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Relevant bibliographies by topics / Data / knowledge partitioning and distribution

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Journal articles

Academic literature on the topic 'Data / knowledge partitioning and distribution'

Author: Grafiati

Published: 4 May 2024

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Journal articles on the topic "Data / knowledge partitioning and distribution"

1

Rota, Jadranka, Tobias Malm, Nicolas Chazot, Carlos Peña, and Niklas Wahlberg. "A simple method for data partitioning based on relative evolutionary rates." PeerJ 6 (August 28, 2018): e5498. http://dx.doi.org/10.7717/peerj.5498.

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Background Multiple studies have demonstrated that partitioning of molecular datasets is important in model-based phylogenetic analyses. Commonly, partitioning is done a priori based on some known properties of sequence evolution, e.g. differences in rate of evolution among codon positions of a protein-coding gene. Here we propose a new method for data partitioning based on relative evolutionary rates of the sites in the alignment of the dataset being analysed. The rates are inferred using the previously published Tree Independent Generation of Evolutionary Rates (TIGER), and the partitioning

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2

Shaikh, M. Bilal, M. Abdul Rehman, and Attaullah Sahito. "Optimizing Distributed Machine Learning for Large Scale EEG Data Set." Sukkur IBA Journal of Computing and Mathematical Sciences 1, no. 1 (2017): 114. http://dx.doi.org/10.30537/sjcms.v1i1.14.

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Distributed Machine Learning (DML) has gained its importance more than ever in this era of Big Data. There are a lot of challenges to scale machine learning techniques on distributed platforms. When it comes to scalability, improving the processor technology for high level computation of data is at its limit, however increasing machine nodes and distributing data along with computation looks as a viable solution. Different frameworks and platforms are available to solve DML problems. These platforms provide automated random data distribution of datasets which miss the power of user defined int

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3

Liu, Richen, Liming Shen, Xueyi Chen, et al. "Sketch-Based Slice Interpretative Visualization for Stratigraphic Data." Journal of Imaging Science and Technology 63, no. 6 (2019): 60505–1. http://dx.doi.org/10.2352/j.imagingsci.technol.2019.63.6.060505.

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Abstract In this article, the authors propose a stratigraphic slice interpretative visualization system, namely slice analyzer. It enables the domain experts, i.e., geologists and oil/gas exploration experts, to interactively interpret the slices with domain knowledge, which helps them get a better understanding of stratigraphic structures and the distribution of the geological materials, e.g., underground flow path (UFP), river delta, floodplain, slump fan, etc. In addition to some domain-specific slice edit manipulations, a sketch-based sub-region partitioning approach is further presented t

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Zhu, Zichen, Xiao Hu, and Manos Athanassoulis. "NOCAP: Near-Optimal Correlation-Aware Partitioning Joins." Proceedings of the ACM on Management of Data 1, no. 4 (2023): 1–27. http://dx.doi.org/10.1145/3626739.

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Storage-based joins are still commonly used today because the memory budget does not always scale with the data size. One of the many join algorithms developed that has been widely deployed and proven to be efficient is the Hybrid Hash Join (HHJ), which is designed to exploit any available memory to maximize the data that is joined directly in memory. However, HHJ cannot fully exploit detailed knowledge of the join attribute correlation distribution. In this paper, we show that given a correlation skew in the join attributes, HHJ partitions data in a suboptimal way. To do that, we derive the o

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Sineglazov, Victor, Olena Chumachenko, and Eduard Heilyk. "Semi-controlled Learning in Information Processing Problems." Electronics and Control Systems 4, no. 70 (2022): 37–43. http://dx.doi.org/10.18372/1990-5548.70.16754.

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The article substantiates the need for further research of known methods and the development of new methods of machine learning – semi-supervized learning. It is shown that knowledge of the probability distribution density of the initial data obtained using unlabeled data should carry information useful for deriving the conditional probability distribution density of labels and input data. If this is not the case, semi-supervised learning will not provide any improvement over supervised learning. It may even happen that the use of unlabeled data reduces the accuracy of the prediction. For semi

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6

Sirbiladze, Gia, Bidzina Matsaberidze, Bezhan Ghvaberidze, Bidzina Midodashvili, and David Mikadze. "Fuzzy TOPSIS based selection index in the planning of emergency service facilities locations and goods transportation." Journal of Intelligent & Fuzzy Systems 41, no. 1 (2021): 1949–62. http://dx.doi.org/10.3233/jifs-210636.

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The attributes influencing the decision-making process in planning transportation of goods from selected facilities locations in disaster zones are considered. Experts evaluate each candidate for humanitarian aid distribution centers (HADCs) (service centers) against each uncertainty factor in q-rung orthopair fuzzy sets (q-ROFS). For representation of experts’ knowledge in the input data for planning emergency service facilities locations a q-rung orthopair fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) approach is developed. Based on the offered fuzzy TOPSIS ag

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7

Smith, Bruce R., Christophe M. Herbinger, and Heather R. Merry. "Accurate Partition of Individuals Into Full-Sib Families From Genetic Data Without Parental Information." Genetics 158, no. 3 (2001): 1329–38. http://dx.doi.org/10.1093/genetics/158.3.1329.

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Abstract Two Markov chain Monte Carlo algorithms are proposed that allow the partitioning of individuals into full-sib groups using single-locus genetic marker data when no parental information is available. These algorithms present a method of moving through the sibship configuration space and locating the configuration that maximizes an overall score on the basis of pairwise likelihood ratios of being full-sib or unrelated or maximizes the full joint likelihood of the proposed family structure. Using these methods, up to 757 out of 759 Atlantic salmon were correctly classified into 12 full-s

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Grard, Aline, and Jean-François Deliège. "Characterizing Trace Metal Contamination and Partitioning in the Rivers and Sediments of Western Europe Watersheds." Hydrology 10, no. 2 (2023): 51. http://dx.doi.org/10.3390/hydrology10020051.

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Adsorption and desorption processes occurring on suspended and bed sediments were studied in two datasets from western Europe watersheds (Meuse and Mosel). Copper and zinc dissolved and total concentrations, total suspended sediment concentrations, mass concentrations, and grain sizes were analyzed. Four classes of mineral particle size were determined. Grain size distribution had to be considered in order to assess the trace metal particulate phase in the water column. The partitioning coefficients of trace metals between the dissolved and particulate phases were calculated. The objective of

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9

McDonald, H. Gregory. "Yukon to the Yucatan: Habitat partitioning in North American Late Pleistocene ground sloths (Xenarthra, Pilosa)." Journal of Palaeosciences 70, no. (1-2) (2021): 237–52. http://dx.doi.org/10.54991/jop.2021.17.

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The late Pleistocene mammalian fauna of North America included seven genera of ground sloth, representing four families. This cohort of megaherbivores had an extensive geographic range in North America from the Yukon in Canada to the Yucatan Peninsula in Mexico and inhabited a variety of biomes. Within this latitudinal range there are taxa with a distribution limited to temperate latitudes while others have a distribution restricted to tropical latitudes. Some taxa are better documented than others and more is known about their palaeoecology and habitat preferences, while our knowledge of the

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Dalton, Lori A., and Mohammadmahdi R. Yousefi. "Data Requirements for Model-Based Cancer Prognosis Prediction." Cancer Informatics 14s5 (January 2015): CIN.S30801. http://dx.doi.org/10.4137/cin.s30801.

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Cancer prognosis prediction is typically carried out without integrating scientific knowledge available on genomic pathways, the effect of drugs on cell dynamics, or modeling mutations in the population. Recent work addresses some of these problems by formulating an uncertainty class of Boolean regulatory models for abnormal gene regulation, assigning prognosis scores to each network based on intervention outcomes, and partitioning networks in the uncertainty class into prognosis classes based on these scores. For a new patient, the probability distribution of the prognosis class was evaluated

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