Relevant bibliographies by topics / Discovery of the large

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Discovery of the large'

Author: Grafiati
Published: 4 June 2021
Last updated: 5 February 2022

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Journal articles on the topic "Discovery of the large"

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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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Discovery of the large"

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Kohlsdorf, Daniel. "Data mining in large audio collections of dolphin signals." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/53968.

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The study of dolphin cognition involves intensive research of animal vocal- izations recorded in the field. In this dissertation I address the automated analysis of audible dolphin communication. I propose a system called the signal imager that automatically discovers patterns in dolphin signals. These patterns are invariant to frequency shifts and time warping transformations. The discovery algorithm is based on feature learning and unsupervised time series segmentation using hidden Markov models. Researchers can inspect the patterns visually and interactively run com- parative statistics between the distribution of dolphin signals in different behavioral contexts. The required statistics for the comparison describe dolphin communication as a combination of the following models: a bag-of-words model, an n-gram model and an algorithm to learn a set of regular expressions. Furthermore, the system can use the patterns to automatically tag dolphin signals with behavior annotations. My results indicate that the signal imager provides meaningful patterns to the marine biologist and that the comparative statistics are aligned with the biologists’ domain knowledge.
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Tedeschi, Cédric. "Peer-to-Peer Prefix Tree for Large Scale Service Discovery." Phd thesis, Ecole normale supérieure de lyon - ENS LYON, 2008. http://tel.archives-ouvertes.fr/tel-00529666.

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Cette thèse étudie la découverte de services (composants logiciels, exécutables, librairies scientifiques) sur des plates-formes distribuées à grande échelle. Les approches traditionnelles, proposées pour des environnements stables et relativement petits, s'appuient sur des techniques centralisées impropres au passage à l'échelle dans des environnements géographiquement distribués et instables. Notre contribution s'articule autour de trois axes. 1) Nous proposons une nouvelle approche appelée DLPT (Distributed Lexicographic Placement Table), qui s'inspire des systèmes pair-à-pair et s'appuie sur un réseau de recouvrement structuré en arbre de préfixes. Cette structure permet des recherches multi-attributs sur des plages de valeurs. 2) Nous étudions la distribution des noeuds de l'arbre sur les processeurs de la plate-forme sous-jacente, distribuée, dynamique et hétérogène. Nous proposons et adaptons des heuristiques de répartition de la charge pour ce type d'architectures. 3) Notre plate-forme cible, par nature instable, nécessite des mécanismes robustes pour la tolérance aux pannes. La réplication traditionnellement utilisée s'y avère coûteuse et incapable de gérer des fautes transitoires. Nous proposons des techniques de tolérance aux pannes best-effort fondées sur la théorie de l'auto-stabilisation pour la construction d'arbres de préfixes dans des environnements pair-à-pair. Nous présentons deux approches. La première, écrite dans un modèle théorique à gros grain, permet de maintenir des arbres de préfixes instantanément stabilisants, c'est-à-dire reconstruits en un temps optimal après un nombre arbitraire de fautes. La deuxième, écrite dans le modèle à passage de messages, permet l'implantation d'une telle architecture dans des réseaux très dynamiques. Enfin, nous présentons un prototype logiciel mettant en oeuvre cette architecture et présentons ses premières expérimentations sur la plate-forme Grid'5000.
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Zhang, Xi. "Knowledge discovery from large-scale biological networks and their relationships." Thesis, University of British Columbia, 2010. http://hdl.handle.net/2429/23353.

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The ultimate aim of postgenomic biomedical research is to understand mechanisms of cellular systems in a systematical way. It is therefore necessary to examine various biomolecular networks and to investigate how the interactions between biomolecules determine biological functions within cellular systems. Rapid advancement in high-throughput techniques provides us with increasing amounts of large-scale datasets that could be transformed into biomolecular networks. Analyzing and integrating these biomolecular networks have become major challenges. I approached these challenges by developing novel methods to extract new knowledge from various types of biomolecular networks. Protein-protein interactions and domain-domain interactions are extremely important in a wide range of biological functions. However, the interaction data are incomplete and inaccurate due to experimental limitations. Therefore, I developed a novel algorithm to predict interactions between membrane proteins in yeast based on the protein interaction network and the domain interaction network. In addition, I also developed a novel algorithm, a gram-based interaction analysis tool (GAIA), to identify interacting domains by integrating the protein primary sequences, the domain annotations and interactions and the structural annotations of proteins. Biological assessment against several metrics indicated that both algorithms were capable of satisfactory performance, facilitating the elucidation of cell interactome. Predicting biological pathways is one of major challenges in systems biology. I proposed a novel integrated approach, called Pandora, which used network topology to predict biological pathways by integrating four types of biological evidence (protein-protein interactions, genetic interactions, domain-domain interactions, and semantic similarity of GO terms). I demonstrated that Pandora achieved better performance compared to other predictive approaches, allowing the reconstruction of biological pathways and the delineation of cellular machinery in a systematic view. Finally, I focused on investigating biological network perturbations in diseases. I developed a novel algorithm to capture highly disturbed sub-networks in the human interactome as the signatures linked to cancer outcomes. This method was applied to breast cancer and yielded improved predictive performance, providing the possibility to predict the outcome of cancers based on “network-based gene signatures”. These methods and tools contributed to the analysis and understanding of a wide variety of biological networks and the relationships between them.
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Lam, Lap-Hing Raymond. "Design and analysis of large chemical databases for drug discovery." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp05/NQ65249.pdf.

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Binder, Polina. "Unsupervised discovery of emphysema subtypes in a large clinical cohort." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/105678.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 45-47).
Emphysema is one of the hallmarks of Chronic Obstructive Pulmonary Disease (COPD), a devastating lung disease often caused by smoking. Emphysema appears on Computed Tomography (CT) scans as a variety of textures that correlate with the disease subtypes. It has been shown that the disease subtypes and the lung texture are linked to physiological indicators and prognosis, although neither is well characterized clinically. Most previous computational approaches to modeling emphysema imaging data have focused on supervised classification of lung textures in patches of CT scans. In this work, we describe a generative model that jointly captures heterogeneity of disease subtypes and of the patient population. We also derive a corresponding inference algorithm that simultaneously discovers disease subtypes and population structure in an unsupervised manner. This approach enables us to create image-based descriptors of emphysema beyond those that can be identified through manual labeling of currently defined phenotypes. By applying the resulting algorithm to a large data set, we identify groups of patients and disease subtypes that correlate with distinct physiological indicators.
by Polina Binder.
S.M.
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Elsilä, U. (Ulla). "Knowledge discovery method for deriving conditional probabilities from large datasets." Doctoral thesis, University of Oulu, 2007. http://urn.fi/urn:isbn:9789514286698.

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Abstract In today's world, enormous amounts of data are being collected everyday. Thus, the problems of storing, handling, and utilizing the data are faced constantly. As the human mind itself can no longer interpret the vast datasets, methods for extracting useful and novel information from the data are needed and developed. These methods are collectively called knowledge discovery methods. In this thesis, a novel combination of feature selection and data modeling methods is presented in order to help with this task. This combination includes the methods of basic statistical analysis, linear correlation, self-organizing map, parallel coordinates, and k-means clustering. The presented method can be used, first, to select the most relevant features from even hundreds of them and, then, to model the complex inter-correlations within the selected ones. The capability to handle hundreds of features opens up the possibility to study more extensive processes instead of just looking at smaller parts of them. The results of k-nearest-neighbors study show that the presented feature selection procedure is valid and appropriate. A second advantage of the presented method is the possibility to use thousands of samples. Whereas the current rules of selecting appropriate limits for utilizing the methods are theoretically proved only for small sample sizes, especially in the case of linear correlation, this thesis gives the guidelines for feature selection with thousands of samples. A third positive aspect is the nature of the results: given that the outcome of the method is a set of conditional probabilities, the derived model is highly unrestrictive and rather easy to interpret. In order to test the presented method in practice, it was applied to study two different cases of steel manufacturing with hot strip rolling. In the first case, the conditional probabilities for different types of retentions were derived and, in the second case, the rolling conditions for the occurrence of wedge were revealed. The results of both of these studies show that steel manufacturing processes are indeed very complex and highly dependent on the various stages of the manufacturing. This was further confirmed by the fact that with studies of k-nearest-neighbors and C4.5, it was impossible to derive useful models concerning the datasets as a whole. It is believed that the reason for this lies in the nature of these two methods, meaning that they are unable to grasp such manifold inter-correlations in the data. On the contrary, the presented method of conditional probabilities allowed new knowledge to be gained of the studied processes, which will help to better understand these processes and to enhance them.
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Zhang, ZengHua. "Discovery and characterisation of ultra-cool dwarfs in large scale surveys." Thesis, University of Hertfordshire, 2013. http://hdl.handle.net/2299/13900.

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Ultracool dwarfs including the lowest mass stars and substellar dwarfs (or brown dwarfs) is a rapidly evolving and very active field. In this thesis I present the discovery and characterization of ultracool dwarfs and their binary systems with solar and subsolar abundances and try to answer a few scientific questions related to these ultracool objects. I use different techniques based on photometric and astrometric data of modern large scale surveys to identify ultracool dwarfs and their binaries. I identify around 1000 ultracool dwarfs from SDSS, 2MASS and UKIDSS surveys, including 82 L dwarfs and 129 L dwarf candidates (Chapter 2 and 4). This work largely increases the known number of ultracool dwarfs and aid the statistic study of these objects. Eighteen ultracool dwarfs in my sample are found to be in wide binary systems by common proper motion (Chapter 4 and 5). Wide binary systems are often used to test formation theories of low mass stars and brown dwarfs, which have different predictions of separations and binary fractions. One of these binary systems is the first L dwarf companion to a giant star eta Cancri. The eta Cancri B is clearly a useful benchmark object, with constrained distance, age, and metallicity. Further more, the L3.5 dwarf companion eta Cancri B is found to be a potential L4 + T4 binary. I focus on the studies of low mass stars and brown dwarfs with subsolar abundance referred as red and ultracool subdwarfs. They belong to the older Population II of the Galactic halo contain more information of the formation, early evolution and structure of the Milky Way. Using the most extensive optical survey, the Sloan Digital Sky Survey (SDSS), to select low mass stars with subsolar abundance, referred as red subdwarfs with spectral types of late K and M. I identify about 1800 M subdwarfs including 30 new >M6 subdwarfs and five M ultra subdwarfs with very high gravity as well as 14 carbon enhanced red subdwarfs. I also identify 45 red subdwarf binary systems from my red subdwarf sample. Thirty of them are in wide binary systems identified by common proper motion. Fifteen binaries are partially resolved in SDSS and UKIDSS. I estimate the M subdwarf binary fraction. I fit the relationships of spectral types and absolute magnitudes of optical and near infrared bands for M and L subdwarfs. I also measure $UVW$ space velocities of the my M subdwarf sample (Chapter 5). Our studies of the lowest mass stars and brown dwarfs of the Galactic halo are limited by the lack of known objects. There are only seven L subdwarfs published in the literature. I search for ultracool subdwarfs by a combine use of the most extensive optical and near infrared surveys, the SDSS and the UKIRT Infrared Deep Sky Survey. I identify three new L subdwarfs with spectral types of sdL3, sdL7 and esdL6. I re-examine the spectral types and metal classes of all known L subdwarfs and propose to use 2.3 um CO line as an indicator of L subdwarfs. Two of my new L subdwarfs are found to be candidates of halo brown dwarfs (or substellar subdwarfs). I find four of these known ten L subdwarfs could be halo brown dwarfs. I propose a new name "purple dwarf" for lowest-mass stars and brown dwarfs with subsolar abundance (Chapter 3). Finally I summarize and discuss the thesis project in Chapter 6 and describe future research plans in Chapter 7.
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Graham, Eleanor(Eleanor L. ). "Sensitivity Models for [Beta]+/EC Discovery in Large-Volume Scintillation Detectors." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/127094.

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Thesis: S.B., Massachusetts Institute of Technology, Department of Physics, 2020
In title on title page, "[Beta]" is the Greek letter. Cataloged from the official PDF of thesis.
Includes bibliographical references (pages 47-49).
In this thesis, we consider the [Beta]+/EC decay of 124Xe and take the first steps towards characterizing a hypothetical experiment to detect it, making use of techniques traditionally employed in neutrinoless double beta decay experiments. We use a simulated large-volume scintillation detector modeled on the Super-Kamiokande experiment, fully implementing this detector in RAT/Geant4. This allows us to extract authentic spectra for the experimental signature of the [Beta]+/EC decay in 124Xe, paving the way for future sensitivity studies. We also consider the relevance of next-generation techniques for background discrimination, specifically particle identification based on counting Cherenkov photons. We find that discrimination between [Beta] and [Beta] particles is readily possible in experiments run at the 1.25 MeV energy scale and also see evidence for the possibility of distinguishing between [Beta]+ and [Beta]- particles via their Cherenkov signatures
by Eleanor Graham.
S.B.
S.B. Massachusetts Institute of Technology, Department of Physics
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Ewert, Kevin. "An Adaptive Machine Learning Approach to Knowledge Discovery in Large Datasets." NSUWorks, 2006. http://nsuworks.nova.edu/gscis_etd/510.

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Large text databases, such as medical records, on-line journals, or the Internet, potentially contain a great wealth of data and knowledge. However, text representation of factual information and knowledge is difficult to process. Analyzing these large text databases often rely upon time consuming human resources for data mining. Since a textual format is a very flexible way to describe and store various types of information, large amounts of information are often retained and distributed as text. 'The amount of accessible textual data has been increasing rapidly. Such data may potentially contain a great wealth of knowledge. However, analyzing huge amounts of textual data requires a tremendous amount of work in reading al l of the text and organizing the content. Thus, the increase in accessible textual data has caused an information flood in spite of hope of becoming knowledgeable about various topics" (Nasukawa and Nagano, 2001). Preliminary research focused on key concepts and techniques derived from clustering methodology, machine learning, and other communities within the arena of data mining. The research was based on a two-stage machine-intelligence system that clustered and filtered large datasets. The overall objective was to optimize response time through parallel processing while attempting to reduce potential errors due to knowledge manipulation. The results generated by the two-stage system were reviewed by domain experts and tested using traditional methods that included multi variable regression analysis and logic testing for accuracy. The two-stage prototype developed a model that was 85 to 90% accurate in determining childhood asthma and disproved existing stereotypes related to sleep breathing disorders. Detail results will be discussed in the proposed dissertation. While the initial research demonstrated positive results in processing large text datasets limitations were identified. These limitations included processing de lays resulting from equal distribution of processing in a heterogeneous client environment and utilizing the results derived from the second-stage as inputs for the first-stage. To address these limitations the proposed doctoral research will investigate the dynamic distribution of processing in heterogeneous environment and cyclical learning involving the second stage neural network clients modifying the first-stage expert systems.
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Weninger, Timothy Edwards. "Link discovery in very large graphs by constructive induction using genetic programming." Thesis, Manhattan, Kan. : Kansas State University, 2008. http://hdl.handle.net/2097/1087.

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Books on the topic "Discovery of the large"

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The Baby Discovery. Toronto: Harlequin Books, 2001.

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Gabriel's discovery. Waterville, Me: Thorndike Press, 2005.

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Wolf, Roger. The Higgs Boson Discovery at the Large Hadron Collider. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18512-5.

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Addie, Siobhan, Amanda Wagner Gee, Steve Olson, and Sarah H. Beachy, eds. Deriving Drug Discovery Value from Large-Scale Genetic Bioresources. Washington, D.C.: National Academies Press, 2016. http://dx.doi.org/10.17226/23601.

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Balesi, Vincent. Méharées: Au grand large du fort Coppolani de Tidjikja dans le Sahara occidental. Paris: Editions ARCAM, 1995.

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Cordeiro, Robson L. F. Data Mining in Large Sets of Complex Data. London: Springer London, 2013.

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How to meditate: A guide to self-discovery. Boston: Little, Brown, 1999.

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Hunting Mister Heartbreak: A discovery of America. New York, NY: Edward Burlingame Books, 1991.

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Hunting Mister Heartbreak: A discovery of America. New York: Vintage Books, 1998.

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Hunting Mister Heartbreak: A discovery of America. Thorndike, Me: Thorndike Press, 1991.

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Book chapters on the topic "Discovery of the large"

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Frank, Eibe, Geoffrey Holmes, Richard Kirkby, and Mark Hall. "Racing Committees for Large Datasets." In Discovery Science, 153–64. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-36182-0_15.

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Yates, John R. "Large-Scale Phosphoproteomics." In Proteomics for Biological Discovery, 291–309. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2019. http://dx.doi.org/10.1002/9781119081661.ch12.

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Asai, Tatsuya, Hiroki Arimura, Takeaki Uno, and Shin-ichi Nakano. "Discovering Frequent Substructures in Large Unordered Trees." In Discovery Science, 47–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-39644-4_6.

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Alison, John. "The Large Hadron Collider." In The Road to Discovery, 11–14. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10344-0_2.

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Freitas, Alex A., and Simon H. Lavington. "Knowledge Discovery Tasks." In Mining Very Large Databases with Parallel Processing, 7–17. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-5521-6_2.

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Freitas, Alex A., and Simon H. Lavington. "Knowledge Discovery Paradigms." In Mining Very Large Databases with Parallel Processing, 19–29. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-5521-6_3.

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Hébert, Céline, and Bruno Crémilleux. "Mining Frequent δ-Free Patterns in Large Databases." In Discovery Science, 124–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11563983_12.

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Albersmeyer, Uwe, Ralf Malessa, and Ulrich Storz. "Patenting Small and Large Pharmaceutical Molecules." In Successful Drug Discovery, 41–64. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA, 2018. http://dx.doi.org/10.1002/9783527808694.ch2.

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Hargittai, I. "Lessons of a Discovery." In Large Clusters of Atoms and Molecules, 423–35. Dordrecht: Springer Netherlands, 1996. http://dx.doi.org/10.1007/978-94-009-0211-4_16.

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Seifert, Christin, Vedran Sabol, Wolfgang Kienreich, Elisabeth Lex, and Michael Granitzer. "Visual Analysis and Knowledge Discovery for Text." In Large-Scale Data Analytics, 189–218. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-9242-9_7.

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Conference papers on the topic "Discovery of the large"

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Simas, Tiago, Gabriel Silva, Bruno Miranda, Andre Moitinho, Rita Ribeiro, and Coryn A. L. Bailer-Jones. "Knowledge Discovery in Large Data Sets." In CLASSIFICATION AND DISCOVERY IN LARGE ASTRONOMICAL SURVEYS: Proceedings of the International Conference: “Classification and Discovery in Large Astronomical Surveys”. AIP, 2008. http://dx.doi.org/10.1063/1.3059044.

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Fernandez, Raul Castro, Ziawasch Abedjan, Samuel Madden, and Michael Stonebraker. "Towards large-scale data discovery." In SIGMOD/PODS'16: International Conference on Management of Data. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2948674.2948675.

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Staelens, Michael. "MoEDAL - Expanding the LHC's Discovery Frontier." In 7th Annual Conference on Large Hadron Collider Physics. Trieste, Italy: Sissa Medialab, 2019. http://dx.doi.org/10.22323/1.350.0031.

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Brakerski, Zvika, and Boaz Patt-Shamir. "Distributed discovery of large near-cliques." In the 28th ACM symposium. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1582716.1582790.

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Caron, Eddy, Florent Chuffart, Haiwu He, Anissa Lamani, Philippe Le Brouster, and Olivier Richard. "Large scale P2P discovery middleware demonstration." In 2011 IEEE International Conference on Peer-to-Peer Computing (P2P). IEEE, 2011. http://dx.doi.org/10.1109/p2p.2011.6038672.

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Wu, Xindong. "Knowledge discovery in very large databases." In the 14th international conference. New York, New York, USA: ACM Press, 2002. http://dx.doi.org/10.1145/568760.568764.

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Lowekamp, Bruce, David O'Hallaron, and Thomas Gross. "Topology discovery for large ethernet networks." In the 2001 conference. New York, New York, USA: ACM Press, 2001. http://dx.doi.org/10.1145/383059.383078.

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Cao, Chu, Zhidan Liu, Mo Li, Wenqiang Wang, and Zheng Qin. "Walkway Discovery from Large Scale Crowdsensing." In 2018 17th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN). IEEE, 2018. http://dx.doi.org/10.1109/ipsn.2018.00009.

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Zhu, Zhemin, Chen Wang, Li Ma, Yue Pan, and Zhiming Ding. "Scalable Community Discovery of Large Networks." In 2008 9th International Conference on Web-Age Information Management (WAIM). IEEE, 2008. http://dx.doi.org/10.1109/waim.2008.13.

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Trimble, Virginia. "HTRA Discovery Potentials (Invited Review)." In High Time Resolution Astrophysics (HTRA) IV - The Era of Extremely Large Telescopes. Trieste, Italy: Sissa Medialab, 2011. http://dx.doi.org/10.22323/1.108.0025.

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Reports on the topic "Discovery of the large"

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Baldwin, C., and G. Abdulla. Efficient Data Management for Knowledge Discovery in Large-Scale Geospatial Imagery Collections. Office of Scientific and Technical Information (OSTI), January 2006. http://dx.doi.org/10.2172/889968.

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Dunbar, L., W. Kumari, and I. Gashinsky. Practices for Scaling ARP and Neighbor Discovery (ND) in Large Data Centers. RFC Editor, August 2014. http://dx.doi.org/10.17487/rfc7342.

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Croft, W. Bruce. Browsing, Discovery, and Search in Large Distributed Databases of Complex and Scanned Documents. Fort Belvoir, VA: Defense Technical Information Center, January 2000. http://dx.doi.org/10.21236/ada372353.

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Tononi, Giulio, Ruth Benca, and Chiara Cirelli. Rapid Discovery of Continuous-Performance Compounds and Powernap Compounds Through Large-Scale Mutagenesis in Drosophila. Fort Belvoir, VA: Defense Technical Information Center, April 2008. http://dx.doi.org/10.21236/ada498600.

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Duffie, Darrell, and Haoxiang Zhu. Size Discovery. Cambridge, MA: National Bureau of Economic Research, November 2015. http://dx.doi.org/10.3386/w21696.

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Cheshire, S. Discovery Proxy for Multicast DNS-Based Service Discovery. RFC Editor, June 2020. http://dx.doi.org/10.17487/rfc8766.

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Conta, A. Extensions to IPv6 Neighbor Discovery for Inverse Discovery Specification. RFC Editor, June 2001. http://dx.doi.org/10.17487/rfc3122.

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Maenpaa, J., and G. Camarillo. Service Discovery Usage for REsource LOcation And Discovery (RELOAD). RFC Editor, October 2014. http://dx.doi.org/10.17487/rfc7374.

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Haberman, B., and J. Martin. Multicast Router Discovery. RFC Editor, December 2005. http://dx.doi.org/10.17487/rfc4286.

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Green, L. The Klondike discovery. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1996. http://dx.doi.org/10.4095/298502.

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