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Surv."],"published-print":{"date-parts":[[2023,7,31]]},"abstract":"<jats:p>\n                    This two-part comprehensive survey is devoted to a computing framework most commonly known under the names Hyperdimensional Computing and Vector Symbolic Architectures (HDC\/VSA). Both names refer to a family of computational models that use high-dimensional distributed representations and rely on the algebraic properties of their key operations to incorporate the advantages of structured symbolic representations and distributed vector representations. Notable models in the HDC\/VSA family are Tensor Product Representations, Holographic Reduced Representations, Multiply-Add-Permute, Binary Spatter Codes, and Sparse Binary Distributed Representations but there are other models too. HDC\/VSA is a highly interdisciplinary field with connections to computer science, electrical engineering, artificial intelligence, mathematics, and cognitive science. This fact makes it challenging to create a thorough overview of the field. However, due to a surge of new researchers joining the field in recent years, the necessity for a comprehensive survey of the field has become extremely important. Therefore, amongst other aspects of the field, this Part I surveys important aspects such as: known computational models of HDC\/VSA and transformations of various input data types to high-dimensional distributed representations. Part\u00a0II of this survey\u00a0[\n                    <jats:xref ref-type=\"bibr\">84<\/jats:xref>\n                    ] is devoted to applications, cognitive computing and architectures, as well as directions for future work. The survey is written to be useful for both newcomers and practitioners.\n                  <\/jats:p>","DOI":"10.1145\/3538531","type":"journal-article","created":{"date-parts":[[2022,5,19]],"date-time":"2022-05-19T08:53:49Z","timestamp":1652950429000},"page":"1-40","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":124,"title":["A Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part I: Models and Data Transformations"],"prefix":"10.1145","volume":"55","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6032-6155","authenticated-orcid":false,"given":"Denis","family":"Kleyko","sequence":"first","affiliation":[{"name":"University of California at Berkeley, USA and Research Institutes of Sweden, Kista, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3414-5334","authenticated-orcid":false,"given":"Dmitri A.","family":"Rachkovskij","sequence":"additional","affiliation":[{"name":"International Research and Training Center for Information Technologies and Systems, Ukraine and Lule\u00e5 University of Technology, Lule\u00e5, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0069-640X","authenticated-orcid":false,"given":"Evgeny","family":"Osipov","sequence":"additional","affiliation":[{"name":"Lule\u00e5 University of Technology, Lule\u00e5, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3141-4970","authenticated-orcid":false,"given":"Abbas","family":"Rahimi","sequence":"additional","affiliation":[{"name":"IBM Research \u2013 Zurich, Zurich, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,12,7]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0022-0000(03)00025-4"},{"issue":"012316","key":"e_1_3_2_3_2","first-page":"1","article-title":"Tensor-product versus geometric-product coding","volume":"77","author":"Aerts D.","year":"2008","unstructured":"D. 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