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155 results on '"Bugiotti, Francesca"'

1. A Hybrid GNN approach for predicting node data for 3D meshes

Author

Salimath, Shwetha, Bugiotti, Francesca, and Magoules, Frederic

Subjects
Computer Science - Machine Learning, Mathematics - Numerical Analysis
Abstract

Metal forging is used to manufacture dies. We require the best set of input parameters for the process to be efficient. Currently, we predict the best parameters using the finite element method by generating simulations for the different initial conditions, which is a time-consuming process. In this paper, introduce a hybrid approach that helps in processing and generating new data simulations using a surrogate graph neural network model based on graph convolutions, having a cheaper time cost. We also introduce a hybrid approach that helps in processing and generating new data simulations using the model. Given a dataset representing meshes, our focus is on the conversion of the available information into a graph or point cloud structure. This new representation enables deep learning. The predicted result is similar, with a low error when compared to that produced using the finite element method. The new models have outperformed existing PointNet and simple graph neural network models when applied to produce the simulations.

Published
2023

2. Point-Cloud-based Deep Learning Models for Finite Element Analysis

Author

Shivaditya, Meduri Venkata, Bugiotti, Francesca, and Magoules, Frederic

Subjects
Mathematics - Numerical Analysis
Abstract

In this paper, we explore point-cloud based deep learning models to analyze numerical simulations arising from finite element analysis. The objective is to classify automatically the results of the simulations without tedious human intervention. Two models are here presented: the Point-Net classification model and the Dynamic Graph Convolutional Neural Net model. Both trained point-cloud deep learning models performed well on experiments with finite element analysis arising from automotive industry. The proposed models show promise in automatizing the analysis process of finite element simulations. An accuracy of 79.17% and 94.5% is obtained for the Point-Net and the Dynamic Graph Convolutional Neural Net model respectively.

Published
2022

3. Graph Neural Network-based Surrogate Models for Finite Element Analysis

Author

Shivaditya, Meduri Venkata, Alves, José, Bugiotti, Francesca, and Magoules, Frederic

Subjects
Mathematics - Numerical Analysis, Computer Science - Distributed, Parallel, and Cluster Computing
Abstract

Current simulation of metal forging processes use advanced finite element methods. Such methods consist of solving mathematical equations, which takes a significant amount of time for the simulation to complete. Computational time can be prohibitive for parametric response surface exploration tasks. In this paper, we propose as an alternative, a Graph Neural Network-based graph prediction model to act as a surrogate model for parameters search space exploration and which exhibits a time cost reduced by an order of magnitude. Numerical experiments show that this new model outperforms the Point-Net model and the Dynamic Graph Convolutional Neural Net model.

Published
2022

4. Smart Crawling: A New Approach toward Focus Crawling from Twitter

Author

Khazaie, Ahmad, Seghouani, Nacéra Bennacer, and Bugiotti, Francesca

Subjects
Computer Science - Information Retrieval, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Computer Science - Social and Information Networks
Abstract

Twitter is a social network that offers a rich and interesting source of information challenging to retrieve and analyze. Twitter data can be accessed using a REST API. The available operations allow retrieving tweets on the basis of a set of keywords but with limitations such as the number of calls per minute and the size of results. Besides, there is no control on retrieved results and finding tweets which are relevant to a specific topic is a big issue. Given these limitations, it is important that the query keywords cover unambiguously the topic of interest in order to both reach the relevant answers and decrease the number of API calls. In this paper, we introduce a new crawling algorithm called "SmartTwitter Crawling" (STiC) that retrieves a set of tweets related to a target topic. In this algorithm, we take an initial keyword query and enrich it using a set of additional keywords that come from different data sources. STiC algorithm relies on a DFS search in Twittergraph where each reached tweet is considered if it is relevant with the query keywords using a scoring, updated throughout the whole crawling process. This scoring takes into account the tweet text, hashtags and the users who have posted the tweet, replied to the tweet, been mentioned in the tweet or retweeted the tweet. Given this score, STiC is able to select relevant tweets in each iteration and continue by adding the related valuable tweets. Several experiments have been achieved for different kinds of queries, the results showedthat the precision increases compared to a simple BFS search.

Published
2021

7. Weakly Supervised Named Entity Recognition for Carbon Storage Using Deep Neural Networks

Author

Londoño, René Gómez, Wlodarczyk, Sylvain, Arman, Molood, Bugiotti, Francesca, Seghouani, Nacéra Bennacer, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Pascal, Poncelet, editor, and Ienco, Dino, editor

Published
2022
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8. Addressing Data Challenges to Drive the Transformation of Smart Cities.

Author

Gilman, Ekaterina, Bugiotti, Francesca, Khalid, Ahmed, Mehmood, Hassan, Kostakos, Panos, Tuovinen, Lauri, Ylipulli, Johanna, Su, Xiang, and Ferreira, Denzil

Subjects
*SMART cities, *INFORMATION & communication technologies, *CITIES & towns, *ENVIRONMENTAL protection, *DEEP diving
Abstract

Cities serve as vital hubs of economic activity and knowledge generation and dissemination. As such, cities bear a significant responsibility to uphold environmental protection measures while promoting the welfare and living comfort of their residents. There are diverse views on the development of smart cities, from integrating Information and Communication Technologies into urban environments for better operational decisions to supporting sustainability, wealth, and comfort of people. However, for all these cases, data are the key ingredient and enabler for the vision and realization of smart cities. This article explores the challenges associated with smart city data. We start with gaining an understanding of the concept of a smart city, how to measure that the city is a smart one, and what architectures and platforms exist to develop one. Afterwards, we research the challenges associated with the data of the cities, including availability, heterogeneity, management, analysis, privacy, and security. Finally, we discuss ethical issues. This article aims to serve as a "one-stop shop" covering data-related issues of smart cities with references for diving deeper into particular topics of interest. [ABSTRACT FROM AUTHOR]

Published
2024
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10. A Graph Partitioning Algorithm for Edge or Vertex Balance

Author

Moussawi, Adnan El, Seghouani, Nacéra Bennacer, Bugiotti, Francesca, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Hartmann, Sven, editor, Küng, Josef, editor, Kotsis, Gabriele, editor, Tjoa, A Min, editor, and Khalil, Ismail, editor

Published
2020
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13. Modeling Strategies for Storing Data in Distributed Heterogeneous NoSQL Databases

Author

Hewasinghage, Moditha, Seghouani, Nacéra Bennacer, Bugiotti, Francesca, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Trujillo, Juan C., editor, Davis, Karen C., editor, Du, Xiaoyong, editor, Li, Zhanhuai, editor, Ling, Tok Wang, editor, Li, Guoliang, editor, and Lee, Mong Li, editor

Published
2018
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18. Eliminating Incorrect Cross-Language Links in Wikipedia

Author

Bennacer, Nacéra, Bugiotti, Francesca, Galicia, Jorge, Patricio, Mariana, Quercini, Gianluca, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Weikum, Gerhard, Series Editor, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bouguettaya, Athman, editor, Gao, Yunjun, editor, Klimenko, Andrey, editor, Chen, Lu, editor, Zhang, Xiangliang, editor, Dzerzhinskiy, Fedor, editor, Jia, Weijia, editor, Klimenko, Stanislav V., editor, and Li, Qing, editor

Published
2017
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19. Interpreting Reputation Through Frequent Named Entities in Twitter

Author

Bennacer, Nacéra, Bugiotti, Francesca, Hewasinghage, Moditha, Isaj, Suela, Quercini, Gianluca, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Weikum, Gerhard, Series Editor, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bouguettaya, Athman, editor, Gao, Yunjun, editor, Klimenko, Andrey, editor, Chen, Lu, editor, Zhang, Xiangliang, editor, Dzerzhinskiy, Fedor, editor, Jia, Weijia, editor, Klimenko, Stanislav V., editor, and Li, Qing, editor

Published
2017
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22. Detecting, Repairing, and Enhancing Raw Metadata

Author

Zimanyi, Esteban, Sakr, Mahmoud, Sacharidis, Dimitrios, Solodovnikova, Darja, Bugiotti, Francesca Dr, Khalid, Hiba, Zimanyi, Esteban, Sakr, Mahmoud, Sacharidis, Dimitrios, Solodovnikova, Darja, Bugiotti, Francesca Dr, and Khalid, Hiba

Abstract

With the increased data production, a significant surge has appeared in metadata generation or collection as a part of the process. Metadata, when captured in accordance with standardized procedures, can offer valuable insights, leading to improved data analytics, data integration, and resource management. However, the absence of standardized practices often leads to inconsistencies in metadata recording, such as missing attribute information, absent publishing URLs, or inadequate provenance details. Furthermore, recorded metadata may exhibit inconsistencies, including variations in value formats, presence of special characters in values, and incorrectly entered values, among others.While the database community has made substantial progress in cleaning, preparing, and transforming data, the research focus on organizing and cleaning metadata has not progressed at the same pace. Despite the similarities between metadata and data files, and the applicability of data preparation and transformation techniques to metadata files in theory, practical implementation is hindered by the unique challenges presented by metadata files. Raw metadata files typically lack a well-defined structure due to the absence of standards, as users record metadata according to their individual preferences. Consequently, these files present not only structural challenges, such as recognizing layouts, elements, and value boundaries, but also semantic issues, including missing information, aliases, and duplicates. Although several approaches have shown promise in metadata collection and discovery, implementing an automated metadata preprocessing pipeline for existing metadata introduces numerous challenges.To address these challenges, this thesis proposes novel approaches to enhance the quality of existing metadata. The first step involves understanding the organization of information within raw metadata files using a system called MDOrg. This system employs rule-based agents to discern how infor, Doctorat en Sciences de l'ingénieur et technologie, info:eu-repo/semantics/nonPublished

Published
2023

23. Database Design for NoSQL Systems

Author

Bugiotti, Francesca, Cabibbo, Luca, Atzeni, Paolo, Torlone, Riccardo, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Yu, Eric, editor, Dobbie, Gillian, editor, Jarke, Matthias, editor, and Purao, Sandeep, editor

Published
2014
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25. Uniform Access to Non-relational Database Systems: The SOS Platform

Author

Atzeni, Paolo, Bugiotti, Francesca, Rossi, Luca, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Ralyté, Jolita, editor, Franch, Xavier, editor, Brinkkemper, Sjaak, editor, and Wrycza, Stanislaw, editor

Published
2012
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30. MISM: A Platform for Model-Independent Solutions to Model Management Problems

Author

Atzeni, Paolo, Bellomarini, Luigi, Bugiotti, Francesca, Gianforme, Giorgio, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Spaccapietra, Stefano, editor, and Delcambre, Lois, editor

Published
2009
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31. From Schema and Model Translation to a Model Management System

Author

Atzeni, Paolo, Bellomarini, Luigi, Bugiotti, Francesca, Gianforme, Giorgio, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Gray, Alex, editor, Jeffery, Keith, editor, and Shao, Jianhua, editor

Published
2008
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33. Graph Neural Network-based Surrogate Models for Finite Element Analysis

Author

Meduri, Venkata, Alves, José, Bugiotti, Francesca, Magoules, Frédéric, CentraleSupélec, Transvalor, Transvalor S. A., Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), and Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)

Subjects
FOS: Computer and information sciences, Point-Net, Deep Learning Graph Neural Network Point-Net Dynamic Graph Convolutional Neural Net Finite Element Analysis GPU, Finite Element Analysis, GPU, Numerical Analysis (math.NA), Dynamic Graph Convolutional Neural Net, Graph Neural Network, Deep Learning, Computer Science - Distributed, Parallel, and Cluster Computing, FOS: Mathematics, [INFO]Computer Science [cs], Mathematics - Numerical Analysis, Distributed, Parallel, and Cluster Computing (cs.DC)
Abstract

International audience; Current simulation of metal forging processes use advanced finite element methods. Such methods consist of solving mathematical equations, which takes a significant amount of time for the simulation to complete. Computational time can be prohibitive for parametric response surface exploration tasks. In this paper, we propose as an alternative, a Graph Neural Networkbased graph prediction model to act as a surrogate model for parameters search space exploration and which exhibits a time cost reduced by an order of magnitude. Numerical experiments show that this new model outperforms the Point-Net model and the Dynamic Graph Convolutional Neural Net model.

Published
2022
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34. SPARQL Query Processing in the Cloud

Author

Bugiotti, Francesca, primary, Camacho-Rodríguez, Jesús, additional, Goasdoué, François, additional, Kaoudi, Zoi, additional, Manolescu, Ioana, additional, and Zampetakis, Stamatis, additional

Published
2014
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37. Graph Data Warehousing: Database and Multidimensional Modeling of Graphs

Author

Zimanyi, Esteban, Romero, Oscar, Vansummeren, Stijn, Sakr, Mahmoud, Voigt, Hannes, Bugiotti, Francesca Dr, Ghrab, Amine, Zimanyi, Esteban, Romero, Oscar, Vansummeren, Stijn, Sakr, Mahmoud, Voigt, Hannes, Bugiotti, Francesca Dr, and Ghrab, Amine

Abstract

Over the last decade, we have witnessed the emergence of networks in a wide spectrum of application domains, ranging from social and information networks to biological and transportation networks.Graphs provide a solid theoretical foundation for modeling complex networks and revealing valuable insights from both the network structure and the data embedded within its entities.As the business and social environments are getting increasingly complex and interconnected, graphs became a widespread abstraction at the core of the information infrastructure supporting those environments. Modern information systems consist of a large number of sophisticated and interacting business entities that naturally form graphs. In particular, integrating graphs into data warehouse systems received a lot of interest from both academia and industry. Indeed, data warehouses are the central enterprise's information repository and are critical for proper decision support and future planning. Graph warehousing is emerging as the field that extends current information systems with graph management and analytics capabilities. Many approaches were proposed to address the graph data warehousing challenge. These efforts laid the foundation for multidimensional modeling and analysis of graphs. However, most of the proposed approaches partially tackle the graph warehousing problem by being restricted to simple abstractions such as homogeneous graphs or ignoring important topics such as multidimensional integrity constraints and dimension hierarchies.In this dissertation, we conduct a systematic study of the graph data warehousing topic and address the key challenges of database and multidimensional modeling of graphs.We first propose GRAD, a new graph database model tailored for graph warehousing and OLAP analytics. GRAD aims to provide analysts with a set of simple, well-defined, and adaptable conceptual components to support rich semantics and perform complex analysis on graphs.Then, we define t, Doctorat en Sciences de l'ingénieur et technologie, info:eu-repo/semantics/nonPublished

Published
2020

41. Élimination des liens inter-langues erronés dans Wikipédia

Author

Bennacer Seghouani, Nacéra, Bugiotti, Francesca, Galicia, Jorge, Patricio, Mariana, Quercini, Gianluca, CentraleSupélec, Modélisation hétérogène (LRI) (MODHEL - LRI), Laboratoire de Recherche en Informatique (LRI), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), and Quercini, Gianluca

Subjects
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-WB] Computer Science [cs]/Web, [INFO.INFO-WB]Computer Science [cs]/Web, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract

International audience; Many Wikipedia articles that cover the same topic in different language editions are interconnectedvia cross-language links However, cross-language links are added manually by theusers of Wikipedia and, as such, are often incorrect. In this paper, we propose an approach toautomatically eliminate incorrect cross-language links. The rationale is that the presence of anincorrect cross-language link is revealed by the existence of a path of cross-links between twoarticles of the same language edition. Our approach removes cross-languages links, startingfrom those having a low correctness score, until there is no path between two articles of thesame language edition. The results of our evaluation on a snapshot ofWikipedia in 8 languagesindicates that our approach shows quantitative promise.

Published
2018

42. A frequent named entities-based approach for interpreting reputation in Twitter

Author

Universitat Politècnica de Catalunya. Departament d'Enginyeria de Serveis i Sistemes d'Informació, Universitat Politècnica de Catalunya. inSSIDE - integrated Software, Service, Information and Data Engineering, Bennacer Seghouani, Nacéra, Bugiotti, Francesca, Hewasinghage, Moditha Lakshan Dharmasir, Isaj, Suela, Quercini, Gianluca, Universitat Politècnica de Catalunya. Departament d'Enginyeria de Serveis i Sistemes d'Informació, Universitat Politècnica de Catalunya. inSSIDE - integrated Software, Service, Information and Data Engineering, Bennacer Seghouani, Nacéra, Bugiotti, Francesca, Hewasinghage, Moditha Lakshan Dharmasir, Isaj, Suela, and Quercini, Gianluca

Abstract

Twitter is a social network that provides a powerful source of data. The analysis of those data offers many challenges among those stands out the opportunity to find reputation of a product, a person or any other entity of interest. Several approaches for sentiment analysis have been proposed in the literature to assess the general opinion expressed in tweets on an entity. Nevertheless, these methods aggregate sentiment scores retrieved from tweets, which is a static view to evaluate the overall reputation of an entity. The reputation of an entity is not static; entities collaborate with each other, and they get involved in different events over time. A simple aggregation of sentiment scores is then not sufficient to represent this dynamism. In this paper, we present a new approach to determine the reputation of an entity on the basis of the set of events in which it is involved. To achieve this, we propose a new sampling method driven by a tweet weighting measure to give a better quality and summary of the target entity. We introduce the concept of Frequent Named Entities to determine the events involving the target entity. Our evaluation achieved for different entities shows that 90% of the reputation of an entity originates from the events it is involved in and the breakdown into events allows interpreting the reputation in a transparent and self-explanatory way., Peer Reviewed, Postprint (published version)

Published
2018

43. Estocada: Stockage Hybride et Ré-écriture sous Contraintes d'Intégrité

Author

AL-Otaibi, Rana B., Bugiotti, Francesca, Bursztyn, Damian, Deutsch, Alin, Manolescu, Ioana, Zampetakis, Stamatis, Department of Computer Science and Engineering [Univ California San Diego] (CSE - UC San Diego), University of California [San Diego] (UC San Diego), University of California (UC)-University of California (UC), CentraleSupélec, Rich Data Analytics at Cloud Scale (CEDAR), Laboratoire d'informatique de l'École polytechnique [Palaiseau] (LIX), École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS), Department of Computer Science and Engineering [San Diego] (CSE-UCSD), University of California-University of California, Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Inria Saclay - Ile de France, and Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)

Subjects
[INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB]
Abstract

National audience; La production croissante de données numériques a conduit a l'´ emergence d'une grande variété de systemes de gestion de données (Data Management Systems, ou DMS). Dans ce contexte, les applications a usage intensif de données ont besoin (i) d' accéder a des données hétérogenes de grande taille (" Big Data "), ayant une structure potentiellement complexe, et (ii) de manipuler des données de façon efficace afin de garantir une bonne performance de l'application. Comme ces différents systemes sont spécialisés sur certaines opérations mais sont moins performants sur d'autres, il peut s' avérer essentiel pour une application d'utiliser plusieurs DMS en même temps. Dans ce contexte nous présentons Estocada, une application donnant la possibilité de tirer profit simultanément de plusieurs DMSs et permettant une manipulation efficace et automatique de données de grande taille et hétérogenes, offrant ainsi un meilleur support aux applications a usage intensif de données. Dans Estocada, les données sont reparties dans plusieurs fragments qui sont stockés dans différents DMSs. Pour répondrè a une requêtè a partir de ces fragments , Estocada est basé sur la reecriture de requêtes sous contraintes; cesdernìeres sont utilisées pour représenter les différents modeles de données et la répartition des fragments entre les differents DMSs.

Published
2016

45. Toward Scalable Hybrid Stores

Author

Bugiotti, Francesca, Bursztyn, Damian, Deutsch, Alin, Ileana, Ioana, Manolescu, Ioana, Database optimizations and architectures for complex large data (OAK), Laboratoire de Recherche en Informatique (LRI), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), CentraleSupélec, Université Paris-Sud - Paris 11 (UP11), Inria@SiliconValley (Inria@SiliconValley), Stanford University-University of California [Santa Cruz] (UC Santa Cruz), University of California (UC)-University of California (UC)-Institut National de Recherche en Informatique et en Automatique (Inria)-University of California [Santa Barbara] (UC Santa Barbara), University of California (UC)-University of California [San Diego] (UC San Diego), University of California (UC)-Ministère de l'Europe et des Affaires étrangères (MEAE)-University of Southern California (USC)-CITRIS-University of California [Irvine] (UC Irvine), University of California (UC), Department of Computer Science and Engineering [Univ California San Diego] (CSE - UC San Diego), University of California [San Diego] (UC San Diego), University of California (UC)-University of California (UC), OAKSAD, Stanford University-University of Southern California (USC)-Institut National de Recherche en Informatique et en Automatique (Inria)-University of California [Santa Barbara] (UCSB), University of California-University of California-University of California [San Diego] (UC San Diego), University of California-Ministère de l'Europe et des Affaires étrangères (MEAE)-CITRIS-University of California [Santa Cruz] (UCSC), University of California-University of California [Irvine] (UCI), University of California, Database group, Computer Science and engeenering Department [San Diego] (DB CSE UCSD), and University of California-University of California

Subjects
[INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB]
Abstract

National audience; Data centric applications often use heterogeneous datasets: some very large while others of moderate size, some highly structured (e.g., relations) while others complex structured (e.g., graphs) or little structured (e.g., log data). Facing them is a variety of storage systems but none of which is the best for all, at all times.We present Estocada, an architecture we are currently developing to efficiently handle highly heterogeneous datasets based on a dynamic set of potentially very different data stores. Estocada provides to the ap- plication layer access to each dataset in its native format, while hosting them internally in a set of potentially overlapping fragments, possibly distributed across heterogeneous stores. At the core of Estocada lie powerful view-based rewriting and view selection algorithms to marry correctness with high performance.

Published
2015

46. Invisible Glue: Scalable Self-Tuning Multi-Stores

Author

Bugiotti, Francesca, Bursztyn, Damian, Deutsch, Alin, Ileana, Ioana, Manolescu, Ioana, Inria@SiliconValley (Inria@SiliconValley), Stanford University-University of California [Santa Cruz] (UC Santa Cruz), University of California (UC)-University of California (UC)-Institut National de Recherche en Informatique et en Automatique (Inria)-University of California [Santa Barbara] (UC Santa Barbara), University of California (UC)-University of California [San Diego] (UC San Diego), University of California (UC)-Ministère de l'Europe et des Affaires étrangères (MEAE)-University of Southern California (USC)-CITRIS-University of California [Irvine] (UC Irvine), University of California (UC), Database optimizations and architectures for complex large data (OAK), Laboratoire de Recherche en Informatique (LRI), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université Paris-Sud - Paris 11 (UP11), Department of Computer Science and Engineering [Univ California San Diego] (CSE - UC San Diego), University of California [San Diego] (UC San Diego), University of California (UC)-University of California (UC), Télécom ParisTech, OAKSAD, Stanford University-University of Southern California (USC)-Institut National de Recherche en Informatique et en Automatique (Inria)-University of California [Santa Barbara] (UCSB), University of California-University of California-University of California [San Diego] (UC San Diego), University of California-Ministère de l'Europe et des Affaires étrangères (MEAE)-CITRIS-University of California [Santa Cruz] (UCSC), University of California-University of California [Irvine] (UCI), University of California, Database group, Computer Science and engeenering Department [San Diego] (DB CSE UCSD), and University of California-University of California

Subjects
[INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB]
Abstract

International audience; Next-generation data centric applications often involve di-verse datasets, some very large while others may be of mod-erate size, some highly structured (e.g., relations) while others may have more complex structure (e.g., graphs) or little structure (e.g., text or log data). Facing them is a variety of storage systems, each of which can host some of the datasets (possibly after some data migration), but none of which is likely to be best for all, at all times. Deploying and efficiently running data-centric applications in such a complex setting is very challenging. We propose Estocada, an architecture for efficiently han-dling highly heterogeneous datasets based on a dynamic set of potentially very different data stores. Estocada pro-vides to the application/programming layer access to each data set in its native format, while hosting them internally in a set of potentially overlapping fragments, possibly dis-tributing (fragments of) each dataset across heterogeneous stores. Given workload information, Estocada self-tunes for performance, i.e., it automatically choses the fragments of each data set to be deployed in each store so as to op-timize performance. At the core of Estocada lie powerful view-based rewriting and view selection algorithms, required in order to correctly handle the features (nesting, keys, con-straints etc.) of the diverse data models involved, and thus to marry correctness with high performance.

Published
2015

49. EXLEngine

Author

Atzeni, Paolo, primary, Bellomarini, Luigi, additional, and Bugiotti, Francesca, additional

Published
2013
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50. AMADA

Author

Aranda-Andújar, Andrés, primary, Bugiotti, Francesca, additional, Camacho-Rodríguez, Jesús, additional, Colazzo, Dario, additional, Goasdoué, François, additional, Kaoudi, Zoi, additional, and Manolescu, Ioana, additional

Published
2012
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