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Relevant bibliographies by topics / Data Domains

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Academic literature on the topic 'Data Domains'

Author: Grafiati

Published: 25 May 2024

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Journal articles on the topic "Data Domains"

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Macak, Martin, Mouzhi Ge, and Barbora Buhnova. "A Cross-Domain Comparative Study of Big Data Architectures." International Journal of Cooperative Information Systems 29, no. 04 (October 28, 2020): 2030001. http://dx.doi.org/10.1142/s0218843020300016.

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Nowadays, a variety of Big Data architectures are emerging to organize the Big Data life cycle. While some of these architectures are proposed for general usage, many of them are proposed in a specific application domain such as smart cities, transportation, healthcare, and agriculture. There is, however, a lack of understanding of how and why Big Data architectures vary in different domains and how the Big Data architecture strategy in one domain may possibly advance other domains. Therefore, this paper surveys and compares the Big Data architectures in different application domains. It also chooses a representative architecture of each researched application domain to indicate which Big Data architecture from a given domain the researchers and practitioners may possibly start from. Next, a pairwise cross-domain comparison among the Big Data architectures is presented to outline the similarities and differences between the domain-specific architectures. Finally, the paper provides a set of practical guidelines for Big Data researchers and practitioners to build and improve Big Data architectures based on the knowledge gathered in this study.

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Rui, Xue, Ziqiang Li, Yang Cao, Ziyang Li, and Weiguo Song. "DILRS: Domain-Incremental Learning for Semantic Segmentation in Multi-Source Remote Sensing Data." Remote Sensing 15, no. 10 (May 12, 2023): 2541. http://dx.doi.org/10.3390/rs15102541.

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With the exponential growth in the speed and volume of remote sensing data, deep learning models are expected to adapt and continually learn over time. Unfortunately, the domain shift between multi-source remote sensing data from various sensors and regions poses a significant challenge. Segmentation models face difficulty in adapting to incremental domains due to catastrophic forgetting, which can be addressed via incremental learning methods. However, current incremental learning methods mainly focus on class-incremental learning, wherein classes belong to the same remote sensing domain, and neglect investigations into incremental domains in remote sensing. To solve this problem, we propose a domain-incremental learning method for semantic segmentation in multi-source remote sensing data. Specifically, our model aims to incrementally learn a new domain while preserving its performance on previous domains without accessing previous domain data. To achieve this, our model has a unique parameter learning structure that reparametrizes domain-agnostic and domain-specific parameters. We use different optimization strategies to adapt to domain shift in incremental domain learning. Additionally, we adopt multi-level knowledge distillation loss to mitigate the impact of label space shift among domains. The experiments demonstrate that our method achieves excellent performance in domain-incremental settings, outperforming existing methods with only a few parameters.

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Son, Jiseong, Chul-Su Lim, Hyoung-Seop Shim, and Ji-Sun Kang. "Development of Knowledge Graph for Data Management Related to Flooding Disasters Using Open Data." Future Internet 13, no. 5 (May 11, 2021): 124. http://dx.doi.org/10.3390/fi13050124.

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Despite the development of various technologies and systems using artificial intelligence (AI) to solve problems related to disasters, difficult challenges are still being encountered. Data are the foundation to solving diverse disaster problems using AI, big data analysis, and so on. Therefore, we must focus on these various data. Disaster data depend on the domain by disaster type and include heterogeneous data and lack interoperability. In particular, in the case of open data related to disasters, there are several issues, where the source and format of data are different because various data are collected by different organizations. Moreover, the vocabularies used for each domain are inconsistent. This study proposes a knowledge graph to resolve the heterogeneity among various disaster data and provide interoperability among domains. Among disaster domains, we describe the knowledge graph for flooding disasters using Korean open datasets and cross-domain knowledge graphs. Furthermore, the proposed knowledge graph is used to assist, solve, and manage disaster problems.

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Crooks, Natacha. "Efficient Data Sharing across Trust Domains." ACM SIGMOD Record 52, no. 2 (August 10, 2023): 36–37. http://dx.doi.org/10.1145/3615952.3615962.

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Cross-Trust-Domain Processing. Data is now a commodity. We know how to compute and store it efficiently and reliably at scale. We have, however, paid less attention to the notion of trust. Yet, data owners today are no longer the entities storing or processing their data (medical records are stored on the cloud, data is shared across banks, etc.). In fact, distributed systems today consist of many different parties, whether it is cloud providers, jurisdictions, organisations or humans. Modern data processing and storage always straddles trust domains.

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Jeon, Hyunsik, Seongmin Lee, and U. Kang. "Unsupervised multi-source domain adaptation with no observable source data." PLOS ONE 16, no. 7 (July 9, 2021): e0253415. http://dx.doi.org/10.1371/journal.pone.0253415.

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Given trained models from multiple source domains, how can we predict the labels of unlabeled data in a target domain? Unsupervised multi-source domain adaptation (UMDA) aims for predicting the labels of unlabeled target data by transferring the knowledge of multiple source domains. UMDA is a crucial problem in many real-world scenarios where no labeled target data are available. Previous approaches in UMDA assume that data are observable over all domains. However, source data are not easily accessible due to privacy or confidentiality issues in a lot of practical scenarios, although classifiers learned in source domains are readily available. In this work, we target data-free UMDA where source data are not observable at all, a novel problem that has not been studied before despite being very realistic and crucial. To solve data-free UMDA, we propose DEMS (Data-free Exploitation of Multiple Sources), a novel architecture that adapts target data to source domains without exploiting any source data, and estimates the target labels by exploiting pre-trained source classifiers. Extensive experiments for data-free UMDA on real-world datasets show that DEMS provides the state-of-the-art accuracy which is up to 27.5% point higher than that of the best baseline.

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Kang, Byung Ok, Hyeong Bae Jeon, and Jeon Gue Park. "Speech Recognition for Task Domains with Sparse Matched Training Data." Applied Sciences 10, no. 18 (September 4, 2020): 6155. http://dx.doi.org/10.3390/app10186155.

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We propose two approaches to handle speech recognition for task domains with sparse matched training data. One is an active learning method that selects training data for the target domain from another general domain that already has a significant amount of labeled speech data. This method uses attribute-disentangled latent variables. For the active learning process, we designed an integrated system consisting of a variational autoencoder with an encoder that infers latent variables with disentangled attributes from the input speech, and a classifier that selects training data with attributes matching the target domain. The other method combines data augmentation methods for generating matched target domain speech data and transfer learning methods based on teacher/student learning. To evaluate the proposed method, we experimented with various task domains with sparse matched training data. The experimental results show that the proposed method has qualitative characteristics that are suitable for the desired purpose, it outperforms random selection, and is comparable to using an equal amount of additional target domain data.

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Li, Rumeng, Xun Wang, and Hong Yu. "MetaMT, a Meta Learning Method Leveraging Multiple Domain Data for Low Resource Machine Translation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (April 3, 2020): 8245–52. http://dx.doi.org/10.1609/aaai.v34i05.6339.

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Neural machine translation (NMT) models have achieved state-of-the-art translation quality with a large quantity of parallel corpora available. However, their performance suffers significantly when it comes to domain-specific translations, in which training data are usually scarce. In this paper, we present a novel NMT model with a new word embedding transition technique for fast domain adaption. We propose to split parameters in the model into two groups: model parameters and meta parameters. The former are used to model the translation while the latter are used to adjust the representational space to generalize the model to different domains. We mimic the domain adaptation of the machine translation model to low-resource domains using multiple translation tasks on different domains. A new training strategy based on meta-learning is developed along with the proposed model to update the model parameters and meta parameters alternately. Experiments on datasets of different domains showed substantial improvements of NMT performances on a limited amount of data.

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Silva, Amila, Ling Luo, Shanika Karunasekera, and Christopher Leckie. "Embracing Domain Differences in Fake News: Cross-domain Fake News Detection using Multi-modal Data." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 1 (May 18, 2021): 557–65. http://dx.doi.org/10.1609/aaai.v35i1.16134.

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With the rapid evolution of social media, fake news has become a significant social problem, which cannot be addressed in a timely manner using manual investigation. This has motivated numerous studies on automating fake news detection. Most studies explore supervised training models with different modalities (e.g., text, images, and propagation networks) of news records to identify fake news. However, the performance of such techniques generally drops if news records are coming from different domains (e.g., politics, entertainment), especially for domains that are unseen or rarely-seen during training. As motivation, we empirically show that news records from different domains have significantly different word usage and propagation patterns. Furthermore, due to the sheer volume of unlabelled news records, it is challenging to select news records for manual labelling so that the domain-coverage of the labelled dataset is maximised. Hence, this work: (1) proposes a novel framework that jointly preserves domain-specific and cross-domain knowledge in news records to detect fake news from different domains; and (2) introduces an unsupervised technique to select a set of unlabelled informative news records for manual labelling, which can be ultimately used to train a fake news detection model that performs well for many domains while minimizing the labelling cost. Our experiments show that the integration of the proposed fake news model and the selective annotation approach achieves state-of-the-art performance for cross-domain news datasets, while yielding notable improvements for rarely-appearing domains in news datasets.

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Darch, Peter T., and Christine L. Borgman. "Ship space to database: emerging infrastructures for studies of the deep subseafloor biosphere." PeerJ Computer Science 2 (November 14, 2016): e97. http://dx.doi.org/10.7717/peerj-cs.97.

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BackgroundAn increasing array of scientific fields face a “data deluge.” However, in many fields data are scarce, with implications for their epistemic status and ability to command funding. Consequently, they often attempt to develop infrastructure for data production, management, curation, and circulation. A component of a knowledge infrastructure may serve one or more scientific domains. Further, a single domain may rely upon multiple infrastructures simultaneously. Studying how domains negotiate building and accessing scarce infrastructural resources that they share with other domains will shed light on how knowledge infrastructures shape science.MethodsWe conducted an eighteen-month, qualitative study of scientists studying the deep subseafloor biosphere, focusing on the Center for Dark Energy Biosphere Investigations (C-DEBI) and the Integrated Ocean Drilling Program (IODP) and its successor, the International Ocean Discovery Program (IODP2). Our methods comprised ethnographic observation, including eight months embedded in a laboratory, interviews (n = 49), and document analysis.ResultsDeep subseafloor biosphere research is an emergent domain. We identified two reasons for the domain’s concern with data scarcity: limited ability to pursue their research objectives, and the epistemic status of their research. Domain researchers adopted complementary strategies to acquire more data. One was to establish C-DEBI as an infrastructure solely for their domain. The second was to use C-DEBI as a means to gain greater access to, and reconfigure, IODP/IODP2 to their advantage. IODP/IODP2 functions as infrastructure for multiple scientific domains, which creates competition for resources. C-DEBI is building its own data management infrastructure, both to acquire more data from IODP and to make better use of data, once acquired.DiscussionTwo themes emerge. One is data scarcity, which can be understood only in relation to a domain’s objectives. To justify support for public funding, domains must demonstrate their utility to questions of societal concern or existential questions about humanity. The deep subseafloor biosphere domain aspires to address these questions in a more statistically intensive manner than is afforded by the data to which it currently has access. The second theme is the politics of knowledge infrastructures. A single scientific domain may build infrastructure for itself and negotiate access to multi-domain infrastructure simultaneously. C-DEBI infrastructure was designed both as a response to scarce IODP/IODP2 resources, and to configure the data allocation processes of IODP/IODP2 in their favor.

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Nakahira, Katsuko T., Yoshiki Mikami, Hiroyuki Namba, Minehiro Takeshita, and Shigeaki Kodama. "Country domain governance: an analysis by data-mining of country domains." Artificial Life and Robotics 16, no. 3 (December 2011): 311–14. http://dx.doi.org/10.1007/s10015-011-0937-5.

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Dissertations / Theses on the topic "Data Domains"

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Crockett, Keeley Alexandria. "Fuzzy rule induction from data domains." Thesis, Manchester Metropolitan University, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.243720.

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McLean, David. "Improving generalisation in continuous data domains." Thesis, University of Manchester, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.283816.

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Hsu, Bo-June (Bo-June Paul). "Language Modeling for limited-data domains." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/52796.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.
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 (p. 99-109).
With the increasing focus of speech recognition and natural language processing applications on domains with limited amount of in-domain training data, enhanced system performance often relies on approaches involving model adaptation and combination. In such domains, language models are often constructed by interpolating component models trained from partially matched corpora. Instead of simple linear interpolation, we introduce a generalized linear interpolation technique that computes context-dependent mixture weights from features that correlate with the component confidence and relevance for each n-gram context. Since the n-grams from partially matched corpora may not be of equal relevance to the target domain, we propose an n-gram weighting scheme to adjust the component n-gram probabilities based on features derived from readily available corpus segmentation and metadata to de-emphasize out-of-domain n-grams. In scenarios without any matched data for a development set, we examine unsupervised and active learning techniques for tuning the interpolation and weighting parameters. Results on a lecture transcription task using the proposed generalized linear interpolation and n-gram weighting techniques yield up to a 1.4% absolute word error rate reduction over a linearly interpolated baseline language model. As more sophisticated models are only as useful as they are practical, we developed the MIT Language Modeling (MITLM) toolkit, designed for efficient iterative parameter optimization, and released it to the research community.
(cont.) With a compact vector-based n-gram data structure and optimized algorithm implementations, the toolkit not only improves the running time of common tasks by up to 40x, but also enables the efficient parameter tuning for language modeling techniques that were previously deemed impractical.
by Bo-June (Paul) Hsu.
Ph.D.

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MAHOTO, NAEEM AHMED. "Data mining techniques for complex application domains." Doctoral thesis, Politecnico di Torino, 2013. http://hdl.handle.net/11583/2506368.

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The emergence of advanced communication techniques has increased availability of large collection of data in electronic form in a number of application domains including healthcare, e- business, and e-learning. Everyday a large amount of records are stored electronically. However, finding useful information from such a large data collection is a challenging issue. Data mining technology aims automatically extracting hidden knowledge from large data repositories exploiting sophisticated algorithms. The hidden knowledge in the electronic data may be potentially utilized to facilitate the procedures, productivity, and reliability of several application domains. The PhD activity has been focused on novel and effective data mining approaches to tackle the complex data coming from two main application domains: Healthcare data analysis and Textual data analysis. The research activity, in the context of healthcare data, addressed the application of different data mining techniques to discover valuable knowledge from real exam-log data of patients. In particular, efforts have been devoted to the extraction of medical pathways, which can be exploited to analyze the actual treatments followed by patients. The derived knowledge not only provides useful information to deal with the treatment procedures but may also play an important role in future predictions of potential patient risks associated with medical treatments. The research effort in textual data analysis is twofold. On the one hand, a novel approach to discovery of succinct summaries of large document collections has been proposed. On the other hand, the suitability of an established descriptive data mining to support domain experts in making decisions has been investigated. Both research activities are focused on adopting widely exploratory data mining techniques to textual data analysis, which require overcoming intrinsic limitations for traditional algorithms for handling textual documents efficiently and effectively.

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RICUPERO, GIUSEPPE. "Exploring Data Hierarchies to Discover Knowledge in Different Domains." Doctoral thesis, Politecnico di Torino, 2019. http://hdl.handle.net/11583/2744938.

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Carapelle, Claudia. "On the Satisfiability of Temporal Logics with Concrete Domains." Doctoral thesis, Universitätsbibliothek Leipzig, 2015. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-190987.

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Temporal logics are a very popular family of logical languages, used to specify properties of abstracted systems. In the last few years, many extensions of temporal logics have been proposed, in order to address the need to express more than just abstract properties. In our work we study temporal logics extended by local constraints, which allow to express quantitative properties on data values from an arbitrary relational structure called the concrete domain. An example of concrete domain can be (Z, <, =), where the integers are considered as a relational structure over the binary order relation and the equality relation. Formulas of temporal logics with constraints are evaluated on data-words or data-trees, in which each node or position is labeled by a vector of data from the concrete domain. We call the constraints local because they can only compare values at a fixed distance inside such models. Several positive results regarding the satisfiability of LTL (linear temporal logic) with constraints over the integers have been established in the past years, while the corresponding results for branching time logics were only partial. In this work we prove that satisfiability of CTL* (computation tree logic) with constraints over the integers is decidable and also lift this result to ECTL*, a proper extension of CTL*. We also consider other classes of concrete domains, particularly ones that are \"tree-like\". We consider semi-linear orders, ordinal trees and trees of a fixed height, and prove decidability in this framework as well. At the same time we prove that our method cannot be applied in the case of the infinite binary tree or the infinitely branching infinite tree. We also look into extending the expressiveness of our logic adding non-local constraints, and find that this leads to undecidability of the satisfiability problem, even on very simple domains like (Z, <, =). We then find a way to restrict the power of the non-local constraints to regain decidability.

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McGregor, Simon. "Artificial neural networks for novel data domains : principles and examples." Thesis, University of Sussex, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.497000.

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I assume that the reader of this thesis is reasonably familiar with artificial neural network (ANN) methods in computer science, including the multi-layer perceptron (ML?) and the backpropagation training method. I have not needed to use any difficult or esoteric mathematics; the major mathematical concept encountered in the thesis is the multiset (which is easy to grasp for anyone familiar with set theory). Certain chapters also make use of the notions of partial derivatives. inner products in arbitrary vector spaces, and metrics.

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Ng, Siu Hung. "An extension of the relational data model to incorporate ordered domains." Thesis, University College London (University of London), 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.268033.

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Baxter, Rolf Hugh. "Recognising high-level agent behaviour through observations in data scarce domains." Thesis, Heriot-Watt University, 2012. http://hdl.handle.net/10399/2597.

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This thesis presents a novel method for performing multi-agent behaviour recognition without requiring large training corpora. The reduced need for data means that robust probabilistic recognition can be performed within domains where annotated datasets are traditionally unavailable (e.g. surveillance, defence). Human behaviours are composed from sequences of underlying activities that can be used as salient features. We do not assume that the exact temporal ordering of such features is necessary, so can represent behaviours using an unordered “bag-of-features”. A weak temporal ordering is imposed during inference to match behaviours to observations and replaces the learnt model parameters used by competing methods. Our three-tier architecture comprises low-level video tracking, event analysis and high-level inference. High-level inference is performed using a new, cascading extension of the Rao-Blackwellised Particle Filter. Behaviours are recognised at multiple levels of abstraction and can contain a mixture of solo and multiagent behaviour. We validate our framework using the PETS 2006 video surveillance dataset and our own video sequences, in addition to a large corpus of simulated data. We achieve a mean recognition precision of 96.4% on the simulated data and 89.3% on the combined video data. Our “bag-of-features” framework is able to detect when behaviours terminate and accurately explains agent behaviour despite significant quantities of low-level classification errors in the input, and can even detect agents who change their behaviour.

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Ferguson, Alexander B. "Higher order strictness analysis by abstract interpretation over finite domains." Thesis, University of Glasgow, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.308143.

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Books on the topic "Data Domains"

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Sychev, Alexander, Sergey Makhortov, and Bernhard Thalheim, eds. Data Analytics and Management in Data Intensive Domains. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81200-3.

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Pozanenko, Alexei, Sergey Stupnikov, Bernhard Thalheim, Eva Mendez, and Nadezhda Kiselyova, eds. Data Analytics and Management in Data Intensive Domains. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-12285-9.

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Kalinichenko, Leonid, Yannis Manolopoulos, Oleg Malkov, Nikolay Skvortsov, Sergey Stupnikov, and Vladimir Sukhomlin, eds. Data Analytics and Management in Data Intensive Domains. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96553-6.

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Elizarov, Alexander, Boris Novikov, and Sergey Stupnikov, eds. Data Analytics and Management in Data Intensive Domains. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-51913-1.

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Kalinichenko, Leonid, Sergei O. Kuznetsov, and Yannis Manolopoulos, eds. Data Analytics and Management in Data Intensive Domains. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57135-5.

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Manolopoulos, Yannis, and Sergey Stupnikov, eds. Data Analytics and Management in Data Intensive Domains. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-23584-0.

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Palmer, Martha Stone. Semantic processing for finite domains. Cambridge [England]: Cambridge University Press, 1990.

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Kathleen, Hornsby, Yuan May, and University Consortium for Geographic Information Science., eds. Understanding dynamics of geographic domains. Boca Raton: Taylor & Francis, 2008.

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Furtado, Pedro Nuno San-Banto, 1968-, ed. Evolving application domains of data warehousing and mining: Trends and solutions. Hershey, PA: Information Science Reference, 2010.

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Furtado, Pedro Nuno San-Banto, 1968-, ed. Evolving application domains of data warehousing and mining: Trends and solutions. Hershey, PA: Information Science Reference, 2010.

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Book chapters on the topic "Data Domains"

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Gold, Alex K. "Domains and DNS." In DevOps for Data Science, 169–76. Boca Raton: Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003213345-13.

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Gawlitzek-Maiwald, Ira. "Approaches to bilingual acquisition data." In (In)vulnerable Domains in Multilingualism, 139–59. Amsterdam: John Benjamins Publishing Company, 2003. http://dx.doi.org/10.1075/hsm.1.06gaw.

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Ristoski, Petar. "Towards Linked Open Data Enabled Data Mining." In The Semantic Web. Latest Advances and New Domains, 772–82. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18818-8_50.

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Matter, Ulrich. "The Two Domains of Big Data Analytics." In Big Data Analytics, 13–24. Boca Raton: Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003378822-3.

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Hughes, John. "Strictness detection in non-flat domains." In Programs as Data Objects, 112–35. Berlin, Heidelberg: Springer Berlin Heidelberg, 1986. http://dx.doi.org/10.1007/3-540-16446-4_7.

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Bertram, Martin, Mark A. Duchaineau, Bernd Hamann, and Kenneth I. Joy. "Generalizing Lifted Tensor-Product Wavelets to Irregular Polygonal Domains." In Data Visualization, 289–300. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-1177-9_20.

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Qu, Zhaowei, Yanjiao Zhao, Xiaoru Wang, and Chunye Wu. "A Generic Model Based on Multiple Domains for Sentiment Classification." In Data Mining and Big Data, 393–403. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93803-5_37.

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Wu, Xiaofeng, Peter Lucas, Susan Kerr, and Roelf Dijkhuizen. "Learning Bayesian-Network Topologies in Realistic Medical Domains." In Medical Data Analysis, 302–7. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45497-7_46.

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ter Hofstede, A. H. M., H. A. Proper, and Th P. van der Weide. "Data modelling in complex application domains." In Notes on Numerical Fluid Mechanics and Multidisciplinary Design, 364–77. Cham: Springer International Publishing, 1992. http://dx.doi.org/10.1007/bfb0035142.

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Antiga, Luca, Joaquim Peiró, and David A. Steinman. "From image data to computational domains." In Cardiovascular Mathematics, 123–75. Milano: Springer Milan, 2009. http://dx.doi.org/10.1007/978-88-470-1152-6_4.

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Conference papers on the topic "Data Domains"

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Shieh, Han-Ping D., and Mark H. Kryder. "The effects of wall stiffness on reproducibility of thermo-magnetically written domains*." In Optical Data Storage. Washington, D.C.: Optica Publishing Group, 1985. http://dx.doi.org/10.1364/ods.1985.waa4.

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The reproducibility of thermomagnetically written domains has been studied as a function of writing parameters and intrinsic magnetic parameters of magneto-optic films. Films with higher anisotropy constant, smaller aspect ratio, higher coercivity, and compensation temperature near or above room temperature result in a higher degree of domain reproducibility. It is shown that a wall stiffness model is consistent with the experimental results. Larger wall stiffness makes the domains less deformable from an equilibrium value and consequently results in domains which are more regular in size and shape.

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Wu, Jun, Hanghang Tong, Elizabeth Ainsworth, and Jingrui He. "Adaptive Knowledge Transfer on Evolving Domains." In 2022 IEEE International Conference on Big Data (Big Data). IEEE, 2022. http://dx.doi.org/10.1109/bigdata55660.2022.10020944.

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Li, Xueni, Guanggang Geng, Zhiwei Yan, Yong Chen, and Xiaodong Lee. "Phishing detection based on newly registered domains." In 2016 IEEE International Conference on Big Data (Big Data). IEEE, 2016. http://dx.doi.org/10.1109/bigdata.2016.7841036.

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Ruane, M., S. Gadetsky, and M. Mansuripur. "Observation of Domains and Grooves on MO Disks with Optical Microscopy and Image Processing." In Optical Data Storage. Washington, D.C.: Optica Publishing Group, 1994. http://dx.doi.org/10.1364/ods.1994.wd8.

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Simple, nondestructive observation of grooves and magnetic domain structures is important to improved MO disk quality control, performance evaluation, and development of novel disk systems. Direct observation is difficult because groove and domain dimensions are on the order of one wavelength of visible light and, therefore, at the diffraction limit of optical microscopy. Complicating the matter further are the facts that illumination is somewhat non-uniform, contrast is low, and observations must be made through the substrate. A conventional metallurgical microscope has been modified to allow observation of grooves and domains on standard MO disks, using digital image processing to enhance otherwise obscure features.

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Pine, Kathleen, Claus Bossen, Naja Holten Møller, Milagros Miceli, Alex Jiahong Lu, Yunan Chen, Leah Horgan, Zhaoyuan Su, Gina Neff, and Melissa Mazmanian. "Investigating Data Work Across Domains." In CHI '22: CHI Conference on Human Factors in Computing Systems. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3491101.3503724.

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Silveira, Marcos Rogerio, Leandro Marcos da Silva, Adriano Mauro Cansian, and Hugo Koji Kobayashi. "Detection of Newly Registered Malicious Domains through Passive DNS." In 2021 IEEE International Conference on Big Data (Big Data). IEEE, 2021. http://dx.doi.org/10.1109/bigdata52589.2021.9671348.

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Dunbar, DeJean. "Survey of Secure Network Protocols: United States Related Domains." In 3rd International Conference on Artificial Intelligence and Machine Learning (CAIML 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.121207.

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Over time, the HTTP Protocol has undergone significant evolution. HTTP was the internet's foundation for data communication. When network security threats became prevalent, HTTPS became a widely accepted technology for assisting in a domain’s defense. HTTPS supported two security protocols: secure socket layer (SSL) and transport layer security (TLS). Additionally, the HTTP Strict Transport Security (HSTS) protocol was included to strengthen the HTTPS protocol. Numerous cyber-attacks occurred in the United States, and many of these attacks could have been avoided simply by implementing domains with the most up-to-date HTTP security mechanisms. This study seeks to accomplish two objectives: 1. Determine the degree to which US-related domains are configured optimally for HTTP security protocol setup; 2. Create a generic scoring system for a domain's network security based on the following factors: SSL version, TLS version, and presence of HSTS to easily determine where a domain stands. We found through our analysis and scoring system incorporation that US-related domains showed a positive trend for secure network protocol setup, but there is still room for improvement. In order to safeguard unwanted cyber-attacks, current HTTP domains need to be extensively investigated to identify if they possess security-related components. Due to the infrequent occurrence of HSTS in the evaluated domains, the computer science community necessitates further HSTS education.

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Hoover, Christopher. "A Data-Centric Approach for Integrated Data Center Management." In ASME 2011 Pacific Rim Technical Conference and Exhibition on Packaging and Integration of Electronic and Photonic Systems. ASMEDC, 2011. http://dx.doi.org/10.1115/ipack2011-52159.

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In this paper, we present the Daffy data model and messaging framework for data centers. The model is a hybrid model, combining physical, structural, geometrical, and logical modeling techniques. The messaging scheme has excellent performance and allows for the loose coupling of the various framework components. The framework bridges the gap between facilities and IT domains and enables the holistic, cross-domain management of data centers. The framework supports rich visualization, cross-domain queries and sophisticated cross-domain autonomic control systems.

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Taddeo, Marco, Alberto Trombetta, Danilo Montesi, and Stefano Pierantozzi. "Querying data across different legal domains." In the 17th International Database Engineering & Applications Symposium. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2513591.2513642.

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Lawrence, Michael, Rachel Pottinger, and Sheryl Staub-French. "Coordination of data in heterogenous domains." In 2010 IEEE 26th International Conference on Data Engineering Workshops (ICDEW 2010). IEEE, 2010. http://dx.doi.org/10.1109/icdew.2010.5452757.

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Reports on the topic "Data Domains"

1

Steenkamp, H. M., N. Wodicka, O. M. Weller, J. Kendrick, I. Therriault, T. Peterson, C. J M Lawley, and V. Tschirhart. Bedrock geology, Wager Bay area, Kivalliq, Nunavut, parts of NTS 56-F, G. Natural Resources Canada/CMSS/Information Management, 2023. http://dx.doi.org/10.4095/331890.

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New geological mapping in the Tehery Lake-Wager Bay area of northwestern Hudson Bay, Nunavut, frames the emplacement, depositional, and metamorphic histories of the dominant rock types, major structures, and links to neighbouring areas of the central Rae Craton and Chesterfield Block. The area is divided into six domains (Ukkusiksalik, Douglas Harbour, Gordon, and Lunan domains presented here, and Kummel Lake Domain and Daly Bay Complex on adjoining maps) defined by large-scale structures and characterized by differing metamorphic assemblages, Sm-Nd and U-Pb isotopic data, and/or specific lithologies. Meso- to Neoarchean granitoid rocks underlie most of the area and are tectonically intercalated with Archean (volcano)sedimentary packages (Kummel Lake, Lorillard, and Paliak belts). These rocks are locally intruded by ca. 2.62 to 2.58 Ga Snow Island suite granite and cut by younger, thin, east-trending diabase dykes. Paleoproterozoic (volcano)sedimentary rocks are preserved in the Kingmirit belt (Daly Bay Complex) and in basement-cover infolds of Ketyet River group-equivalent strata (Douglas Harbour and Ukkusiksalik domains). In the south, the Daly Bay Complex (comprising mostly mafic granulite-facies rocks) and Kummel Lake Domain (a granulite-grade core complex) share some characteristics with rocks of the Kramanituar and Uvauk complexes, which may delineate the northeastern segment of the ca. 1.90 Ga Snowbird tectonic zone. The Paleoproterozoic Trans-Hudson Orogeny had widespread, penetrative structural and metamorphic effects on the area, and led to the intrusion of the ca. 1.85 to 1.81 Ga Hudson suite monzogranite and mafic ultrapotassic rocks, and ca. 1.83 Ga monzodiorite in the Ukkusiksalik and Douglas Harbour domains. The area is cut by large, southeast-trending gabbro dykes of the 1.267 Ga Mackenzie igneous event.

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Steenkamp, H. M., N. Wodicka, C. J M Lawley, T. Peterson, W. Garrison, I. Therriault, J. Kendrick, O. M. Weller, and V. Tschirhart. Bedrock geology, Daly Bay area, Kivalliq, Nunavut, NTS 56-A, 46-D west, 46-E southwest, and 56-H south. Natural Resources Canada/CMSS/Information Management, 2023. http://dx.doi.org/10.4095/331888.

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New geological mapping in the Tehery Lake-Wager Bay area of northwestern Hudson Bay, Nunavut, frames the emplacement, depositional, and metamorphic histories of the dominant rock types, major structures, and links to neighbouring areas of the central Rae Craton and Chesterfield Block. The area is divided into six domains (Ukkusiksalik, Douglas Harbour, and Gordon domains and Daly Bay Complex presented here, and Lunan and Kummel Lake domains on adjoining maps) defined by large-scale structures and characterized by differing metamorphic assemblages, Sm-Nd and U-Pb isotopic data, and/or specific lithologies. Meso- to Neoarchean granitoid rocks underlie most of the area and are tectonically intercalated with Archean (volcano)sedimentary packages (Kummel Lake, Lorillard, and Paliak belts). These rocks are locally intruded by ca. 2.62 to 2.58 Ga Snow Island suite granite and cut by younger, thin, east-trending diabase dykes. Paleoproterozoic (volcano)sedimentary rocks are preserved in the Kingmirit belt (Daly Bay Complex) and in basement-cover infolds of Ketyet River group-equivalent strata (Douglas Harbour and Ukkusiksalik domains). In the south, the Daly Bay Complex (comprising mostly mafic granulite-facies rocks) and Kummel Lake Domain (a granulite-grade core complex) share some characteristics with rocks of the Kramanituar and Uvauk complexes, which may delineate the northeastern segment of the ca. 1.90 Ga Snowbird tectonic zone. The Paleoproterozoic Trans-Hudson Orogeny had widespread, penetrative structural and metamorphic effects on the area, and led to the intrusion of the ca. 1.85 to 1.81 Ga Hudson suite monzogranite and mafic ultrapotassic rocks, and ca. 1.83 Ga monzodiorite in the Ukkusiksalik and Douglas Harbour domains. The area is cut by large, southeast-trending gabbro dykes of the 1.267 Ga Mackenzie igneous event.

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3

Steenkamp, H. M., N. Wodicka, C. J M Lawley, T. Peterson, O. M. Weller, J. Kendrick, and V. Tschirhart. Bedrock geology, Armit Lake area, Kivalliq, Nunavut, NTS 56-B and 56-C east. Natural Resources Canada/CMSS/Information Management, 2023. http://dx.doi.org/10.4095/331889.

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New geological mapping in the Tehery Lake-Wager Bay area of northwestern Hudson Bay, Nunavut, frames the emplacement, depositional, and metamorphic histories of the dominant rock types, major structures, and links to neighbouring areas of the central Rae Craton and Chesterfield Block. The area is divided into six domains (Gordon, Lunan, and Kummel Lake domains presented here, and Ukkusiksalik and Douglas Harbour domains and Daly Bay Complex on adjoining maps) defined by large-scale structures and characterized by differing metamorphic assemblages, Sm-Nd and U-Pb isotopic data, and/or specific lithologies. Meso- to Neoarchean granitoid rocks underlie most of the area and are tectonically intercalated with Archean (volcano)sedimentary packages (Kummel Lake, Lorillard, and Paliak belts). These rocks are locally intruded by ca. 2.62 to 2.58 Ga Snow Island suite granite and cut by younger, thin, east-trending diabase dykes. Paleoproterozoic (volcano)sedimentary rocks are preserved in the Kingmirit belt (Daly Bay Complex) and in basement-cover infolds of Ketyet River group-equivalent strata (Douglas Harbour and Ukkusiksalik domains). In the south, the Daly Bay Complex (comprising mostly mafic granulite-facies rocks) and Kummel Lake Domain (a granulite-grade core complex) share some characteristics with rocks of the Kramanituar and Uvauk complexes, which may delineate the northeastern segment of the ca. 1.90 Ga Snowbird tectonic zone. The Paleoproterozoic Trans-Hudson Orogeny had widespread, penetrative structural and metamorphic effects on the area, and led to the intrusion of the ca. 1.85 to 1.81 Ga Hudson suite monzogranite and mafic ultrapotassic rocks, and ca. 1.83 Ga monzodiorite in the Ukkusiksalik and Douglas Harbour domains. The area is cut by large, southeast-trending gabbro dykes of the 1.267 Ga Mackenzie igneous event.

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Woyczynski, Lauren, Christina Misunas, and Md Irfan Hossain. Building the Adolescent Indicators and Gender Gaps Dashboard. Population Council, 2022. http://dx.doi.org/10.31899/sbsr2022.1014.

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The Adolescent Atlas for Action (A3) is a suite of tools that summarizes the lives and needs of adolescents around the world to promote evidence-based decision-making. Through accessible and easy-to-grasp data just one click away, the A3 bridges the gap between decisionmakers and evidence to inform policies and programs. The Adolescent Indicators dashboard and Gender Gaps dashboard are two simple but dynamic dashboards that provide insights on the lives of adolescent girls and boys living in low- and middle income countries (LMICs) across 9 thematic domains of wellbeing. The Adolescent Indicators dashboard showcases how adolescent girls or boys are faring under each theme, globally and subnationally, as well as how the indicators across domains are interlinked. The Gender Gaps dashboard allows you to compare gaps by domain and indicator, both globally and subnationally. This brief elaborates on the methodology for developing both dashboards.

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Velez, Gladis, and Ragvi Shah. Reorienting Smart City Metrics to Emphasize Resident Well-Being: A Disparity-Oriented Approach. University of Miami, 2022. http://dx.doi.org/10.33596/report-1.

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This paper applies a disparity-oriented focus to promote human-centered solutions to smart city planning efforts. For five metropolitan areas (San Jose, Miami, New York, Denver, and Seattle) we explored three smart city domains (socioeconomics, public transit access, and digital divide), identified candidate indicators for each domain using publicly available data, and mapped composite measures generated using principal components analysis. The study identifies areas that may be most and least likely to benefit from smart city investments. Reorienting solutions can ultimately increase community equity and engagement in urban life.

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Hagedorn, G., R. Paulen, R. Fortin, and E. Arnaud. Radiometric domains and the integration of multiple gamma-ray data sources for a remote area of northern Quebec. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2017. http://dx.doi.org/10.4095/306142.

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Hagedorn, G., R. C. Paulen, R. Fortin, and E. Arnaud. Radiometric domains and the integration of multiple gamma-ray data sources for a remote area of northern Quebec. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2018. http://dx.doi.org/10.4095/308209.

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Lora, Eduardo. The Distance between Perception and Reality in the Social Domains of Life. Inter-American Development Bank, August 2013. http://dx.doi.org/10.18235/0011489.

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The distance between perception and reality with respect to the social domains of life is often striking. Using survey data collected on Latin American countries, this paper provides an overview of the main empirical findings on the gaps between perception and reality in four social domains--health, employment, the perception of security, and social ranking. The overview emphasizes the psychological biases that may explain the gaps. Biases associated with cultural values are very relevant with respect to health and job satisfaction. Cultural differences across countries are pronounced in perceptions of health, while cultural differences across socioeconomic groups are more apparent with respect to job satisfaction. Affect and availability heuristics are the dominant sources of bias in the case of perceptions of security. The formation of subjective social rankings appears to be less culturally dependent but more dependent on the socioeconomic development in the country. The gaps between objective and subjective indicators in the social domains of life are a rich source of data to help understand how perceptions are formed, identify important aspects of people's lives that do not appear in official indicators, inform public debate on social policy, and shed light on public attitudes on key social issues.

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Henmi, Teizi. Evaluation Study of Mesoscale Models MM5 and BFM Over the Model Domains of Utah Using Surface Meteorological Data by Mesowest. Fort Belvoir, VA: Defense Technical Information Center, March 2003. http://dx.doi.org/10.21236/ada411840.

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Grumet, Rebecca, and Benjamin Raccah. Identification of Potyviral Domains Controlling Systemic Infection, Host Range and Aphid Transmission. United States Department of Agriculture, July 2000. http://dx.doi.org/10.32747/2000.7695842.bard.

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Potyviruses form one of the largest and most economically important groups of plant viruses. Individual potyviruses and their isolates vary in symptom expression, host range, and ability to overcome host resistance genes. Understanding factors influencing these biological characteristics is of agricultural importance for epidemiology and deployment of resistance strategies. Cucurbit crops are subject to severe losses by several potyviruses including the highly aggressive and variable zucchini yellow mosaic virus (ZYMV). In this project we sought to investigate protein domains in ZYMV that influence systemic infection and host range. Particular emphasis was on coat protein (CP), because of known functions in both cell to cell and long distance movement, and helper component-protease (HC-Pro), which has been implicated to play a role in symptom development and long distance movement. These two genes are also essential for aphid mediated transmission, and domains that influence disease development may also influence transmissibility. The objectives of the approved BARD project were to test roles of specific domains in the CP and HC-Pro by making sequence alterations or switches between different isolates and viruses, and testing for infectivity, host range, and aphid transmissibility. These objectives were largely achieved as described below. Finally, we also initiated new research to identify host factors interacting with potyviral proteins and demonstrated interaction between the ZYMV RNA dependent RNA polymerase and host poly-(A)-binding protein (Wang et al., in press). The focus of the CP studies (MSU) was to investigate the role of the highly variable amino terminus (NT) in host range determination and systemic infection. Hybrid ZYMV infectious clones were produced by substituting the CP-NT of ZYMV with either the CP-NT from watermelon mosaic virus (overlapping, but broader host range) or tobacco etch virus (TEV) (non- overlapping host range) (Grumet et al., 2000; Ullah ct al., in prep). Although both hybrid viruses initially established systemic infection, indicating that even the non-cucurbit adapted TEV CP-NT could facilitate long distance transport in cucurbits, after approximately 4-6, the plants inoculated with the TEV-CPNT hybrid exhibited a distinct recovery of reduced symptoms, virus titer, and virus specific protection against secondary infection. These results suggest that the plant recognizes the presence of the TEV CP-NT, which has not been adapted to infection of cucurbits, and initiates defense responses. The CP-NT also appears to play a role in naturally occurring resistance conferred by the zym locus in the cucumber line 'Dina-1'. Patterns of virus accumulation indicated that expression of resistance is developmentally controlled and is due to a block in virus movement. Switches between the core and NT domains of ZYMV-NAA (does not cause veinal chlorosis on 'Dina-1'), and ZYMV-Ct (causes veinal chlorosis), indicated that the resistance response likely involves interaction with the CP-NT (Ullah and Grumet, submitted). At the Volcani Center the main thrust was to identify domains in the HC-Pro that affect symptom expression or aphid transmissibility. From the data reported in the first and second year report and in the attached publications (Peng et al. 1998; Kadouri et al. 1998; Raccah et al. 2000: it was shown that: 1. The mutation from PTK to PAK resulted in milder symptoms of the virus on squash, 2. Two mutations, PAK and ATK, resulted in total loss of helper activity, 3. It was established for the first time that the PTK domain is involved in binding of the HC-Pro to the potyvirus particle, and 4. Some of these experiments required greater amount of HC-Pro, therefore a simpler and more efficient purification method was developed based on Ni2+ resin.

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