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Опубліковано: 11 вересня 2023

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Статті в журналах з теми "MULTISOURCE DATA"

Li, Chaofeng. "Data Mining-Based Tracking Method for Multisource Target Data of Heterogeneous Networks." Wireless Communications and Mobile Computing 2022 (August 22, 2022): 1–8. http://dx.doi.org/10.1155/2022/1642925.

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In order to solve the problem that the target is easily lost in the process of multisource target data fusion tracking, a multisource target data fusion tracking method based on data mining is proposed. Multisource target data fusion tracking belongs to location level fusion. Firstly, a hybrid heterogeneous network fusion model is established, and then, data features are extracted, and a fusion source big data acquisition algorithm is designed based on compressed sensing to complete data preprocessing to reduce the amount of data acquisition. Based on data mining association multisource fusion target, get the relationship between each measurement and target, and build multisource target data fusion tracking model to ensure the stable state of fusion results. It shows that the proposed method can save the tracking time and improve the tracking accuracy compared with the methods based on NNDA and PDA, which is more conducive to the real-time tracking of multisource targets.

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Guo, Hongyan, and Xintao Li. "Multisource Target Data Fusion Tracking Method for Heterogeneous Network Based on Data Mining." Wireless Communications and Mobile Computing 2022 (June 10, 2022): 1–10. http://dx.doi.org/10.1155/2022/9291319.

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This research is on heterogeneous network fusion method of multisource target data based on data mining. Firstly, it is a distributed storage structure model for building heterogeneous network multisource target data. Then, using the phase space reconstruction method, a grid distribution structure model for data fusion tracking is constructed, and realize visual scheduling and automatic monitoring of multisource target data. Finally, according to the feature extraction results, analyze the statistical characteristics of multisource target data in heterogeneous networks, combined with the fuzzy tomographic analysis method, multilevel fusion, and adaptive mining of multisource target data, extract the associated feature quantities in it, and realize the fusion tracking of data. The simulation results show that, in relatively simple heterogeneous networks, the feature mining error of the proposed method is nearly 2.11% lower than the two traditional methods. In relatively complex heterogeneous networks, the feature mining error of the proposed method is nearly 6.48% lower than the two traditional methods. It can be seen that this method has better adaptability for fusion tracking of heterogeneous network multisource target data, the anti-interference ability is strong, and the tracking accuracy in the data fusion tracking process is also improved.

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Goldring, Ellen B., Madeline Mavrogordato, and Katherine Taylor Haynes. "Multisource Principal Evaluation Data." Educational Administration Quarterly 51, no. 4 (November 4, 2014): 572–99. http://dx.doi.org/10.1177/0013161x14556152.

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Liu, Xiaolun. "Local Government Governance Path Optimization Based on Multisource Big Data." Mathematical Problems in Engineering 2022 (June 21, 2022): 1–10. http://dx.doi.org/10.1155/2022/1941558.

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With the development of Internet technology, multisource big data can collect and analyze information so as to provide people with a good vision. In the process of governance, local governments will have problems of incomplete information. With the development of big data, multisource and big data will have advanced nature. Therefore, based on multisource big data, this paper analyzes the multisource big data algorithm in detail and establishes a local government governance model based on multisource big data. Then, the proposed model is applied to the local government governance process of Beijing, Shanghai, Chongqing, and Tianjin, and the local governance situation of each city is compared and analyzed so as to provide some reference for the optimization of the local government governance path. The experimental results show that the local governance model based on multisource big data can optimize the local government governance path and point out the direction for the local government governance path.

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Zhang, Haibo. "Music Emotion Representation Learning Based on Multisource Data Fusion and Its Application." Mobile Information Systems 2022 (September 27, 2022): 1–9. http://dx.doi.org/10.1155/2022/3983201.

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Based on the multisource data fusion and fusion of music emotion expression learning and its application, this paper further analyzes the actual influence of music emotion and Internet multisource data in emotion learning. This paper uses multisource data to improve the professionalism and concentration of music emotion and uses modern Internet technology to help users quickly integrate into music emotion learning. At the same time, the multisource data structure can develop the music learning structure to a high-quality level. It is precisely because of the emphasis of the multisource data model architecture that the learning mechanism of musical emotion representation can be continuously updated and improved, which is mutually promoted jointly with Internet technology.

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Ju, Ankang, Yuanbo Guo, Ziwei Ye, Tao Li, and Jing Ma. "HeteMSD: A Big Data Analytics Framework for Targeted Cyber-Attacks Detection Using Heterogeneous Multisource Data." Security and Communication Networks 2019 (May 2, 2019): 1–9. http://dx.doi.org/10.1155/2019/5483918.

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In the current enterprise network environment, multistep targeted cyber-attacks with concealment and advanced characteristics have become the main threat. Multisource security data are the prerequisite of targeted cyber-attacks detection. However, these data have characters of heterogeneity and semantic diversity, and existing attack detection methods do not take comprehensive data sources into account. Identifying and predicting attack intention from heterogeneous noisy data can be meaningful work. In this paper, we first review different data fusion mechanisms of correlating heterogeneous multisource data. On this basis, we propose a big data analytics framework for targeted cyber-attacks detection and give the basic idea of correlation analysis. Our approach will offer the ability to correlate multisource heterogeneous security data and analyze attack intention effectively.

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NOVOSAD, Mariia-Ruslana. "MULTISOURCE INTELLIGENT PARKING ASSISTANT." Herald of Khmelnytskyi National University. Technical sciences 313, no. 5 (October 27, 2022): 56–60. http://dx.doi.org/10.31891/2307-5732-2022-313-5-56-60.

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Problem searching for a parking space is time-consuming and highly relevant both in Ukraine and abroad. Time spent searching for a parking space leads to excessive traffic, more traffic jams, air pollution and increased fuel consumption. These factors also affect the daily stress levels of drivers. Due to this, the process of finding a parking space should be fast and convenient. At the same time, there has been significant development of real estate in Lviv over the past few years. Accordingly, the need for organizing the process of parking cars of residents of residential areas is growing. This paper presents the results of the development of an application for a quick and convenient search for a parking space. A review of similar software applications was conducted. Proposed solutions use various technologies to solve the problem of searching for a free parking space including IoT, sensors, machine learning for image recognition. Even though they solve the problem of searching for a free parking space, most of them can be expensive to implement, maintain, they don’t provide the ability to work with different data sources. An activity diagram of system is presented and it shows two main flows of the system: displaying the current state of parking spaces and displaying parking space by the number of the car entering the territory of the complex. System consists of three modules. The first module is responsible for working with different data sources, storing the status of parking spaces, processing requests. Image processing module is responsible for determining the occupied and free parking spaces from the image. The third module is responsible for the correct display of parking spaces and their statuses. It is also demonstrated how the application works with different data sources and how exceptions are handled. The system works correctly and has a сlear interface. The parking assistant is a great helper and significantly reduces the time required to find a free parking space.

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Wang, Zichi. "Multisource Data Hiding in Digital Images." Symmetry 14, no. 5 (April 27, 2022): 890. http://dx.doi.org/10.3390/sym14050890.

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In this paper, we propose a new data-hiding framework: multisource data hiding, in which multiple senders (multiple sources) are able to transmit different secret data to a receiver via the same cover image symmetrically. We propose two multisource data-hiding schemes, i.e., separable and anonymous, according to different applications. In the separable scheme, the receiver can extract the secret data transmitted by all senders using the symmetrical data-hiding key. A sender is unable to know the content of the secret data that is not transmitted by them (non-source sender). In the anonymous scheme, it is unnecessary to extract all secret data on the receiver side. The content extracted by the receiver is a co-determined result of the secret data transmitted by all senders. Details of the secret data are unknown to the receiver and the non-source senders. In addition, the two proposed schemes achieve multisource data hiding without decreasing the undetectability of data hiding.

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Datcu, M., F. Melgani, A. Piardi, and S. B. Serpico. "Multisource data classification with dependence trees." IEEE Transactions on Geoscience and Remote Sensing 40, no. 3 (March 2002): 609–17. http://dx.doi.org/10.1109/tgrs.2002.1000321.

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Amarsaikhan, D., and T. Douglas*. "Data fusion and multisource image classification." International Journal of Remote Sensing 25, no. 17 (September 2004): 3529–39. http://dx.doi.org/10.1080/0143116031000115111.

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Більше джерел

Дисертації з теми "MULTISOURCE DATA"

Fiskio-Lasseter, John Howard Eli. "Specification and solution of multisource data flow problems /." view abstract or download file of text, 2006. http://proquest.umi.com/pqdweb?did=1280151111&sid=1&Fmt=2&clientId=11238&RQT=309&VName=PQD.

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Thesis (Ph. D.)--University of Oregon, 2006.
Typescript. Includes vita and abstract. Includes bibliographical references (leaves 150-162). Also available for download via the World Wide Web; free to University of Oregon users.

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Filiberti, Daniel Paul. "Combined Spatial-Spectral Processing of Multisource Data Using Thematic Content." Diss., Tucson, Arizona : University of Arizona, 2005. http://etd.library.arizona.edu/etd/GetFileServlet?file=file:///data1/pdf/etd/azu%5Fetd%5F1066%5F1%5Fm.pdf&type=application/pdf.

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Kayani, Amina Josetta. "Critical determinants influencing employee reactions to multisource feedback systems." Thesis, Curtin University, 2009. http://hdl.handle.net/20.500.11937/150.

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The current study examines the Multisource Feedback (MSF) system by investigating the impact several MSF design and implementation factors have on employees’ reaction towards the system. The fundamental goal of the research was to advance the understanding of what is currently known about effectively implementing multisource feedback systems to maximize employee favorable reaction, acceptance and perceptions of usefulness.Of the many management feedback trends that have swept organizations in the past decade, few have had the outstanding impact of MSF. Despite the numerous studies on MSF, perusal of empirical literature lacks overall cohesion in identifying critical factors influencing employees’ reactions to MSF. The constructs examined were delimited to those found to have inherent paradoxes, insufficient coverage, be inconclusive and/or have contradictory findings in the extant literature.A series of main research questions, underscoring the main goal of the study, were developed from the gaps identified in literature to establish which predictors were predominant in influencing the employees’ reactions, acceptance and perceptions of usefulness towards the MSF system. These research questions were formed into hypotheses for testing. The relationships to be tested were integrated into a hypothetical model which encompassed four sub-models to be tested. The models, named the Climate, Reaction, Reaction-Acceptance, Reaction-Perceptions of Usefulness and Acceptance-Perceptions of Usefulness Models were tested in parts using a combination of exploratory factor analysis, correlation analysis and multiple regressions. Further, key informants from each organization and HR managers in three large organizations provided post-survey feedback and information to assist with the elucidation of quantitative findings; this represented the pluralist approach taken in the study.Survey items were derived from extant literature as well as developed specifically for the study. Further, the items were refined using expert reviewers and a pilot study. A cross-sectional web-based survey was administered to employees from a range of managerial levels in three large Malaysian multinational organizations. A total of 420 useable surveys were received, representing a response rate of 47%.Self-report data was used to measure the constructs which were perceptions of the various facets of the MSF. An empirical methodology was used to test the hypotheses to enable the research questions to be answered and to suggest a final model of Critical Determinants Influencing Employee Reaction to MSF Systems.The study was conducted in six phases. In the first phase, a literature map was drawn highlighting the gaps in empirical research. In the second stage, a hypothetical model of employees’ reaction to MSF was developed from past empirical research and literature on MSF. The third phase involved drafting a survey questionnaire on the basis of available literature, with input from academics and practitioners alike. The fourth stage entailed pilot testing the survey instrument using both the ‘paper and pencil’ and web-based methods. The surveys were administered with the assistance of the key informants of the participant organizations in the fifth stage of the study; data received were analysed using a range of statistical tools within SPSS version 15. Content analysis was utilized to categorize themes that emerged from an open-ended question. In the sixth and final stage, empirical results from the quantitative analysis were presented to HR managers to glean first hand understanding over the patterns that emerged.Exploratory factor analysis and reliability analysis indicated that the surveyinstrument was sound in terms of validity and reliability. In the Climate model, itwas found that all the hypothesized predictors, feedback-seeking environment,control over organizational processes, understanding over organizational events,operational support and political awareness were positively associated withpsychological climate for MSF implementation. In terms of predictive power, controlover organizational processes failed to attain significance at the 5% level. In theReaction model, it was found that perceived purpose, perceived anonymity,complexity and rater assignment processes had significant associations withemployee reaction to MSF, but perceived anonymity indicated poor predictive powerfrom the regressions results. As hypothesized, employee reaction was found to be related to MSF acceptance and perceptions of usefulness, and results indicated thatthe two latter outcome constructs were related, but statistically distinct.The two-tier pluralist technique of collecting and examining data was a salient feature of the current study. Indeed, such a holistic approach to investigating the determinants of employee reaction to MSF allowed for better integration of its theory and practice. The study is believed to make a modest, but unique contribution to knowledge, advancing the body of knowledge towards a better understanding of MSF design and implementation issues.The results have implications for calibrating MSF systems and evaluating the needfor, and likely effectiveness of, what has been hailed as one of the powerful newmodels for management feedback in the past two decades. Suggestions were madeabout how the results could benefit academia and practitioners alike. Since mostorganizational and management research has a western ethnocentric bias, the current study encompassed eastern evidence, using cases in Malaysia.

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Peterson, Dwight M. "The Merging of Multisource Telemetry Data to Support Over the Horizon Missile Testing." International Foundation for Telemetering, 1995. http://hdl.handle.net/10150/608414.

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International Telemetering Conference Proceedings / October 30-November 02, 1995 / Riviera Hotel, Las Vegas, Nevada
The testing of instrumented missile systems with extended range capabilities present many challenges to existing T&E and training ranges. Providing over-the-horizon (OTH) telemetry data collection and displaying portions of this data in real time for range safety purposes are just a few of many factors required for successful instrumented range support. Techniques typically used for OTH telemetry data collection are to use fixed or portable antennas installed at strategic down-range locations, instrumented relay pods installed on chase aircraft, and instrumented high flying relay aircraft. Multiple data sources from these various locations typically arrive at a central site within a telemetry ground station and must be merged together to determine the best data source for real time and post processing purposes. Before multiple telemetered sources can be merged, the time skews caused by the relay of down-range land and airborne based sources must be taken into account. The time skews are fixed for land based sources, but vary with airborne sources. Various techniques have been used to remove the time skews associated with multiple telemetered sources. These techniques, which involve both hardware and software applications, have been effective, but are expensive and application and range dependent. This paper describes the use of a personal computer (PC) based workstation, configured with independent Pulse Code Modulation (PCM) decommutators/bit synchronizers, Inner-Range Instrumentation Group (IRIG) timing, and data merging resident software to perform the data merging task. Current technology now permits multiple PCM decommutators, each built as a separate virtual memory expansion (VME) card, to be installed within a PC based workstation. Each land based or airborne source is connected to a dedicated VME based PCM decommutator/bit synchronizer within the workstation. After the exercise has been completed, data merging software resident within the workstation is run which reads the digitized data from each of the disk files and aligns the data on a bit by bit basis to determine the optimum merged result. Both time based and event based alignment is performed when merging the multiple sources.This technique has application for current TOMAHAWK exercises performed at the Air Force Development Test Center, Eglin Air Force Base (AFB), Florida and the Naval Air Warfare Center/Weapons Division (NAWC/WD), Point Mugu, California and future TOMAHAWK Baseline Improvement Program (TBIP) testing.

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Papadopoulos, Georgios. "Towards a 3D building reconstruction using spatial multisource data and computational intelligence techniques." Thesis, Limoges, 2019. http://www.theses.fr/2019LIMO0084/document.

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La reconstruction de bâtiments à partir de photographies aériennes et d’autres données spatiales urbaines multi-sources est une tâche qui utilise une multitude de méthodes automatisées et semi-automatisées allant des processus ponctuels au traitement classique des images et au balayage laser. Dans cette thèse, un système de relaxation itératif est développé sur la base de l'examen du contexte local de chaque bord en fonction de multiples sources d'entrée spatiales (masques optiques, d'élévation, d'ombre et de feuillage ainsi que d'autres données prétraitées, décrites au chapitre 6). Toutes ces données multisource et multirésolution sont fusionnées de manière à extraire les segments de ligne probables ou les arêtes correspondant aux limites des bâtiments. Deux nouveaux sous-systèmes ont également été développés dans cette thèse. Ils ont été conçus dans le but de fournir des informations supplémentaires, plus fiables, sur les contours des bâtiments dans une future version du système de relaxation proposé. La première est une méthode de réseau de neurones à convolution profonde (CNN) pour la détection de frontières de construction. Le réseau est notamment basé sur le modèle SRCNN (Dong C. L., 2015) de super-résolution à la pointe de la technologie. Il accepte des photographies aériennes illustrant des données de zones urbaines densément peuplées ainsi que leurs cartes d'altitude numériques (DEM) correspondantes. La formation utilise trois variantes de cet ensemble de données urbaines et vise à détecter les contours des bâtiments grâce à une nouvelle cartographie hétéroassociative super-résolue. Une autre innovation de cette approche est la conception d'une couche de perte personnalisée modifiée appelée Top-N. Dans cette variante, l'erreur quadratique moyenne (MSE) entre l'image de sortie reconstruite et l'image de vérité de sol (GT) fournie des contours de bâtiment est calculée sur les 2N pixels de l'image avec les valeurs les plus élevées. En supposant que la plupart des N pixels de contour de l’image GT figurent également dans les 2N pixels supérieurs de la reconstruction, cette modification équilibre les deux catégories de pixels et améliore le comportement de généralisation du modèle CNN. Les expériences ont montré que la fonction de coût Top-N offre des gains de performance par rapport à une MSE standard. Une amélioration supplémentaire de la capacité de généralisation du réseau est obtenue en utilisant le décrochage. Le deuxième sous-système est un réseau de convolution profonde à super-résolution, qui effectue un mappage associatif à entrée améliorée entre les images d'entrée à basse résolution et à haute résolution. Ce réseau a été formé aux données d’altitude à basse résolution et aux photographies urbaines optiques à haute résolution correspondantes. Une telle différence de résolution entre les images optiques / satellites optiques et les données d'élévation est souvent le cas dans les applications du monde réel
Building reconstruction from aerial photographs and other multi-source urban spatial data is a task endeavored using a plethora of automated and semi-automated methods ranging from point processes, classic image processing and laser scanning. In this thesis, an iterative relaxation system is developed based on the examination of the local context of each edge according to multiple spatial input sources (optical, elevation, shadow & foliage masks as well as other pre-processed data as elaborated in Chapter 6). All these multisource and multiresolution data are fused so that probable line segments or edges are extracted that correspond to prominent building boundaries.Two novel sub-systems have also been developed in this thesis. They were designed with the purpose to provide additional, more reliable, information regarding building contours in a future version of the proposed relaxation system. The first is a deep convolutional neural network (CNN) method for the detection of building borders. In particular, the network is based on the state of the art super-resolution model SRCNN (Dong C. L., 2015). It accepts aerial photographs depicting densely populated urban area data as well as their corresponding digital elevation maps (DEM). Training is performed using three variations of this urban data set and aims at detecting building contours through a novel super-resolved heteroassociative mapping. Another innovation of this approach is the design of a modified custom loss layer named Top-N. In this variation, the mean square error (MSE) between the reconstructed output image and the provided ground truth (GT) image of building contours is computed on the 2N image pixels with highest values . Assuming that most of the N contour pixels of the GT image are also in the top 2N pixels of the re-construction, this modification balances the two pixel categories and improves the generalization behavior of the CNN model. It is shown in the experiments, that the Top-N cost function offers performance gains in comparison to standard MSE. Further improvement in generalization ability of the network is achieved by using dropout.The second sub-system is a super-resolution deep convolutional network, which performs an enhanced-input associative mapping between input low-resolution and high-resolution images. This network has been trained with low-resolution elevation data and the corresponding high-resolution optical urban photographs. Such a resolution discrepancy between optical aerial/satellite images and elevation data is often the case in real world applications. More specifically, low-resolution elevation data augmented by high-resolution optical aerial photographs are used with the aim of augmenting the resolution of the elevation data. This is a unique super-resolution problem where it was found that many of -the proposed general-image SR propositions do not perform as well. The network aptly named building super resolution CNN (BSRCNN) is trained using patches extracted from the aforementioned data. Results show that in comparison with a classic bicubic upscale of the elevation data the proposed implementation offers important improvement as attested by a modified PSNR and SSIM metric. In comparison, other proposed general-image SR methods performed poorer than a standard bicubic up-scaler.Finally, the relaxation system fuses together all these multisource data sources comprising of pre-processed optical data, elevation data, foliage masks, shadow masks and other pre-processed data in an attempt to assign confidence values to each pixel belonging to a building contour. Confidence is augmented or decremented iteratively until the MSE error fails below a specified threshold or a maximum number of iterations have been executed. The confidence matrix can then be used to extract the true building contours via thresholding

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Bascol, Kevin. "Adaptation de domaine multisource sur données déséquilibrées : application à l'amélioration de la sécurité des télésièges." Thesis, Lyon, 2019. http://www.theses.fr/2019LYSES062.

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Bluecime a mis au point un système de vidéosurveillance à l'embarquement de télésièges qui a pour but d'améliorer la sécurité des passagers. Ce système est déjà performant, mais il n'utilise pas de techniques d'apprentissage automatique et nécessite une phase de configuration chronophage. L’apprentissage automatique est un sous-domaine de l'intelligence artificielle qui traite de l'étude et de la conception d'algorithmes pouvant apprendre et acquérir des connaissances à partir d'exemples pour une tâche donnée. Une telle tâche pourrait consister à classer les situations sûres ou dangereuses dans les télésièges à partir d'exemples d'images déjà étiquetées dans ces deux catégories, appelés exemples d’entraînement. L'algorithme d'apprentissage automatique apprend un modèle capable de prédire la catégories de nouveaux cas. Depuis 2012, il a été démontré que les modèles d'apprentissage profond sont les modèles d'apprentissage machine les mieux adaptés pour traiter les problèmes de classification d'images lorsque de nombreuses données d’entraînement sont disponibles. Dans ce contexte, cette thèse, financée par Bluecime, vise à améliorer à la fois le coût et l'efficacité du système actuel de Bluecime grâce à l'apprentissage profond
Bluecime has designed a camera-based system to monitor the boarding station of chairlifts in ski resorts, which aims at increasing the safety of all passengers. This already successful system does not use any machine learning component and requires an expensive configuration step. Machine learning is a subfield of artificial intelligence which deals with studying and designing algorithms that can learn and acquire knowledge from examples for a given task. Such a task could be classifying safe or unsafe situations on chairlifts from examples of images already labeled with these two categories, called the training examples. The machine learning algorithm learns a model able to predict one of these two categories on unseen cases. Since 2012, it has been shown that deep learning models are the best suited machine learning models to deal with image classification problems when many training data are available. In this context, this PhD thesis, funded by Bluecime, aims at improving both the cost and the effectiveness of Bluecime's current system using deep learning

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Ben, Hassine Soumaya. "Évaluation et requêtage de données multisources : une approche guidée par la préférence et la qualité des données : application aux campagnes marketing B2B dans les bases de données de prospection." Thesis, Lyon 2, 2014. http://www.theses.fr/2014LYO22012/document.

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Avec l’avènement du traitement distribué et l’utilisation accrue des services web inter et intra organisationnels alimentée par la disponibilité des connexions réseaux à faibles coûts, les données multisources partagées ont de plus en plus envahi les systèmes d’informations. Ceci a induit, dans un premier temps, le changement de leurs architectures du centralisé au distribué en passant par le coopératif et le fédéré ; et dans un deuxième temps, une panoplie de problèmes d’exploitation allant du traitement des incohérences des données doubles à la synchronisation des données distribuées. C’est le cas des bases de prospection marketing où les données sont enrichies par des fichiers provenant de différents fournisseurs.Nous nous intéressons au cadre particulier de construction de fichiers de prospection pour la réalisation de campagnes marketing B-to-B, tâche traitée manuellement par les experts métier. Nous visons alors à modéliser le raisonnement de brokers humains, afin d’optimiser et d’automatiser la sélection du « plan fichier » à partir d’un ensemble de données d’enrichissement multisources. L’optimisation en question s’exprimera en termes de gain (coût, qualité) des données sélectionnées, le coût se limitant à l’unique considération du prix d’utilisation de ces données.Ce mémoire présente une triple contribution quant à la gestion des bases de données multisources. La première contribution concerne l’évaluation rigoureuse de la qualité des données multisources. La deuxième contribution porte sur la modélisation et l’agrégation préférentielle des critères d’évaluation qualité par l’intégrale de Choquet. La troisième contribution concerne BrokerACO, un prototype d’automatisation et d’optimisation du brokering multisources basé sur l’algorithme heuristique d’optimisation par les colonies de fourmis (ACO) et dont la Pareto-optimalité de la solution est assurée par l’utilisation de la fonction d’agrégation des préférences des utilisateurs définie dans la deuxième contribution. L’efficacité du prototype est montrée par l’analyse de campagnes marketing tests effectuées sur des données réelles de prospection
In Business-to-Business (B-to-B) marketing campaigns, manufacturing “the highest volume of sales at the lowest cost” and achieving the best return on investment (ROI) score is a significant challenge. ROI performance depends on a set of subjective and objective factors such as dialogue strategy, invested budget, marketing technology and organisation, and above all data and, particularly, data quality. However, data issues in marketing databases are overwhelming, leading to insufficient target knowledge that handicaps B-to-B salespersons when interacting with prospects. B-to-B prospection data is indeed mainly structured through a set of independent, heterogeneous, separate and sometimes overlapping files that form a messy multisource prospect selection environment. Data quality thus appears as a crucial issue when dealing with prospection databases. Moreover, beyond data quality, the ROI metric mainly depends on campaigns costs. Given the vagueness of (direct and indirect) cost definition, we limit our focus to price considerations.Price and quality thus define the fundamental constraints data marketers consider when designing a marketing campaign file, as they typically look for the "best-qualified selection at the lowest price". However, this goal is not always reachable and compromises often have to be defined. Compromise must first be modelled and formalized, and then deployed for multisource selection issues. In this thesis, we propose a preference-driven selection approach for multisource environments that aims at: 1) modelling and quantifying decision makers’ preferences, and 2) defining and optimizing a selection routine based on these preferences. Concretely, we first deal with the data marketer’s quality preference modelling by appraising multisource data using robust evaluation criteria (quality dimensions) that are rigorously summarized into a global quality score. Based on this global quality score and data price, we exploit in a second step a preference-based selection algorithm to return "the best qualified records bearing the lowest possible price". An optimisation algorithm, BrokerACO, is finally run to generate the best selection result

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Mondésir, Jacques Philémon. "Apports de la texture multibande dans la classification orientée-objets d'images multisources (optique et radar)." Mémoire, Université de Sherbrooke, 2016. http://hdl.handle.net/11143/9706.

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Résumé : La texture dispose d’un bon potentiel discriminant qui complète celui des paramètres radiométriques dans le processus de classification d’image. L’indice Compact Texture Unit (CTU) multibande, récemment mis au point par Safia et He (2014), permet d’extraire la texture sur plusieurs bandes à la fois, donc de tirer parti d’un surcroît d’informations ignorées jusqu’ici dans les analyses texturales traditionnelles : l’interdépendance entre les bandes. Toutefois, ce nouvel outil n’a pas encore été testé sur des images multisources, usage qui peut se révéler d’un grand intérêt quand on considère par exemple toute la richesse texturale que le radar peut apporter en supplément à l’optique, par combinaison de données. Cette étude permet donc de compléter la validation initiée par Safia (2014) en appliquant le CTU sur un couple d’images optique-radar. L’analyse texturale de ce jeu de données a permis de générer une image en « texture couleur ». Ces bandes texturales créées sont à nouveau combinées avec les bandes initiales de l’optique, avant d’être intégrées dans un processus de classification de l’occupation du sol sous eCognition. Le même procédé de classification (mais sans CTU) est appliqué respectivement sur : la donnée Optique, puis le Radar, et enfin la combinaison Optique-Radar. Par ailleurs le CTU généré sur l’Optique uniquement (monosource) est comparé à celui dérivant du couple Optique-Radar (multisources). L’analyse du pouvoir séparateur de ces différentes bandes à partir d’histogrammes, ainsi que l’outil matrice de confusion, permet de confronter la performance de ces différents cas de figure et paramètres utilisés. Ces éléments de comparaison présentent le CTU, et notamment le CTU multisources, comme le critère le plus discriminant ; sa présence rajoute de la variabilité dans l’image permettant ainsi une segmentation plus nette, une classification à la fois plus détaillée et plus performante. En effet, la précision passe de 0.5 avec l’image Optique à 0.74 pour l’image CTU, alors que la confusion diminue en passant de 0.30 (dans l’Optique) à 0.02 (dans le CTU).
Abstract : Texture has a good discriminating power which complements the radiometric parameters in the image classification process. The index Compact Texture Unit multiband, recently developed by Safia and He (2014), allows to extract texture from several bands at a time, so taking advantage of extra information not previously considered in the traditional textural analysis: the interdependence between bands. However, this new tool has not yet been tested on multi-source images, use that could be an interesting added-value considering, for example, all the textural richness the radar can provide in addition to optics, by combining data. This study allows to complete validation initiated by Safia (2014), by applying the CTU on an optics-radar dataset. The textural analysis of this multisource data allowed to produce a "color texture" image. These newly created textural bands are again combined with the initial optical bands before their use in a classification process of land cover in eCognition. The same classification process (but without CTU) was applied respectively to: Optics data, then Radar, finally on the Optics-Radar combination. Otherwise, the CTU generated on the optics separately (monosource) was compared to CTU arising from Optical-Radar couple (multisource). The analysis of the separating power of these different bands (radiometric and textural) with histograms, and the confusion matrix tool allows to compare the performance of these different scenarios and classification parameters. These comparators show the CTU, including the CTU multisource, as the most discriminating criterion; his presence adds variability in the image thus allowing a clearer segmentation (homogeneous and non-redundant), a classification both more detailed and more efficient. Indeed, the accuracy changes from 0.5 with the Optics image to 0.74 for the CTU image while confusion decreases from 0.30 (in Optics) to 0.02 (in the CTU).

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Zamite, João Miguel Quintino de Morais 1985. "Multisource epidemic data collector." Master's thesis, 2010. http://hdl.handle.net/10451/2346.

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Tese de mestrado. Biologia (Bioinformática e Biologia Computacional). Universidade de Lisboa, Faculdade de Ciências, 2010
Epidemic surveillance has recently been subject to the development of Web based information retrieval systems. The majority of these systems extract information directly from users, official epidemic reports or news sources. Others extract epidemic data fromInternet based social network services. The currently existing epidemic surveillance systems are mostly monolithic, not being designed for knowledge share or their integration with other applications such as epidemic forecasting tools. In this dissertation, an approach is presented to the creation of a data collection system which enables the integration of data from diverse sources. Based on the principles of interoperability and modularity, this system not only addresses the current needs for data integration but is also expansible to enable data extraction from future epidemic data sources. This system was developed as a module for the ”Epidemic Marketplace” under the EPIWORK project with the objective of becoming a valuable data source for epidemic modeling and forecasting tools. This document describes the requirements and development stages for this epidemic surveillance system and its evaluation.
Nos últimos anos, a vigilância epidemiológica tem sido um campo de desenvolvimento de sistemas de recolha de informação da Web. A maioria destes sistemas extraem informação directamente dos utilizadores, de relatórios oficiais ou de fontes noticiosas, enquanto outros extraem dados epidemiológicos de redes sociais da Internet. Estes sistemas de vigilância epidemiológica são na sua maioria monolíticos, não sendo desenhados para a partilha de dados e sua integração com outras aplicacões, como ferramentas de previsão epidemiológica. Ao longo desta dissertação apresento uma abordagempara a criação de um sistema de colecta de dados que permite a integração de dados de diversas fontes. Baseado nos princípios de interoperabilidade e modularidade, este sistema não só aborda a necessidade para a integração de informação mas é também expansível para permitir a extracção de dados de fontes futuras de dados epidemiológicos. Este sistema foi desenvolvido como um módulo para o ”Epidemic Marketplace” no projecto EPIWORK com o objectivo de se tornar uma fonte de dados para ferramentas de modelação e previsão epidemiológica. Este documento descreve os requisitos e fases de desenvolvimento deste sistema de vigilância epidemiológica bem como a sua avaliação.
European Commission - EPIWORK project under the Seventh Framework Programme (Grant # 231807), the EPIWORK project partners, CMU-Portugal partnership and FCT (Portuguese research funding agency) for its LaSIGE Multi-annual support

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You, MIn-Ruei, and 游旻叡. "Deriving deep sea seafood tracability maps using multisource data aggregation." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/remqvk.

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碩士
國立臺灣海洋大學
資訊工程學系
106
Taiwan is an island country surrounded by the ocean that is rich in marine resources. According to Taiwan Fishery Agency's statistical data, the fishery industry is very important since it produces an estimated value of 86 billion NTD per year. However, marine resources are not unlimited and may deplete if not sustained. Illegal, unreported, and unregulated (IUU) fishing is a major reason of unsustained fishery and has aroused concerned of different sectors in the community. To strengthen the management of deep sea fisheries, Fisheries Agency, Council of Agriculture, Executive Yuan established Fisheries Management Center. Since July 1, 2016, the Fisheries Agency implemented a declaration system for landing and began to use a new generation eLogbook system, hoping that these strategies can make monitoring and management more complete. The international regulation attaches great importance to the traceability of seafood, which is a key to battle IUU fishing. In Taiwan, the Agricultural Traceability System has already been established. However, there is no such system yet in the deep sea fishery sector. From the landing declarations and eLogbook system developed by the Fisheries Agency, we can construct a traceability map of deep sea seafood. We apply data aggregation techniques on multisource data and use Closest Point of Approach (CPA) algorithm to estimate the position where the fishing vessel transships to another vessel. This seafood traceability map system can map catch and transshipment information of major fish products. It provides a web-based visualization interface which can show either the region of catch or travel map of produces. Authorities and users can use this system to understand the source and validity of fish catches.

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Книги з теми "MULTISOURCE DATA"

E, Wright Bruce, and Geological Survey (U.S.). National Mapping Division, eds. Integrating multisource land use and land cover data. [Reston, Va.]: U.S. Dept. of the Interior, U.S. Geological Survey, National Mapping Division, 1995.

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Mahler, Ronald P. S. Advances in statistical multisource-multitarget information fusion. Boston: Artech House, 2014.

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V, Dasarathy Belur, Society of Photo-optical Instrumentation Engineers., and Ball Aerospace & Technologies Corporation (USA), eds. Multisensor, multisource information fusion : architectures, algorithms, and applications 2005: 30-31 March, 2005, Orlando, Florida, USA. Bellingham, Wash: SPIE, 2005.

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V, Dasarathy Belur, and Society of Photo-optical Instrumentation Engineers., eds. Multisensor, multisource information fusion: Architectures, algorithms, and applications 2006 : 19-20 April 2006, Kissimmee, Florida, USA. Bellingham, Wash: SPIE, 2006.

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Braun, Jerome J. Multisensor, multisource information fusion: Architectures, algorithms, and applications 2010 : 7-8 April 2010, Orlando, Florida, United States. Bellingham, Wash: SPIE, 2010.

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(Society), SPIE, ed. Multisensor, multisource information fusion: Architectures, algorithms, and applications 2009 : 16-17 April 2009, Orlando, Florida, United States. Bellingham, Wash: SPIE, 2009.

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Braun, Jerome J. Multisensor, multisource information fusion: Architectures, algorithms, and applications 2011 : 27-28 April 2011, Orlando, Florida, United States. Bellingham, Wash: SPIE, 2011.

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V, Dasarathy Belur, and Society of Photo-optical Instrumentation Engineers., eds. Multisensor, multisource information fusion--architectures, algorithms, and applications 2003: 23-25 April 2003, Orlando, Florida, USA. Bellingham, Wash: SPIE, 2003.

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V, Dasarathy Belur, and Society of Photo-optical Instrumentation Engineers., eds. Multisensor, multisource information fusion: Architectures, algorithms, and applications 2007 : 11-12 April, 2007, Orlando, Florida, USA. Bellingham, Wash: SPIE, 2007.

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Kim, Hakil. A method of classification for multisource data in remote sensing based on interval-valued probabilties. West Lafayette, Indiana: Laboratory for Applications of Remote Sensing and School of Electrical Engineering, Purdue University, 1990.

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Zamite, João, Fabrício A. B. Silva, Francisco Couto, and Mário J. Silva. "MEDCollector: Multisource Epidemic Data Collector." In Transactions on Large-Scale Data- and Knowledge-Centered Systems IV, 40–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23740-9_3.

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Zamite, João, Fabrício A. B. Silva, Francisco Couto, and Mário J. Silva. "MEDCollector: Multisource Epidemic Data Collector." In Information Technology in Bio- and Medical Informatics, ITBAM 2010, 16–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15020-3_2.

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Gao, Xueyuan, and Fuyuan Xiao. "A Generalized $$\chi ^2$$ Divergence for Multisource Information Fusion." In Data Mining and Big Data, 175–84. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-7502-7_20.

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Waske, Björn, and Jón Atli Benediktsson. "Decision Fusion, Classification of Multisource Data." In Encyclopedia of Remote Sensing, 140–44. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-0-387-36699-9_34.

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Ardagna, Danilo, Cinzia Cappiello, Chiara Francalanci, and Annalisa Groppi. "Brokering Multisource Data with Quality Constraints." In On the Move to Meaningful Internet Systems 2006: CoopIS, DOA, GADA, and ODBASE, 807–17. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11914853_49.

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Liu, Yan. "Traps in Multisource Heterogeneous Big Data Processing." In Artificial Intelligence on Fashion and Textiles, 229–35. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99695-0_28.

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Rhodes, Philip J., R. Daniel Bergeron, and Ted M. Sparr. "Database Support for Multisource Multiresolution Scientific Data." In SOFSEM 2002: Theory and Practice of Informatics, 94–114. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-36137-5_5.

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Rhodes, Philip J., R. Daniel Bergeron, and Ted M. Sparr. "A Data Model for Distributed Multiresolution Multisource Scientific Data." In Mathematics and Visualization, 297–317. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-642-55787-3_18.

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Ignaciuk, Przemysław, and Andrzej Bartoszewicz. "Flow Control in a Multisource Discrete-Time System." In Congestion Control in Data Transmission Networks, 197–288. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-4147-1_6.

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Benediktsson, Jon Atli, and Johannes R. Sveinsson. "Consensus Based Classification of Multisource Remote Sensing Data." In Multiple Classifier Systems, 280–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-45014-9_27.

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Тези доповідей конференцій з теми "MULTISOURCE DATA"

Woodley, Robert, Michael Gosnell, and Kevin Shallenberger. "Multisource causal data mining." In SPIE Defense, Security, and Sensing, edited by Jerome J. Braun. SPIE, 2012. http://dx.doi.org/10.1117/12.919399.

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Guðgeirsson, Jon, Anna Soffia Hauksdottir, and Palmi Simonarson. "Multisource Flight Surveillance Radar Data Fusion." In AIAA Guidance, Navigation, and Control Conference and Exhibit. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2002. http://dx.doi.org/10.2514/6.2002-4934.

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Yang, Nian-Xiang, Xue-Bo Jin, Ting-Li Su, and Jian-Lei Kong. "Multisource Data Analysis for Stock Prediction." In 2018 10th International Conference on Modelling, Identification and Control (ICMIC). IEEE, 2018. http://dx.doi.org/10.1109/icmic.2018.8529942.

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Fernandez-Prieto, Diego, and Olivier Arino. "Partially supervised classification of multisource data." In International Symposium on Remote Sensing, edited by Sebastiano B. Serpico. SPIE, 2003. http://dx.doi.org/10.1117/12.463173.

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Carchiolo, Vincenza, Alessandro Longheu, Michele Malgeri, and Giuseppe Mangioni. "Multisource agent-based healthcare data gathering." In 2015 Federated Conference on Computer Science and Information Systems. IEEE, 2015. http://dx.doi.org/10.15439/2015f302.

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Alam, M. S. "Data fusion based target tracking in FLIR imagery." In Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2008. SPIE, 2008. http://dx.doi.org/10.1117/12.778390.

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Chibunichev, Alexander G., Denis V. Zhuravlev, and Vladimir A. Knyaz. "Multisource data fusion for documenting archaeological sites." In Image and Signal Processing for Remote Sensing, edited by Lorenzo Bruzzone, Francesca Bovolo, and Jon Atli Benediktsson. SPIE, 2017. http://dx.doi.org/10.1117/12.2278736.

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Fassinut-Mombot, B., and J. B. Choquel. "An entropy method for multisource data fusion." In Proceedings of the Third International Conference on Information Fusion. IEEE, 2000. http://dx.doi.org/10.1109/ific.2000.859901.

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Ma, Chunlai, Chao Chang, Tao Ma, Jun Huang, and Zhao Niu. "User Identity Matching for Multisource Location Data." In 2021 IEEE 21st International Conference on Communication Technology (ICCT). IEEE, 2021. http://dx.doi.org/10.1109/icct52962.2021.9657893.

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El-Fallah, A., A. Zatezalo, R. Mahler, and R. K. Mehra. "Unified robust-Bayes multisource ambiguous data rule fusion." In Defense and Security, edited by Ivan Kadar. SPIE, 2005. http://dx.doi.org/10.1117/12.605466.

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Toutin, Th. Multisource Data Integration: Comparison of Geometric and Radiometric Methods. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1995. http://dx.doi.org/10.4095/219858.

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Toutin, Th. Multisource Data Fusion with an Integrated and Unified Geometric Modelling. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1995. http://dx.doi.org/10.4095/218015.

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Wilson, D., Matthew Kamrath, Caitlin Haedrich, Daniel Breton, and Carl Hart. Urban noise distributions and the influence of geometric spreading on skewness. Engineer Research and Development Center (U.S.), November 2021. http://dx.doi.org/10.21079/11681/42483.

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Statistical distributions of urban noise levels are influenced by many complex phenomena, including spatial and temporal variations in the source level, multisource mixtures, propagation losses, and random fading from multipath reflections. This article provides a broad perspective on the varying impacts of these phenomena. Distributions incorporating random fading and averaging (e.g., gamma and noncentral Erlang) tend to be negatively skewed on logarithmic (decibel) axes but can be positively skewed if the fading process is strongly modulated by source power variations (e.g., compound gamma). In contrast, distributions incorporating randomly positioned sources and explicit geometric spreading [e.g., exponentially modified Gaussian (EMG)] tend to be positively skewed with exponential tails on logarithmic axes. To evaluate the suitability of the various distributions, one-third octave band sound-level data were measured at 37 locations in the North End of Boston, MA. Based on the Kullback-Leibler divergence as calculated across all of the locations and frequencies, the EMG provides the most consistently good agreement with the data, which were generally positively skewed. The compound gamma also fits the data well and even outperforms the EMG for the small minority of cases exhibiting negative skew. The lognormal provides a suitable fit in cases in which particular non-traffic noise sources dominate.

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