SBVR

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.

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

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