Relevant bibliographies by topics / Data representation

Academic literature on the topic 'Data representation'

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Published: 4 June 2021
Last updated: 25 May 2024

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Journal articles on the topic "Data representation"

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Frederick, Karen, Lucy Barnard-Brak, and Tracey Sulak. "Under-representation in nationally representative secondary data." International Journal of Research & Method in Education 35, no. 1 (April 2012): 31–40. http://dx.doi.org/10.1080/1743727x.2011.609545.

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Yusriyah, Yais, and Mega Achdisty Noordyana. "Kemampuan Representasi Matematis Siswa SMP pada Materi Penyajian Data di Desa Bungbulang." Plusminus: Jurnal Pendidikan Matematika 1, no. 1 (March 31, 2021): 47–60. http://dx.doi.org/10.31980/plusminus.v1i1.1025.

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Several studies have shown that students' mathematical representation skills are still low. The research objective was to analyze the mathematical representation ability of junior high school students on data presentation material. The research was conducted in Citalahab Kaler Village, Bungbulang-Garut Village. This research uses a qualitative approach. The method used is the descriptive analysis method, which involved 3 students as a sample, using a simple random sampling technique. The test instrument for the students' mathematical representation ability consisted of 5 questions in the form of descriptions. The results of the study are (1) The ability of mathematical representations on the pictorial representation indicator is that almost all students from the three samples have been able to solve a problem using a visual representation. (2) The ability of mathematical representation in the symbolic representation indicator is that almost all students from the three samples have not been able to solve problems using symbolic representations. (3) The ability of mathematical representation on the verbal representation indicator is that some students can use verbal representations, but some of them are still unable to convey their mathematical ideas in their language.
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Mishra, Sandip. "Analytical Data Representation." International Journal of Computer Sciences and Engineering 7, no. 11 (November 30, 2019): 68–72. http://dx.doi.org/10.26438/ijcse/v7i11.6872.

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Hawkins, Peter, Alex Aiken, Kathleen Fisher, Martin Rinard, and Mooly Sagiv. "Data representation synthesis." ACM SIGPLAN Notices 47, no. 6 (August 6, 2012): 38. http://dx.doi.org/10.1145/2345156.1993504.

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Hawkins, Peter, Alex Aiken, Kathleen Fisher, Martin Rinard, and Mooly Sagiv. "Data representation synthesis." ACM SIGPLAN Notices 46, no. 6 (June 4, 2011): 38–49. http://dx.doi.org/10.1145/1993316.1993504.

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Lestari, Nurcholif Diah Sri, Wasilatul Murtafiah, Marheny Lukitasari, Suwarno Suwarno, and Inge Wiliandani Setya Putri. "IDENTIFIKASI RAGAM DAN LEVEL KEMAMPUAN REPRESENTASI PADA DESAIN MASALAH LITERASI MATEMATIS DARI MAHASISWA CALON GURU." KadikmA 13, no. 1 (April 30, 2022): 11. http://dx.doi.org/10.19184/kdma.v13i1.31538.

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Representation is one of the fundamental abilities of mathematics reflected by students understanding of mathematics concepts, principles, or procedures, so it becomes crucial for teachers to develop students' mathematical representation skills. This research was time to describe the representation used in the problem and the level of mathematical representation ability needed to solve mathematical literacy problems. The data was collected through the assignment to design mathematical literacy problems between 3-10 pieces and interview as triangulation on 35 prospective elementary school teacher students. The data are grouped based on various representations and analyzed quantitatively and descriptively. Then one problem is chosen randomly for each type of representation to describe the level of representation ability needed to solve the problem qualitatively. The results show that the mathematical representations used in designed mathematical literacy problems are pictorial-verbal, pictorial-symbolic, verbal-symbolic, pictorial, verbal, symbolic, and pictorial-verbal-symbolic representations. The level of representational ability that tends to be needed to solve problems is levels 0 and 1. This study suggests that prospective teacher students should develop mathematical representation knowledge to improve the quality of their learning in the future
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Kadi, Hafid, Mohammed Rebbah, Boudjelal Meftah, and Olivier Lézoray. "A Data Representation Model for Personalized Medicine." International Journal of Healthcare Information Systems and Informatics 16, no. 4 (October 2021): 1–25. http://dx.doi.org/10.4018/ijhisi.295822.

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Personalized medicine exploits the patient data, for example, genetic compositions, and key biomarkers. During the data mining process, the key challenges are the information loss, the data types heterogeneity and the time series representation. In this paper, a novel data representation model for personalized medicine is proposed in light of these challenges. The proposed model will account for the structured, temporal and non-temporal data and their types, namely, numeric, nominal, date, and Boolean. After the "Date and Boolean" data transformation, the nominal data are treated by dispersion while several clustering techniques are deployed to control the numeric data distribution. Ultimately, the transformation process results in three homogeneous representations with these representations having only two dimensions to ease the exploration of the represented dataset. Compared to the Symbolic Aggregate Approximation technique, the proposed model preserves the time-series information, conserves as much data as possible and offers multiple simple representations to be explored.
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Ott, Frédéric, and Sergey Kozhevnikov. "Off-specular data representations in neutron reflectivity." Journal of Applied Crystallography 44, no. 2 (February 11, 2011): 359–69. http://dx.doi.org/10.1107/s0021889811002858.

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The different methods of data acquisition and representation in neutron reflectometry measurements are discussed. The different representations of diffuse scattering are compared and the off-specular features that can be observed in neutron reflectivity are described. The representation of diffuse data in the `natural' reciprocal-space coordinates (Qx, Qz) leads to a loss of information for smallQzscattering vector. It is suggested that an intermediate representation (Qx/Qz, Qz) allows the unification of data measured on different types of spectrometers and permits a straightforward comparison and understanding while keeping all the interesting features of the off-specular scattering. The discussion is illustrated by diffuse scattering data measured on neutron waveguides obtained on both fixed-wavelength and time-of-flight spectrometers. A simple procedure allowing for dense remapping between different representations is described.
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Corcoran, Padraig, and Irena Spasić. "Self-Supervised Representation Learning for Geographical Data—A Systematic Literature Review." ISPRS International Journal of Geo-Information 12, no. 2 (February 12, 2023): 64. http://dx.doi.org/10.3390/ijgi12020064.

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Self-supervised representation learning (SSRL) concerns the problem of learning a useful data representation without the requirement for labelled or annotated data. This representation can, in turn, be used to support solutions to downstream machine learning problems. SSRL has been demonstrated to be a useful tool in the field of geographical information science (GIS). In this article, we systematically review the existing research literature in this space to answer the following five research questions. What types of representations were learnt? What SSRL models were used? What downstream problems were the representations used to solve? What machine learning models were used to solve these problems? Finally, does using a learnt representation improve the overall performance?
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Vrotsou, Katerina, Camilla Forsell, and Matthew Cooper. "2D and 3D Representations for Feature Recognition in Time Geographical Diary Data." Information Visualization 9, no. 4 (December 3, 2009): 263–76. http://dx.doi.org/10.1057/ivs.2009.30.

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Time geographical representations are becoming a common approach to analysing spatio-temporal data. Such representations appear intuitive in the process of identifying patterns and features as paths of populations form tracks through the 3D space, which can be seen converging and diverging over time. In this article, we compare 2D and 3D representations within a time geographical visual analysis tool for activity diary data. We identify a representative task and evaluate task performance between the two representations. The results show that the 3D representation has benefits over the 2D representation for feature identification but also indicate that these benefits can be lost if the 3D representation is not carefully constructed to help the user to see them.
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Dissertations / Theses on the topic "Data representation"

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Chintala, Venkatram Reddy. "Digital image data representation." Ohio : Ohio University, 1986. http://www.ohiolink.edu/etd/view.cgi?ohiou1183128563.

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Lansley, Guy David. "Big data : geodemographics and representation." Thesis, University College London (University of London), 2018. http://discovery.ucl.ac.uk/10045119/.

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Due to the harmonisation of data collection procedures with everyday activities, Big Data can be harnessed to produce geodemographic representations to supplement or even replace traditional sources of population data which suffer from low response rates or intermittent refreshes. Furthermore, the velocity and diversity of new forms of data also enable the creation entirely new forms of geodemographic insight. However, their miscellaneous data collection procedures are inconsistent, unregulated and are not robustly sampled like conventional social sciences data sources. Therefore, uncertainty is inherent when attempting to glean representative research on the population at large from Big Data. All data are of partial coverage; however, the provenance Big Data is poorly understood. Consequently, the use of said data has epistemologically shifted how geographers build representations of the population. In repurposing Big Data, researchers might encounter a variety of data types that are not readily suitable for quantitative analysis and may represent geodemographic phenomena indirectly. Furthermore, whilst there are considerable barriers acquiring data pertaining to people and their actions, it is also challenging to link Big Data. In light of this, this work explores the fundamental challenges of using geospatial Big Data to represent the population and their activities across space and time. These are demonstrated through original research on various big datasets, they include Consumer Registers (which comprise public versions of the Electoral Register and consumer data), Driver and Vehicle Licencing Agency (DVLA) car registration data, and geotagged Twitter posts. While this thesis is critical of Big Data, it remains optimistic of their potential value and demonstrates techniques through which uncertainty can be identified or mitigated to an extent. In the process it also exemplifies how new forms of data can produce geodemographic insight that was previously unobservable on a large scale.
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Dos, Santos Ludovic. "Representation learning for relational data." Thesis, Paris 6, 2017. http://www.theses.fr/2017PA066480/document.

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L'utilisation croissante des réseaux sociaux et de capteurs génère une grande quantité de données qui peuvent être représentées sous forme de graphiques complexes. Il y a de nombreuses tâches allant de l'analyse de l'information à la prédiction et à la récupération que l'on peut imaginer sur ces données où la relation entre les noeuds de graphes devrait être informative. Dans cette thèse, nous avons proposé différents modèles pour trois tâches différentes: - Classification des noeuds graphiques - Prévisions de séries temporelles relationnelles - Filtrage collaboratif. Tous les modèles proposés utilisent le cadre d'apprentissage de la représentation dans sa variante déterministe ou gaussienne. Dans un premier temps, nous avons proposé deux algorithmes pour la tâche de marquage de graphe hétérogène, l'un utilisant des représentations déterministes et l'autre des représentations gaussiennes. Contrairement à d'autres modèles de pointe, notre solution est capable d'apprendre les poids de bord lors de l'apprentissage simultané des représentations et des classificateurs. Deuxièmement, nous avons proposé un algorithme pour la prévision des séries chronologiques relationnelles où les observations sont non seulement corrélées à l'intérieur de chaque série, mais aussi entre les différentes séries. Nous utilisons des représentations gaussiennes dans cette contribution. C'était l'occasion de voir de quelle manière l'utilisation de représentations gaussiennes au lieu de représentations déterministes était profitable. Enfin, nous appliquons l'approche d'apprentissage de la représentation gaussienne à la tâche de filtrage collaboratif. Ceci est un travail préliminaire pour voir si les propriétés des représentations gaussiennes trouvées sur les deux tâches précédentes ont également été vérifiées pour le classement. L'objectif de ce travail était de généraliser ensuite l'approche à des données plus relationnelles et pas seulement des graphes bipartis entre les utilisateurs et les items
The increasing use of social and sensor networks generates a large quantity of data that can be represented as complex graphs. There are many tasks from information analysis, to prediction and retrieval one can imagine on those data where relation between graph nodes should be informative. In this thesis, we proposed different models for three different tasks: - Graph node classification - Relational time series forecasting - Collaborative filtering. All the proposed models use the representation learning framework in its deterministic or Gaussian variant. First, we proposed two algorithms for the heterogeneous graph labeling task, one using deterministic representations and the other one Gaussian representations. Contrary to other state of the art models, our solution is able to learn edge weights when learning simultaneously the representations and the classifiers. Second, we proposed an algorithm for relational time series forecasting where the observations are not only correlated inside each series, but also across the different series. We use Gaussian representations in this contribution. This was an opportunity to see in which way using Gaussian representations instead of deterministic ones was profitable. At last, we apply the Gaussian representation learning approach to the collaborative filtering task. This is a preliminary work to see if the properties of Gaussian representations found on the two previous tasks were also verified for the ranking one. The goal of this work was to then generalize the approach to more relational data and not only bipartite graphs between users and items
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Dos, Santos Ludovic. "Representation learning for relational data." Electronic Thesis or Diss., Paris 6, 2017. http://www.theses.fr/2017PA066480.

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L'utilisation croissante des réseaux sociaux et de capteurs génère une grande quantité de données qui peuvent être représentées sous forme de graphiques complexes. Il y a de nombreuses tâches allant de l'analyse de l'information à la prédiction et à la récupération que l'on peut imaginer sur ces données où la relation entre les noeuds de graphes devrait être informative. Dans cette thèse, nous avons proposé différents modèles pour trois tâches différentes: - Classification des noeuds graphiques - Prévisions de séries temporelles relationnelles - Filtrage collaboratif. Tous les modèles proposés utilisent le cadre d'apprentissage de la représentation dans sa variante déterministe ou gaussienne. Dans un premier temps, nous avons proposé deux algorithmes pour la tâche de marquage de graphe hétérogène, l'un utilisant des représentations déterministes et l'autre des représentations gaussiennes. Contrairement à d'autres modèles de pointe, notre solution est capable d'apprendre les poids de bord lors de l'apprentissage simultané des représentations et des classificateurs. Deuxièmement, nous avons proposé un algorithme pour la prévision des séries chronologiques relationnelles où les observations sont non seulement corrélées à l'intérieur de chaque série, mais aussi entre les différentes séries. Nous utilisons des représentations gaussiennes dans cette contribution. C'était l'occasion de voir de quelle manière l'utilisation de représentations gaussiennes au lieu de représentations déterministes était profitable. Enfin, nous appliquons l'approche d'apprentissage de la représentation gaussienne à la tâche de filtrage collaboratif. Ceci est un travail préliminaire pour voir si les propriétés des représentations gaussiennes trouvées sur les deux tâches précédentes ont également été vérifiées pour le classement. L'objectif de ce travail était de généraliser ensuite l'approche à des données plus relationnelles et pas seulement des graphes bipartis entre les utilisateurs et les items
The increasing use of social and sensor networks generates a large quantity of data that can be represented as complex graphs. There are many tasks from information analysis, to prediction and retrieval one can imagine on those data where relation between graph nodes should be informative. In this thesis, we proposed different models for three different tasks: - Graph node classification - Relational time series forecasting - Collaborative filtering. All the proposed models use the representation learning framework in its deterministic or Gaussian variant. First, we proposed two algorithms for the heterogeneous graph labeling task, one using deterministic representations and the other one Gaussian representations. Contrary to other state of the art models, our solution is able to learn edge weights when learning simultaneously the representations and the classifiers. Second, we proposed an algorithm for relational time series forecasting where the observations are not only correlated inside each series, but also across the different series. We use Gaussian representations in this contribution. This was an opportunity to see in which way using Gaussian representations instead of deterministic ones was profitable. At last, we apply the Gaussian representation learning approach to the collaborative filtering task. This is a preliminary work to see if the properties of Gaussian representations found on the two previous tasks were also verified for the ranking one. The goal of this work was to then generalize the approach to more relational data and not only bipartite graphs between users and items
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Penton, Dave. "Linguistic data models : presentation and representation /." Connect to thesis, 2006. http://eprints.unimelb.edu.au/archive/00002875.

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Sanches, Pedro. "Health Data : Representation and (In)visibility." Doctoral thesis, KTH, Programvaruteknik och Datorsystem, SCS, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-158909.

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Health data requires context to be understood. I show how, by examining two areas: self-surveillance, with a focus on representation of bodily data, and mass-surveillance, with a focus on representing populations. I critically explore how Information and Communication Technology (ICT) can be made to represent individuals and populations, and identify implications of such representations. My contributions are: (i) the design of a self-tracking stress management system, (ii) the design of a mass-surveillance system based on mobile phone data, (iii) an empirical study exploring how users of a fitness tracker make sense of their generated data, (iv) an analysis of the discourse of designers of a syndrome surveillance system, (v) a critical analysis of the design process of a mass-surveillance system, and (vi) an analysis of the historicity of the concepts and decisions taken during the design of a stress management system. I show that producing health data, and subsequently the technological characteristics of algorithms that produce them depend on factors present in the ICT design process. These factors determine how data is made to represent individuals and populations in ways that may selectively make invisible parts of the population, determinants of health, or individual conception of self and wellbeing. In addition, I show that the work of producing data does not stop with the work of the engineers who produce ICT-based systems: maintenance is constantly required.
För att förstå hälsodata krävs sammanhang. Jag visar hur detta kan erhållas, genom två fallstudier: en om självövervakning, med fokus på representation av kroppsdata, samt en om massövervakning, med fokus på representation av populationer. Jag granskar kritiskt hur informationsteknologi (IT) kan fås att representera såväl individer som populationer och vilka följder det får. Mina bidrag är: (i) utformningen av ett självövervakningssystem för stresshantering, (ii) utformningen av ett massövervakningssystem baserat på data från mobiltelefonanvändning, (iii) en empirisk studie av hur användare av en hälsosensor begriper det data som sensorn genererar, (iv) en diskursiv analys av hur syndromövervakningssystem utformas, (v) en kritisk analys av processer kring att utforma ett massövervakningssystem, samt (vi) en analys av den historiska korrektheten i begrepp och beslutsfattande i samband med utformningen av ett stresshanteringssystem. Jag visar att produktion av hälsodata, liksom tekniska beskrivningar av de algoritmer som används i den processen, beror av faktorer som hänger samman med IT-utformningsprocessen. Dessa faktorer avgör sedan hur data kan fås att representera individer och populationer på sätt som kan rendera delar av en population, hälsodeterminanter, eller individens självuppfattning och förståelse av välmående osynliga. Jag visar också att arbetet med att producera data inte är avslutat i och med det ingenjörsarbete som krävs för att IT-systemen ska byggas: konstant underhåll krävs också.

QC 20150114

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Parvathala, Rajeev (Rajeev Krishna). "Representation learning for non-sequential data." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/119581.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 85-90).
In this thesis, we design and implement new models to learn representations for sets and graphs. Typically, data collections in machine learning problems are structured as arrays or sequences, with sequential relationships between successive elements. Sets and graphs both break this common mold of data collections that have been extensively studied in the machine learning community. First, we formulate a new method for performing diverse subset selection using a neural set function approximation method. This method relies on the deep sets idea, which says that any set function s(X) has a universal approximator of the form f([sigma]x[xi]X [phi](x)). Second, we design a new variational autoencoding model for highly structured, sparse graphs, such as chemical molecules. This method uses the graphon, a probabilistic graphical model from mathematics, as inspiration for the decoder. Furthermore, an adversary is employed to force the distribution of vertex encodings to follow a target distribution, so that new graphs can be generated by sampling from this target distribution. Finally, we develop a new framework for performing encoding of graphs in a hierarchical manner. This approach partitions an input graph into multiple connected subgraphs, and creates a new graph where each node represents one such subgraph. This allows the model to learn a higher level representation for graphs, and increases robustness of graphical encoding to varying graph input sizes.
by Rajeev Parvathala.
M. Eng.
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Andersson, Elin, and Hanna Bengtsson. "Geovisualisering: En rumslig representation av data." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-43221.

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Internet of things ger oss möjligheten att kunna identifiera, kontrollera och övervaka objekt över hela världen. För att mängden rådata som strömmar in ska kunna skapa mening och insikter till människan krävs det att den presenteras på rätt sätt. Studien undersöker därför om geovisualisering bättre kan möta människans kognitiva förmåga vid intag och tolkning av information. Geovisualisering innebär att rumslig data kan utforskas på en karta via en interaktiv display och är en länk mellan den mänskliga beslutsprocessen, interaktiva gränssnitt och data [21]. Mer forskning behövs inom området för att undersöka hur geovisualisering kan ta plats i system där stora datamängder behöver presenteras på ett överskådligt sätt och stödja beslutsprocesser. Studien syftar till att jämföra geovisualiseringar med ett befintligt system som tillhandahåller kontinuerlig uppdatering och övervakning av nätverkskameror genom utförande av användbarhetstester och intervjuer. Det som undersökts är om geovisualisering kan ge en ökad förståelse och bättre interaktion i ett utrymme som efterliknar den fysiska världen, samt undersöka potentiella problem för att hitta framtida förbättringar. Resultaten visade att navigering och informationsöverbelastning var återkommande problem under testerna av det befintliga systemet. För geovisualiseringarna visade resultaten det motsatta då de istället underlättade förståelsen för interaktion och information. Vissa problem identifierades dock för de framtagna geovisualiseringarna, som exempelvis dess begränsade interaktion och misstolkningar av objekt. Trots detta visade det sig vara fördelaktigt att placera ut enheter i deras verkliga miljö med hjälp av geovisualisering då det bidrog till en bättre översikt och förståelse av systemets sammanhang.
The Internet of Things gives us the ability to identify, control and monitor objects around the world. In order to get meaning and knowledge from the amount of raw data, it needs to be presented in the right way for people to get insights from it. The study therefore examines whether geovisualization can better meet human cognitive ability in  interpretation of information. Geovisualization means that spatial data can be explored on a map through an interactive display and is a link between the human decision-making process, interactive interfaces and data [21]. More research is needed in the area to investigate how geovisualization can take place in systems where large amounts of data needs to be presented and how it can support decision-making processes. The study aims to compare geovisualizations with an existing system that provides continuous updating and monitoring of network cameras by performing usability tests and interviews. Geovisualization has been investigated to see if it can contribute an increased understanding and better navigation in a space that mimics the physical world, as well as investigate potential problems to find future improvements. The results proved that navigation and information overload were recurring problems during the tests of the existing system. For the geovisualizations, the results proved the opposite as they instead facilitated the understanding of navigation and information. However, some problems were identified for the developed geovisualizations, such as its limited interaction and misinterpretations of objects. Despite this, it proved to be advantageous to place units in their real environment using geovisualization as it contributed to a better overview and understanding of the system's context.
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Friedman, Marc T. "Representation and optimization for data integration /." Thesis, Connect to this title online; UW restricted, 1999. http://hdl.handle.net/1773/6979.

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Jansson, Erika. "Data-model representation for non-programmers." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-394277.

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Nowadays, there are people working within the IT-industry without any major knowledge in programming. Some of them sometimes need to make changes that currently only can be done in the actual code. This project is about finding the best way for non-programmers to make changes in a data-model without having to change the code. The project is divided into three parts where the first two is about finding different ways to solve this problem and then evaluate them through expert evaluation and based on relevant theory. The third part is about taking the result from part one and two and develop it. The third part ends with user-tests and follow-up interviews with 12 test-participants. In this part, also programmers will participate to get a complete overview of all the intended user’s experience. The result is that a graphical concept is to be preferred for users with minor/without programming experience. For programmers, it is harder to tell which concept is best and a more extensive investigation probably has to be done to get a fair result. These conclusions are based on the results from the conducted tests/interviews together with available external theory. The results could be improved with more users and more extensive tests. Worth mentioning is since all users are individuals, different concepts suit different persons and what suits one user best might not suit another at all, despite background as programmer or non-programmer.
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Books on the topic "Data representation"

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Davie, Lynn. Data collection and representation. Melbourne: Macmillan Australia, 1991.

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Li, Sheng, and Yun Fu. Robust Representation for Data Analytics. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-60176-2.

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Conway, Joseph B. Analysis and representation of fatigue data. Materials Park, Ohio: ASM International, 1991.

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Peeters, Yvo J. D., 1949- and Williams Colin H. 1950-, eds. The Cartographic representation of linguistic data. [Stafford]: Staffordshire University, 1993.

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Ding, Zhengming, Handong Zhao, and Yun Fu. Learning Representation for Multi-View Data Analysis. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-00734-8.

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Computers and the representation of geographical data. Chichester: John Wiley, 1987.

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Blackledge, J. M. Spatial data representation for rotation invariant correlation. Leicester: De Montfort University, SERCentre, 1996.

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Sharan, Girja. Fourier representation of climatic data of Kothara-Kutch. Ahmedabad: Indian Institute of Management, 2004.

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United States. Government Accountability Office. Data on hispanic representation in the federal workforce. Washington, D.C: United States Government Accountability Office, 2007.

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Simon, J. C. Patterns and operators: The foundations of data representation. London: North Oxford Academic, 1986.

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Book chapters on the topic "Data representation"

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Drach, Robert, and John Caron. "Data Representation." In Earth System Modelling - Volume 4, 25–37. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36464-8_5.

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Barnes, Timothy J., David Harrison, A. Richard Newton, and Rick L. Spickelmier. "Data Representation." In Electronic CAD Frameworks, 29–50. Boston, MA: Springer US, 1992. http://dx.doi.org/10.1007/978-1-4615-3558-4_3.

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Ortolani, Claudio. "Data Representation." In Flow Cytometry Today, 157–70. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-10836-5_11.

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Sannella, Donald, Michael Fourman, Haoran Peng, and Philip Wadler. "Data Representation." In Undergraduate Topics in Computer Science, 189–204. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-76908-6_20.

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Hahs-Vaughn, Debbie L., and Richard G. Lomax. "Data Representation." In Statistical Concepts, 25–84. New York, NY : Routledge, 2019.: Routledge, 2020. http://dx.doi.org/10.4324/9780429261268-2.

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Zong, Chengqing, Rui Xia, and Jiajun Zhang. "Text Representation." In Text Data Mining, 33–73. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0100-2_3.

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Lavrač, Nada, Vid Podpečan, and Marko Robnik-Šikonja. "Propositionalization of Relational Data." In Representation Learning, 83–105. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68817-2_4.

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Servin, Claude. "Representation of Data." In Telecommunications, 1–9. London: Springer London, 1999. http://dx.doi.org/10.1007/978-1-4471-0893-1_1.

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Jackson, Keith. "Representation of Data." In C Programming for Electronic Engineers, 23–30. London: Macmillan Education UK, 1995. http://dx.doi.org/10.1007/978-1-349-13747-3_3.

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Dubuisson, Séverine. "Data Representation Models." In Tracking with Particle Filter for High-Dimensional Observation and State Spaces, 29–77. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2015. http://dx.doi.org/10.1002/9781119004868.ch2.

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Conference papers on the topic "Data representation"

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Hawkins, Peter, Alex Aiken, Kathleen Fisher, Martin Rinard, and Mooly Sagiv. "Data representation synthesis." In the 32nd ACM SIGPLAN conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/1993498.1993504.

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Panaye, A. "MOLECULAR SHAPE REPRESENTATION AND CODES." In Data For Discovery. Connecticut: Begellhouse, 2023. http://dx.doi.org/10.1615/1-56700-002-9.190.

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Leskovec, Jure. "Large-scale graph representation learning." In 2017 IEEE International Conference on Big Data (Big Data). IEEE, 2017. http://dx.doi.org/10.1109/bigdata.2017.8257903.

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Du, Boxin, Changhe Yuan, Robert Barton, Tal Neiman, and Hanghang Tong. "Self-supervised Hypergraph Representation Learning." In 2022 IEEE International Conference on Big Data (Big Data). IEEE, 2022. http://dx.doi.org/10.1109/bigdata55660.2022.10020240.

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Zhang, Xiang, Siwei Ma, Zhouchen Lin, Jian Zhang, Shiqi Wang, and Wen Gao. "Globally Variance-Constrained Sparse Representation for Rate-Distortion Optimized Image Representation." In 2017 Data Compression Conference (DCC). IEEE, 2017. http://dx.doi.org/10.1109/dcc.2017.63.

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Patterson, Evan, Ioana Baldini, Aleksandra Mojsilović, and Kush R. Varshney. "Semantic Representation of Data Science Programs." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/858.

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Your computer is continuously executing programs, but does it really understand them? Not in any meaningful sense. That burden falls upon human knowledge workers, who are increasingly asked to write and understand code. They would benefit greatly from intelligent tools that reveal the connections between their code and its subject matter. Towards this prospect, we present an AI system that forms semantic representations of computer programs, using techniques from knowledge representation and program analysis. These representations are created through a novel algorithm for the semantic enrichment of dataflow graphs. We illustrate its workings with examples from the field of data science. The algorithm is undergirded by a new ontology language for modeling computer programs and a new ontology about data science, written in this language.
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Qian, Sheng, Guanyue Li, Wen-Ming Cao, Cheng Liu, Si Wu, and Hau San Wong. "Improving representation learning in autoencoders via multidimensional interpolation and dual regularizations." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/453.

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Autoencoders enjoy a remarkable ability to learn data representations. Research on autoencoders shows that the effectiveness of data interpolation can reflect the performance of representation learning. However, existing interpolation methods in autoencoders do not have enough capability of traversing a possible region between two datapoints on a data manifold, and the distribution of interpolated latent representations is not considered.To address these issues, we aim to fully exert the potential of data interpolation and further improve representation learning in autoencoders. Specifically, we propose the multidimensional interpolation to increase the capability of data interpolation by randomly setting interpolation coefficients for each dimension of latent representations. In addition, we regularize autoencoders in both the latent and the data spaces by imposing a prior on latent representations in the Maximum Mean Discrepancy (MMD) framework and encouraging generated datapoints to be realistic in the Generative Adversarial Network (GAN) framework. Compared to representative models, our proposed model has empirically shown that representation learning exhibits better performance on downstream tasks on multiple benchmarks.
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Zhuge, Wenzhang, Chenping Hou, Xinwang Liu, Hong Tao, and Dongyun Yi. "Simultaneous Representation Learning and Clustering for Incomplete Multi-view Data." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/623.

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Incomplete multi-view clustering has attracted various attentions from diverse fields. Most existing methods factorize data to learn a unified representation linearly. Their performance may degrade when the relations between the unified representation and data of different views are nonlinear. Moreover, they need post-processing on the unified representations to extract the clustering indicators, which separates the consensus learning and subsequent clustering. To address these issues, in this paper, we propose a Simultaneous Representation Learning and Clustering (SRLC) method. Concretely, SRLC constructs similarity matrices to measure the relations between pair of instances, and learns low-dimensional representations of present instances on each view and a common probability label matrix simultaneously. Thus, the nonlinear information can be reflected by these representations and the clustering results can obtained from label matrix directly. An efficient iterative algorithm with guaranteed convergence is presented for optimization. Experiments on several datasets demonstrate the advantages of the proposed approach.
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Hawkins, Peter, Alex Aiken, Kathleen Fisher, Martin Rinard, and Mooly Sagiv. "Concurrent data representation synthesis." In the 33rd ACM SIGPLAN conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2254064.2254114.

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Serra, Edoardo, Mikel Joaristi, and Alfredo Cuzzocrea. "Large-scale Sparse Structural Node Representation." In 2020 IEEE International Conference on Big Data (Big Data). IEEE, 2020. http://dx.doi.org/10.1109/bigdata50022.2020.9377854.

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Reports on the topic "Data representation"

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Tafolla, Tanya, Eappen Nelluvelil, Jacob Moore, Daniel Dunning, Nathaniel Morgan, and Robert Robey. MATAR: Data-Oriented Sparse Data Representation. Office of Scientific and Technical Information (OSTI), March 2021. http://dx.doi.org/10.2172/1773304.

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DeSchon, A. L. Survey of data representation standards. RFC Editor, January 1986. http://dx.doi.org/10.17487/rfc0971.

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Srinivasan, R. XDR: External Data Representation Standard. RFC Editor, August 1995. http://dx.doi.org/10.17487/rfc1832.

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Eisler, M., ed. XDR: External Data Representation Standard. RFC Editor, May 2006. http://dx.doi.org/10.17487/rfc4506.

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Smith, Bradford M. External representation of product definition data. Gaithersburg, MD: National Institute of Standards and Technology, 1989. http://dx.doi.org/10.6028/nist.ir.89-4166.

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Measure, Edward. Glossary and Catalog of MeT Data Representation. Fort Belvoir, VA: Defense Technical Information Center, February 2003. http://dx.doi.org/10.21236/ada411989.

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Schunn, Christian D., Lelyn D. Saner, Susan K. Kirschenbaum, J. G. Trafton, and Eliza B. Littleton. Complex Visual Data Analysis, Uncertainty, and Representation. Fort Belvoir, VA: Defense Technical Information Center, January 2007. http://dx.doi.org/10.21236/ada479656.

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Jenkins, N., and R. Stepanek. JSCalendar: A JSON Representation of Calendar Data. RFC Editor, July 2021. http://dx.doi.org/10.17487/rfc8984.

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Stepanek, R., and M. Loffredo. JSContact: A JSON Representation of Contact Data. RFC Editor, May 2024. http://dx.doi.org/10.17487/rfc9553.

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Currie, A., and B. Ady. GEOSIS project: knowledge representation and data structures for geoscience data. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1990. http://dx.doi.org/10.4095/128053.

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