are not only close to a primary data source but also required to be different from auxiliary data sources. For this problem, we enrich both GANs' formulations and applications by introducing pushing forces that thrust generated samples away from given auxiliary data sources. We term our method Push-and-Pull GAN (P2GAN). We conduct extensive experiments to demonstrate the merit of P2GAN in two applications: generating data with constraints and addressing the mode collapsing problem. We use CIFAR-10, STL-10, and ImageNet datasets and compute Fréchet Inception Distance to evaluate P2GAN's effectiveness in addressing the mode collapsing problem. The results show that P2GAN outperforms the state-of-the-art baselines. For the problem of generating data with constraints, we show that P2GAN can successfully avoid generating specific features such as black hair.
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Tan, Ben, Erheng Zhong, Evan Wei Xiang, and Qiang Yang. "Multi-Transfer: Transfer Learning with Multiple Views and Multiple Sources." In Proceedings of the 2013 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, 2013. http://dx.doi.org/10.1137/1.9781611972832.27.

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Batista, Natércia A., Michele A. Brandão, Michele B. Pinheiro, Daniel H. Dalip, and Mirella M. Moro. "Dealing with Data from Multiple Web Sources." In WebMedia '18: Brazilian Symposium on Multimedia and the Web. ACM, 2018. http://dx.doi.org/10.1145/3243082.3264609.

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Reports on the topic "Multiple data sources"

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Arens, Yigal, Chin Y. Chee, Chun-Nan Hsu, and Craig A. Knoblock. Retrieving and Integrating Data from Multiple Information Sources. Defense Technical Information Center, 1993. http://dx.doi.org/10.21236/ada269588.

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Kum, Hye-Chung, Eric Ragan, Alva Ferdinand, and Cason Schmit. Developing and Testing Software for Linking Patient Data from Multiple Sources. Patient-Centered Outcomes Research Institute® (PCORI), 2022. http://dx.doi.org/10.25302/04.2022.me.160234486.

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Dickersin, Kay, Tianjing Li, and Evan Mayo-Wilson. Integrating Multiple Data Sources for Meta-analysis to Improve Patient-Centered Outcomes Research. Patient-Centered Outcomes Research Institute (PCORI), 2018. http://dx.doi.org/10.25302/3.2018.me.13035785.

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Cajner, Tomaz, Leland Crane, Ryan Decker, Adrian Hamins-Puertolas, and Christopher Kurz. Improving the Accuracy of Economic Measurement with Multiple Data Sources: The Case of Payroll Employment Data. National Bureau of Economic Research, 2019. http://dx.doi.org/10.3386/w26033.

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Silowash, George J., and Christopher King. Insider Threat Control: Understanding Data Loss Prevention (DLP) and Detection by Correlating Events from Multiple Sources. Defense Technical Information Center, 2013. http://dx.doi.org/10.21236/ada610587.

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Gutman, Roee, and Gauri Kamat. Developing and Testing New Methods for Linking and Analyzing Multiple Data Sources with Limited Patient Identifiers. Patient-Centered Outcomes Research Institute® (PCORI), 2023. http://dx.doi.org/10.25302/11.2023.me.2017c39241.

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Hagedorn, G., R. Paulen, R. Fortin, and E. Arnaud. Radiometric domains and the integration of multiple gamma-ray data sources for a remote area of northern Quebec. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2017. http://dx.doi.org/10.4095/306142.

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Hagedorn, G., R. C. Paulen, R. Fortin, and E. Arnaud. Radiometric domains and the integration of multiple gamma-ray data sources for a remote area of northern Quebec. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2018. http://dx.doi.org/10.4095/308209.

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Martinez-Espiñeira, Roberto, and María Pérez Urdiales. Water Affordability Measures Under Multiple and Non-Exclusive Sources in Latin America and the Caribbean. Inter-American Development Bank, 2024. http://dx.doi.org/10.18235/0005529.

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Standard water affordability measures that only account for expenditure on piped water are unlikely to adequately capture the situation of all consumers in developing countries, who often experience water service quality issues and must rely on coping strategies. We construct and compare a series of water affordability ratios including coping costs, and we also adjust these ratios by normative judgements about the need for coping strategies. We use nationally representative household-level data from 18 countries in Latin America and the Caribbean, providing, for the first time, a regional perspective on water affordability. We show that the share of income devoted to water expenses substantially increases when we consider coping costs, particularly affecting the bottom 20% of the income distribution. These findings should be of interest to policy makers aiming at promoting access to safe and affordable water as we also identify the characteristics associated with water affordability issues.
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Moreno, Isaac. Poverty Dynamics in Bangladesh – Selective Review. Data and Evidence to End Extreme Poverty, 2023. http://dx.doi.org/10.55158/deepwp19.

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