Bibliografías temáticas / Ceramics, multidimensional data analysis

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Literatura académica sobre el tema "Ceramics, multidimensional data analysis"

Autor: Grafiati
Publicado: 14 de diciembre de 2024

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Tesis sobre el tema "Ceramics, multidimensional data analysis"

1

Passuti, Sara. "Electrοn crystallοgrathy οf nanοdοmains in functiοnal materials". Electronic Thesis or Diss., Normandie, 2024. http://www.theses.fr/2024NORMC230.

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L’étude des matériaux fonctionnels se concentre de plus en plus sur des échantillonscaractérisés par des nano-domaines (allant de tailles submicroniques à des dizaines denanomètres) en raison de leurs propriétés physiques intéressantes, telles que celles observéesdans les films minces ou les matériaux céramiques. Lorsqu’il faut déterminer des phases inconnuesou obtenir des informations détaillées sur la structure cristalline de ces matériaux, ladiffraction des rayons X et la microscopie électronique à transmission (MET) se heurtent à desdifficultés. Pour résoudre ce problème, une nouvelle tech
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2

Westerlund, Per. "Business Intelligence: Multidimensional Data Analysis." Thesis, Umeå universitet, Institutionen för datavetenskap, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-138758.

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The relational database model is probably the most frequently used database model today. It has its strengths, but it doesn’t perform very well with complex queries and analysis of very large sets of data. As computers have grown more potent, resulting in the possibility to store very large data volumes, the need for efficient analysis and processing of such data sets has emerged. The concept of Online Analytical Processing (OLAP) was developed to meet this need. The main OLAP component is the data cube, which is a multidimensional database model that with various techniques has accomplished a
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3

Duch, Brown Amàlia. "Design and Analysis of Multidimensional Data Structures." Doctoral thesis, Universitat Politècnica de Catalunya, 2004. http://hdl.handle.net/10803/6647.

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Aquesta tesi està dedicada al disseny i a l'anàlisi d'estructures de dades multidimensionals, és a dir, estructures de dades que serveixen per emmagatzemar registres $K$-dimensionals que solen representar-se com a punts en l'espai $[0,1]^K$. Aquestes estructures tenen aplicacions en diverses àrees de la informàtica com poden ser els sistemes d'informació geogràfica, la robòtica, el processament d'imatges, la world wide web, el data mining, entre d'altres. <br/><br/>Les estructures de dades multidimensionals també es poden utilitzar com a indexos d'estructures de dades que emmagatzemen, possibl
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4

Schroeder, Michael Philipp 1986. "Analysis and visualization of multidimensional cancer genomics data." Doctoral thesis, Universitat Pompeu Fabra, 2014. http://hdl.handle.net/10803/301436.

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Cancer is a complex disease caused by somatic alterations of the genome and epigenome in tumor cells. Increased investments and cheaper access to various technologies have built momentum for the generation of cancer genomics data. The availability of such large datasets offers many new possibilities to gain insight into cancer molecular properties. Within this scope I present two methods that exploit the broad availability of cancer genomic data: OncodriveROLE, an approach to classify mutational cancer driver genes into activating and loss of function mode of actions and MutEx, a statistical m
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5

Nam, Beomseok. "Distributed multidimensional indexing for scientific data analysis applications." College Park, Md. : University of Maryland, 2007. http://hdl.handle.net/1903/6795.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2007.<br>Thesis research directed by: Computer Science. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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6

Palmas, Gregorio. "Visual Analysis of Multidimensional Data for Biomechanics and HCI." Doctoral thesis, KTH, Beräkningsvetenskap och beräkningsteknik (CST), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-193713.

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Multidimensional analysis is performed in many scientific fields.Its main tasks involve the identification of correlations between data dimensions,the investigation of data clusters, and the identification of outliers. Visualization techniques often help in getting a better understanding. In this thesis, we present our work on improving visual multidimensional analysis by exploiting the semantics of the data and enhancing the perception of existing visualizations. Firstly, we exploit the semantics of the data by creating new visualizations which present visual encodings specifically tailoredto
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7

Odondi, Maurice Jacob. "Multidimensional analysis of successive categories (rating) data by dual scaling." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp02/NQ28031.pdf.

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8

Weherage, Pradeep Peiris. "BigDataCube: Distributed Multidimensional Data Cube Over Apache Spark : An OLAP framework that brings Multidimensional Data Analysis to modern Distributed Storage Systems." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-215696.

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Multidimensional Data Analysis is an important subdivision of Data Analytic paradigm. Data Cube provides the base abstraction for Multidimensional Data Analysis and helps in discovering useful insights of a dataset. On-Line Analytical Processing (OLAP) enhanced it to the next level supporting online responses to analytical queries with the underlying technique that precomputes (materializes) the data cubes. Data Cube Materialization is significant for OLAP, but it is an expensive task in term of data processing and storage. Most of the early decision support system benefits the value of multid
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9

Jernberg, Robert, and Tobias Hultgren. "Flexible Data Extraction for Analysis using Multidimensional Databases and OLAP Cubes." Thesis, KTH, Data- och elektroteknik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-123393.

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Bright is a company that provides customer and employee satisfaction surveys, and uses this information to provide feedback to their customers. Data from the surveys are stored in a relational database and information is generated both by directly querying the database as well as doing analysis on extracted data. As the amount of data grows, generating this information takes increasingly more time. Extracting the data requires significant manual work and is in practice avoided. As this is not an uncommon issue, there is a substantial theoretical framework around the area. The aim of this degre
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10

Johnson, Kevin J. "Strategies for chemometric analysis of gas chromatographic data /." Thesis, Connect to this title online; UW restricted, 2003. http://hdl.handle.net/1773/8513.

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