Literatura académica sobre el tema "Data / knowledge partitioning and distribution"
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Artículos de revistas sobre el tema "Data / knowledge partitioning and distribution"
Rota, Jadranka, Tobias Malm, Nicolas Chazot, Carlos Peña y Niklas Wahlberg. "A simple method for data partitioning based on relative evolutionary rates". PeerJ 6 (28 de agosto de 2018): e5498. http://dx.doi.org/10.7717/peerj.5498.
Texto completoShaikh, M. Bilal, M. Abdul Rehman y Attaullah Sahito. "Optimizing Distributed Machine Learning for Large Scale EEG Data Set". Sukkur IBA Journal of Computing and Mathematical Sciences 1, n.º 1 (30 de junio de 2017): 114. http://dx.doi.org/10.30537/sjcms.v1i1.14.
Texto completoLiu, Richen, Liming Shen, Xueyi Chen, Genlin Ji, Bin Zhao, Chao Tan y Mingjun Su. "Sketch-Based Slice Interpretative Visualization for Stratigraphic Data". Journal of Imaging Science and Technology 63, n.º 6 (1 de noviembre de 2019): 60505–1. http://dx.doi.org/10.2352/j.imagingsci.technol.2019.63.6.060505.
Texto completoZhu, Zichen, Xiao Hu y Manos Athanassoulis. "NOCAP: Near-Optimal Correlation-Aware Partitioning Joins". Proceedings of the ACM on Management of Data 1, n.º 4 (8 de diciembre de 2023): 1–27. http://dx.doi.org/10.1145/3626739.
Texto completoSineglazov, Victor, Olena Chumachenko y Eduard Heilyk. "Semi-controlled Learning in Information Processing Problems". Electronics and Control Systems 4, n.º 70 (4 de enero de 2022): 37–43. http://dx.doi.org/10.18372/1990-5548.70.16754.
Texto completoSirbiladze, Gia, Bidzina Matsaberidze, Bezhan Ghvaberidze, Bidzina Midodashvili y David Mikadze. "Fuzzy TOPSIS based selection index in the planning of emergency service facilities locations and goods transportation". Journal of Intelligent & Fuzzy Systems 41, n.º 1 (11 de agosto de 2021): 1949–62. http://dx.doi.org/10.3233/jifs-210636.
Texto completoSmith, Bruce R., Christophe M. Herbinger y Heather R. Merry. "Accurate Partition of Individuals Into Full-Sib Families From Genetic Data Without Parental Information". Genetics 158, n.º 3 (1 de julio de 2001): 1329–38. http://dx.doi.org/10.1093/genetics/158.3.1329.
Texto completoGrard, Aline y Jean-François Deliège. "Characterizing Trace Metal Contamination and Partitioning in the Rivers and Sediments of Western Europe Watersheds". Hydrology 10, n.º 2 (16 de febrero de 2023): 51. http://dx.doi.org/10.3390/hydrology10020051.
Texto completoMcDonald, H. Gregory. "Yukon to the Yucatan: Habitat partitioning in North American Late Pleistocene ground sloths (Xenarthra, Pilosa)". Journal of Palaeosciences 70, n.º (1-2) (10 de septiembre de 2021): 237–52. http://dx.doi.org/10.54991/jop.2021.17.
Texto completoDalton, Lori A. y Mohammadmahdi R. Yousefi. "Data Requirements for Model-Based Cancer Prognosis Prediction". Cancer Informatics 14s5 (enero de 2015): CIN.S30801. http://dx.doi.org/10.4137/cin.s30801.
Texto completoTesis sobre el tema "Data / knowledge partitioning and distribution"
De, Oliveira Joffrey. "Gestion de graphes de connaissances dans l'informatique en périphérie : gestion de flux, autonomie et adaptabilité". Electronic Thesis or Diss., Université Gustave Eiffel, 2023. http://www.theses.fr/2023UEFL2069.
Texto completoThe research work carried out as part of this PhD thesis lies at the interface between the Semantic Web, databases and edge computing. Indeed, our objective is to design, develop and evaluate a database management system (DBMS) based on the W3C Resource Description Framework (RDF) data model, which must be adapted to the terminals found in Edge computing.The possible applications of such a system are numerous and cover a wide range of sectors such as industry, finance and medicine, to name but a few. As proof of this, the subject of this thesis was defined with the team from the Computer Science and Artificial Intelligence Laboratory (CSAI) at ENGIE Lab CRIGEN. The latter is ENGIE's research and development centre dedicated to green gases (hydrogen, biogas and liquefied gases), new uses of energy in cities and buildings, industry and emerging technologies (digital and artificial intelligence, drones and robots, nanotechnologies and sensors). CSAI financed this thesis as part of a CIFRE-type collaboration.The functionalities of a system satisfying these characteristics must enable anomalies and exceptional situations to be detected in a relevant and effective way from measurements taken by sensors and/or actuators. In an industrial context, this could mean detecting excessively high measurements, for example of pressure or flow rate in a gas distribution network, which could potentially compromise infrastructure or even the safety of individuals. This detection must be carried out using a user-friendly approach to enable as many users as possible, including non-programmers, to describe risk situations. The approach must therefore be declarative, not procedural, and must be based on a query language, such as SPARQL.We believe that Semantic Web technologies can make a major contribution in this context. Indeed, the ability to infer implicit consequences from explicit data and knowledge is a means of creating new services that are distinguished by their ability to adjust to the circumstances encountered and to make autonomous decisions. This can be achieved by generating new queries in certain alarming situations, or by defining a minimal sub-graph of knowledge that an instance of our DBMS needs in order to respond to all of its queries.The design of such a DBMS must also take into account the inherent constraints of Edge computing, i.e. the limits in terms of computing capacity, storage, bandwidth and sometimes energy (when the terminal is powered by a solar panel or a battery). Architectural and technological choices must therefore be made to meet these limitations. With regard to the representation of data and knowledge, our design choice fell on succinct data structures (SDS), which offer, among other advantages, the fact that they are very compact and do not require decompression during querying. Similarly, it was necessary to integrate data flow management within our DBMS, for example with support for windowing in continuous SPARQL queries, and for the various services supported by our system. Finally, as anomaly detection is an area where knowledge can evolve, we have integrated support for modifications to the knowledge graphs stored on the client instances of our DBMS. This support translates into an extension of certain SDS structures used in our prototype
HE, AIJING. "UNSUPERVISED DATA MINING BY RECURSIVE PARTITIONING". University of Cincinnati / OhioLINK, 2002. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1026406153.
Texto completoEberhagen, Niclas. "An investigation of emerging knowledge distribution means and their characterization". Licentiate thesis, Department of Computer and Systems Sciences, Stockholm University, 1999. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-8262.
Texto completoLicentiate thesis in partial fulfillment of the Licentiate of Philosophy degree in Computer and Systems Sciences, Stockholm University
George, Chadrick Hendrik. "Knowledge management infrastructure and knowledge sharing: The case of a large fast moving consumer goods distribution centre in the Western Cape". Thesis, University of the Western Cape, 2014. http://hdl.handle.net/11394/3943.
Texto completoThe aim of this study is to understand how knowledge is created, shared and used within the fast moving consumer goods distribution centre in the Western Cape (WC). It also aims to understand knowledge sharing between individuals in the organisation. A literature review was conducted, in order to answer the research questions- this covered the background of knowledge management (KM) and KS and its current status with particular reference to SA’s private sector. The study found that technological KM infrastructure, cultural KM infrastructure and organisational KM infrastructure are important enablers of KS. A conceptual model was developed around these concepts. In order to answer the research questions, the study identified a FMCG DC in the WC, where KS is practiced
Arres, Billel. "Optimisation des performances dans les entrepôts distribués avec Mapreduce : traitement des problèmes de partionnement et de distribution des données". Thesis, Lyon, 2016. http://www.theses.fr/2016LYSE2012.
Texto completoIn this manuscript, we addressed the problems of data partitioning and distribution for large scale data warehouses distributed with MapReduce. First, we address the problem of data distribution. In this case, we propose a strategy to optimize data placement on distributed systems, based on the collocation principle. The objective is to optimize queries performances through the definition of an intentional data distribution schema of data to reduce the amount of data transferred between nodes during treatments, specifically during MapReduce’s shuffling phase. Secondly, we propose a new approach to improve data partitioning and placement in distributed file systems, especially Hadoop-based systems, which is the standard implementation of the MapReduce paradigm. The aim is to overcome the default data partitioning and placement policies which does not take any relational data characteristics into account. Our proposal proceeds according to two steps. Based on queries workload, it defines an efficient partitioning schema. After that, the system defines a data distribution schema that meets the best user’s needs, and this, by collocating data blocks on the same or closest nodes. The objective in this case is to optimize queries execution and parallel processing performances, by improving data access. Our third proposal addresses the problem of the workload dynamicity, since users analytical needs evolve through time. In this case, we propose the use of multi-agents systems (MAS) as an extension of our data partitioning and placement approach. Through autonomy and self-control that characterize MAS, we developed a platform that defines automatically new distribution schemas, as new queries appends to the system, and apply a data rebalancing according to this new schema. This allows offloading the system administrator of the burden of managing load balance, besides improving queries performances by adopting careful data partitioning and placement policies. Finally, to validate our contributions we conduct a set of experiments to evaluate our different approaches proposed in this manuscript. We study the impact of an intentional data partitioning and distribution on data warehouse loading phase, the execution of analytical queries, OLAP cubes construction, as well as load balancing. We also defined a cost model that allowed us to evaluate and validate the partitioning strategy proposed in this work
Antoine, Emilien. "Distributed data management with a declarative rule-based language webdamlog". Phd thesis, Université Paris Sud - Paris XI, 2013. http://tel.archives-ouvertes.fr/tel-00933808.
Texto completoGalicia, Auyón Jorge Armando. "Revisiting Data Partitioning for Scalable RDF Graph Processing Combining Graph Exploration and Fragmentation for RDF Processing Query Optimization for Large Scale Clustered RDF Data RDFPart- Suite: Bridging Physical and Logical RDF Partitioning. Reverse Partitioning for SPARQL Queries: Principles and Performance Analysis. ShouldWe Be Afraid of Querying Billions of Triples in a Graph-Based Centralized System? EXGRAF: Exploration et Fragmentation de Graphes au Service du Traitement Scalable de Requˆetes RDF". Thesis, Chasseneuil-du-Poitou, Ecole nationale supérieure de mécanique et d'aérotechnique, 2021. http://www.theses.fr/2021ESMA0001.
Texto completoThe Resource Description Framework (RDF) and SPARQL are very popular graph-based standards initially designed to represent and query information on the Web. The flexibility offered by RDF motivated its use in other domains and today RDF datasets are great information sources. They gather billions of triples in Knowledge Graphs that must be stored and efficiently exploited. The first generation of RDF systems was built on top of traditional relational databases. Unfortunately, the performance in these systems degrades rapidly as the relational model is not suitable for handling RDF data inherently represented as a graph. Native and distributed RDF systems seek to overcome this limitation. The former mainly use indexing as an optimization strategy to speed up queries. Distributed and parallel RDF systems resorts to data partitioning. The logical representation of the database is crucial to design data partitions in the relational model. The logical layer defining the explicit schema of the database provides a degree of comfort to database designers. It lets them choose manually or automatically (through advisors) the tables and attributes to be partitioned. Besides, it allows the partitioning core concepts to remain constant regardless of the database management system. This design scheme is no longer valid for RDF databases. Essentially, because the RDF model does not explicitly enforce a schema since RDF data is mostly implicitly structured. Thus, the logical layer is inexistent and data partitioning depends strongly on the physical implementations of the triples on disk. This situation contributes to have different partitioning logics depending on the target system, which is quite different from the relational model’s perspective. In this thesis, we promote the novel idea of performing data partitioning at the logical level in RDF databases. Thereby, we first process the RDF data graph to support logical entity-based partitioning. After this preparation, we present a partitioning framework built upon these logical structures. This framework is accompanied by data fragmentation, allocation, and distribution procedures. This framework was incorporated to a centralized (RDF_QDAG) and a distributed (gStoreD) triple store. We conducted several experiments that confirmed the feasibility of integrating our framework to existent systems improving their performances for certain queries. Finally, we design a set of RDF data partitioning management tools including a data definition language (DDL) and an automatic partitioning wizard
Meiring, Linda. "A distribution model for the assessment of database systems knowledge and skills among second-year university students". Thesis, [Bloemfontein?] : Central University of Technology, Free State, 2009. http://hdl.handle.net/11462/44.
Texto completoDasgupta, Arghya. "How can the ‘Zeigarnik effect’ becombined with analogical reasoning inorder to enhance understanding ofcomplex knowledge related to computerscience?" Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-143636.
Texto completoCoullon, Hélène. "Modélisation et implémentation de parallélisme implicite pour les simulations scientifiques basées sur des maillages". Thesis, Orléans, 2014. http://www.theses.fr/2014ORLE2029/document.
Texto completoParallel scientific computations is an expanding domain of computer science which increases the speed of calculations and offers a way to deal with heavier or more accurate calculations. Thus, the interest of scientific computations increases, with more precised results and bigger physical domains to study. In the particular case of scientific numerical simulations, solving partial differential equations (PDEs) is an especially heavy calculation and a perfect applicant to parallel computations. On one hand, it is more and more easy to get an access to very powerfull parallel machines and clusters, but on the other hand parallel programming is hard to democratize, and most scientists are not able to use these machines. As a result, high level programming models, framework, libraries, languages etc. have been proposed to hide technical details of parallel programming. However, in this “implicit parallelism” field, it is difficult to find the good abstraction level while keeping a low programming effort. This thesis proposes a model to write implicit parallelism solutions for numerical simulations such as mesh-based PDEs computations. This model is called “Structured Implicit Parallelism for scientific SIMulations” (SIPSim), and proposes an approach at the crossroads of existing solutions, taking advantage of each one. A first implementation of this model is proposed, as a C++ library called SkelGIS, for two dimensional Cartesian meshes. A second implementation of the model, and an extension of SkelGIS, proposes an implicit parallelism solution for network-simulations (which deals with simulations with multiple physical phenomenons), and is studied in details. A performance analysis of both these implementations is given on real case simulations, and it demonstrates that the SIPSim model can be implemented efficiently
Libros sobre el tema "Data / knowledge partitioning and distribution"
Kjaerulff, Uffe B. Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis. 2a ed. New York, NY: Springer New York, 2013.
Buscar texto completoPetchey, Owen L., Andrew P. Beckerman, Natalie Cooper y Dylan Z. Childs. Insights from Data with R. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198849810.001.0001.
Texto completoMadsen, Anders L. y Uffe B. B. Kjærulff. Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis. Springer, 2014.
Buscar texto completoFörster, Michael y Brian Nolan. Inequality and Living Standards. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198807032.003.0002.
Texto completoFerreira, Eliel Alves y João Vicente Zamperion. Excel: Uma ferramenta estatística. Brazil Publishing, 2021. http://dx.doi.org/10.31012/978-65-5861-400-5.
Texto completoTebaldi, Claudia y Richard Smith. Indirect elicitation from ecological experts: From methods and software to habitat modelling and rock-wallabies. Editado por Anthony O'Hagan y Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.19.
Texto completoO'Donoghue, Cathal. Practical Microsimulation Modelling. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198852872.001.0001.
Texto completoAshby, F. Gregory y Fabian A. Soto. Multidimensional Signal Detection Theory. Editado por Jerome R. Busemeyer, Zheng Wang, James T. Townsend y Ami Eidels. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780199957996.013.2.
Texto completoMassimi, Michela. Perspectival Realism. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9780197555620.001.0001.
Texto completoGarnett, Stephen, Judit Szabo y Guy Dutson. Action Plan for Australian Birds 2010. CSIRO Publishing, 2011. http://dx.doi.org/10.1071/9780643103696.
Texto completoCapítulos de libros sobre el tema "Data / knowledge partitioning and distribution"
Tsai, Kao-Tai. "Examining Data Distribution". En Machine Learning for Knowledge Discovery with R, 9–28. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003205685-2.
Texto completoAslam, Adeel, Giovanni Simonini, Luca Gagliardelli, Angelo Mozzillo y Sonia Bergamaschi. "HKS: Efficient Data Partitioning for Stateful Streaming". En Big Data Analytics and Knowledge Discovery, 386–91. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-39831-5_35.
Texto completoGalicia, Jorge, Amin Mesmoudi y Ladjel Bellatreche. "RDFPartSuite: Bridging Physical and Logical RDF Partitioning". En Big Data Analytics and Knowledge Discovery, 136–50. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-27520-4_10.
Texto completoBae, Jinuk y Sukho Lee. "Partitioning Algorithms for the Computation of Average Iceberg Queries". En Data Warehousing and Knowledge Discovery, 276–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-44466-1_27.
Texto completoBellatreche, Ladjel, Kamel Boukhalfa y Pascal Richard. "Data Partitioning in Data Warehouses: Hardness Study, Heuristics and ORACLE Validation". En Data Warehousing and Knowledge Discovery, 87–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-85836-2_9.
Texto completoBodra, Jay, Soumyava Das, Abhishek Santra y Sharma Chakravarthy. "Query Processing on Large Graphs: Scalability Through Partitioning". En Big Data Analytics and Knowledge Discovery, 271–88. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-98539-8_21.
Texto completoLi, Haoran, Li Xiong, Zhanglong Ji y Xiaoqian Jiang. "Partitioning-Based Mechanisms Under Personalized Differential Privacy". En Advances in Knowledge Discovery and Data Mining, 615–27. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57454-7_48.
Texto completoJiang, Hansi y Carl Meyer. "Relations Between Adjacency and Modularity Graph Partitioning". En Advances in Knowledge Discovery and Data Mining, 189–200. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-33377-4_15.
Texto completoBauer, H. H., M. Staat y M. Hammerschmidt. "Value Based Benchmarking and Market Partitioning". En Studies in Classification, Data Analysis, and Knowledge Organization, 422–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-642-55721-7_43.
Texto completoTsuchiya, Takahiro. "Homogeneity Analysis for Partitioning Qualitative Variables". En Studies in Classification, Data Analysis, and Knowledge Organization, 452–59. Tokyo: Springer Japan, 1998. http://dx.doi.org/10.1007/978-4-431-65950-1_50.
Texto completoActas de conferencias sobre el tema "Data / knowledge partitioning and distribution"
Nishimura, Joel y Johan Ugander. "Restreaming graph partitioning". En KDD' 13: The 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2013. http://dx.doi.org/10.1145/2487575.2487696.
Texto completoFetai, Ilir, Damian Murezzan y Heiko Schuldt. "Workload-driven adaptive data partitioning and distribution — The Cumulus approach". En 2015 IEEE International Conference on Big Data (Big Data). IEEE, 2015. http://dx.doi.org/10.1109/bigdata.2015.7363940.
Texto completoPacaci, Anil y M. Tamer Özsu. "Distribution-Aware Stream Partitioning for Distributed Stream Processing Systems". En SIGMOD/PODS '18: International Conference on Management of Data. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3206333.3206338.
Texto completoHigham, Catherine F., Desmond J. Higham y Francesco Tudisco. "Core-periphery Partitioning and Quantum Annealing". En KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3534678.3539261.
Texto completoZhang, Chenzi, Fan Wei, Qin Liu, Zhihao Gavin Tang y Zhenguo Li. "Graph Edge Partitioning via Neighborhood Heuristic". En KDD '17: The 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3097983.3098033.
Texto completoAwadelkarim, Amel y Johan Ugander. "Prioritized Restreaming Algorithms for Balanced Graph Partitioning". En KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3394486.3403239.
Texto completoOtgonbayar, Ankhbayar, Zeeshan Pervez y Keshav Dahal. "Partitioning based incremental marginalization algorithm for anonymizing missing data streams". En 2019 13th International Conference on Software, Knowledge, Information Management and Applications (SKIMA). IEEE, 2019. http://dx.doi.org/10.1109/skima47702.2019.8982399.
Texto completoXie, Xiao-Min y Yun Li. "Bisecting data partitioning methods for Min-Max Modular Support Vector Machine". En 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2011). IEEE, 2011. http://dx.doi.org/10.1109/fskd.2011.6019750.
Texto completoKor, Yashar, Liang Tan, Marek Z. Reformat y Petr Musilek. "GridKG: Knowledge Graph Representation of Distribution Grid Data". En 2020 IEEE Electric Power and Energy Conference (EPEC). IEEE, 2020. http://dx.doi.org/10.1109/epec48502.2020.9320066.
Texto completoGupta, Gaurav, Tharun Medini, Anshumali Shrivastava y Alexander J. Smola. "BLISS: A Billion scale Index using Iterative Re-partitioning". En KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3534678.3539414.
Texto completoInformes sobre el tema "Data / knowledge partitioning and distribution"
Schoen, Robert, Xiaotong Yang y Gizem Solmaz. Psychometric Report for the 2019 Knowledge for Teaching Early Elementary Mathematics (K-TEEM) Test. Florida State University Libraries, mayo de 2021. http://dx.doi.org/10.33009/lsi.1620243057.
Texto completoWolf, Shmuel y William J. Lucas. Involvement of the TMV-MP in the Control of Carbon Metabolism and Partitioning in Transgenic Plants. United States Department of Agriculture, octubre de 1999. http://dx.doi.org/10.32747/1999.7570560.bard.
Texto completoIdakwo, Gabriel, Sundar Thangapandian, Joseph Luttrell, Zhaoxian Zhou, Chaoyang Zhang y Ping Gong. Deep learning-based structure-activity relationship modeling for multi-category toxicity classification : a case study of 10K Tox21 chemicals with high-throughput cell-based androgen receptor bioassay data. Engineer Research and Development Center (U.S.), julio de 2021. http://dx.doi.org/10.21079/11681/41302.
Texto completoMudge, Christopher, Glenn Suir y Benjamin Sperry. Unmanned aircraft systems and tracer dyes : potential for monitoring herbicide spray distribution. Engineer Research and Development Center (U.S.), octubre de 2023. http://dx.doi.org/10.21079/11681/47705.
Texto completoBédard, K., A. Marsh, M. Hillier y Y. Music. 3D geological model of the Western Canadian Sedimentary Basin in Saskatchewan, Canada. Natural Resources Canada/CMSS/Information Management, 2023. http://dx.doi.org/10.4095/331747.
Texto completoBaker, Michael. DTRS56-02-D-70036-16 Mechanical Damage. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), abril de 2009. http://dx.doi.org/10.55274/r0011844.
Texto completoMcMartin, I., D. E. Kerr, M. B. McClenaghan, A. Duk-Rodkin, T. Tremblay, M. Parent y J. M. Rice. Introduction and Summary. Natural Resources Canada/CMSS/Information Management, 2023. http://dx.doi.org/10.4095/331419.
Texto completoMcMartin, I., D. E. Kerr, M. B. McClenaghan, A. Duk-Rodkin, T. Tremblay, M. Parent y J. M. Rice. Introduction et Sommaire. Natural Resources Canada/CMSS/Information Management, 2023. http://dx.doi.org/10.4095/331427.
Texto completoGantzer, Clark J., Shmuel Assouline y Stephen H. Anderson. Synchrotron CMT-measured soil physical properties influenced by soil compaction. United States Department of Agriculture, febrero de 2006. http://dx.doi.org/10.32747/2006.7587242.bard.
Texto completoChopra, Deepta, Kas Sempere y Meenakshi Krishnan. Assessing Unpaid Care Work: A Participatory Toolkit. Institute of Development Studies, marzo de 2021. http://dx.doi.org/10.19088/ids.2021.016.
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