Relevant bibliographies by topics / Approximate database

Contents

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

Academic literature on the topic 'Approximate database'

Author: Grafiati
Published: 4 June 2021
Last updated: 18 February 2022

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Journal articles on the topic "Approximate database"

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Saharia, Aditya N., and Terence M. Barron. "Approximate dependencies in database systems." Decision Support Systems 13, no. 3-4 (March 1995): 335–47. http://dx.doi.org/10.1016/0167-9236(93)e0049-j.

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Kläbe, Steffen, Kai-Uwe Sattler, and Stephan Baumann. "PatchIndex: exploiting approximate constraints in distributed databases." Distributed and Parallel Databases 39, no. 3 (March 6, 2021): 833–53. http://dx.doi.org/10.1007/s10619-021-07326-1.

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AbstractCloud data warehouse systems lower the barrier to access data analytics. These applications often lack a database administrator and integrate data from various sources, potentially leading to data not satisfying strict constraints. Automatic schema optimization in self-managing databases is difficult in these environments without prior data cleaning steps. In this paper, we focus on constraint discovery as a subtask of schema optimization. Perfect constraints might not exist in these unclean datasets due to a small set of values violating the constraints. Therefore, we introduce the concept of a generic PatchIndex structure, which handles exceptions to given constraints and enables database systems to define these approximate constraints. We apply the concept to the environment of distributed databases, providing parallel index creation approaches and optimization techniques for parallel queries using PatchIndexes. Furthermore, we describe heuristics for automatic discovery of PatchIndex candidate columns and prove the performance benefit of using PatchIndexes in our evaluation.
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TamilSelvi, M., and R. Renuga. "Approximate String Search in Large Spatial Database." Procedia Computer Science 47 (2015): 92–100. http://dx.doi.org/10.1016/j.procs.2015.03.187.

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Intan, Rolly, and Masao Mukaidono. "Approximate Data Querying in Fuzzy Relational Database." Journal of Advanced Computational Intelligence and Intelligent Informatics 6, no. 1 (February 20, 2002): 33–40. http://dx.doi.org/10.20965/jaciii.2002.p0033.

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Fuzzy relational database was proposed for dealing with imprecise data or fuzzy information in a relational database. In order to provide a more realistic relation in representing similarity between two imprecise data, we need to weaken fuzzy similarity relation to be weak fuzzy similarity relation in which fuzzy conditional probability relation (FCPR, for short) is regarded as a concrete example of the weak fuzzy similarity relation. In this paper, application of approximate data querying is discussed induced by FCPR in the presence of the fuzzy relational database. Application of approximate data querying in order to provide fuzzy query relation is presented into two frameworks, namely dependent inputs and independent inputs. Finally, related to join operator, approximate join of two or more fuzzy query relations is given for the purpose of extending query system.
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Mazlack, Lawrence J. "Approximate reasoning applied to unsupervised database mining." International Journal of Intelligent Systems 12, no. 5 (May 1997): 391–414. http://dx.doi.org/10.1002/(sici)1098-111x(199705)12:5<391::aid-int3>3.0.co;2-i.

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Valiullin, Timur, Zhexue Huang, Chenghao Wei, Jianfei Yin, Dingming Wu, and Luliia Egorova. "A new approximate method for mining frequent itemsets from big data." Computer Science and Information Systems, no. 00 (2020): 15. http://dx.doi.org/10.2298/csis200124015v.

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Mining frequent itemsets in transaction databases is an important task in many applications. It becomes more challenging when dealing with a large transaction database because traditional algorithms are not scalable due to the memory limit. In this paper, we propose a new approach for approximately mining of frequent itemsets in a big transaction database. Our approach is suitable for mining big transaction databases since it produces approximate frequent itemsets from a subset of the entire database, and can be implemented in a distributed environment. Our algorithm is able to efficiently produce high-accurate results, however it misses some true frequent itemsets. To address this problem and reduce the number of false negative frequent itemsets we introduce an additional parameter to the algorithm to discover most of the frequent itemsets contained in the entire data set. In this article, we show an empirical evaluation of the results of the proposed approach.
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Huh, Soon-Young, and Jung-Whan Lee. "Providing Approximate Answers Using a Knowledge Abstraction Database." Journal of Database Management 12, no. 2 (April 2001): 14–24. http://dx.doi.org/10.4018/jdm.2001040102.

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Breitinger, Frank, Harald Baier, and Douglas White. "On the database lookup problem of approximate matching." Digital Investigation 11 (May 2014): S1—S9. http://dx.doi.org/10.1016/j.diin.2014.03.001.

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Kum, Hye-Chung, and Joong-Hyuk Chang. "Mining Approximate Sequential Patterns in a Large Sequence Database." KIPS Transactions:PartD 13D, no. 2 (April 1, 2006): 199–206. http://dx.doi.org/10.3745/kipstd.2006.13d.2.199.

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Fisher, Danyel, Steven M. Drucker, and A. Christian Knig. "Exploratory Visualization Involving Incremental, Approximate Database Queries and Uncertainty." IEEE Computer Graphics and Applications 32, no. 4 (July 2012): 55–62. http://dx.doi.org/10.1109/mcg.2012.48.

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Dissertations / Theses on the topic "Approximate database"

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Jermaine, Christopher. "Approximate answering of aggregate queries in relational databases." Diss., Georgia Institute of Technology, 2002. http://hdl.handle.net/1853/9221.

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Cheng, Lok-lam, and 鄭樂霖. "Approximate string matching in DNA sequences." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2003. http://hub.hku.hk/bib/B29350591.

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Sjö, Kristoffer. "Semantics and Implementation of Knowledge Operators in Approximate Databases." Thesis, Linköping University, Department of Computer and Information Science, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-2438.

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In order that epistemic formulas might be coupled with approximate databases, it is necessary to have a well-defined semantics for the knowledge operator and a method of reducing epistemic formulas to approximate formulas. In this thesis, two possible definitions of a semantics for the knowledge operator are proposed for use together with an approximate relational database:

* One based upon logical entailment (being the dominating notion of knowledge in literature); sound and complete rules for reduction to approximate formulas are explored and found not to be applicable to all formulas.

* One based upon algorithmic computability (in order to be practically feasible); the correspondence to the above operator on the one hand, and to the deductive capability of the agent on the other hand, is explored.

Also, an inductively defined semantics for a"know whether"-operator, is proposed and tested. Finally, an algorithm implementing the above is proposed, carried out using Java, and tested.

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Geum, Seong. "An approximate load balancing parallel hash join algorithm to handle data skew in a parallel data base system." Thesis, Georgia Institute of Technology, 1995. http://hdl.handle.net/1853/9222.

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Linari, Alessandro <1977&gt. "Models and techniques for approximate similarity search in large databases." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2007. http://amsdottorato.unibo.it/398/.

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CALENDER, CHRISTOPHER R. "APPROXIMATE N-NEAREST NEIGHBOR CLUSTERING ON DISTRIBUTED DATABASES USING ITERATIVE REFINEMENT." University of Cincinnati / OhioLINK, 2004. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1092929952.

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Monge, Alvaro Edmundo. "Adaptive detection of approximately duplicate database records and the database integration approach to information discovery /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 1997. http://wwwlib.umi.com/cr/ucsd/fullcit?p9804033.

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Bechchi, Mounir. "Clustering-based Approximate Answering of Query Result in Large and Distributed Databases." Phd thesis, Université de Nantes, 2009. http://tel.archives-ouvertes.fr/tel-00475917.

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Les utilisateurs des bases de données doivent faire face au problème de surcharge d'information lors de l'interrogation de leurs données, qui se traduit par un nombre de réponses trop élevé à des requêtes exploratoires. Pour remédier à ce problème, nous proposons un algorithme efficace et rapide, ap- pelé ESRA (Explore-Select-Rearrange Algorithm), qui utilise les résumés SAINTETIQ pré-calculés sur l'ensemble des données pour regrouper les réponses à une requête utilisateur en un ensemble de classes (ou résumés) organisées hiérarchiquement. Chaque classe décrit un sous-ensemble de résul- tats dont les propriétés sont voisines. L'utilisateur pourra ainsi explorer la hiérarchie pour localiser les données qui l'intéressent et en écarter les autres. Les résultats expérimentaux montrent que l'al- gorithme ESRA est efficace et fournit des classes bien formées (i.e., leur nombre reste faible et elles sont bien séparées). Cependant, le modèle SAINTETIQ, utilisé par l'algorithme ESRA, exige que les données soient disponibles sur le serveur des résumés. Cette hypothèse rend inapplicable l'algo- rithme ESRA dans des environnements distribués où il est souvent impossible ou peu souhaitable de rassembler toutes les données sur un même site. Pour remédier à ce problème, nous proposons une collection d'algorithmes qui combinent deux résumés générés localement et de manière autonome sur deux sites distincts pour en produire un seul résumant l'ensemble des données distribuées, sans accéder aux données d'origine. Les résultats expérimentaux montrent que ces algorithmes sont aussi performants que l'approche centralisée (i.e., SAINTETIQ appliqué aux données après regroupement sur un même site) et produisent des hiérarchies très semblables en structure et en qualité à celles produites par l'approche centralisée.
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Brodsky, Lloyd. "A knowledge-based preprocessor for approximate joins in improperly designed transaction databases." Thesis, Massachusetts Institute of Technology, 1991. http://hdl.handle.net/1721.1/13744.

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Ozturk, Ozgur. "Feature extraction and similarity-based analysis for proteome and genome databases." The Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=osu1190138805.

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Books on the topic "Approximate database"

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Vilarem, Charlotte. Approximate key and foreign key discovery in relational databases. Ottawa: National Library of Canada, 2002.

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Paul, Wasserman, and Grefsheim Suzanne, eds. Encyclopedia of health information sources: A bibliographic guide to approximately 13,000 citations for publications, organizations, and other sources of information on more than 450 health-related subjects ... Detroit, Mich: Gale Research Co., 1987.

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Paul, Wasserman, Kelly James R. 1947-, and Vikor Desider L. 1950-, eds. Encyclopedia of public affairs information sources: A bibliographic guide to approximately 8,000 citations for publications, organizations, and other sources of information on nearly 300 subjects relating to public affairs ... Detroit, Mich: Gale Research Co., 1988.

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CeciliaAnn, Marlow, and Thomas Robert C, eds. The directory of directories: An annotated guide to approximately 9600 business and industrial directories, professional and scientific rosters, directory databases, and other lists and guides of all kinds. Detroit: Gale Research, 1987.

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Directories in print, 1990: An annotated guide to approximately 10,000 business and industrial directories, professional and scientific rosters, directory databases, and other lists and guides of all kinds that are published in the United States or that are national or regional in scope or interest. 7th ed. Detroit: Gale Research Inc., 1990.

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Book chapters on the topic "Approximate database"

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Novák, Vilém. "Approximate Reasoning." In Encyclopedia of Database Systems, 146–47. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_5012.

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Novák, Vilém. "Approximate Reasoning." In Encyclopedia of Database Systems, 119–20. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_5012.

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Novák, Vilém. "Approximate Reasoning." In Encyclopedia of Database Systems, 1–2. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4899-7993-3_5012-2.

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Liu, Qing. "Approximate Query Processing." In Encyclopedia of Database Systems, 139–46. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_534.

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Liu, Qing. "Approximate Query Processing." In Encyclopedia of Database Systems, 113–19. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_534.

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Liu, Qing. "Approximate Query Processing." In Encyclopedia of Database Systems, 1–7. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4899-7993-3_534-2.

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Sistla, A. Prasad, and Clement Yu. "Retrieval of Pictures Using Approximate Matching." In Multimedia Database Systems, 101–12. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-642-60950-3_4.

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Kivinen, Jyrki, and Heikki Mannila. "Approximate dependency inference from relations." In Database Theory — ICDT '92, 86–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/3-540-56039-4_34.

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Lin, Chunbo, Jingdong Li, Xiaoling Wang, Xingjian Lu, and Ji Zhang. "WFApprox: Approximate Window Functions Processing." In Database Systems for Advanced Applications, 72–87. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59410-7_5.

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Siberski, Wolf, and Wolfgang Nejdl. "Approximate Queries in Peer-to-Peer Systems." In Encyclopedia of Database Systems, 137–39. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_1229.

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Conference papers on the topic "Approximate database"

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Leong, Hong Va, Alvin Chan, and Grace Ngai. "Approximate Web Database Snapshots." In 2015 IEEE 39th Annual Computer Software and Applications Conference (COMPSAC). IEEE, 2015. http://dx.doi.org/10.1109/compsac.2015.144.

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Ferro, Alfredo, Giovanni Gallo, and Rosalba Giugno. "Approximate search in image database." In Electronic Imaging, edited by Minerva M. Yeung, Boon-Lock Yeo, and Charles A. Bouman. SPIE, 1999. http://dx.doi.org/10.1117/12.373551.

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Korotkov, Alexander. "Database Index for Approximate String Matching." In Spring/Summer Young Researchers' Colloquium on Software Engineering. Institute for System Programming of the Russian Academy of Sciences, 2010. http://dx.doi.org/10.15514/syrcose-2010-4-27.

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Zheng, Bolong, Nicholas Jing Yuan, Kai Zheng, Xing Xie, Shazia Sadiq, and Xiaofang Zhou. "Approximate keyword search in semantic trajectory database." In 2015 IEEE 31st International Conference on Data Engineering (ICDE). IEEE, 2015. http://dx.doi.org/10.1109/icde.2015.7113349.

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Perera, Kasun S., Martin Hahmann, Wolfgang Lehner, Torben Bach Pedersen, and Christian Thomsen. "Efficient Approximate OLAP Querying Over Time Series." In the 20th International Database Engineering & Applications Symposium. New York, New York, USA: ACM Press, 2016. http://dx.doi.org/10.1145/2938503.2938526.

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Bergami, Giacomo, Flavio Bertini, and Danilo Montesi. "On approximate nesting of multiple social network graphs." In the 23rd International Database Applications & Engineering Symposium. New York, New York, USA: ACM Press, 2019. http://dx.doi.org/10.1145/3331076.3331081.

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Gao, Linlin, Haiwei Pan, Qilong Han, Xiaoqin Xie, Zhiqiang Zhang, Xiao Zhai, and Pengyuan Li. "Finding Frequent Approximate Subgraphs in medical image database." In 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2015. http://dx.doi.org/10.1109/bibm.2015.7359821.

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Li, Xin, and Jun Zhang. "E-Commerce Web Database Approximate Query Results Ranking." In 2011 International Conference on Internet Technology and Applications (iTAP). IEEE, 2011. http://dx.doi.org/10.1109/itap.2011.6006139.

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Fiot, Celine, Anne Laurent, and Maguelonne Teisseire. "Approximate Sequential Patterns for Incomplete Sequence Database Mining." In 2007 IEEE International Fuzzy Systems Conference. IEEE, 2007. http://dx.doi.org/10.1109/fuzzy.2007.4295445.

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Agarwal, Nitin, Magdiel Galan Oliveras, and Yi Chen. "Approximate Structural Matching over Ordered XML Documents." In 11th International Database Engineering and Applications Symposium (IDEAS 2007). IEEE, 2007. http://dx.doi.org/10.1109/ideas.2007.4318089.

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Reports on the topic "Approximate database"

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Liu, J. W. Approximate Database Queries and Updates. Fort Belvoir, VA: Defense Technical Information Center, November 1995. http://dx.doi.org/10.21236/ada311068.

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Garton, Timothy. Data enrichment and enhanced accessibility of waterborne commerce numerical data : spatially depicting the National Waterway Network. Engineer Research and Development Center (U.S.), December 2020. http://dx.doi.org/10.21079/11681/39223.

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This report provides methodologies and processes of data enrichment and enhanced accessibility of Waterborne Commerce and Statistics Center (WCSC) maintained databases. These databases house tabular and statistical data that reports on The U.S. Army Corps of Engineers (USACE) Civil Works Division National Waterway Network (NWN), which geospatially represents approximately 1,000 harbors and 25,000 miles of channels and waterways. WCSC is a division of The Institute for Water Resources (IWR). They have been tasked with the international collection, maintenance, and archival of all records involving commercial movements and commerce that occur on federal waterways. The current records structure is a large, tabular dataset and limited to the systems and processes put in place prior to the computing standards and capabilities available today. Methods have been tested and utilized to bring the tabular datasets into an optimized, modern geospatial network and expanded upon to create a higher resolution than previously maintained by the WCSC. This report will expand upon the applied methodologies to optimize data queries and the overall enhancement of the data system to allow for linkages to various other sources of information for commerce data enhancement for decision support assistance.
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Evans, Julie, Kendra Sikes, and Jamie Ratchford. Vegetation classification at Lake Mead National Recreation Area, Mojave National Preserve, Castle Mountains National Monument, and Death Valley National Park: Final report (Revised with Cost Estimate). National Park Service, October 2020. http://dx.doi.org/10.36967/nrr-2279201.

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Vegetation inventory and mapping is a process to document the composition, distribution and abundance of vegetation types across the landscape. The National Park Service’s (NPS) Inventory and Monitoring (I&M) program has determined vegetation inventory and mapping to be an important resource for parks; it is one of 12 baseline inventories of natural resources to be completed for all 270 national parks within the NPS I&M program. The Mojave Desert Network Inventory & Monitoring (MOJN I&M) began its process of vegetation inventory in 2009 for four park units as follows: Lake Mead National Recreation Area (LAKE), Mojave National Preserve (MOJA), Castle Mountains National Monument (CAMO), and Death Valley National Park (DEVA). Mapping is a multi-step and multi-year process involving skills and interactions of several parties, including NPS, with a field ecology team, a classification team, and a mapping team. This process allows for compiling existing vegetation data, collecting new data to fill in gaps, and analyzing the data to develop a classification that then informs the mapping. The final products of this process include a vegetation classification, ecological descriptions and field keys of the vegetation types, and geospatial vegetation maps based on the classification. In this report, we present the narrative and results of the sampling and classification effort. In three other associated reports (Evens et al. 2020a, 2020b, 2020c) are the ecological descriptions and field keys. The resulting products of the vegetation mapping efforts are, or will be, presented in separate reports: mapping at LAKE was completed in 2016, mapping at MOJA and CAMO will be completed in 2020, and mapping at DEVA will occur in 2021. The California Native Plant Society (CNPS) and NatureServe, the classification team, have completed the vegetation classification for these four park units, with field keys and descriptions of the vegetation types developed at the alliance level per the U.S. National Vegetation Classification (USNVC). We have compiled approximately 9,000 existing and new vegetation data records into digital databases in Microsoft Access. The resulting classification and descriptions include approximately 105 alliances and landform types, and over 240 associations. CNPS also has assisted the mapping teams during map reconnaissance visits, follow-up on interpreting vegetation patterns, and general support for the geospatial vegetation maps being produced. A variety of alliances and associations occur in the four park units. Per park, the classification represents approximately 50 alliances at LAKE, 65 at MOJA and CAMO, and 85 at DEVA. Several riparian alliances or associations that are somewhat rare (ranked globally as G3) include shrublands of Pluchea sericea, meadow associations with Distichlis spicata and Juncus cooperi, and woodland associations of Salix laevigata and Prosopis pubescens along playas, streams, and springs. Other rare to somewhat rare types (G2 to G3) include shrubland stands with Eriogonum heermannii, Buddleja utahensis, Mortonia utahensis, and Salvia funerea on rocky calcareous slopes that occur sporadically in LAKE to MOJA and DEVA. Types that are globally rare (G1) include the associations of Swallenia alexandrae on sand dunes and Hecastocleis shockleyi on rocky calcareous slopes in DEVA. Two USNVC vegetation groups hold the highest number of alliances: 1) Warm Semi-Desert Shrub & Herb Dry Wash & Colluvial Slope Group (G541) has nine alliances, and 2) Mojave Mid-Elevation Mixed Desert Scrub Group (G296) has thirteen alliances. These two groups contribute significantly to the diversity of vegetation along alluvial washes and mid-elevation transition zones.
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Ruiz, Pablo, Craig Perry, Alejando Garcia, Magali Guichardot, Michael Foguer, Joseph Ingram, Michelle Prats, Carlos Pulido, Robert Shamblin, and Kevin Whelan. The Everglades National Park and Big Cypress National Preserve vegetation mapping project: Interim report—Northwest Coastal Everglades (Region 4), Everglades National Park (revised with costs). National Park Service, November 2020. http://dx.doi.org/10.36967/nrr-2279586.

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The Everglades National Park and Big Cypress National Preserve vegetation mapping project is part of the Comprehensive Everglades Restoration Plan (CERP). It is a cooperative effort between the South Florida Water Management District (SFWMD), the United States Army Corps of Engineers (USACE), and the National Park Service’s (NPS) Vegetation Mapping Inventory Program (VMI). The goal of this project is to produce a spatially and thematically accurate vegetation map of Everglades National Park and Big Cypress National Preserve prior to the completion of restoration efforts associated with CERP. This spatial product will serve as a record of baseline vegetation conditions for the purpose of: (1) documenting changes to the spatial extent, pattern, and proportion of plant communities within these two federally-managed units as they respond to hydrologic modifications resulting from the implementation of the CERP; and (2) providing vegetation and land-cover information to NPS park managers and scientists for use in park management, resource management, research, and monitoring. This mapping project covers an area of approximately 7,400 square kilometers (1.84 million acres [ac]) and consists of seven mapping regions: four regions in Everglades National Park, Regions 1–4, and three in Big Cypress National Preserve, Regions 5–7. The report focuses on the mapping effort associated with the Northwest Coastal Everglades (NWCE), Region 4 , in Everglades National Park. The NWCE encompasses a total area of 1,278 square kilometers (493.7 square miles [sq mi], or 315,955 ac) and is geographically located to the south of Big Cypress National Preserve, west of Shark River Slough (Region 1), and north of the Southwest Coastal Everglades (Region 3). Photo-interpretation was performed by superimposing a 50 × 50-meter (164 × 164-feet [ft] or 0.25 hectare [0.61 ac]) grid cell vector matrix over stereoscopic, 30 centimeters (11.8 inches) spatial resolution, color-infrared aerial imagery on a digital photogrammetric workstation. Photo-interpreters identified the dominant community in each cell by applying majority-rule algorithms, recognizing community-specific spectral signatures, and referencing an extensive ground-truth database. The dominant vegetation community within each grid cell was classified using a hierarchical classification system developed specifically for this project. Additionally, photo-interpreters categorized the absolute cover of cattail (Typha sp.) and any invasive species detected as either: Sparse (10–49%), Dominant (50–89%), or Monotypic (90–100%). A total of 178 thematic classes were used to map the NWCE. The most common vegetation classes are Mixed Mangrove Forest-Mixed and Transitional Bayhead Shrubland. These two communities accounted for about 10%, each, of the mapping area. Other notable classes include Short Sawgrass Marsh-Dense (8.1% of the map area), Mixed Graminoid Freshwater Marsh (4.7% of the map area), and Black Mangrove Forest (4.5% of the map area). The NWCE vegetation map has a thematic class accuracy of 88.4% with a lower 90th Percentile Confidence Interval of 84.5%.
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