Intelligent System for Providing Migration Through Dynamic Data Deserialization

R. A. Tomakova, D. V. Ivanov, N. A. Korsunsky
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Abstract

The purpose of research. Timely provision of data transfer between information systems allows you to quickly exchange resources. However, applications may have different data formats and structures. Therefore, the aim of the research was to develop models, methods and algorithms for a system of asynchronous deserialization of a data string, providing an increase in the efficiency of determining data models by generating strongly typed objects.Methods. The way to deserialize models from data involves line-by-line decomposition of a JSON-file line with the definition of key-value types and their correlation with the data model: character, string, number, boolean value. After that, the web controller conducts asynchronous generation of the class and its objects. To classify string values, serialized string value classifiers are used. For asynchronous generation of objects, a system of “contracts” of models and algorithms for executing and converting these models are used.Results. The deserializer consists of a system of four model analysis controllers and a value generation algorithm. A simple single model deserialization model allows the model to be mapped to relational database table headers to enable model migration between systems. The generated objects are represented by static data types, which ensures that they can be written to any DBMS system using built-in tools. A complex model represents a block of values as a system of different models. Software has been developed for connecting source and target databases, which allows you to migrate data from the created models. Generated values are represented as full-fledged objects and can be used to create a web interface for applications, edit data models, and manage the model system.Conclusion. Experimental studies on deserialization of models from a JSON string containing complex model classes showed an average accuracy of determining the data type of models in 92% of cases, in particular when determining the types of values "character" and "string". The created models are presented in the form of a data table and can be used to ensure the migration of these models.
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通过动态数据反序列化提供迁移的智能系统
研究的目的。在信息系统之间及时提供数据传输,可以快速交换资源。然而,应用程序可能有不同的数据格式和结构。因此,研究的目的是为数据字符串的异步反序列化系统开发模型、方法和算法,通过生成强类型对象来提高确定数据模型的效率。从数据反序列化模型的方法包括逐行分解 JSON 文件行,定义键值类型及其与数据模型的相关性:字符、字符串、数字、布尔值。然后,网络控制器异步生成类及其对象。要对字符串值进行分类,需要使用序列化字符串值分类器。为了异步生成对象,使用了模型 "合约 "系统以及执行和转换这些模型的算法。反序列化器包括一个由四个模型分析控制器和一个值生成算法组成的系统。简单的单一模型反序列化模型可将模型映射到关系数据库表头,从而实现系统间的模型迁移。生成的对象由静态数据类型表示,这确保它们可以使用内置工具写入任何数据库管理系统。复杂模型将一个数值块表示为一个由不同模型组成的系统。已开发出用于连接源数据库和目标数据库的软件,可以从创建的模型中迁移数据。生成的值表示为成熟的对象,可用于创建应用程序的网络接口、编辑数据模型和管理模型系统。对包含复杂模型类的 JSON 字符串进行模型反序列化的实验研究表明,在 92% 的情况下,确定模型数据类型的平均准确率很高,尤其是在确定值类型为 "字符 "和 "字符串 "时。创建的模型以数据表的形式呈现,可用于确保这些模型的迁移。
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