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Enregistrement W2913631344

Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data

2014· article· en· W2913631344 sur OpenAlexaffabout
Curtis Dyreson, Feifei Li, M. TAMER ÖZSU

Notice bibliographique

Revuenon disponible
Typearticle
Langueen
DomaineBusiness, Management and Accounting
ThématiqueBig Data and Business Intelligence
Établissements canadiensUniversity of Waterloo
Organismes subventionnairesnon disponible
Mots-clésComputer scienceInformation retrieval
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

We are delighted to welcome you to SIGMOD 2014, the 2014 edition of the ACM SIGMOD International Conference on Management of Data, at Snowbird, Utah, in the Rocky Mountains of the United States. Snowbird is a mountain ski and summer resort. Many attractions, parks, and recreational areas -- including the Yellowstone, Zion, Bryce Canyon, Capitol Reef, Great Basin, Canyonlands, and Arches national parks -- are within a day's drive. The mountain scenery of Snowbird is also adjacent to the urban environment of Salt Lake City. SIGMOD 2014 hosts an exciting technical program, with two keynote talks, by Maurice Herlihy (Brown University) and Eric Sedlar (Oracle Labs); a panel session on Should we all be teaching 'Intro to Data Science' instead of 'Intro to Databases'?, by Bill Howe (University of Washington), Michael Franklin (University of California, Berkeley), Juliana Freire (New York University), James Frew (University of California, Santa Barbara), Tim Kraska (Brown University), and Raghu Ramakrishnan (Microsoft Corporation); an industrial panel on Are We Experiencing a Big Data Bubble?, by Fatma Ozcan (IBM Almaden Research Center), Daniel Abadi (Yale University), Marcel Kornacker (Cloudera), C Mohan (IBM Almaden Research Center), Karthik Ramasamy (Twitter), and Janet Wiener (Facebook, Inc. ); 107 research paper presentations; 6 tutorials; 30 demonstrations; and 15 industrial presentations selected from among 44 submissions. In addition to having 25-minute presentation slots, research papers are included in one of two Research Plenary Poster Sessions. One of these sessions is jointly for PODS and SIGMOD research papers, to deepen the ties between the two conferences. SIGMOD 2014 includes several technical and social events designed specifically for student attendees. The SIGMOD/PODS 2014 Ph.D. Symposium, the Undergraduate Research Poster Competition, and the New Researcher Symposium are all established components of the SIGMOD program and are all part of SIGMOD 2014. The conference also hosts a session dedicated to highlighting the finalists of the SIGMOD Programming Contest. (This year's task is to implement a multidimensional, high-throughput, in-memory indexing system.) In addition, the conference includes a Sponsor Session for our industry partners to present the cool things they have done and introduce career opportunities in industry. This helps bring student attendees together with our Gold, Platinum, and Diamond sponsors. A vis-a-vis meeting aimed at helping Ph.D. students meet internationally recognized researchers in their research areas, will also be organized to exchange ideas and receive guidance in a relaxed social setting. We are immensely grateful to the many researchers who have shaped the conference program. In particular, we thank the Keynotes Chair Gustavo Alonso (ETH Zurich), the Panels Chairs Susan Davidson (University of Pennsylvania) and Sunita Sarawagi (IIT Bombay), the Tutorial Chairs Chris Jermaine (Rice University) and Yufei Tao (Chinese University of Hong Kong), the Industrial Program Chairs Fatma Ozcan (IBM Almaden) and Nesime Tatbul (Intel Labs and MIT), together with the 13 members of the Industrial Program Committee, the Demonstrations Chairs Wolfgang Lehner (Technische Universitat Dresden) and Bettina Kemme (McGill University), together with the 53 members of the Demonstration Program Committee, the Workshops Chair Anastasia Ailamaki (EPFL), the Undergraduate Research Program Chairs Mario Nascimento (University of Alberta) and Anastasios Kementsietsidis (IBM TJ Watson Laboratories), the New Researcher Symposium Chairs Anish Das Sarma (Google Research) and Alexandra Meliou (University of Massachusetts, Amherst). We also express our enormous gratitude to the 13 Program Committee coordinators, Lei Chen (Hong Kong University of Science and Technology), Michael Franklin (University of California, Berkeley), Alfons Kemper (Technical University of Munich), Laks Lakshmanan (University of British Columbia), Ioana Manolescu (Inria Saclay), Tova Milo (Tel Aviv University), Elke Rundensteiner (Worcester Polytechnic Institute), Ken Salem (University of Waterloo), Dennis Shasha (New York University), Divesh Srivastava (AT&T Labs-Research), Kian-Lee Tan (National University of Singapore), Patrick Valduriez (Inria Sophia- Antipolis Mediterranee), and Xiaokui Xiao (Nanyang Technological University) along with the 111 members of the SIGMOD 2014 Program Committee. This year we had two submission cycles for SIGMOD research papers -- one in mid-September and the second one in mid-December. We received 421 research paper submissions across two cycles. As usual, each paper was reviewed by at least three program committee members, followed by a discussion led by the area coordinators. We followed a review process that resembles journal reviewing -- papers went through multiple rounds of reviews until a final decision was taken. Out of 421 submissions, 29 were directly accepted, 102 went through a major revision process, and 290 were rejected. Out of the 102 that went through a major revision 56 were accepted, a further 22 were accepted following a third round of minor revisions, and 24 were rejected. Since the review process resembled journal-style, the acceptance rates are also similar to top journals. We also acknowledge all members of the SIGMOD 2014 organizing committee, as well as the SIGMOD Executive Committee, for invaluable help and guidance throughout the many months leading to the conference. The strong organizing team for SIGMOD 2014 includes proceedings chairs Xiang Lian (University of Texas - Pan American) and Florin Rusu (University of California Merced), finance chair Nikolaus Augsten (University of Salzburg), publicity chairs Guoliang Li (Tsinghua University) and Ashwin Machanavajjhala (Duke University), social media chair Ming Hua (Facebook), sponsorship chairs Yi Chen (New Jersey Institute of Technology) and Nick Koudas (University of Toronto), exhibit chairs Rui Zhang (University of Melbourne) and Peixiang Zhao (Florida State University), local arrangements chair Ryan Johnson (University of Toronto), registration chairs Tingjian Ge (University of Massachusetts, Lowell) and Gabriel Ghinita (University of Massachusetts, Boston), demonstration and workshop local arrangement chair Jeff Phillips (University of Utah), web chairs Huiping Cao (New Mexico State University) and Bin Yao (Shanghai Jiao Tong University), travel award committee chairs Alexandra Meliou (University of Massachusetts, Amherst) and Cong Yu (Google Research), and programming contest chairs Sam Madden (MIT) and Jeong-Hyon Hwang (University of Albany). The success of this conference is impossible without their hard work and persistent dedication. K. Selcuk Candan deserves special thanks for providing us with his advice and SIGMOD institutional memory, as well as for his help with logistics throughout the conference planning. We are also grateful to the Microsoft Research Conference Management Toolkit (CMT) team for their extremely prompt and helpful support throughout the complex conference reviewing process. (We used CMT for the submission and reviewing of research papers, demonstrations, and industrial presentations.) We also extend our appreciation to the student volunteers.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,008
score de la tête « metaresearch » (Gemma)0,017
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Autre · Signal consensuel: Autre
Score de désaccord entre enseignants0,110
Score d'incertitude au seuil0,369

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0080,017
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0030,001
Bibliométrie0,0040,003
Études des sciences et des technologies0,0020,002
Communication savante0,0120,015
Science ouverte0,0030,009
Intégrité de la recherche0,0030,007
Charge utile insuffisante (le modèle a refusé de juger)0,1100,093

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,160
Tête enseignante GPT0,322
Écart entre enseignants0,163 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreAutre

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2014
Routes d'admission2
Résumé présentoui

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