Relevant bibliographies by topics / Synthetic Database Generation

Academic literature on the topic 'Synthetic Database Generation'

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Published: 6 September 2023

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Journal articles on the topic "Synthetic Database Generation"

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Priyadarshini, Pallavi, Fengqiong Qin, Ee-Peng Lim, and Wee-Keong Ng. "Parameter driven synthetic web database generation." Journal of Systems and Software 69, no. 1-2 (January 2004): 29–42. http://dx.doi.org/10.1016/s0164-1212(03)00002-5.

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Sanghi, Anupam, Shadab Ahmed, and Jayant R. Haritsa. "Projection-compliant database generation." Proceedings of the VLDB Endowment 15, no. 5 (January 2022): 998–1010. http://dx.doi.org/10.14778/3510397.3510398.

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Synthesizing data using declarative formalisms has been persuasively advocated in contemporary data generation frameworks. In particular, they specify operator output volumes through row-cardinality constraints. However, thus far, adherence to these volumetric constraints has been limited to the Filter and Join operators. A critical deficiency is the lack of support for the Projection operator, which is at the core of basic SQL constructs such as Distinct, Union and Group By. The technical challenge here is that cardinality unions in multi-dimensional space, and not mere summations, need to be captured in the generation process. Further, dependencies across different data subspaces need to be taken into account. We address the above lacuna by presenting PiGen , a dynamic data generator that incorporates Projection cardinality constraints in its ambit. The design is based on a projection subspace division strategy that supports the expression of constraints using optimized linear programming formulations. Further, techniques of symmetric refinement and workload decomposition are introduced to handle constraints across different projection subspaces. Finally, PiGen supports dynamic generation, where data is generated on-demand during query processing, making it amenable to Big Data environments. A detailed evaluation on workloads derived from real-world and synthetic benchmarks demonstrates that PiGen can accurately and efficiently model Projection outcomes, representing an essential step forward in customized database generation.
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Pujol, David, Amir Gilad, and Ashwin Machanavajjhala. "PreFair: Privately Generating Justifiably Fair Synthetic Data." Proceedings of the VLDB Endowment 16, no. 6 (February 2023): 1573–86. http://dx.doi.org/10.14778/3583140.3583168.

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When a database is protected by Differential Privacy (DP), its usability is limited in scope. In this scenario, generating a synthetic version of the data that mimics the properties of the private data allows users to perform any operation on the synthetic data, while maintaining the privacy of the original data. Therefore, multiple works have been devoted to devising systems for DP synthetic data generation. However, such systems may preserve or even magnify properties of the data that make it unfair, rendering the synthetic data unfit for use. In this work, we present PreFair, a system that allows for DP fair synthetic data generation. PreFair extends the state-of-the-art DP data generation mechanisms by incorporating a causal fairness criterion that ensures fair synthetic data. We adapt the notion of justifiable fairness to fit the synthetic data generation scenario. We further study the problem of generating DP fair synthetic data, showing its intractability and designing algorithms that are optimal under certain assumptions. We also provide an extensive experimental evaluation, showing that PreFair generates synthetic data that is significantly fairer than the data generated by leading DP data generation mechanisms, while remaining faithful to the private data.
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Pavez, Vicente, Gabriel Hermosilla, Francisco Pizarro, Sebastián Fingerhuth, and Daniel Yunge. "Thermal Image Generation for Robust Face Recognition." Applied Sciences 12, no. 1 (January 5, 2022): 497. http://dx.doi.org/10.3390/app12010497.

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This article shows how to create a robust thermal face recognition system based on the FaceNet architecture. We propose a method for generating thermal images to create a thermal face database with six different attributes (frown, glasses, rotation, normal, vocal, and smile) based on various deep learning models. First, we use StyleCLIP, which oversees manipulating the latent space of the input visible image to add the desired attributes to the visible face. Second, we use the GANs N’ Roses (GNR) model, a multimodal image-to-image framework. It uses maps of style and content to generate thermal imaging from visible images, using generative adversarial approaches. Using the proposed generator system, we create a database of synthetic thermal faces composed of more than 100k images corresponding to 3227 individuals. When trained and tested using the synthetic database, the Thermal-FaceNet model obtained a 99.98% accuracy. Furthermore, when tested with a real database, the accuracy was more than 98%, validating the proposed thermal images generator system.
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Dinges, Laslo, Ayoub Al-Hamadi, Moftah Elzobi, Sherif El-etriby, and Ahmed Ghoneim. "ASM Based Synthesis of Handwritten Arabic Text Pages." Scientific World Journal 2015 (2015): 1–18. http://dx.doi.org/10.1155/2015/323575.

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Document analysis tasks, as text recognition, word spotting, or segmentation, are highly dependent on comprehensive and suitable databases for training and validation. However their generation is expensive in sense of labor and time. As a matter of fact, there is a lack of such databases, which complicates research and development. This is especially true for the case of Arabic handwriting recognition, that involves different preprocessing, segmentation, and recognition methods, which have individual demands on samples and ground truth. To bypass this problem, we present an efficient system that automatically turns Arabic Unicode text into synthetic images of handwritten documents and detailed ground truth. Active Shape Models (ASMs) based on 28046 online samples were used for character synthesis and statistical properties were extracted from the IESK-arDB database to simulate baselines and word slant or skew. In the synthesis step ASM based representations are composed to words and text pages, smoothed by B-Spline interpolation and rendered considering writing speed and pen characteristics. Finally, we use the synthetic data to validate a segmentation method. An experimental comparison with the IESK-arDB database encourages to train and test document analysis related methods on synthetic samples, whenever no sufficient natural ground truthed data is available.
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Sazonova, Kateryna, Olena Nosovets, Vitalii Babenko, and Olga Averianova. "GENERATION OF SYNTHETICAL MEDICAL DATA BY MDR-ANALYSIS." Proceedings of the National Aviation University 87, no. 2 (July 27, 2021): 31–36. http://dx.doi.org/10.18372/2306-1472.87.15719.

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Purpose: The purpose of this article is to outline an algorithm for generating synthetic medical data in order to augment small samples of data. Methods: To achieve the research goal, methods such as: correlation analysis (to identify significant variables and the relationships between them), MDR analysis (to build logical chains of relationships between medical data), and regression analysis (to model medical data variables to use this to generate synthetic data) were used. Results: A database of heart failure patients that is publicly available was used to test the developed algorithm for generating synthetic medical data in action; as a result, statistical relationships between data were found and used to build linear regression models. Discussion: The proposed algorithm allows, with a few simple, yet important actions, to perform the generation of medical data, which makes it possible to obtain large data sets that can be used to implement machine learning methods in any tasks related to medicine.
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Burman, Nitin, Claudia Manetti, Paulo Tostes, Joost Lumens, and Jan D'hooge. "A pipeline to enable large-scale generation of diverse 2D cardiac synthetic ultrasound recordings corresponding to healthy and heart failure virtual patients." Journal of the Acoustical Society of America 152, no. 4 (October 2022): A279. http://dx.doi.org/10.1121/10.0016267.

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Simulated ultrasound (US) data are widely used in echocardiography to develop and validate rapidly growing convolutional neural networks (CNNs) based learning algorithms for image processing and analysis. In this context, a large and diverse database of synthetic US scans is considered vital for CNN training purposes, as clinical US data are scarce and difficult to access. Major hurdles in creating an extensive database are the long US simulation time and unstable heart models for extreme parameter settings. Here, we developed and implemented a cardiac US simulation pipeline that kinematically connects two state-of-the-art solutions in the field of US simulation (COLE) and cardiac modelling (CircAdapt), benefiting from the fast simulation time of the convolution-based ultrasound simulator and stability of the mechanical heart model to produce 2D synthetic cardiac US recordings. Furthermore, using our pipeline, we generated diverse set of 600 2D synthetic cardiac US recordings of healthy and heart failure virtual patients with variations in the shapes, motion patterns, and functions of the heart, along with their ground truth 2D myocardial velocity profiles and deformation curves. The resulting database is a potential tool for augmenting training databases of machine learning based US image processing algorithms. [Work funded by European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 860745.]
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Kuriki, Mikaele Silva, Francisco Lledo Santos, and Cristiano Poleto. "Small-Scale Wetland Model for Synthetic Sewage Treatment." Ciência e Natura 44 (April 21, 2022): e25. http://dx.doi.org/10.5902/2179460x68834.

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With the demand for electricity growing, the migration to renewable sources is a reality. In distributed generation, photovoltaic systems are a renewable and sustainable alternative to the main energy sources to generate electricity. Monitoring a photovoltaic system over its operating time guarantees its good performance. This requires solar radiation and temperature data measured at the installation site or the use of solarimetric stations databases. However, the differences between the results simulated with databases and with data measured at the installation site are not widely known, which would be the ideal case from a technical point of view. The aim of this study was to verify the feasibility of monitoring the performance of a 2.5 kWp photovoltaic system located in the city of Porto Alegre - Brazil using the System Advisor Model (SAM) modeling tool and a public database. Simulation results were compared using data provided by a station of the National Institute of Meteorology (INMET) with the results obtained with data measured at the site of the photovoltaic system. Differences were verified between the solar radiation measured on site and that of the INMET database, and the difference in accumulated radiation was 9.2% for the entire period analyzed. When comparing the measured and simulated alternating current energy using the radiation and temperature data measured on site for the non-shading time, it was found that the difference between the results was 0.5%. Using the INMET climate file, the monthly differences ranged from -6% to 14% and the difference in accumulated energy for the entire measurement period was 2.5%. The results showed that the use of a database measured by a public solarimetric station close to the site, in this case approximately 6 km away from the installation, is feasible for monitoring photovoltaic systems, since the differences found were not significant. This monitoring can identify system failures and performance loss over time.
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Baowaly, Mrinal Kanti, Chia-Ching Lin, Chao-Lin Liu, and Kuan-Ta Chen. "Synthesizing electronic health records using improved generative adversarial networks." Journal of the American Medical Informatics Association 26, no. 3 (December 7, 2018): 228–41. http://dx.doi.org/10.1093/jamia/ocy142.

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AbstractObjectiveThe aim of this study was to generate synthetic electronic health records (EHRs). The generated EHR data will be more realistic than those generated using the existing medical Generative Adversarial Network (medGAN) method.Materials and MethodsWe modified medGAN to obtain two synthetic data generation models—designated as medical Wasserstein GAN with gradient penalty (medWGAN) and medical boundary-seeking GAN (medBGAN)—and compared the results obtained using the three models. We used 2 databases: MIMIC-III and National Health Insurance Research Database (NHIRD), Taiwan. First, we trained the models and generated synthetic EHRs by using these three 3 models. We then analyzed and compared the models’ performance by using a few statistical methods (Kolmogorov–Smirnov test, dimension-wise probability for binary data, and dimension-wise average count for count data) and 2 machine learning tasks (association rule mining and prediction).ResultsWe conducted a comprehensive analysis and found our models were adequately efficient for generating synthetic EHR data. The proposed models outperformed medGAN in all cases, and among the 3 models, boundary-seeking GAN (medBGAN) performed the best.DiscussionTo generate realistic synthetic EHR data, the proposed models will be effective in the medical industry and related research from the viewpoint of providing better services. Moreover, they will eliminate barriers including limited access to EHR data and thus accelerate research on medical informatics.ConclusionThe proposed models can adequately learn the data distribution of real EHRs and efficiently generate realistic synthetic EHRs. The results show the superiority of our models over the existing model.
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Loisel, Hubert, Daniel Schaffer Ferreira Jorge, Rick A. Reynolds, and Dariusz Stramski. "A synthetic optical database generated by radiative transfer simulations in support of studies in ocean optics and optical remote sensing of the global ocean." Earth System Science Data 15, no. 8 (August 18, 2023): 3711–31. http://dx.doi.org/10.5194/essd-15-3711-2023.

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Abstract. Radiative transfer (RT) simulations have long been used to study the relationships between the inherent optical properties (IOPs) of seawater and light fields within and leaving the ocean, from which ocean apparent optical properties (AOPs) can be calculated. For example, inverse models used to estimate IOPs from ocean color radiometric measurements have been developed and validated using the results of RT simulations. Here we describe the development of a new synthetic optical database based on hyperspectral RT simulations across the spectral range of near-ultraviolet to near-infrared performed with the HydroLight radiative transfer code. The key component of this development is the generation of a synthetic dataset of seawater IOPs that serves as input to RT simulations. Compared to similar developments of optical databases in the past, the present dataset of IOPs is characterized by the probability distributions of IOPs that are consistent with global distributions representative of vast areas of open-ocean pelagic environments and coastal regions, covering a broad range of optical water types. The generation of synthetic data of IOPs associated with particulate and dissolved constituents of seawater was driven largely by an extensive set of field measurements of the phytoplankton absorption coefficient collected in diverse oceanic environments. Overall, the synthetic IOP dataset consists of 3320 combinations of IOPs. Additionally, the pure seawater IOPs were assumed following recent recommendations. The RT simulations were performed using 3320 combinations of input IOPs, assuming vertical homogeneity within an infinitely deep ocean. These input IOPs were used in three simulation scenarios associated with assumptions about inelastic radiative processes in the water column (not considered in previous synthetically generated optical databases) and three simulation scenarios associated with the sun zenith angle. Specifically, the simulations were made assuming no inelastic processes, the presence of Raman scattering by water molecules, and the presence of both Raman scattering and fluorescence of chlorophyll a pigment. Fluorescence of colored dissolved organic matter was omitted from all simulations. For each of these three simulation scenarios, the simulations were made for three sun zenith angles of 0, 30, and 60∘ assuming clear skies, standard atmosphere, and a wind speed of 5 m s−1. Thus, overall 29 880 RT simulations were performed. The output results of these simulations include radiance distributions, plane and scalar irradiances, and a whole set of AOPs, including remote-sensing reflectance, vertical diffuse attenuation coefficients, and mean cosines, where all optical variables are reported in the spectral range of 350 to 750 nm at 5 nm intervals for different depths between the sea surface and 50 m. The consistency of this new synthetic database has been assessed through comparisons with in situ data and previously developed empirical relationships involving IOPs and AOPs. The database is available at the Dryad open-access repository of research data (https://doi.org/10.6076/D1630T, Loisel et al., 2023).
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Dissertations / Theses on the topic "Synthetic Database Generation"

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Gwinnett, Claire M. B. "The Use of Inexperienced Personnel in the Analysis of Synthetic Textile Fibres using Polarized Light Microscopy for the Generation of Data Suitable for the Production of a Synthetic Fibres Database." Thesis, Staffordshire University, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.522253.

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Kříž, Blažej. "Framework pro tvorbu generátorů dat." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2012. http://www.nusl.cz/ntk/nusl-236623.

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This master's thesis is focused on the problem of data generation. At the beginning, it presents several applications for data generation and describes the data generation process. Then it deals with development of framework for data generators and demonstrational application for validating the framework.
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Knoors, Daan. "Utility of Differentially Private Synthetic Data Generation for High-Dimensional Databases." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-235640.

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When processing data that contains sensitive information, careful consideration is required with regard to privacy-preservation to prevent disclosure of confidential information. Privacy engineering enables one to extract valuable patterns, safely, without compromising anyone’s privacy. Over the last decade, academics have actively sought to find stronger definitions and methodologies to achieve data privacy while preserving the data utility. Differential privacy emerged and became the de facto standard for achieving data privacy and numerous techniques are continuously proposed based on this definition. One method in particular focuses on the generation of private synthetic databases, that mimic statistical patterns and characteristics of a confidential data source in a privacy-preserving manner. Original data format and utility is preserved in a new database that can be shared and analyzed safely without the risk of privacy violation. However, while this privacy approach sounds promising there has been little application beyond academic research. Hence, we investigate the potential of private synthetic data generation for real-world applicability. We propose a new utility evaluation framework that provides a unified approach upon which various algorithms can be assessed and compared. This framework extends academic evaluation methods by incorporating a user-oriented perspective and varying industry requirements, while also examining performance on real-world use cases. Finally, we implement multiple general-purpose algorithms and evaluate them based on our framework to ultimately determine the potential of private synthetic data generation beyond the academic domain.
Vid databehandling av känslig information måste särskild hänsyn tas till sekretessbevarande för att undvika oavsiktligt röjande av konfidentiell information. Med sekretessingenjörsskap menas möjliggörandet av informationssäker mönsterextraktion utan att kompromissa någons rätt tillett privatliv. Under det senaste decenniet har akademiker aktivt försökt finna starkare definitioner och metodiker för att uppnå ett sekretessbevarande men endå bibehålla datats nytta. Differentielt hemlighållande(eng. Differential Privacy) framkom som en "de facto" standard för att uppnå sekretessbevarande och det föreslås kontinuerligt nya tekniker baserade på denna. I synnerhet en metod fokuserar på generering av privata syntetiska databaser vilka härmar de statistiska mönster och särdrag från en konfidentiell datakälla på ett sekretessbevarande sätt. På grund av detta kan originaldatats format och nytta bibehållas men fortfarande delas och analyseras utan risk för sekretessöverträdelser. Tyvärr har denna metod sett liten tillämpning utanför akademia. Därför undersöker vi härmed dess potential för användande av hemlighållande syntetisk datagenerering i verkliga användarfall. Vi föreslår vidare ett nytt nyttjandegradsutvärderingramverk vilket ger ett enhetligt sätt att utvärdera diverse algorithmer gentemot varandra. Detta ramverk bygger vidare på de typiska akademiska utvärderingsmetoderna genom att inkorporera ett användarorienterat perspektiv och industrikrav samtidigt som prestandautvärdering av verkliga användarfall görs. Slutligen implementerar vi flera algoritmer med allmänt ändamål och utvärderar dem utifrån kriterierna för detta ramverk med syftet att i slutändan bestämma potentialen för hemlighållande syntetisk datagenerering utanför den akademiska domänen.
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Knoors, Daan Josephus. "Utility of Differentially Private Synthetic Data Generation for High-Dimensional Databases." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-237424.

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When processing data that contains sensitive information, careful consideration is required with regard to privacy-preservation to prevent disclosure of confidential information. Privacy engineering enables one to extract valuable patterns, safely, without compromising anyone’s privacy. Over the last decade, academics have actively sought to find stronger definitions and methodologies to achieve data privacy while preserving the data utility. Differential privacy emerged and became the de facto standard for achieving data privacy and numerous techniques are continuously proposed based on this definition. One method in particular focuses on the generation of private synthetic databases, that mimic statistical patterns and characteristics of a confidential data source in a privacy-preserving manner. Original data format and utility is preserved in a new database that can be shared and analyzed safely without the risk of privacy violation. However, while this privacy approach sounds promising there has been little application beyond academic research. Hence, we investigate the potential of private synthetic data generation for real-world applicability. We propose a new utility evaluation framework that provides a unified approach upon which various algorithms can be assessed and compared. This framework extends academic evaluation methods by incorporating a user-oriented perspective and varying industry requirements, while also examining performance on real-world use cases. Finally, we implement multiple general-purpose algorithms and evaluate them based on our framework to ultimately determine the potential of private synthetic data generation beyond the academic domain.
Vid databehandling av känslig information måste särskild hänsyn tas till sekretessbevarande för att undvika oavsiktligt röjande av konfidentiell information. Med skretessingenjörsskap menas möjliggörandet av informationssäker mönsterextraktion utan att kompromissa någons rätt till ett privatliv. Under det senaste decenniet har akademiker aktivt försökt finna starkare definitioner och metodiker för att uppnå ett sekretessbevarande men endå bibehålla datats nytta. Differentielt hemlighållande (eng. Differential Privacy) framkom som en "de facto" standard för att uppnå sekretessbevarande och det föreslås kontinuerligt nya tekniker baserade på denna. I synnerhet en metod fokuserar på generering av privata syntetiska databaser vilka härmar de statistiska mönster och särdrag från en konfidentiell datakälla på ett sekretessbevarande sätt. På grund av detta kan originaldatats format och nytta bibehållas men fortfarande delas och analyseras utan risk för sekretessöverträdelser. Tyvärr har denna metod sett liten tillämpning utanför akademia. Därför undersöker vi härmed dess potential för användande av hemlighållande syntetisk datagenerering i verkliga användarfall. Vi föreslår vidare ett nytt nyttjandegradsutvärderingramverk vilket ger ett enhetligt sätt att utvärdera diverse algorithmer gentemot varandra. Detta ramverk bygger vidare på de typiska akademiska utvärderingsmetoderna genom att inkorporera ett användarorienterat perspektiv och industrikrav samtidigt som prestandautvärdering av verkliga användarfall görs. Slutligen implementerar vi flera algoritmer med allmänt ändamål och utvärderar dem utifrån kriterierna för detta ramverk med syftet att i slutändan bestämma potentialen för hemlighållande syntetisk datagenerering utanför den akademiska domänen.
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Patki, Neha (Neha R. ). "The Synthetic Data Vault : generative modeling for relational databases." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/109616.

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Thesis: M. Eng. in Computer Science and Engineering, Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 79-80).
The goal of this thesis is to build a system that automatically creates synthetic data for enabling data science endeavors. To meet this goal, we present the Synthetic Data Vault (SDV), a system that builds generative models of relational databases. We are able to sample from the model and create synthetic data, hence the name SDV. When implementing the SDV, we developed an algorithm that computes statistics at the intersection of related database tables. We then use a state-of-the-art multivariate modeling approach to model this data. The SDV iterates through all possible relations, ultimately creating a model for the entire database. Once this model is computed, the same relational information allows the SDV to synthesize data by sampling from any part of the database. After building the SDV, we used it to generate synthetic data for five different publicly available datasets. We then published the datasets and asked data scientists to develop predictive models for them as part of a crowdsourced experiment. On May 18, 2016, preliminary analysis from the ongoing experiment provided evidence that the synthetic data can successfully replace original data for data science. Our analysis indicates that there is no significant difference in the work produced by data scientists who used synthetic data as opposed to real data. We conclude that the SDV is a viable solution for synthetic data generation. Our primary contribution is that we designed and implemented the first generative modeling system for relational databases that demonstratively synthesizes realistic data.
by Neha Patki.
M. Eng. in Computer Science and Engineering
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Al-Hakawati, al-Dakkak Oumayma. "Extraction automatique de paramètres formantiques guidée par le contexte et élaboration de règles de synthèse." Grenoble INPG, 1988. http://www.theses.fr/1988INPG0056.

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Formation des connaissances permettant la generation automatique des trajectoires de parametres d'un synthetiseur a formants. Ce travail a ete applique a: l'analyse automatique a formants par des connaissances a priori sur le contexte et a l'elaboration de regles permettant une synthese par regles a partir du texte
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Book chapters on the topic "Synthetic Database Generation"

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Hall, David, and Lena Strömbäck. "Generation of Synthetic XML for Evaluation of Hybrid XML Systems." In Database Systems for Advanced Applications, 191–202. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14589-6_20.

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Alammar, Ammar, and Wassim Jabi. "Generation of a Large Synthetic Database of Office Tower's Energy Demand Using Simulation and Machine Learning." In Formal Methods in Architecture, 479–500. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-2217-8_27.

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Mateo-Sanz, Josep Maria, Antoni Martínez-Ballesté, and Josep Domingo-Ferrer. "Fast Generation of Accurate Synthetic Microdata." In Privacy in Statistical Databases, 298–306. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-25955-8_24.

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Blanco-Justicia, Alberto, Najeeb Moharram Jebreel, Jesús A. Manjón, and Josep Domingo-Ferrer. "Generation of Synthetic Trajectory Microdata from Language Models." In Privacy in Statistical Databases, 172–87. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13945-1_13.

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Pires, Carlos Eduardo, Priscilla Vieira, Márcio Saraiva, and Denilson Barbosa. "Generating Synthetic Database Schemas for Simulation Purposes." In Lecture Notes in Computer Science, 502–10. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23091-2_44.

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Neves, João C., Ruben Tolosana, Ruben Vera-Rodriguez, Vasco Lopes, Hugo Proença, and Julian Fierrez. "GAN Fingerprints in Face Image Synthesis." In Multimedia Forensics, 175–204. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7621-5_8.

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AbstractThe availability of large-scale facial databases, together with the remarkable progresses of deep learning technologies, in particular Generative Adversarial Networks (GANs), have led to the generation of extremely realistic fake facial content, raising obvious concerns about the potential for misuse. Such concerns have fostered the research on manipulation detection methods that, contrary to humans, have already achieved astonishing results in various scenarios. This chapter is focused on the analysis of GAN fingerprints in face image synthesis.
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Sakshaug, Joseph W., and Trivellore E. Raghunathan. "Nonparametric Generation of Synthetic Data for Small Geographic Areas." In Privacy in Statistical Databases, 213–31. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11257-2_17.

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Drechsler, Jörg. "Using Support Vector Machines for Generating Synthetic Datasets." In Privacy in Statistical Databases, 148–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15838-4_14.

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Ayala-Rivera, Vanessa, A. Omar Portillo-Dominguez, Liam Murphy, and Christina Thorpe. "COCOA: A Synthetic Data Generator for Testing Anonymization Techniques." In Privacy in Statistical Databases, 163–77. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-45381-1_13.

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Yu, Chengcheng, Fan Xia, Qunyan Zhang, Haixin Ma, Weining Qian, Minqi Zhou, Cheqing Jin, and Aoying Zhou. "BSMA-Gen: A Parallel Synthetic Data Generator for Social Media Timeline Structures." In Database Systems for Advanced Applications, 539–42. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05813-9_40.

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Conference papers on the topic "Synthetic Database Generation"

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Lindahl, Charles E., Alan Cockcroft, Thomas Derryberry, John Sigler, and Mark Yablonski. "Synthetic multisensor database generation and validation." In Orlando '90, 16-20 April, edited by Hatem N. Nasr and Firooz A. Sadjadi. SPIE, 1990. http://dx.doi.org/10.1117/12.21797.

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Endres, Markus, Asha Mannarapotta Venugopal, and Tung Son Tran. "Synthetic Data Generation: A Comparative Study." In IDEAS'22: International Database Engineered Applications Symposium. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3548785.3548793.

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Rigney, Matthew, Brad Seal, and Chris Porter. "Generation of high fidelity signature for AI/ML training database generation." In Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications, edited by Kimberly E. Manser, Raghuveer M. Rao, and Christopher L. Howell. SPIE, 2023. http://dx.doi.org/10.1117/12.2663906.

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Placidi, Simone, Alessandro Vetere, Eugenio Pino, and Adriano Meta. "Advanced SAR simulator for ATR and AI database generation." In 2021 7th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR). IEEE, 2021. http://dx.doi.org/10.1109/apsar52370.2021.9688497.

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Li, Wanxin. "Supporting Database Constraints in Synthetic Data Generation based on Generative Adversarial Networks." In SIGMOD/PODS '20: International Conference on Management of Data. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3318464.3384414.

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Viscaino - Quito, Andres, and Luis Serpa-Andrade. "Proposal for the Generation of Profiles using a Synthetic Database." In 13th International Conference on Applied Human Factors and Ergonomics (AHFE 2022). AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1001462.

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Abstract:
The lack of data to perform various models that feed an artificial intelligence with which you can get or discover various patterns of behavior in a set of data. Therefore, due to this lack of data, the systems are not well nourished with data large enough to fulfill its learning function, presenting a synthetic database which is parameterized with restrictions on the characteristics of graphomotor and language elements, which develops a set of combinations that will be the model for the AI. As effect to all this gave a commensurable amount of 777,600 combinations at the moment of applying the first filter with the respective restrictions, when taking the valid combinations that are 77304 a second filter is applied with the remaining restrictions that gave 57,672 valid combinations for the generation of the synthetic database that will feed the AI. It is concluded that the generation of synthetic data helps to create, according to its importance, more or less similar to real data and in this way ensures a quantity and no dependence on real or original data.
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Bonjean, Maxime E., Jacques G. Verly, and Jens Schiefele. "Generation of infrared imagery from an aviation synthetic vision database." In Defense and Security, edited by Jacques G. Verly. SPIE, 2005. http://dx.doi.org/10.1117/12.604462.

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Won, Jin-Ju, Sungho Kim, Youngrea Cho, Woo-Jin Song, and So-Hyun Kim. "Synthetic SAR/IR database generation for sensor fusion-based A.T.R." In 2015 12th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI). IEEE, 2015. http://dx.doi.org/10.1109/urai.2015.7358893.

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Friedrich, Axel, Helmut Raabe, Jens Schiefele, and Kai Uwe Doerr. "Airport databases for 3D synthetic-vision flight-guidance displays: database design, quality assessment, and data generation." In AeroSense '99, edited by Jacques G. Verly. SPIE, 1999. http://dx.doi.org/10.1117/12.354413.

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Rogozik, Juergen, Jens Schiefele, Axel Friedrich, Thorsten Wiesemann, and Wolfgang Kubbat. "Requirements and generation of a terrain and obstacle database for synthetic vision." In Aerospace/Defense Sensing, Simulation, and Controls, edited by Jacques G. Verly. SPIE, 2001. http://dx.doi.org/10.1117/12.438013.

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Reports on the topic "Synthetic Database Generation"

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Erner, Karen A. Methods for Generating Synthetic Databases with Specified Statistical Properties. Fort Belvoir, VA: Defense Technical Information Center, March 1996. http://dx.doi.org/10.21236/ada307081.

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