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Journal articles on the topic 'Discovery of the large'

Author: Grafiati

Published: 4 June 2021

Last updated: 5 February 2022

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1

Ouyang, Wei Min, and Qin Hua Huang. "Mining Indirect Temporal Sequential Patterns in Large Transaction Databases." Applied Mechanics and Materials 385-386 (August 2013): 1362–65. http://dx.doi.org/10.4028/www.scientific.net/amm.385-386.1362.

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Sequential pattern is an important research topic in data mining and knowledge discovery. Traditional algorithms for mining sequential patterns focus on the frequent sequences, which do not consider the infrequent sequences and lifespan of each sequence. On the one hand, some infrequent patterns can provide very useful insight view into the data set, on the other hand, without taking lifespan of each sequence into account, not only some discovered patterns may be invalid, but also some useful patterns may not be discovered. So, we extend the sequential patterns to the indirect temporal sequential patterns, and put forward an algorithm to discover indirect temporal sequential patterns in this paper.

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2

ANDREEV, YU M., N. V. KRASNIKOV, and A. N. TOROPIN. "THE MSSM WITH LARGE GLUINO MASS." Modern Physics Letters A 24, no. 17 (June 7, 2009): 1317–24. http://dx.doi.org/10.1142/s0217732309030771.

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We study the Minimal Supersymmetric Standard Model (MSSM) with large gluino mass [Formula: see text]. In particular, we discuss the LHC supersymmetry discovery signatures with [Formula: see text], n ≥ 0 for the MSSM with large gluino mass. We show that for some relations among squark and neutralino masses leptonic signatures with [Formula: see text], n ≥ 1 do not allow to discover supersymmetry at the LHC and the only supersymmetry discovery signature is the signature with no [Formula: see text]. Moreover, for LSP mass close to squark masses the LHC discovery potential for this signature is strongly reduced.

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3

Huang, Ying, Liyun Zhong, and Yan Chen. "Filtering Infrequent Behavior in Business Process Discovery by Using the Minimum Expectation." International Journal of Cognitive Informatics and Natural Intelligence 14, no. 2 (April 2020): 1–15. http://dx.doi.org/10.4018/ijcini.2020040101.

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The aim of process discovery is to discover process models from the process execution data stored in event logs. In the era of “Big Data,” one of the key challenges is to analyze the large amounts of collected data in meaningful and scalable ways. Most process discovery algorithms assume that all the data in an event log fully comply with the process execution specification, and the process event logs are no exception. However, real event logs contain large amounts of noise and data from irrelevant infrequent behavior. The infrequent behavior or noise has a negative influence on the process discovery procedure. This article presents a technique to remove infrequent behavior from event logs by calculating the minimum expectation of the process event log. The method was evaluated in detail, and the results showed that its application in existing process discovery algorithms significantly improves the quality of the discovered process models and that it scales well to large datasets.

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4

Seghier, Mohamed L., and Karl J. Friston. "Network discovery with large DCMs." NeuroImage 68 (March 2013): 181–91. http://dx.doi.org/10.1016/j.neuroimage.2012.12.005.

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5

Zambrano, Diego. "Discovery as Regulation." Michigan Law Review, no. 119.1 (2020): 71. http://dx.doi.org/10.36644/mlr.119.1.discovery.

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This article develops an approach to discovery that is grounded in regulatory theory and administrative subpoena power. The conventional judicial and scholarly view about discovery is that it promotes fair and accurate outcomes and nudges the parties toward settlement. While commonly held, however, this belief is increasingly outdated and suffers from limitations. Among them, it has generated endless controversy about the problem of discovery costs. Indeed, a growing chorus of scholars and courts has offered an avalanche of reforms, from cost shifting and bespoke discovery contracts to outright elimination. Recently, Judge Thomas Hardiman quipped that if he had absolute power, he would abolish discovery for cases involving less than $500,000. These debates, however, are at a standstill, and existing scholarship offers incomplete treatment of discovery theory that might move debates forward. The core insight of the project is that in the private-enforcement context—where Congress deliberately employs private litigants as the main method of statutory enforcement—there is a surprisingly strong case that our current discovery system should be understood in part as serving regulatory goals analogous to administrative subpoena power. That is, discovery here can be seen as an extension of the subpoena power that agencies like the SEC, FTC, and EPA possess and is the lynchpin of a system that depends on private litigants to enforce our most important statutes. By forcing parties to disclose large amounts of information, discovery deters harm and, most importantly, shapes industry-wide practices and the primary behavior of regulated entities. This approach has a vast array of implications for the scope of discovery as well as the debate over costs. Scholars and courts should thus grapple with the consequences of what I call “regulatory discovery” for the entire legal system.

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6

Mason, Jonathan S. "Computational screening: large-scale drug discovery." Trends in Biotechnology 17 (January 1999): 34–36. http://dx.doi.org/10.1016/s0167-5699(99)01478-4.

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7

Lowekamp, Bruce, David O'Hallaron, and Thomas Gross. "Topology discovery for large ethernet networks." ACM SIGCOMM Computer Communication Review 31, no. 4 (October 2001): 237–48. http://dx.doi.org/10.1145/964723.383078.

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8

Brakerski, Zvika, and Boaz Patt-Shamir. "Distributed discovery of large near-cliques." Distributed Computing 24, no. 2 (April 26, 2011): 79–89. http://dx.doi.org/10.1007/s00446-011-0132-x.

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9

SON, M. "Topology Discovery in Large Ethernet Mesh Networks." IEICE Transactions on Communications E89-B, no. 1 (January 1, 2006): 66–75. http://dx.doi.org/10.1093/ietcom/e89-b.1.66.

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10

Chum, O., and J. Matas. "Large-Scale Discovery of Spatially Related Images." IEEE Transactions on Pattern Analysis and Machine Intelligence 32, no. 2 (February 2010): 371–77. http://dx.doi.org/10.1109/tpami.2009.166.

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11

Donnet, Benoit, Philippe Raoult, Timur Friedman, and Mark Crovella. "Efficient algorithms for large-scale topology discovery." ACM SIGMETRICS Performance Evaluation Review 33, no. 1 (June 6, 2005): 327–38. http://dx.doi.org/10.1145/1071690.1064256.

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12

Cheung, David W., Vincent T. Ng, and Benjamin W. Tam. "Incremental Updates of Discovered Multi-Level Association Rules." International Journal on Artificial Intelligence Tools 06, no. 02 (June 1997): 273–90. http://dx.doi.org/10.1142/s0218213097000153.

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Update of the single- and multi-level association rules discovered in large databases is inherently costly. The straight forward approach of re-running the discovery algorithm on the entire updated database to re-discover the association rules is not cost-effective. An incremental algorithm FUP have been proposed for the update of discovered single-level association rules. In this study, we have shown that the incremental technique in FUP can be generalized to other data mining systems. An efficient algorithm MLUp has been proposed for the updating of discovered multi-level association rules. Our performance study shows that MLUp has a superior performance over the representative mining algorithm such as ML-T2 in updating discovered multi-level association rules.

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13

Pedreira, Júlia G. B., Lucas S. Franco, and Eliezer J. Barreiro. "Chemical Intuition in Drug Design and Discovery." Current Topics in Medicinal Chemistry 19, no. 19 (October 21, 2019): 1679–93. http://dx.doi.org/10.2174/1568026619666190620144142.

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The medicinal chemist plays the most important role in drug design, discovery and development. The primary goal is to discover leads and optimize them to develop clinically useful drug candidates. This process requires the medicinal chemist to deal with large sets of data containing chemical descriptors, pharmacological data, pharmacokinetics parameters, and in silico predictions. The modern medicinal chemist has a large number of tools and technologies to aid him in creating strategies and supporting decision-making. Alongside with these tools, human cognition, experience and creativity are fundamental to drug research and are important for the chemical intuition of medicinal chemists. Therefore, fine-tuning of data processing and in-house experience are essential to reach clinical trials. In this article, we will provide an expert opinion on how chemical intuition contributes to the discovery of drugs, discuss where it is involved in the modern drug discovery process, and demonstrate how multidisciplinary teams can create the optimal environment for drug design, discovery, and development.

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14

Al-Ahmadi, Saad, and Abdullah Al-Dhelaan. "Scalable Resource Discovery Architecture for Large Scale MANETs." Research Journal of Applied Sciences, Engineering and Technology 7, no. 7 (February 20, 2014): 1351–63. http://dx.doi.org/10.19026/rjaset.7.401.

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15

Elshater, Yehia, Khalid Elgazzar, and Patrick Martin. "WEB SERVICE DISCOVERY FOR LARGE SCALE IOT DEPLOYMENTS." Services Transactions on Services Computing 4, no. 1 (January 2016): 55–68. http://dx.doi.org/10.29268/stsc.2016.4.1.5.

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16

Huang, Chi-Sheng, Meng-Feng Tsai, Po-Hsuan Huang, Li-Ding Su, and Kuei-Sheng Lee. "Distributed asteroid discovery system for large astronomical data." Journal of Network and Computer Applications 93 (September 2017): 27–37. http://dx.doi.org/10.1016/j.jnca.2017.03.013.

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17

Akpinar, Bala Ani, Stuart Lucas, and Hikmet Budak. "A large-scale chromosome-specific SNP discovery guideline." Functional & Integrative Genomics 17, no. 1 (November 29, 2016): 97–105. http://dx.doi.org/10.1007/s10142-016-0536-6.

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18

Macropol, Kathy, and Ambuj Singh. "Scalable discovery of best clusters on large graphs." Proceedings of the VLDB Endowment 3, no. 1-2 (September 2010): 693–702. http://dx.doi.org/10.14778/1920841.1920930.

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19

Shi, Leming M., Yi Fan, Jae K. Lee, Mark Waltham, Darren T. Andrews, Uwe Scherf, Kenneth D. Paull, and John N. Weinstein. "Mining and Visualizing Large Anticancer Drug Discovery Databases†." Journal of Chemical Information and Computer Sciences 40, no. 2 (March 2000): 367–79. http://dx.doi.org/10.1021/ci990087b.

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20

Tanaka, Isao. "Data-Driven Materials Discovery from Large Chemistry Spaces." Matter 3, no. 2 (August 2020): 327–28. http://dx.doi.org/10.1016/j.matt.2020.07.010.

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21

Tang, Feng, Hoi Chun Po, Ashvin Vishwanath, and Xiangang Wan. "Topological materials discovery by large-order symmetry indicators." Science Advances 5, no. 3 (March 2019): eaau8725. http://dx.doi.org/10.1126/sciadv.aau8725.

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Crystalline symmetries play an important role in the classification of band structures, and their richness leads to various topological crystalline phases. On the basis of our recently developed method for the efficient discovery of topological materials using symmetry indicators, we explore topological materials in five space groups (SGs), which are diagnosed by large-order symmetry indicators (ℤ8 and ℤ12) and support the coexistence of several kinds of gapless boundary states in a single compound. We predict many candidate materials; some representatives include Pt3Ge (SG140), graphite (SG194), XPt3 (SG221, X = Sn, Pb), Au4Ti (SG87), and Ti2Sn (SG194). As by-products, we also find that AgXF3 (SG140, X = Rb, Cs) and AgAsX (SG194, X = Sr, Ba) are good Dirac semimetals with clean Fermi surfaces. The proposed materials provide a good platform for studying the novel properties emerging from the interplay between different types of boundary states.

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22

Harvey-Smith, L., and R. J. Cohen. "Discovery of large-scale masers in W3(OH)." Proceedings of the International Astronomical Union 2, S237 (August 2006): 422. http://dx.doi.org/10.1017/s1743921307002104.

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AbstractWe report a vast filament of hydroxyl and methanol maser emission surrounding the ultra-compact HII region W3(OH). The filament stretches 3100 AU and has a linear velocity gradient. By studying the velocity structure, line profiles and extended methanol maser structures we believe we have located the position of the central star and detected around it a circumstellar disc with a large velocity gradient of 47 km s−1 arcsec−1.

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23

Bailey, David H., Jonathan M. Borwein, Jason S. Kimberley, and Watson Ladd. "Computer Discovery and Analysis of Large Poisson Polynomials." Experimental Mathematics 26, no. 3 (August 24, 2016): 349–63. http://dx.doi.org/10.1080/10586458.2016.1180565.

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24

KITAURA, Kazuo. "Large Scale Quantum Chemical Calculation for Drug Discovery." YAKUGAKU ZASSHI 131, no. 8 (August 1, 2011): 1163–69. http://dx.doi.org/10.1248/yakushi.131.1163.

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25

Wild, David J. "Mining large heterogeneous data sets in drug discovery." Expert Opinion on Drug Discovery 4, no. 10 (August 28, 2009): 995–1004. http://dx.doi.org/10.1517/17460440903233738.

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26

Lee, Kyoung-Soo, Arjun Dey, Sungryong Hong, Naveen Reddy, Christian Wilson, Buell T. Jannuzi, Hanae Inami, and Anthony H. Gonzalez. "DISCOVERY OF A VERY LARGE STRUCTURE ATZ= 3.78." Astrophysical Journal 796, no. 2 (November 13, 2014): 126. http://dx.doi.org/10.1088/0004-637x/796/2/126.

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27

Robison, Keith, Walter Gilbert, and George M. Church. "Large scale bacterial gene discovery by similarity search." Nature Genetics 7, no. 2 (June 1994): 205–14. http://dx.doi.org/10.1038/ng0694-205.

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28

Hong, Yinghan, Zhusong Liu, and Guizhen Mai. "An efficient algorithm for large-scale causal discovery." Soft Computing 21, no. 24 (August 3, 2016): 7381–91. http://dx.doi.org/10.1007/s00500-016-2281-0.

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29

Goizueta, Alberto, Rabih El-Bizri, Anthony Moulton, and Hiran Fernando. "Incidental Discovery of a Large Complicated Arteriovenous Hemangioma." Chest 150, no. 4 (October 2016): 551A. http://dx.doi.org/10.1016/j.chest.2016.08.639.

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30

Hrovat, Goran, Gregor Stiglic, Peter Kokol, and Milan Ojsteršek. "Contrasting temporal trend discovery for large healthcare databases." Computer Methods and Programs in Biomedicine 113, no. 1 (January 2014): 251–57. http://dx.doi.org/10.1016/j.cmpb.2013.09.005.

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31

BHATTACHARYA, SATYAKI. "Higgs Search with the Compact Muon Solenoid(CMS) detector at the Large Hadron Collider(LHC)." International Journal of Modern Physics A 20, no. 15 (June 20, 2005): 3400–3402. http://dx.doi.org/10.1142/s0217751x05026649.

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The Large Hadron Collider(LHC) is a proton proton collider being built at CERN, Geneva which will collide two 7 TeV proton beams giving a center of mass energy of 14 TeV. The Compact Muon Solenoid (CMS) is a multi-purpose detector at the LHC which is designed to discover the Higgs boson over the mass range of 90 to 1000 GeV. Since LEP searches have put a 95% C.L. lower bound on (standard model) Higgs mass of 114.4 GeV and theory excludes mass above about 1 TeV, CMS should discover the Higgs if it exists. In this paper, we will review CMS's Higgs-discovery potential both in the Standard Model and the Minimal Supersymmetric Standard Model for Higgs bosons produced in gluon-gluon fusion and in vector boson fusion mechanisms. Particular emphasis will be placed on discovery in the early years of running with luminosity of about 2 × 1033cm-2/s.

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32

Yu, Jianbin, and Neal H. Hooker. "Exploring relationships among recall effectiveness indicators in the US meat and poultry industry." International Food and Agribusiness Management Review 22, no. 1 (January 28, 2019): 97–106. http://dx.doi.org/10.22434/ifamr2018.0026.

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Food recalls need to balance speed and completeness, consumer and firm interests and thus meet managerial and social goals. Effective recalls play a vital role in protecting public health and reducing economic consequences. This paper develops a simultaneous equation model to explore the relationships among three effectiveness indicators; discovery time, completion time and recovery rate. A three-stage least square estimator is applied to control for endogeneity among these indicators. The results suggest that higher recovery rates are associated with shorter discovery times. Longer discovery times led to longer completion times. Longer completion times elicited higher recovery rates. Recalls with high risk to human health had shorter discovery times but longer completion times and lower recovery rates. Recalls issued by large plants had shorter discovery times. Large recalls and national distribution channels negatively impacted discovery times. Compared to other stakeholders, government agencies took longer to discover the problem leading to a recall.

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33

Amshakala, K., R. Nedunchezhian, and M. Rajalakshmi. "Extracting Functional Dependencies in Large Datasets Using MapReduce Model." International Journal of Intelligent Information Technologies 10, no. 3 (July 2014): 19–35. http://dx.doi.org/10.4018/ijiit.2014070102.

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Over the last few years, data are generated in large volume at a faster rate and there has been a remarkable growth in the need for large scale data processing systems. As data grows larger in size, data quality is compromised. Functional dependencies representing semantic constraints in data are important for data quality assessment. Executing functional dependency discovery algorithms on a single computer is hard and laborious with large data sets. MapReduce provides an enabling technology for large scale data processing. The open-source Hadoop implementation of MapReduce has provided researchers a powerful tool for tackling large-data problems in a distributed manner. The objective of this study is to extract functional dependencies between attributes from large datasets using MapReduce programming model. Attribute entropy is used to measure the inter attribute correlations, and exploited to discover functional dependencies hidden in the data.

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34

Carroll, Raymond L., and C. A. Tuggle. "The World Outside: Local TV News Treatment of Imported News." Journalism & Mass Communication Quarterly 74, no. 1 (March 1997): 123–33. http://dx.doi.org/10.1177/107769909707400110.

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This study sought to determine whether stations located in larger or smaller markets gave different treatment to news and to resolve whether disparities noted among small and large television market news programs extended to their treatment of news imported from outside the market. McManus's economic model of inexpensive, passive discovery held true over the journalistic model of active surveillance in smaller markets, where stations not only devoted less time to news than those in larger markets, but a greater proportion of their news content was imported, thus passively discovered. The larger the market size, the more active the discovery. Some evidence that imported news supplants strictly local news in smaller television markets was found. Furthermore, although major-, large-, and medium-market stations devoted higher proportions of their news hole to sensational and human interest news, stations in the smallest markets imported a greater proportion of sensational/human interest news than they originated locally.

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35

Mollamotalebi, M., R. Maghami, and A. S. Ismail. "A Weight-based Query Forwarding Technique for Super-peer-based Grid Resource Discovery." Engineering, Technology & Applied Science Research 7, no. 1 (February 12, 2017): 1398–404. http://dx.doi.org/10.48084/etasr.952.

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Grid computing environments include heterogeneous resources shared by a large number of computers to handle data and process intensive applications. The required resources must be accessible for the grid applications on demand, which makes resource discovery a critical service. In recent years, different techniques are provided to index and discover grid resources. Response time and message load during the search process highly affect the efficiency of resource discovery. This paper proposes a technique to forward the queries based on the resource types accessible through each neighbor in super-peer-based grid resource discovery approaches. The proposed technique is simulated in GridSim and the experimental results indicated that it is able to reduce the response time and message load during the search process especially when the grid environment contains a large number of nodes.

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36

Blower Jr., Paul, Kevin Cross, Michael Fligner, Glenn Myatt, Joseph Verducci, and Chihae Yang. "Systematic Analysis of Large Screening Sets in Drug Discovery." Current Drug Discovery Technologies 1, no. 1 (January 1, 2004): 37–47. http://dx.doi.org/10.2174/1570163043484879.

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37

Peakman, Tim, Steven Franks, Colm White, and Mark Beggs. "Delivering the power of discovery in large pharmaceutical organizations." Drug Discovery Today 8, no. 5 (March 2003): 203–11. http://dx.doi.org/10.1016/s1359-6446(03)02612-6.

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38

Lavington, S., N. Dewhurst, E. Wilkins, and A. Freitas. "Interfacing knowledge discovery algorithms to large database management systems." Information and Software Technology 41, no. 9 (June 1999): 605–17. http://dx.doi.org/10.1016/s0950-5849(99)00024-5.

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39

Gaulton, A., L. J. Bellis, A. P. Bento, J. Chambers, M. Davies, A. Hersey, Y. Light, et al. "ChEMBL: a large-scale bioactivity database for drug discovery." Nucleic Acids Research 40, no. D1 (September 23, 2011): D1100—D1107. http://dx.doi.org/10.1093/nar/gkr777.

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40

Sun, Wenguang, Brian J. Reich, T. Tony Cai, Michele Guindani, and Armin Schwartzman. "False discovery control in large-scale spatial multiple testing." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 77, no. 1 (April 8, 2014): 59–83. http://dx.doi.org/10.1111/rssb.12064.

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41

Donnet, B., P. Raoult, T. Friedman, and M. Crovella. "Deployment of an Algorithm for Large-Scale Topology Discovery." IEEE Journal on Selected Areas in Communications 24, no. 12 (December 2006): 2210–20. http://dx.doi.org/10.1109/jsac.2006.884019.

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42

Gao, Jun, Ruoming Jin, Jiashuai Zhou, Jeffrey Xu Yu, Xiao Jiang, and Tengjiao Wang. "Relational approach for shortest path discovery over large graphs." Proceedings of the VLDB Endowment 5, no. 4 (December 2011): 358–69. http://dx.doi.org/10.14778/2095686.2095694.

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43

Dobrzycki, A., L. Eyer, K. Z. Stanek, and L. M. Macri. "Discovery of nine quasars behind the Large Magellanic Cloud." Astronomy & Astrophysics 442, no. 2 (October 7, 2005): 495–500. http://dx.doi.org/10.1051/0004-6361:20053494.

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44

Deshpande, Deepa S. "An efficient method for discovery of large item sets." International Journal of Data Mining, Modelling and Management 8, no. 4 (2016): 303. http://dx.doi.org/10.1504/ijdmmm.2016.081244.

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45

Deshpande, Deepa S. "An efficient method for discovery of large item sets." International Journal of Data Mining, Modelling and Management 8, no. 4 (2016): 303. http://dx.doi.org/10.1504/ijdmmm.2016.10002310.

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46

Down, Thomas A., Casey M. Bergman, Jing Su, and Tim J. P. Hubbard. "Large-Scale Discovery of Promoter Motifs in Drosophila melanogaster." PLoS Computational Biology 3, no. 1 (January 19, 2007): e7. http://dx.doi.org/10.1371/journal.pcbi.0030007.

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47

Down, Thomas A., Casey M. Bergman, Jing Su, and Tim J. Hubbard. "Large Scale Discovery of Promoter Motifs in Drosophila melanogaster." PLoS Computational Biology preprint, no. 2006 (2005): e7. http://dx.doi.org/10.1371/journal.pcbi.0030007.eor.

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48

May, Dalit, Matthew J. Blow, Tommy Kaplan, David J. McCulley, Brian C. Jensen, Jennifer A. Akiyama, Amy Holt, et al. "Large-scale discovery of enhancers from human heart tissue." Nature Genetics 44, no. 1 (December 4, 2011): 89–93. http://dx.doi.org/10.1038/ng.1006.

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49

Christen, Peter, Ross W. Gayler, Khoi-Nguyen Tran, Jeffrey Fisher, and Dinusha Vatsalan. "Automatic Discovery of Abnormal Values in Large Textual Databases." Journal of Data and Information Quality 7, no. 1-2 (June 6, 2016): 1–31. http://dx.doi.org/10.1145/2889311.

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Saha, Ashis, Aik Choon Tan, and Jaewoo Kang. "Automatic Context-Specific Subnetwork Discovery from Large Interaction Networks." PLoS ONE 9, no. 1 (January 1, 2014): e84227. http://dx.doi.org/10.1371/journal.pone.0084227.

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