Research on optimization of real-time efficient storage algorithm in data information serialization

Huang, Bin; Tang, Yibo

doi:10.1371/journal.pone.0260697

Cited by 4 publications

(2 citation statements)

References 19 publications

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“…The Phenopacket Schema was created using the open source protocol buffers framework (“protobuf” for short) developed by Google [ 13 ]. Protocol buffers provide a language-neutral, platform-neutral, and extensible mechanism for serializing structured data.…”

Section: Methodsmentioning

confidence: 99%

Phenopacket-tools: Building and validating GA4GH Phenopackets

et al. 2023

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The Global Alliance for Genomics and Health (GA4GH) is a standards-setting organization that is developing a suite of coordinated standards for genomics. The GA4GH Phenopacket Schema is a standard for sharing disease and phenotype information that characterizes an individual person or biosample. The Phenopacket Schema is flexible and can represent clinical data for any kind of human disease including rare disease, complex disease, and cancer. It also allows consortia or databases to apply additional constraints to ensure uniform data collection for specific goals. We present phenopacket-tools, an open-source Java library and command-line application for construction, conversion, and validation of phenopackets. Phenopacket-tools simplifies construction of phenopackets by providing concise builders, programmatic shortcuts, and predefined building blocks (ontology classes) for concepts such as anatomical organs, age of onset, biospecimen type, and clinical modifiers. Phenopacket-tools can be used to validate the syntax and semantics of phenopackets as well as to assess adherence to additional user-defined requirements. The documentation includes examples showing how to use the Java library and the command-line tool to create and validate phenopackets. We demonstrate how to create, convert, and validate phenopackets using the library or the command-line application. Source code, API documentation, comprehensive user guide and a tutorial can be found at https://github.com/phenopackets/phenopacket-tools. The library can be installed from the public Maven Central artifact repository and the application is available as a standalone archive. The phenopacket-tools library helps developers implement and standardize the collection and exchange of phenotypic and other clinical data for use in phenotype-driven genomic diagnostics, translational research, and precision medicine applications.

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Section: Methodsmentioning

confidence: 99%

Phenopacket-tools: Building and validating GA4GH Phenopackets

et al. 2023

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“…Additionally, protobuf can easily be converted into JSON or YAML for human consumption. [14] In protobuf terminology, a message is a data structure that can contain multiple fields. Messages can be combined hierarchically so that one message contains another message.…”

Section: Basic Introduction To Protobufmentioning

confidence: 99%

GA4GH Phenopackets: A Practical Introduction

et al. 2022

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The Global Alliance for Genomics and Health (GA4GH) is developing a suite of coordinated standards for genomics for healthcare. The Phenopacket is a new GA4GH standard for sharing disease and phenotype information that characterizes an individual person, linking that individual to detailed phenotypic descriptions, genetic information, diagnoses, and treatments. A detailed example is presented that illustrates how to use the schema to represent the clinical course of a patient with retinoblastoma, including demographic information, the clinical diagnosis, phenotypic features and clinical measurements, an examination of the extirpated tumor, therapies, and the results of genomic analysis. The Phenopacket Schema, together with other GA4GH data and technical standards, will enable data exchange and provide a foundation for the computational analysis of disease and phenotype information to improve our ability to diagnose and conduct research on all types of disorders, including cancer and rare diseases.

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Intelligent System for Providing Migration Through Dynamic Data Deserialization

Tomakova,

Ivanov,

Korsunsky

2024

jour

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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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Research on optimization of real-time efficient storage algorithm in data information serialization

Cited by 4 publications

References 19 publications

Phenopacket-tools: Building and validating GA4GH Phenopackets

Phenopacket-tools: Building and validating GA4GH Phenopackets

GA4GH Phenopackets: A Practical Introduction

Intelligent System for Providing Migration Through Dynamic Data Deserialization

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