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Related papers: Relational databases for data management in PHENIX

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High energy physics experiments including those at the Tevatron and the upcoming LHC require analysis of large data sets which are best handled by distributed computation. We present the design and development of a distributed data analysis…

Distributed, Parallel, and Cluster Computing · Computer Science 2007-05-23 Jeremiah Mans , David Bengali

As the volume of data being produced is increasing at an exponential rate that needs to be processed quickly, it is reasonable that the data needs to be available very close to the compute devices to reduce transfer latency. Due to this…

Performance · Computer Science 2024-08-06 Sohail Shaikh

The demand for data analytics has been consistently increasing in the past years at Twitter. In order to fulfill the requirements and provide a highly scalable and available query experience, a large-scale in-house SQL system is heavily…

The use of large-scale machine learning methods is becoming ubiquitous in many applications ranging from business intelligence to self-driving cars. These methods require a complex computation pipeline consisting of various types of…

Databases · Computer Science 2021-11-10 Yongyang Yu , Mingjie Tang , Walid G. Aref

With the explosive growth of big data, workloads tend to get more complex and computationally demanding. Such applications are processed on distributed interconnected resources that are becoming larger in scale and computational capacity.…

Distributed, Parallel, and Cluster Computing · Computer Science 2025-10-30 Georgios L. Stavrinides , Helen D. Karatza

The four LHC experiments at CERN have decided to use a commercial SCADA (Supervisory Control And Data Acquisition) product for the supervision of their DCS (Detector Control System). The selected SCADA, which is therefore used for the CMS…

Instrumentation and Detectors · Physics 2007-05-23 P. Gras , F. Drouhin , W. Funk , L. Gross , D. Vintache

Approximate Nearest Neighbor Search (ANNS) is essential for various data-intensive applications, including recommendation systems, image retrieval, and machine learning. Scaling ANNS to handle billions of high-dimensional vectors on a…

Databases · Computer Science 2025-06-18 Qian Xu , Feng Zhang , Chengxi Li , Lei Cao , Zheng Chen , Jidong Zhai , Xiaoyong Du

Recent Results from the PHENIX Collaboration on Au+Au and d+Au collisions at the Relativistic Heavy Ion Collider.

Nuclear Experiment · Physics 2019-08-14 Barbara Jacak

Heavy quarks are good probes of the hot and dense medium created in relativistic heavy ion collisions since they are mainly generated early in the collision and interact with the medium in all collision stages. In addition, heavy flavor…

Nuclear Experiment · Physics 2019-08-13 Rachid Nouicer

High-energy physics (HEP) provides ever-growing amount of data. To analyse these, continuously-evolving computational power is required in parallel by extending the storage capacity. Such developments play key roles in the future of this…

Distributed, Parallel, and Cluster Computing · Computer Science 2023-03-10 Gábor Bíró , Gergely Gábor Barnaföldi , Péter Lévai

Relational Databases (RDBs) are the backbone of modern business, yet they lack foundation models comparable to those in text or vision. A key obstacle is that high-quality RDBs are private, scarce, and structurally heterogeneous, making…

Machine Learning · Computer Science 2026-05-29 Yanbo Wang , Jiaxuan You , Chuan Shi , Muhan Zhang

Existing differentially private (DP) synthetic data generation mechanisms typically assume a single-source table. In practice, data is often distributed across multiple tables with relationships across tables. In this paper, we introduce…

Machine Learning · Computer Science 2025-01-22 Kaveh Alimohammadi , Hao Wang , Ojas Gulati , Akash Srivastava , Navid Azizan

Recursive query processing has experienced a recent resurgence, as a result of its use in many modern application domains, including data integration, graph analytics, security, program analysis, networking and decision making. Due to the…

Databases · Computer Science 2018-12-11 Zhiwei Fan , Jianqiao Zhu , Zuyu Zhang , Aws Albarghouthi , Paraschos Koutris , Jignesh Patel

Optimising use of the Web (WWW) for LHC data analysis is a complex problem and illustrates the challenges arising from the integration of and computation across massive amounts of information distributed worldwide. Finding the right piece…

Instrumentation and Detectors · Physics 2014-11-18 Nigel Baker , Peter Brooks , Richard McClatchey , Zsolt Kovacs , Jean-Marie Le Goff

Novel large scale research projects often require cooperation between various different project partners that are spread among the entire world. They do not only need huge computing resources, but also a reliable network to operate on. The…

Networking and Internet Architecture · Computer Science 2015-03-17 Patricia Marcu , David Schmitz , Wolfgang Fritz , Mark Yampolskiy , Wolfgang Hommel

The PHENIX Experiment on the Relativistic Heavy Ion Collider (RHIC) with its use of beams of polarized protons, provides a unique environment of hard scattering between gluons and quarks complementary to that provided by deep inelastic…

Nuclear Experiment · Physics 2008-08-25 Astrid Morreale

In today's Web and social network environments, query workloads include ad hoc and OLAP queries, as well as iterative algorithms that analyze data relationships (e.g., link analysis, clustering, learning). Modern DBMSs support ad hoc and…

Databases · Computer Science 2012-08-02 Svilen R. Mihaylov , Zachary G. Ives , Sudipto Guha

In this document the PHENIX collaboration proposes a major upgrade to the PHENIX detector at the Relativistic Heavy Ion Collider. This upgrade, sPHENIX, enables an extremely rich jet and beauty quarkonia physics program addressing…

Nuclear Experiment · Physics 2015-01-27 A. Adare , S. Afanasiev , C. Aidala , N. N. Ajitanand , Y. Akiba , R. Akimoto , J. Alexander , K. Aoki , N. Apadula , H. Asano , E. T. Atomssa , T. C. Awes , B. Azmoun , V. Babintsev , M. Bai , X. Bai , N. Bandara , B. Bannier , K. N. Barish , O. Baron , B. Bassalleck , S. Bathe , V. Baublis , S. Baumgart , A. Bazilevsky , M. Beaumier , S. Beckman , R. Belmont , G. Benjamin , A. Berdnikov , Y. Berdnikov , J. Blackburn , D. S. Blau , M. Bobrek , J. Bok , S. Boose , K. Boyle , C. L. Britton, , M. L. Brooks , J. Bryslawskyj , V. Bumazhnov , C. Butler , S. Butsyk , S. Campbell , A. Carollo , J. -S. Chai , C. -H. Chen , S. Chernichenko , C. Y. Chi , M. Chiu , I. J. Choi , J. B. Choi , S. Choi , S. Chollet , P. Christiansen , T. Chujo , V. Cianciolo , Z. Citron , B. A. Cole , N. Cronin , N. Crossett , M. Csanád , L. D'Orazio , S. Dairaku , D. Danley , A. Datta , M. S. Daugherity , G. David , K. DeBlasio , A. Debraine , K. Dehmelt , A. Denisov , A. Deshpande , E. J. Desmond , O. Dietzsch , L. Ding , A. Dion , P. B. Diss , J. H. Do , M. Donadelli , O. Drapier , A. Drees , K. A. Drees , J. M. Durham , A. Durum , L. Eberle , Y. V. Efremenko , T. Engelmore , A. Enokizono , S. Esumi , K. O. Eyser , B. Fadem , N. Feege , D. E. Fields , M. Finger , M. FingerJr. , F. Fleuret , S. L. Fokin , J. E. Frantz , A. Franz , A. D. Frawley , Y. Fukao , T. Fusayasu , K. Gainey , C. Gal , P. Gallus , P. Garg , A. Garishvili , I. Garishvili , F. Gastaldi , H. Ge , P. Giannotti , F. Giordarno , A. Glenn , X. Gong , M. Gonin , Y. Goto , R. Granier de Cassagnac , N. Grau , S. V. Greene , M. Grosse Perdekamp , Y. Gu , T. Gunji , H. Guragain , T. Hachiya , J. S. Haggerty , K. I. Hahn , H. Hamagaki , H. F. Hamilton , S. Y. Han , J. Hanks , S. Hasegawa , T. O. S. Haseler , K. Hashimoto , R. Hayano , S. Hayashi , X. He , T. K. Hemmick , T. Hester , J. C. Hill , M. Hoefferkamp , R. S. Hollis , K. Homma , B. Hong , Y. Hori , T. Hoshino , J. Huang , S. Huang , J. R. Hutchins , T. Ichihara , Y. Ikeda , K. Imai , Y. Imazu , J. Imrek , M. Inaba , A. Iordanova , D. Isenhower , A. Isinhue , A. Isupov , D. Ivanischev , V. Ivanov , B. V. Jacak , S. J. Jeon , M. Jezghani , J. Jia , X. Jiang , B. M. Johnson , K. S. Joo , D. Jouan , D. S. Jumper , J. Kamin , S. Kanda , B. H. Kang , J. H. Kang , J. S. Kang , J. Kapustinsky , K. Karatsu , D. Kawall , A. V. Kazantsev , H. -J. Kehayias , J. A. Key , V. Khachatryan , P. K. Khandai , A. Khanzadeev , K. M. Kijima , C. Kim , D. H. Kim , D. J. Kim , E. -J. Kim , H. J. Kim , K. -B. Kim , M. Kim , Y. -J. Kim , Y. K. Kim , B. Kimelman , Á. Kiss , E. Kistenev , R. Kitamura , J. Klatsky , D. Kleinjan , P. Kline , T. Koblesky , L. Kochenda , M. Kofarago , Y. Komatsu , B. Komkov , J. Koster , D. Kotchetkov , D. Kotov , P. Kravtsov , F. Krizek , K. Kurita , M. Kuriyama , M. Kurosawa , Y. Kwon , R. Lacey , Y. S. Lai , J. G. Lajoie , A. Lebedev , G. H. Lee , J. Lee , K. B. Lee , K. S. Lee , S. Lee , S. H. Lee , R. Lefferts , M. J. Leitch , M. A. L. Leite , M. Leitgab , B. Lewis , X. Li , S. H. Lim , A. Lipski , A. Litvinenko , M. X. Liu , B. Love , D. Lynch , M. Lynch , C. F. Maguire , Y. I. Makdisi , M. Makek , A. Malakhov , A. Manion , V. I. Manko , E. Mannel , T. Maruyama , S. Masumoto , M. McCumber , P. L. McGaughey , D. McGlinchey , R. McKay , C. McKinney , A. Meles , M. Mendoza , R. Menegasso , B. Meredith , Y. Miake , T. Mibe , A. C. Mignerey , A. Milov , D. K. Mishra , J. T. Mitchell , S. Miyasaka , S. Mizuno , A. K. Mohanty , P. Montuenga , T. Moon , D. P. Morrison , M. Moskowitz , S. Motschwiller , T. V. Moukhanova , T. Murakami , J. Murata , A. Mwai , T. Nagae , S. Nagamiya , K. Nagashima , J. L. Nagle , M. I. Nagy , I. Nakagawa , H. Nakagomi , Y. Nakamiya , K. R. Nakamura , T. Nakamura , K. Nakano , C. Nattrass , A. Nederlof , P. K. Netrakanti , M. Nihashi , T. Niida , K. Ninomiya , S. Nishimura , D. Northacker , R. Nouicer , T. Novak , N. Novitzky , A. Nukariya , A. S. Nyanin , E. O'Brien , C. A. Ogilvie , H. Oide , K. Okada , J. D. Orjuela Koop , J. D. Osborn , A. Oskarsson , L. Österman , K. Ozawa , C. Pancake , V. Pantuev , V. Papavassiliou , I. H. Park , J. S. Park , S. Park , S. K. Park , S. F. Pate , L. Patel , M. Patel , J. -C. Peng , D. Perepelitsa , G. D. N. Perera , V. Peresedov , D. Yu. Peressounko , J. Perry , R. Petti , C. Pinkenburg , R. Pinson , R. P. Pisani , J. Popule , M. L. Purschke , H. Qu , S. Radhakrishnan , J. Rak , B. J. Ramson , I. Ravinovich , K. F. Read , D. Reynolds , R. Reynolds , V. Riabov , Y. Riabov , E. Richardson , T. Rinn , N. Riveli , D. Roach , S. D. Rolnick , M. Rosati , E. Roschin , Z. Rowan , J. G. Rubin , P. Rukoyatkin , M. S. Ryu , A. Safonov , B. Sahlmueller , N. Saito , T. Sakaguchi , H. Sako , V. Samsonov , M. Sano , M. Sarsour , S. Sato , S. Sawada , B. Schaefer , B. K. Schmoll , K. Sedgwick , J. Seele , R. Seidl , Y. Sekiguchi , A. Sen , R. Seto , P. Sett , A. Sexton , E. Shafto , D. Sharma , A. Shaver , I. Shein , T. -A. Shibata , K. Shigaki , M. Shimomura , K. Shoji , P. Shukla , P. Sicho , A. Sickles , C. L. Silva , D. Silvermyr , B. K. Singh , C. P. Singh , V. Singh , F. W. Sippach , M. Skolnik , M. Snowball , S. Solano , A. Soldatov , R. A. Soltz , W. E. Sondheim , S. P. Sorensen , M. Soumya , I. V. Sourikova , P. W. Stankus , P. Steinberg , E. Stenlund , M. Stepanov , A. Ster , L. Stevens , S. P. Stoll , M. R. Stone , T. Sugitate , A. Sukhanov , T. Sumita , J. Sun , J. Sziklai , E. M. Takagui , A. Takahara , A. Taketani , Y. Tanaka , K. Tanida , M. J. Tannenbaum , S. Tarafdar , A. Taranenko , P. Tarján , A. Tate , E. Tennant , E. Thorsland , R. Tieulent , A. Timilsina , T. Todoroki , H. Torii , C. Towell , R. Towell , R. S. Towell , V. Trofimov , I. Tserruya , T. Tsuji , A. Tullo , H. W. van Hecke , M. Vargyas , E. Vazquez-Zambrano , A. Veicht , J. Velkovska , R. Vértesi , M. Virius , V. Vrba , E. Vznuzdaev , X. R. Wang , D. Watanabe , K. Watanabe , Y. Watanabe , Y. S. Watanabe , T. S. Watson , F. Wei , R. Wei , S. Whitaker , A. S. White , D. Winter , S. Wolin , C. L. Woody , M. Wysocki , B. Xia , L. Xue , S. Yalcin , Y. L. Yamaguchi , R. Yang , A. Yanovich , S. Yokkaichi , J. H. Yoo , I. Yoon , M. Young , I. Younus , H. Yu , I. E. Yushmanov , W. A. Zajc , E. Zarndt , A. Zelenski , L. Zhang , S. Zhou , L. Zolin , L. Zou , C. Zumberge

This is a thought piece on data-intensive science requirements for databases and science centers. It argues that peta-scale datasets will be housed by science centers that provide substantial storage and processing for scientists who access…

Databases · Computer Science 2007-05-23 Jim Gray , David T. Liu , Maria Nieto-Santisteban , Alexander S. Szalay , David DeWitt , Gerd Heber

In this paper we describe the support for data feed ingestion in AsterixDB, an open-source Big Data Management System (BDMS) that provides a platform for storage and analysis of large volumes of semi-structured data. Data feeds are a…

Databases · Computer Science 2014-05-08 Raman Grover , Michael J. Carey
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