Jaroslaw Zelinski (Independent Researcher, UK)
Source Title: Emerging Challenges, Solutions, and Best Practices for Digital Enterprise Transformation

Copyright: ? 2021 | Pages: 24 DOI: 10.4018/978 – 1‑7998 – 8587‑0.ch003

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Abstract

This stu­dy pre­sents a method for the sto­ra­ge of data orga­ni­zed in digi­tal docu­ments, which is pro­ven in prac­ti­ce. The discus­sed method does not bear any disa­dvan­ta­ges of the rela­tio­nal model used for data orga­ni­za­tion, such as the loss of data con­text and com­pli­ca­tions evo­ked by the lack of data redun­dan­cy. The method pre­sen­ted here can be used for data orga­ni­za­tion into docu­ments (digi­tal and paper) as clas­si­fied aggre­ga­tes and for data clas­si­fi­ca­tion. The stu­dy also descri­bes a new meta­mo­del for the data struc­tu­re which assu­mes that docu­ments, being data struc­tu­res, form com­pact aggre­ga­tes, clas­si­fied as objects, or event descrip­tions, thus always assi­gning them a spe­ci­fic and unam­bi­gu­ous con­text. Furthermore, the stu­dy pre­sents a design method for docu­ments as con­text aggre­ga­tes that allows leve­ling the disa­dvan­ta­ges of the rela­tio­nal model and ensu­res effi­cient infor­ma­tion mana­ge­ment. The work also con­ta­ins prac­ti­cal exam­ples of the appli­ca­tion of the descri­bed method.Chapter PreviewTop

Background

Documents often con­ta­in a high volu­me of dif­fe­rent data join­tly for­ming mul­ti­ple con­text data­sets (aggre­ga­tes). Application of rela­tio­nal model for the orga­ni­za­tion of such data leads to the gene­ra­tion of a com­pre­hen­si­ve sys­tem of rela­tio­nal lin­ked tables, whi­le the remo­val of redun­dan­cies often results in the loss of con­tent con­text of indi­vi­du­al fields in the tables. This gives rise to a neces­si­ty to use the high­ly com­plex SQL queries so that the­se docu­ments can be saved in and retrie­ved from this data­ba­se. Thus, the data­ba­se itself sole­ly con­ta­ins data depri­ved of the con­text pre­sent in tables, not the documents.

Many authors have poin­ted out the pro­blem of com­ple­xi­ty and the loss of uni­form rela­tio­nal model con­text (Ślęzak et al., 2018). Those authors have sug­ge­sted that con­te­xts sho­uld be sepa­ra­ted in lar­ge rela­tio­nal data models. However, recom­men­da­tion of con­text sepa­ra­tion (Evans, 2003; Fowler, 1997; Fowler & Rice, 2005), whi­le main­ta­ining the rela­tio­nal model, does not help to solve the issue com­ple­te­ly (Awang et al., 2012, 2012, 2012).

Context chan­ge often alters the meaning of data (Danesi, 2004). Attempts made for main­ta­ining the meaning of data fre­qu­en­tly result in the for­ma­tion of com­pre­hen­si­ve rela­tio­nal data models, thus gene­ra­ting addi­tio­nal costs. Therefore, using one rela­tio­nal data model to save the con­tents of nume­ro­us dif­fe­rent docu­ments can make such a sys­tem an enor­mo­us and indi­vi­si­ble mono­lith, which is expen­si­ve to deve­lop as well as maintain.

Key Terms in this Chapter

Document Database: A class of non-rela­tio­nal, some­ti­mes cal­led NoSQL data­ba­se, the docu­ment data model has beco­me the most popu­lar alter­na­ti­ve to tabu­lar, rela­tio­nal databases.

Notion Model (Also Fact Model, Conceptual Schema): Represented by a set of sen­ten­ces, each of which con­no­tes either a rule or a gro­und fact (OMG​.org, 2019).

Object Paradigm: A para­digm that reco­gni­zes that the sys­tem is built of inde­pen­dent and encap­su­la­ted objects with spe­ci­fic responsibilities.

Source: Digital Documents as Data Carriers and a Method of Data Management Guaranteeing the Unambiguity of the Recorded Information: Ontology-Oriented Data Management and Document Databases: Business & Management Book Chapter | IGI Global

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