Predictable universally unique identification of sequential events on complex objects
| Autor: | Pereira-Santos, Davi, Dalforno, Gabriel, Carvalho, André C. P. L. F. |
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| Rok vydání: | 2021 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | Universal identifiers and hashing have been widely adopted in computer science from distributed financial transactions to data science. This is a consequence of their capability to avoid many shortcomings of relative identifiers, such as limited scope and the need for central management. However, the current identifiers in use are isolated entities which cannot provide much information about the relationship between objects. As an example, if one has both the identifiers of an object and its transformed version, no information about how they are related can be obtained without resorting to some kind of authority or additionally appended information. Moreover, given an input object and an arbitrarily long sequence of costly steps, one cannot currently predict the identifier of the outcome without actually processing the entire sequence. The capability of predicting the resulting identifier and avoiding redundant calculations is highly desirable in an efficient unmanaged system. In this paper, we propose a new kind of unique identifier that is calculated from the list of events that can produce an object, instead of directly identifying it by content. This provides an identification scheme regardless of the object's current existence, thus allowing to inexpensively check for its content in a database and retrieve it when it has already been calculated before. These identifiers are proposed in the context of abstract algebra, where objects are represented by elements that can be operated according to useful properties, such as associativity, order sensitivity when desired, and reversibility, while simplicity of implementation and strong guarantees are given by well known group theory results. Comment: 23 pages, 1 figure, for associated software package, see https://github.com/davips/garoupa, submitted to Data & Knowledge Engineering - Elsevier |
| Databáze: | arXiv |
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