Sparse p-adic data coding for computationally efficient and effective big data analytics

Hdl Handle:
http://hdl.handle.net/10545/619218
Title:
Sparse p-adic data coding for computationally efficient and effective big data analytics
Authors:
Murtagh, Fionn ( 0000-0002-0589-6892 )
Abstract:
We develop the theory and practical implementation of p-adic sparse coding of data. Rather than the standard, sparsifying criterion that uses the $L_0$ pseudo-norm, we use the p-adic norm. We require that the hierar- chy or tree be node-ranked, as is standard practice in agglomerative and other hierarchical clustering, but not necessarily with decision trees. In order to structure the data, all computational processing operations are direct reading of the data, or are bounded by a constant number of direct readings of the data, implying linear computational time. Through p-adic sparse data coding, effi cient storage results, and for bounded p-adic norm stored data, search and retrieval are constant time operations. Examples show the e ffectiveness of this new approach to content-driven encoding and displaying of data.
Affiliation:
Department of Computing and Mathematics, Big Data Lab, University of Derby
Citation:
Murtagh, F. 'Sparse p-adic data coding for computationally efficient and effective big data analytics' 2016, 8 (3):236 P-Adic Numbers, Ultrametric Analysis, and Applications
Publisher:
Pleiades Publishing Ltd. (Springer)
Journal:
P-Adic Numbers, Ultrametric Analysis, and Applications
Issue Date:
14-Aug-2016
URI:
http://hdl.handle.net/10545/619218
DOI:
10.1134/S2070046616030055
Additional Links:
http://link.springer.com/10.1134/S2070046616030055; https://arxiv.org/pdf/1604.06961v1.pdf
Type:
Article
Language:
en
ISSN:
2070-0466; 2070-0474
Appears in Collections:
Department of Electronics, Computing & Maths

Full metadata record

DC FieldValue Language
dc.contributor.authorMurtagh, Fionnen
dc.date.accessioned2016-09-01T11:02:34Z-
dc.date.available2016-09-01T11:02:34Z-
dc.date.issued2016-08-14-
dc.identifier.citationMurtagh, F. 'Sparse p-adic data coding for computationally efficient and effective big data analytics' 2016, 8 (3):236 P-Adic Numbers, Ultrametric Analysis, and Applicationsen
dc.identifier.issn2070-0466-
dc.identifier.issn2070-0474-
dc.identifier.doi10.1134/S2070046616030055-
dc.identifier.urihttp://hdl.handle.net/10545/619218-
dc.description.abstractWe develop the theory and practical implementation of p-adic sparse coding of data. Rather than the standard, sparsifying criterion that uses the $L_0$ pseudo-norm, we use the p-adic norm. We require that the hierar- chy or tree be node-ranked, as is standard practice in agglomerative and other hierarchical clustering, but not necessarily with decision trees. In order to structure the data, all computational processing operations are direct reading of the data, or are bounded by a constant number of direct readings of the data, implying linear computational time. Through p-adic sparse data coding, effi cient storage results, and for bounded p-adic norm stored data, search and retrieval are constant time operations. Examples show the e ffectiveness of this new approach to content-driven encoding and displaying of data.en
dc.language.isoenen
dc.publisherPleiades Publishing Ltd. (Springer)en
dc.relation.urlhttp://link.springer.com/10.1134/S2070046616030055en
dc.relation.urlhttps://arxiv.org/pdf/1604.06961v1.pdfen
dc.rightsArchived with thanks to P-Adic Numbers, Ultrametric Analysis, and Applicationsen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.subjectBig dataen
dc.subjectP-adic numbersen
dc.subjectUltrametric topologyen
dc.subjectHierarchical clusteringen
dc.subjectBinary rooted treeen
dc.subjectComputational and storage complexityen
dc.titleSparse p-adic data coding for computationally efficient and effective big data analyticsen
dc.typeArticleen
dc.contributor.departmentDepartment of Computing and Mathematics, Big Data Lab, University of Derbyen
dc.identifier.journalP-Adic Numbers, Ultrametric Analysis, and Applicationsen
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