Relevant bibliographies by topics / Data patterns

Contents

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

Academic literature on the topic 'Data patterns'

Author: Grafiati
Published: 10 January 2023
Last updated: 28 January 2023

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Journal articles on the topic "Data patterns"

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S., Sivaranjani. "Detecting Congestion Patterns in Spatio Temporal Traffic Data Using Frequent Pattern Mining." Bonfring International Journal of Networking Technologies and Applications 5, no. 1 (March 30, 2018): 21–23. http://dx.doi.org/10.9756/bijnta.8372.

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McGuirl, Melissa R., Alexandria Volkening, and Björn Sandstede. "Topological data analysis of zebrafish patterns." Proceedings of the National Academy of Sciences 117, no. 10 (February 25, 2020): 5113–24. http://dx.doi.org/10.1073/pnas.1917763117.

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Self-organized pattern behavior is ubiquitous throughout nature, from fish schooling to collective cell dynamics during organism development. Qualitatively these patterns display impressive consistency, yet variability inevitably exists within pattern-forming systems on both microscopic and macroscopic scales. Quantifying variability and measuring pattern features can inform the underlying agent interactions and allow for predictive analyses. Nevertheless, current methods for analyzing patterns that arise from collective behavior capture only macroscopic features or rely on either manual inspection or smoothing algorithms that lose the underlying agent-based nature of the data. Here we introduce methods based on topological data analysis and interpretable machine learning for quantifying both agent-level features and global pattern attributes on a large scale. Because the zebrafish is a model organism for skin pattern formation, we focus specifically on analyzing its skin patterns as a means of illustrating our approach. Using a recent agent-based model, we simulate thousands of wild-type and mutant zebrafish patterns and apply our methodology to better understand pattern variability in zebrafish. Our methodology is able to quantify the differential impact of stochasticity in cell interactions on wild-type and mutant patterns, and we use our methods to predict stripe and spot statistics as a function of varying cellular communication. Our work provides an approach to automatically quantifying biological patterns and analyzing agent-based dynamics so that we can now answer critical questions in pattern formation at a much larger scale.
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Singh, Sakshi, Harsh Mittal, and Archana Purwar. "Prediction of Investment Patterns Using Data Mining Techniques." International Journal of Computer and Communication Engineering 3, no. 2 (2014): 145–48. http://dx.doi.org/10.7763/ijcce.2014.v3.309.

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Zvyagin, L. S. "DATA MINING: BIG DATA AND DATA SCIENCE." SOFT MEASUREMENTS AND COMPUTING 5, no. 54 (2022): 81–90. http://dx.doi.org/10.36871/2618-9976.2022.05.006.

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Data mining is the process of discovering information that can be used in large amounts of data. This method uses mathematical analysis, which helps to identify patterns and trends in the data. Such patterns cannot be noticed during normal data viewing due to the complexity of the relationships that arise with a large amount of data. All of them are a set of tools and methods that help humanity in the changing world around us. It is becoming more and more voluminous, we receive huge aggregates of data on various processes. Big Data and Data Science allow large companies to systematize information about the markets in which they operate, which allows them to get a large amount of profit and benefits.
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Liu, Shihu, Li Deng, Haiyan Gao, and Xueyu Ma. "Relative Entropy-Based Similarity for Patterns in Graph Data." Wireless Communications and Mobile Computing 2022 (July 26, 2022): 1–20. http://dx.doi.org/10.1155/2022/7490656.

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How to make a correct similarity between patterns is a groundwork in data mining, especially for graph data. Despite these methods that can obtain great results, there may be still some limitations, for instance, the similarity of patterns in directed weighted graph data. Here, we introduce a new approach by taking the so-called the second-order neighbors into consideration. The proposed new similarity approach is named as relative entropy-based similarity for patterns in graph data, wherein the relative entropy provides a brand new aspect to make the difference between patterns in directed weighted graph data. The proposed similarity measure can be partitioned under three phases. First of all, strength set is given by degree and weight of patterns; in this phase, four variables holding the strength about out-degree, in-degree, out-weight, and in-weight are constructed. Then, with the help of Euclidean metric, pattern’s probability set is constructed, which contains influence of similarity between pattern and its all one-order neighbors. Finally, relative entropy is used to measure the difference between patterns. In order to examine the validity of our approach as well as its advantage comparing with the state-of-art approach, two sorts of experiments are suggested for real-world and synthetic graph data. The outcomes of experiment indicate that the recommended method get handy execution done measuring similarity and gain accurate results.
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Batra, Dinesh. "Conceptual Data Modeling Patterns." Journal of Database Management 16, no. 2 (April 2005): 84–106. http://dx.doi.org/10.4018/jdm.2005040105.

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Wagner, Peter, Ragna Hoffmann, Marek Junghans, Andreas Leich, and Hagen Saul. "Visualizing crash data patterns." Transactions on Transport Sciences 11, no. 2 (September 11, 2020): 77–83. http://dx.doi.org/10.5507/tots.2020.008.

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Labra Gayo, Jose Emilio, Dimitris Kontokostas, and Sören Auer. "Multilingual linked data patterns." Semantic Web 6, no. 4 (August 7, 2015): 319–37. http://dx.doi.org/10.3233/sw-140136.

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Muley, Abhinav, and Manish Gudadhe. "Synthesizing High-Utility Patterns from Different Data Sources." Data 3, no. 3 (September 3, 2018): 32. http://dx.doi.org/10.3390/data3030032.

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In large organizations, it is often required to collect data from the different geographic branches spread over different locations. Extensive amounts of data may be gathered at the centralized location in order to generate interesting patterns via mono-mining the amassed database. However, it is feasible to mine the useful patterns at the data source itself and forward only these patterns to the centralized company, rather than the entire original database. These patterns also exist in huge numbers, and different sources calculate different utility values for each pattern. This paper proposes a weighted model for aggregating the high-utility patterns from different data sources. The procedure of pattern selection was also proposed to efficiently extract high-utility patterns in our weighted model by discarding low-utility patterns. Meanwhile, the synthesizing model yielded high-utility patterns, unlike association rule mining, in which frequent itemsets are generated by considering each item with equal utility, which is not true in real life applications such as sales transactions. Extensive experiments performed on the datasets with varied characteristics show that the proposed algorithm will be effective for mining very sparse and sparse databases with a huge number of transactions. Our proposed model also outperforms various state-of-the-art distributed models of mining in terms of running time.
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Zhou, C., W. D. Xiao, and D. Q. Tang. "MINING CO-LOCATION PATTERNS FROM SPATIAL DATA." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-2 (June 2, 2016): 85–90. http://dx.doi.org/10.5194/isprsannals-iii-2-85-2016.

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Due to the widespread application of geographic information systems (GIS) and GPS technology and the increasingly mature infrastructure for data collection, sharing, and integration, more and more research domains have gained access to high-quality geographic data and created new ways to incorporate spatial information and analysis in various studies. There is an urgent need for effective and efficient methods to extract unknown and unexpected information, e.g., co-location patterns, from spatial datasets of high dimensionality and complexity. A co-location pattern is defined as a subset of spatial items whose instances are often located together in spatial proximity. Current co-location mining algorithms are unable to quantify the spatial proximity of a co-location pattern. We propose a co-location pattern miner aiming to discover co-location patterns in a multidimensional spatial data by measuring the cohesion of a pattern. We present a model to measure the cohesion in an attempt to improve the efficiency of existing methods. The usefulness of our method is demonstrated by applying them on the publicly available spatial data of the city of Antwerp in Belgium. The experimental results show that our method is more efficient than existing methods.
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Dissertations / Theses on the topic "Data patterns"

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Voß, Jakob. "Describing data patterns." Doctoral thesis, Humboldt-Universität zu Berlin, Philosophische Fakultät I, 2013. http://dx.doi.org/10.18452/16794.

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Diese Arbeit behandelt die Frage, wie Daten grundsätzlich strukturiert und beschrieben sind. Im Gegensatz zu vorhandenen Auseinandersetzungen mit Daten im Sinne von gespeicherten Beobachtungen oder Sachverhalten, werden Daten hierbei semiotisch als Zeichen aufgefasst. Diese Zeichen werden in Form von digitalen Dokumenten kommuniziert und sind mittels zahlreicher Standards, Formate, Sprachen, Kodierungen, Schemata, Techniken etc. strukturiert und beschrieben. Diese Vielfalt von Mitteln wird erstmals in ihrer Gesamtheit mit Hilfe der phenomenologischen Forschungsmethode analysiert. Ziel ist es dabei, durch eine genaue Erfahrung und Beschreibung von Mitteln zur Strukturierung und Beschreibung von Daten zum allgemeinen Wesen der Datenstrukturierung und -beschreibung vorzudringen. Die Ergebnisse dieser Arbeit bestehen aus drei Teilen. Erstens ergeben sich sechs Prototypen, die die beschriebenen Mittel nach ihrem Hauptanwendungszweck kategorisieren. Zweitens gibt es fünf Paradigmen, die das Verständnis und die Anwendung von Mitteln zur Strukturierung und Beschreibung von Daten grundlegend beeinflussen. Drittens legt diese Arbeit eine Mustersprache der Datenstrukturierung vor. In zwanzig Mustern werden typische Probleme und Lösungen dokumentiert, die bei der Strukturierung und Beschreibung von Daten unabhängig von konkreten Techniken immer wieder auftreten. Die Ergebnisse dieser Arbeit können dazu beitragen, das Verständnis von Daten --- das heisst digitalen Dokumente und ihre Metadaten in allen ihren Formen --- zu verbessern. Spezielle Anwendungsgebiete liegen unter Anderem in den Bereichen Datenarchäologie und Daten-Literacy.
Many methods, technologies, standards, and languages exist to structure and describe data. The aim of this thesis is to find common features in these methods to determine how data is actually structured and described. Existing studies are limited to notions of data as recorded observations and facts, or they require given structures to build on, such as the concept of a record or the concept of a schema. These presumed concepts have been deconstructed in this thesis from a semiotic point of view. This was done by analysing data as signs, communicated in form of digital documents. The study was conducted by a phenomenological research method. Conceptual properties of data structuring and description were first collected and experienced critically. Examples of such properties include encodings, identifiers, formats, schemas, and models. The analysis resulted in six prototypes to categorize data methods by their primary purpose. The study further revealed five basic paradigms that deeply shape how data is structured and described in practice. The third result consists of a pattern language of data structuring. The patterns show problems and solutions which occur over and over again in data, independent from particular technologies. Twenty general patterns were identified and described, each with its benefits, consequences, pitfalls, and relations to other patterns. The results can help to better understand data and its actual forms, both for consumption and creation of data. Particular domains of application include data archaeology and data literacy.
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Jones, Mary Elizabeth Song Il-Yeol. "Dimensional modeling : identifying patterns, classifying patterns, and evaluating pattern impact on the design process /." Philadelphia, Pa. : Drexel University, 2006. http://dspace.library.drexel.edu/handle/1860/743.

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Tronicke, Jens. "Patterns in geophysical data and models." Universität Potsdam, 2006. http://www.uni-potsdam.de/imaf/events/ge_work0602.html.

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Muzammal, Muhammad. "Mining sequential patterns from probabilistic data." Thesis, University of Leicester, 2012. http://hdl.handle.net/2381/27638.

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Sequential Pattern Mining (SPM) is an important data mining problem. Although it is assumed in classical SPM that the data to be mined is deterministic, it is now recognized that data obtained from a wide variety of data sources is inherently noisy or uncertain, such as data from sensors or data being collected from the web from different (potentially conflicting) data sources. Probabilistic databases is a popular framework for modelling uncertainty. Recently, several data mining and ranking problems have been studied in probabilistic databases. To the best of our knowledge, this is the first systematic study of mining sequential patterns from probabilistic databases. In this work, we consider the kind of uncertainties that could arise in SPM. We propose four novel uncertainty models for SPM, namely tuple-level uncertainty, event-level uncertainty, source-level uncertainty and source-level uncertainty in deduplication, all of which fit into the probabilistic databases framework, and motivate them using potential real-life scenarios. We then define the interestingness predicate for two measures of interestingness, namely expected support and probabilistic frequentness. Next, we consider the computational complexity of evaluating the interestingness predicate, for various combinations of uncertainty models and interestingness measures, and show that different combinations have very different outcomes from a complexity theoretic viewpoint: whilst some cases are computationally tractable, we show other cases to be computationally intractable. We give a dynamic programming algorithm to compute the source support probability and hence the expected support of a sequence in a source-level uncertain database. We then propose optimizations to speedup the support computation task. Next, we propose probabilistic SPM algorithms based on the candidate generation and pattern growth frameworks for the source-level uncertainty model and the expected support measure. We implement these algorithms and give an empirical evaluation of the probabilistic SPM algorithms and show the scalability of these algorithms under different parameter settings using both real and synthetic datasets. Finally, we demonstrate the effectiveness of the probabilistic SPM framework at extracting meaningful patterns in the presence of noise.
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陳志昌 and Chee-cheong Chan. "Compositional data analysis of voting patterns." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1993. http://hub.hku.hk/bib/B31977236.

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McDermott, Philip. "Patterns of data management in bioinformatics." Thesis, University of Manchester, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.705544.

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Momsen, Eric. "Vector-Vector Patterns for Agricultural Data." Thesis, North Dakota State University, 2013. https://hdl.handle.net/10365/27040.

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Agriculture is increasingly driven by massive data, and some challenges are not covered by existing statistics, machine learning, or data mining techniques. Many crops are characterized not only by yield but also by quality measures, such as sugar content and sugar lost to molasses for sugarbeets. The set of features furthermore contains time series data, such as rainfall and periodic satellite imagery. This study examines the problem of identifying relationships in a complex data set, in which there are vectors (multiple attributes) for both the explanatory and response conditions. This problem can be characterized as a vector-vector pattern mining problem. The proposed algorithm uses one of the vector representations to determine the neighbors of a randomly picked instance, and then tests the randomness of that subset within the other vector representation. Compared to conventional approaches, the vector-vector algorithm shows better performance for distinguishing existing relationships.
National Science Foundation Partnerships for Innovation program Grant No. 1114363
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Chan, Chee-cheong. "Compositional data analysis of voting patterns." [Hong Kong : University of Hong Kong], 1993. http://sunzi.lib.hku.hk/hkuto/record.jsp?B13787160.

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Tiddi, Ilaria. "Explaining data patterns using knowledge from the Web of Data." Thesis, Open University, 2016. http://oro.open.ac.uk/47827/.

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Knowledge Discovery (KD) is a long-tradition field aiming at developing methodologies to detect hidden patterns and regularities in large datasets, using techniques from a wide range of domains, such as statistics, machine learning, pattern recognition or data visualisation. In most real world contexts, the interpretation and explanation of the discovered patterns is left to human experts, whose work is to use their background knowledge to analyse, refine and make the patterns understandable for the intended purpose. Explaining patterns is therefore an intensive and time-consuming process, where parts of the knowledge can remain unrevealed, especially when the experts lack some of the required background knowledge. In this thesis, we investigate the hypothesis that such interpretation process can be facilitated by introducing background knowledge from the Web of (Linked) Data. In the last decade, many areas started publishing and sharing their domain-specific knowledge in the form of structured data, with the objective of encouraging information sharing, reuse and discovery. With a constantly increasing amount of shared and connected knowledge, we thus assume that the process of explaining patterns can become easier, faster, and more automated. To demonstrate this, we developed Dedalo, a framework that automatically provides explanations to patterns of data using the background knowledge extracted from the Web of Data. We studied the elements required for a piece of information to be considered an explanation, identified the best strategies to automatically find the right piece of information in the Web of Data, and designed a process able to produce explanations to a given pattern using the background knowledge autonomously collected from the Web of Data. The final evaluation of Dedalo involved users within an empirical study based on a real-world scenario. We demonstrated that the explanation process is complex when not being familiar with the domain of usage, but also that this can be considerably simplified when using the Web of Data as a source of background knowledge.
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Kamra, Varun. "Mining discriminating patterns in data with confidence." Thesis, California State University, Long Beach, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10196147.

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There are many pattern mining algorithms available for classifying data. The main drawback of most of the algorithms is that they always focus on mining frequent patterns in data that may not always be discriminative enough for classification. There could exist patterns that are not frequent, but are efficient discriminators. In such cases these algorithms might not perform well. This project proposes the MDP algorithm, which aims to search for patterns that are good at discriminating between classes rather than searching for frequent patterns. The MDP ensures that there is at least one most discriminative pattern (MDP) per record. The purpose of the project is to investigate how a structural approach to classification compares to a functional approach. The project has been developed in Java programming language.

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Books on the topic "Data patterns"

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Patterns of data modeling. Boca Raton, FL: CRC Press, 2010.

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Blaha, Michael. Patterns of data modeling. Boca Raton, FL: CRC Press, 2010.

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Galic, Michele. Patterns: Applying pattern approaches. 2nd ed. [Research Triangle Park, N.C.]: IBM International Technical Support Organization, 2004.

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Carr, Daniel B. Visualizing data patterns with micromaps. Boca Raton: Chapman & Hall/CRC, 2010.

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Data model patterns: Conventions of thought. New York: Dorset House Pub., 1996.

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Starr, Alexander J. Idealised data communications patterns in parallel programs. Manchester: University of Manchester, Department of Computer Science, 1995.

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Trude, Sally. Measuring physician practice patterns with medicare data. Santa Monica, CA (P.O. Box 2138, Santa Monica 90407-2138): Rand, 1993.

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1973-, Wang Wei, and Yang Jiong, eds. Mining sequential patterns from large data sets. New York: Springer, 2005.

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Souloglou, Jason. Idealised data communications patterns in parallel programs. Manchester: University of Manchester, Department of Computer Science, 1995.

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Pascal, Poncelet, Masseglia Florent, and Teisseire Maguelonne, eds. Data mining patterns: New methods and applications. Hershey PA: Information Science Reference, 2007.

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Book chapters on the topic "Data patterns"

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Estrada, Raul, and Isaac Ruiz. "Fast Data Patterns." In Big Data SMACK, 207–24. Berkeley, CA: Apress, 2016. http://dx.doi.org/10.1007/978-1-4842-2175-4_9.

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Lindsey, James K. "Patterns." In The Analysis of Categorical Data Using GLIM, 97–110. New York, NY: Springer New York, 1989. http://dx.doi.org/10.1007/978-1-4684-7448-0_6.

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Leonard, Andy, Tim Mitchell, Matt Masson, Jessica Moss, and Michelle Ufford. "Data Warehouse Patterns." In SQL Server Integration Services Design Patterns, 227–50. Berkeley, CA: Apress, 2014. http://dx.doi.org/10.1007/978-1-4842-0082-7_11.

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Masri, David. "Data Synchronization Patterns." In Developing Data Migrations and Integrations with Salesforce, 181–202. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-4209-4_9.

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Leonard, Andy, Matt Masson, Tim Mitchell, Jessica M. Moss, and Michelle Ufford. "Data Warehouse Patterns." In SQL Server 2012 Integration Services Design Patterns, 227–49. Berkeley, CA: Apress, 2012. http://dx.doi.org/10.1007/978-1-4302-3772-3_11.

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Kettner, Benjamin, and Frank Geisler. "Data Storage Patterns." In Pro Serverless Data Handling with Microsoft Azure, 279–95. Berkeley, CA: Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-8067-6_16.

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Kettner, Benjamin, and Frank Geisler. "Data-Loading Patterns." In Pro Serverless Data Handling with Microsoft Azure, 263–77. Berkeley, CA: Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-8067-6_15.

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Reinders, James, Ben Ashbaugh, James Brodman, Michael Kinsner, John Pennycook, and Xinmin Tian. "Common Parallel Patterns." In Data Parallel C++, 323–52. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-5574-2_14.

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Abstract When we are at our best as programmers, we recognize patterns in our work and apply techniques that are time proven to be the best solution. Parallel programming is no different, and it would be a serious mistake not to study the patterns that have proven to be useful in this space. Consider the MapReduce frameworks adopted for Big Data applications; their success stems largely from being based on two simple yet effective parallel patterns—map and reduce.
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Rajendran, Karthikeyan, Assimakis Kattis, Alexander Holiday, Risi Kondor, and Ioannis G. Kevrekidis. "Data Mining When Each Data Point is a Network." In Patterns of Dynamics, 289–317. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-64173-7_17.

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Valentine, James W. "Introduction. Diversity As Data." In Phanerozoic Diversity Patterns, 1–8. Princeton: Princeton University Press, 1986. http://dx.doi.org/10.1515/9781400855056.1.

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Conference papers on the topic "Data patterns"

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Mikami, Tomoya, Masaki Matsubara, Takashi Harada, and Atsuyuki Morishima. "Worker Classification based on Answer Pattern for Finding Typical Mistake Patterns." In 2018 IEEE International Conference on Big Data (Big Data). IEEE, 2018. http://dx.doi.org/10.1109/bigdata.2018.8622188.

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Zhu, Feida, Xifeng Yan, Jiawei Han, Philip S. Yu, and Hong Cheng. "Mining Colossal Frequent Patterns by Core Pattern Fusion." In 2007 IEEE 23rd International Conference on Data Engineering. IEEE, 2007. http://dx.doi.org/10.1109/icde.2007.367916.

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Sukhobok, Dina, Nikolay Nikolov, and Dumitru Roman. "Tabular Data Anomaly Patterns." In 2017 International Conference on Big Data Innovations and Applications (Innovate-Data). IEEE, 2017. http://dx.doi.org/10.1109/innovate-data.2017.10.

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Aïzan, Josky, Cina Motamed, and Eugene C. Ezin. "LEARNING TRAJECTORY PATTERNS BY SEQUENTIAL PATTERN MINING FROM PROBABILISTIC DATABASES." In 3rd International Conference on Data Mining & Knowledge Management. AIRCC Publication Corporation, 2018. http://dx.doi.org/10.5121/csit.2018.81505.

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Ugarte, Willy, Alexandre Termier, and Miguel Santana. "Steady Patterns." In 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW). IEEE, 2016. http://dx.doi.org/10.1109/icdmw.2016.0103.

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Wei, Mingliang, Changhao Jiang, and Marc Snir. "Programming Patterns for Architecture-Level Software Optimizations on Frequent Pattern Mining." In 2007 IEEE 23rd International Conference on Data Engineering. IEEE, 2007. http://dx.doi.org/10.1109/icde.2007.367879.

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Wang, Shihan, and Takao Terano. "Detecting rumor patterns in streaming social media." In 2015 IEEE International Conference on Big Data (Big Data). IEEE, 2015. http://dx.doi.org/10.1109/bigdata.2015.7364071.

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Demesmaeker, Florian, Amine Ghrab, Siegfried Nijssen, and Sabri Skhiri. "Discovering interesting patterns in large graph cubes." In 2017 IEEE International Conference on Big Data (Big Data). IEEE, 2017. http://dx.doi.org/10.1109/bigdata.2017.8258317.

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Almuammar, Manal, and Maria Fasli. "Learning Patterns from Imbalanced Evolving Data Streams." In 2018 IEEE International Conference on Big Data (Big Data). IEEE, 2018. http://dx.doi.org/10.1109/bigdata.2018.8622108.

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Ho, Kin-Hon, Tse-Tin Chan, Haoyuan Pan, and Chin Li. "Do Candlestick Patterns Work in Cryptocurrency Trading?" In 2021 IEEE International Conference on Big Data (Big Data). IEEE, 2021. http://dx.doi.org/10.1109/bigdata52589.2021.9671826.

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Reports on the topic "Data patterns"

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EATON, SHELLEY M., and GREGORY N. CONRAD. Identifying and Implementing Patterns in Data Models. Office of Scientific and Technical Information (OSTI), March 2003. http://dx.doi.org/10.2172/809995.

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Ndoye, M., and C. Kamath. Understanding Diurnal Patterns in Wind Power Generation Data. Office of Scientific and Technical Information (OSTI), November 2011. http://dx.doi.org/10.2172/1107316.

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Lutz, Jim, and Moya Melody. Typical hot water draw patterns based on field data. Office of Scientific and Technical Information (OSTI), November 2012. http://dx.doi.org/10.2172/1127143.

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Engelhardt, M. E. Modeling patterns in data using linear and related models. Office of Scientific and Technical Information (OSTI), June 1996. http://dx.doi.org/10.2172/266746.

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Koller, Daphne. Learning Statistical Patterns in Relational Data Using Probabilistic Relational Models. Fort Belvoir, VA: Defense Technical Information Center, January 2005. http://dx.doi.org/10.21236/ada430268.

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Neel, Michael M. Data Analysis of High Temperature Superconductive Spiral's Antenna Chamber Patterns. Fort Belvoir, VA: Defense Technical Information Center, December 1995. http://dx.doi.org/10.21236/ada304248.

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Atwood, C. L., C. D. Gentillon, and G. E. Wilson. Data and statistical methods for analysis of trends and patterns. Office of Scientific and Technical Information (OSTI), November 1992. http://dx.doi.org/10.2172/6683876.

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Atwood, C. L. Modeling patterns in count data using loglinear and related models. Office of Scientific and Technical Information (OSTI), December 1995. http://dx.doi.org/10.2172/172140.

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Atwood, C. L., C. D. Gentillon, and G. E. Wilson. Data and statistical methods for analysis of trends and patterns. Office of Scientific and Technical Information (OSTI), November 1992. http://dx.doi.org/10.2172/10126801.

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Albrecht, Jochen, Andreas Petutschnig, Laxmi Ramasubramanian, Bernd Resch, and Aleisha Wright. Comparing Twitter and LODES Data for Detecting Commuter Mobility Patterns. Mineta Transportation Institute, May 2021. http://dx.doi.org/10.31979/mti.2021.2037.

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Abstract:
Local and regional planners struggle to keep up with rapid changes in mobility patterns. This exploratory research is framed with the overarching goal of asking if and how geo-social network data (GSND), in this case, Twitter data, can be used to understand and explain commuting and non-commuting travel patterns. The research project set out to determine whether GSND may be used to augment US Census LODES data beyond commuting trips and whether it may serve as a short-term substitute for commuting trips. It turns out that the reverse is true and the common practice of employing LODES data to extrapolate to overall traffic demand is indeed justified. This means that expensive and rarely comprehensive surveys are now only needed to capture trip purposes. Regardless of trip purpose (e.g., shopping, regular recreational activities, dropping kids at school), the LODES data is an excellent predictor of overall road segment loads.
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