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14 results on '"Milo, Tova"'

1. SubDEx: Exploring Ratings in Subjective Databases (Authors' Copy)

Author

Amer-Yahia, Sihem, Milo, Tova, Youngmann, Brit, Laboratoire d'Informatique de Grenoble (LIG), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), Tel Aviv University [Tel Aviv], ANR-19-P3IA-0003,MIAI,MIAI @ Grenoble Alpes(2019), European Project: H2020,INODE, and Tel Aviv University (TAU)

Subjects
[INFO]Computer Science [cs]
Abstract

International audience; We demonstrate SubDEx, a dedicated framework for Subjective Data Exploration (SDE). SubDEx enables the joint exploration of items, people, and people's opinions on items, in a guided multi-step process where each step aggregates the most useful and diverse trends in the form of rating maps. Because of the large search space of possible rating maps, we leverage pruning strategies to enable interactive running times. We demonstrate the need for a dedicated SDE framework and the effectiveness and efficiency of our approach, by interacting with the ICDE'21 participants who will act as data analysts.

Published
2021

2. Machine Learning to Data Management: A Round Trip

Author

Milo Tova, Bonifati Angela, Berti-Equille Laure, Aix Marseille Université (AMU), Base de Données (BD), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2), Tel Aviv University [Tel Aviv], Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS), and Tel Aviv University (TAU)

Subjects
[INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], Standardization, business.industry, Computer science, Data management, Inference, 02 engineering and technology, computer.software_genre, Machine learning, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], 020204 information systems, Schema (psychology), Scalability, 0202 electrical engineering, electronic engineering, information engineering, Data deduplication, 020201 artificial intelligence & image processing, Artificial intelligence, Cluster analysis, business, computer, Data integration
Abstract

International audience; With the emergence of machine learning (ML) techniques in database research, ML has already proved a tremendous potential to dramatically impact the foundations, algorithms, and models of several data management tasks, such as error detection, data cleaning, data integration, and query inference. Part of the data preparation, standardization, and cleaning processes, such as data matching and deduplication for instance, could be automated by making a ML model " learn " and predict the matches routinely. Data integration can also benefit from ML as the data to be integrated can be sampled and used to design the data integration algorithms. After the initial manual work to setup the labels, ML models can start learning from the new incoming data that are being submitted for standardization, integration, and cleaning. The more data supplied to the model, the better the ML algorithm can perform and deliver accurate results. Therefore, ML is more scalable compared to traditional and time-consuming approaches. Nevertheless, many ML algorithms require an out-of-the-box tuning and their parameters and scope are often not adapted to the problem at hand. To make an example, in cleaning and integration processes, the window sizes of values used for the ML models cannot be arbitrarily chosen and require an adaptation of the learning parameters. This tutorial will survey the recent trend of applying machine learning solutions to improve data management tasks and establish new paradigms to sharpen data error detection, cleaning, and integration at the data instance level, as well as at schema, system, and user levels.

Published
2018

3. Research Directions for Principles of Data Management

Author

Abiteboul, Serge, Arenas, Marcelo, Barceló, Pablo, Bienvenu, Meghyn, Calvanese, Diego, David, Claire, Hull, Richard, Hullermeier, Eyke, Kimelfeld, Benny, Libkin, Leonid, Martens, Wim, Milo, Tova, Murlak, Filip, Neven, Frank, Ortiz, Magdalena, Schwentick, Thomas, Stoyanovich, Julia, Su, Jianwen, Suciu, Dan, Vianu, Victor, Yi, Ke, Laboratoire Spécification et Vérification [Cachan] (LSV), École normale supérieure - Cachan (ENS Cachan)-Centre National de la Recherche Scientifique (CNRS), Pontificia Universidad Católica de Chile (UC), Universidad de Chile = University of Chile [Santiago] (UCHILE), Université de Montpellier (UM), Free University of Bozen-Bolzano, Laboratoire d'Informatique Gaspard-Monge (LIGM), Centre National de la Recherche Scientifique (CNRS)-Fédération de Recherche Bézout-ESIEE Paris-École des Ponts ParisTech (ENPC)-Université Paris-Est Marne-la-Vallée (UPEM), IBM Thomas J. Watson Research Center, IBM, Universität Paderborn (UPB), Technion - Israel Institute of Technology [Haifa], Laboratory for the Foundations of Computer Science [Edinburgh] (LFCS), University of Edinburgh, Universität Bayreuth, School of Computer Science (TAU-CS), Tel Aviv University [Tel Aviv], Institute of Informatics [Warsaw], Faculty of Mathematics, Informatics, and Mechanics [Warsaw] (MIMUW), University of Warsaw (UW)-University of Warsaw (UW), School for Information Technology, Transnational University of Limburg-Hasselt University (UHasselt), Vienna University of Technology (TU Wien), Lehrstuhl Informatik 1 [Dortmund], Technische Universität Dortmund [Dortmund] (TU), Drexel University, Department of Computer Science [Santa Barbara] (CS-UCSB), University of California [Santa Barbara] (UCSB), University of California-University of California, University of Washington [Seattle], Department of Computer Science and Engineering [San Diego] (CSE-UCSD), University of California [San Diego] (UC San Diego), Department of Computer Science and Engineering [HKUST] (CSE), and Hong Kong University of Science and Technology (HKUST)

Subjects
query languages, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], database theory, efficient query processing, heterogeneous data, principles of data management, uncertainty, query optimization
Abstract

International audience; The area of Principles of Data Management (PDM) has made crucial contributions to the development of formal frameworks for understanding and managing data and knowledge. This work has involved a rich cross-fertilization between PDM and other disciplines in mathematics and computer science, including logic, complexity theory, and knowledge representation. We anticipate ongoing expansion of PDM research as the technology and applications involving data management continue to grow and evolve. In particular, the lifecycle of Big Data Analytics raises a wealth of challenge areas that PDM can help with. In this report we identify some of the most important research directions where the PDM community has the potential to make significant contributions. This is done from three perspectives: potential practical relevance, results already obtained, and research questions that appear surmountable in the short and medium term.

Published
2018

4. Research Directions for Principles of Data Management: Manifesto from Dagstuhl Perspectives Workshop 16151

Author

Abiteboul, Serge, Arenas, Marcelo, Barceló, Pablo, Bienvenu, Meghyn, Calvanese, Diego, David, Claire, Hull, Richard, Hullermeier, Eyke, Kimelfeld, Benny, Libkin, Leonid, Martens, Wim, Milo, Tova, Murlak, Filip, Neven, Frank, Ortiz, Magdalena, Schwentick, Thomas, Stoyanovich, Julia, Su, Jianwen, Suciu, Dan, Vianu, Victor, Yi, Ke, Laboratoire Spécification et Vérification [Cachan] (LSV), École normale supérieure - Cachan (ENS Cachan)-Centre National de la Recherche Scientifique (CNRS), Pontificia Universidad Católica de Chile (UC), Universidad de Chile = University of Chile [Santiago] (UCHILE), Université de Montpellier (UM), Free University of Bozen-Bolzano, Laboratoire d'Informatique Gaspard-Monge (LIGM), Centre National de la Recherche Scientifique (CNRS)-Fédération de Recherche Bézout-ESIEE Paris-École des Ponts ParisTech (ENPC)-Université Paris-Est Marne-la-Vallée (UPEM), IBM Thomas J. Watson Research Center, IBM, Universität Paderborn (UPB), Technion - Israel Institute of Technology [Haifa], Laboratory for the Foundations of Computer Science [Edinburgh] (LFCS), University of Edinburgh, Universität Bayreuth, School of Computer Science (TAU-CS), Tel Aviv University [Tel Aviv], Institute of Informatics [Warsaw], Faculty of Mathematics, Informatics, and Mechanics [Warsaw] (MIMUW), University of Warsaw (UW)-University of Warsaw (UW), School for Information Technology, Transnational University of Limburg-Hasselt University (UHasselt), Vienna University of Technology (TU Wien), Lehrstuhl Informatik 1 [Dortmund], Technische Universität Dortmund [Dortmund] (TU), Drexel University, Department of Computer Science [Santa Barbara] (CS-UCSB), University of California [Santa Barbara] (UCSB), University of California-University of California, University of Washington [Seattle], Department of Computer Science and Engineering [San Diego] (CSE-UCSD), University of California [San Diego] (UC San Diego), Department of Computer Science and Engineering [HKUST] (CSE), Hong Kong University of Science and Technology (HKUST), Université Paris-Est Marne-la-Vallée (UPEM)-École des Ponts ParisTech (ENPC)-ESIEE Paris-Fédération de Recherche Bézout-Centre National de la Recherche Scientifique (CNRS), University of California [Santa Barbara] (UC Santa Barbara), University of California (UC)-University of California (UC), and Department of Computer Science and Engineering [Univ California San Diego] (CSE - UC San Diego)

Subjects
query languages, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], database theory, efficient query processing, heterogeneous data, principles of data management, uncertainty, query optimization
Abstract

International audience; The area of Principles of Data Management (PDM) has made crucial contributions to the development of formal frameworks for understanding and managing data and knowledge. This work has involved a rich cross-fertilization between PDM and other disciplines in mathematics and computer science, including logic, complexity theory, and knowledge representation. We anticipate ongoing expansion of PDM research as the technology and applications involving data management continue to grow and evolve. In particular, the lifecycle of Big Data Analytics raises a wealth of challenge areas that PDM can help with. In this report we identify some of the most important research directions where the PDM community has the potential to make significant contributions. This is done from three perspectives: potential practical relevance, results already obtained, and research questions that appear surmountable in the short and medium term.

Published
2018

5. Top-k Querying of Unknown Values under Order Constraints

Author

Amarilli, Antoine, Amsterdamer, Yael, Milo, Tova, Senellart, Pierre, Laboratoire Traitement et Communication de l'Information (LTCI), Télécom ParisTech-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS), Department of Computer Science [Bar Ilan], Bar-Ilan University [Israël], School of Computer Science (TAU-CS), Tel Aviv University (TAU), Département d'informatique - ENS Paris (DI-ENS), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), Value from Data (VALDA ), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria), Tel Aviv University [Tel Aviv], Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Inria de Paris, Département d'informatique de l'École normale supérieure (DI-ENS), École normale supérieure - Paris (ENS Paris), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris)

Subjects
[INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], partial order, unknown values, crowdsourcing, ACM: H.: Information Systems/H.2: DATABASE MANAGEMENT, uncertainty, interpolation
Abstract

International audience; Many practical scenarios make it necessary to evaluate top-k queries over data items with partially unknown values. This paper considers a setting where the values are taken from a numerical domain, and where some partial order constraints are given over known and unknown values: under these constraints, we assume that all possible worlds are equally likely. Our work is the first to propose a principled scheme to derive the value distributions and expected values of unknown items in this setting, with the goal of computing estimated top-k results by interpolating the unknown values from the known ones. We study the complexity of this general task, and show tight complexity bounds, proving that the problem is intractable, but can be tractably approximated. We then consider the case of tree-shaped partial orders, where we show a constructive PTIME solution. We also compare our problem setting to other top-k definitions on uncertain data.

Published
2017

6. Top-k Queries on Unknown Values under Order Constraints

Author

Amarilli, Antoine, Amsterdamer, Yael, Milo, Tova, Senellart, Pierre, Data, Intelligence and Graphs (DIG), Laboratoire Traitement et Communication de l'Information (LTCI), Institut Mines-Télécom [Paris] (IMT)-Télécom Paris-Institut Mines-Télécom [Paris] (IMT)-Télécom Paris, Département Informatique et Réseaux (INFRES), Télécom ParisTech, Department of Computer Science [Bar Ilan], Bar-Ilan University [Israël], School of Computer Science (TAU-CS), Tel Aviv University [Tel Aviv], Département d'informatique de l'École normale supérieure (DI-ENS), École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), Tel Aviv University (TAU), Département d'informatique - ENS Paris (DI-ENS), École normale supérieure - Paris (ENS-PSL), and HAL, TelecomParis

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

International audience

Published
2017

7. Top-k Querying of Unknown Values under Order Constraints (Extended Version)

Author

Amarilli, Antoine, Amsterdamer, Yael, Milo, Tova, Senellart, Pierre, Laboratoire Traitement et Communication de l'Information (LTCI), Télécom ParisTech-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS), Department of Computer Science [Bar Ilan], Bar-Ilan University [Israël], School of Computer Science (TAU-CS), Tel Aviv University (TAU), Département d'informatique - ENS Paris (DI-ENS), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), Value from Data (VALDA ), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria), Tel Aviv University [Tel Aviv], Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Inria de Paris, Senellart, Pierre, Département d'informatique de l'École normale supérieure (DI-ENS), École normale supérieure - Paris (ENS Paris), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris)

Subjects
FOS: Computer and information sciences, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], Computer Science - Databases, [INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB], Databases (cs.DB)
Abstract

Many practical scenarios make it necessary to evaluate top-k queries over data items with partially unknown values. This paper considers a setting where the values are taken from a numerical domain, and where some partial order constraints are given over known and unknown values: under these constraints, we assume that all possible worlds are equally likely. Our work is the first to propose a principled scheme to derive the value distributions and expected values of unknown items in this setting, with the goal of computing estimated top-k results by interpolating the unknown values from the known ones. We study the complexity of this general task, and show tight complexity bounds, proving that the problem is intractable, but can be tractably approximated. We then consider the case of tree-shaped partial orders, where we show a constructive PTIME solution. We also compare our problem setting to other top-k definitions on uncertain data., 32 pages, 1 figure, 1 algorithm, 51 references. Extended version of paper at ICDT'17

Published
2017

8. PROX: Approximated Summarization of Data Provenance

Author

Ainy, Eleanor, Bourhis, Pierre, Davidson, Susan, Deutch, Daniel, Milo, Tova, School of Computer Science (TAU-CS), Tel Aviv University (TAU), Linking Dynamic Data (LINKS), Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), Department of Computer and Information Science [Pennsylvania] (CIS), University of Pennsylvania, ANR-14-CE25-0017,Aggreg,Requêtes d'Agrégation(2014), Links, Inria, Appel à projets générique - Requêtes d'Agrégation - - Aggreg2014 - ANR-14-CE25-0017 - Appel à projets générique - VALID, Tel Aviv University [Tel Aviv], and University of Pennsylvania [Philadelphia]

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

Démonstration; International audience

Published
2016

9. The Complexity of Evaluating Order Queries with the Crowd *

Author

Groz, Benoit, Milo, Tova, Roy, Sudeepa, Laboratoire de Recherche en Informatique (LRI), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), School of Computer Science (TAU-CS), Tel Aviv University [Tel Aviv], Duke University, and Groz, Benoit

Subjects
[INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], [INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS], [INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB], [INFO.INFO-DS] Computer Science [cs]/Data Structures and Algorithms [cs.DS], ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2016

10. On the Complexity of Mining Itemsets from the Crowd Using Taxonomies

Author

Amarilli, Antoine, Amsterdamer, Yael, Milo, Tova, School of Computer Science (TAU-CS), Tel Aviv University [Tel Aviv], Data, Intelligence and Graphs (DIG), Laboratoire Traitement et Communication de l'Information (LTCI), Institut Mines-Télécom [Paris] (IMT)-Télécom Paris-Institut Mines-Télécom [Paris] (IMT)-Télécom Paris, Département Informatique et Réseaux (INFRES), Télécom ParisTech, and Télécom Paristech, Admin

Subjects
FOS: Computer and information sciences, Computer Science - Computational Complexity, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], [INFO.INFO-WB] Computer Science [cs]/Web, Computer Science - Databases, [INFO.INFO-WB]Computer Science [cs]/Web, H.2.8, [INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB], InformationSystems_DATABASEMANAGEMENT, Databases (cs.DB), Computational Complexity (cs.CC), Information Retrieval (cs.IR), Computer Science - Information Retrieval
Abstract

We study the problem of frequent itemset mining in domains where data is not recorded in a conventional database but only exists in human knowledge. We provide examples of such scenarios, and present a crowdsourcing model for them. The model uses the crowd as an oracle to find out whether an itemset is frequent or not, and relies on a known taxonomy of the item domain to guide the search for frequent itemsets. In the spirit of data mining with oracles, we analyze the complexity of this problem in terms of (i) crowd complexity, that measures the number of crowd questions required to identify the frequent itemsets; and (ii) computational complexity, that measures the computational effort required to choose the questions. We provide lower and upper complexity bounds in terms of the size and structure of the input taxonomy, as well as the size of a concise description of the output itemsets. We also provide constructive algorithms that achieve the upper bounds, and consider more efficient variants for practical situations., Comment: 18 pages, 2 figures. To be published to ICDT'13. Added missing acknowledgement

Published
2014

11. Crowd Miner: Mining association rules from the crowd

Author

Amsterdamer, Yael, Grossman, Yael, Milo, Tova, Senellart, Pierre, School of Computer Science (TAU-CS), Tel Aviv University [Tel Aviv], Data, Intelligence and Graphs (DIG), Laboratoire Traitement et Communication de l'Information (LTCI), Institut Mines-Télécom [Paris] (IMT)-Télécom Paris-Institut Mines-Télécom [Paris] (IMT)-Télécom Paris, Département Informatique et Réseaux (INFRES), Télécom ParisTech, and Télécom Paristech, Admin

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

International audience

Published
2013

12. Understanding Local Structure in Ranked Datasets

Author

Stoyanovich, Julia, Amer-Yahia, Sihem, B. Davidson, Susan, Jacob, Marie, Milo, Tova, Drexel University, Skoltech, Heterogeneous and Adaptive distributed DAta management Systems (HADAS), Laboratoire d'Informatique de Grenoble (LIG), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF), Department of Computer and Information Science [Pennsylvania] (CIS), University of Pennsylvania [Philadelphia], School of Computer Science (TAU-CS), Tel Aviv University [Tel Aviv], Google Research Award 'Identifying Ranked Agreement Among Raters', European Research Council (FP7/2007-2013) / ERC grant MoDaS, agreement 291071, Israel Ministry of Science, US-Israel Bi-national Science foundation, European Project: 291071,MoDaS(2007), Samper, Valérie, (FP7) / ERC grant MoDaS, agreement n° 291071 - MoDaS - 2007-00-00 - 2011-00-00 - 291071 - VALID, Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS), University of Pennsylvania, and Tel Aviv University (TAU)

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

Session: Outrageous Ideas and Vision I (Query Processing I) - http://www.cidrdb.org/cidr2013 - Online Proceedings; International audience; Ranked data is ubiquitous in real-world applications. Rankingsarise naturally when users express preferences about products and services, when voters cast ballots in elections, when funding proposals are evaluated based on their merits and university departments based on their reputation, or when genes are ordered based on their expression levels under various experimental conditions. We observe that ranked data exhibits interesting local structure, representing agreement of subsets of rankers, over subsets of items. Being able to model, identify and describe such structure is important, because it enables novel kinds of analysis with the potential of making ground-breaking impact, but is challenging to do effectively and efficiently. We argue for the use of fundamental data management principles such as declarativeness and incremental evaluation, in combination with state-of-the-art machine learning and data mining techniques, for addressing the effectiveness and efficiency challenges. We describe the key ingredients of a solution, and propose a roadmap towards a framework that will enable robust and efficient analysis of large ranked datasets.

Published
2013

13. Optimal Probabilistic Generators for XML Corpora

Author

Abiteboul, Serge, Amsterdamer, Yael, Deutch, Daniel, Milo, Tova, Senellart, Pierre, Verification in databases (DAHU), Laboratoire Spécification et Vérification [Cachan] (LSV), École normale supérieure - Cachan (ENS Cachan)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Cachan (ENS Cachan)-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), Distributed and heterogeneous data and knowledge (LEO), Université Paris-Sud - Paris 11 (UP11)-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)-Centre National de la Recherche Scientifique (CNRS), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria), School of Computer Science (TAU-CS), Tel Aviv University [Tel Aviv], Laboratoire Traitement et Communication de l'Information (LTCI), Télécom ParisTech-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS), Tel Aviv University (TAU), and Télécom Paristech, Admin

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

National audience

Published
2011

14. EDOS: Environment for the Development and Distribution of Open Source Software

Author

Abiteboul, Serge, Leroy, Xavier, Vrdoljak, Boris, Bryce, Ciaran, Di Cosmo, Roberto, Dittrich, Klaus R., Fermigier, Stéphane, Milo, Tova, Sagi, Assaf, Shtossel, Yotam, Laurière, Stéphane, Lepied, Frédéric, Pop, Radu, Panto, Eleonora, Smets, Jean-Paul, Integration of data and knowledge distributed over the web (GEMO), 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), Programming languages, types, compilation and proofs (GALLIUM), Inria Paris-Rocquencourt, Centre Universitaire d'Informatique (CUI), University of Geneva [Switzerland], Preuves, Programmes et Systèmes (PPS), Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), Universität Zürich [Zürich] = University of Zurich (UZH), Nuxeo, nuxeo, Tel Aviv University [Tel Aviv], Mandrakesoft, Grid'5000, Rennes, Ist, Université de Genève = University of Geneva (UNIGE), and Tel Aviv University (TAU)

Subjects
[INFO.INFO-OH] Computer Science [cs]/Other [cs.OH], [INFO.INFO-OH]Computer Science [cs]/Other [cs.OH]
Abstract

International audience; The open-source software community is now comprised of a very large and growing number of contributors and users. The GNU/Linux operating system for instance has an estimated 18 million users worldwide and its contributing developers can be counted by thousands. The critical mass of contributors taking part in various opensource projects has helped to ensure high quality for open source software. However, despite the achievements of the open-source software industry, there are issues in the production of large scale open-source software (OSS) such as the GNU/Linux operating system that have to be addressed as the numbers of users, of contributors, and of available applications grow. EDOS is a European project supported by IST started October 2004 and ending in 2007, whose objective is to provide a new generation of methodologies, theoretical models, technical tools and quality models specifically tailored to OSS engineering and to software distribution over the Internet.

Published
2005

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