Data solutions

SDQ

(Smart Data Quality)

SDQ establishes systematic enterprise data quality processes, improving quality and stability to increase business efficiency. [Smart] Supports data architecture through management of data standards: words, terms and domains. [Data] Its lightweight, accessible design enables fast, reliable quality management. [Quality] Automates quality assessments and analyzes results to support diagnosis and improvement.

SDQ benefits

More efficient data quality management
Clearer diagnostic results make quality status easier to understand. Simple, smart assessments and reports improve the efficiency of data quality management.
Reliable data quality
Schedule regular automated assessments to continuously monitor and improve incoming data quality and ensure reliability.
Stable data operations
Identify suspected erroneous data for root-cause analysis, trace the source programs responsible and improve them to safeguard operational data.

SDQ features	Details
Ministry of the Interior and Safety assessment criteria	Built-in Ministry of the Interior and Safety quality assessment criteria Assess standards and structure as well as data values Public-data-specific criteria, including open data designation
Simple, smart quality assessments	Simple design focused on data quality assessment Convenient domain-based quality assessment User-centered features with an intuitive interface
Fast, convenient quality assessments	Multithreading for strong assessment performance Efficient resource use through scheduling Support for DBMS parallel processing
Built on open-source software	Architecture designed for scalability and flexibility Support for multiple platforms

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SDQ for AI

(AI training data quality management solution)

Developed from B2EN's AI and big data expertise, SDQ for AI provides systematic quality management for AI training data, laying the foundation for high-quality training datasets.

SDQ for AI benefits

Comprehensive quality management for training data across diverse fields
SDQ for AI provides advanced inspection and analysis for natural language, healthcare, autonomous driving and other training data. National-scale experience gained through AI training data syntax accuracy inspection projects underpins its cross-industry expertise and reliability.
Greater usability through GUI-based configuration
GUI-based configuration allows non-developers to inspect training data easily. Complex hierarchical datasets are presented as easy-to-understand grids, enabling clear communication between designers and quality managers.
Fast inspection of large-scale training datasets
Ensures fast inspection and analysis of datasets containing more than five million files.

SDQ for AI features	Details
Automatic inspection rule generation	Automatically generate rules based on the global JSON Schema standard Display complex hierarchies as tables for clear, convenient understanding Edit detailed inspection rules through a GUI
Syntax accuracy inspection	Inspect the three syntax accuracy indicators defined in NIA quality management guidelines File integrity checks Syntax accuracy checks for annotation files in XML, CSV and other formats
Statistical diversity analysis	GUI-based analysis of dataset size, class distributions and other diversity metrics Combine and transform information to calculate custom composite statistics
Automatic comprehensive reporting	Automatically generate Excel reports for syntax accuracy and statistical diversity results Export error lists for each syntax accuracy indicator in CSV format

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SMETA

(Smart Metadata)

SMETA integrates enterprise metadata management, drawing on B2EN's experience and technology in public data access. It provides data standards, structures and change impact information to ensure consistency, establish structural quality and improve accessibility, making data assets more transparent.

SMETA benefits

Reliable standard metadata management
Classification terms ensure consistent domains, while synonym and prohibited-term management keeps expressions consistent for a reliable standard dictionary. Valid-value management for code domains prevents quality degradation, and impact analysis maintains data integrity.
Systematic DBMS object management
Collect enterprise DBMS catalogs and compare them against standards and models to verify currency. Support role-based request and approval workflows and easily manage object change histories.

SMETA features	Details
Data standards management	Register and manage enterprise words, domains and terms Manage domain groups and look up codes Manage prohibited terms, synonyms and administrative terminology dictionaries
Data model management	Register and manage subject areas, entities, attributes and other models Integration with ERwin models
Database management	Register and manage DBMS tables and indexes Automatically generate DDL and objects System DBMS gap analysis and statistics
Additional support tools	Metadata request and approval workflows Standardization tools, notices and unified search Eclipse terminology lookup and utilization plug-in

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SFLOW

(Smart Data Integration)

SFLOW brings together B2EN's data processing technology and expertise to collect and integrate diverse internal and external data quickly and easily. It delivers data to operational and analytical systems, providing a foundation for data-driven business.

SFLOW benefits

Configuration
GUI-based configuration instead of programming ensures development productivity and consistent quality.
Overall
Support HTTP, FTP, JMS and other protocols, with collection from databases, files and sensors.
Realtime
Real-time monitoring through dashboards and topology maps enables rapid fault identification and response.
Enhancement
Extend data value through direct integration of collected data with big data platforms.

SFLOW features	Details
Services	Support both HTTP/FTP services and dedicated messaging between SFLOW agents Message relay enables online processing across segregated networks
Processes	Combine components to meet diverse processing requirements Process design, version control and runtime deployment
Monitoring	Integrated monitoring from topology maps and dashboards to processing history and detailed tracking Multiple alert options help operators quickly detect and respond to failures
Remote control	Central operators can control distributed agents and perform various queries Patches and upgrades through smart updates

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SPORTAL

(Smart Data Portal)

A data utilization portal supporting every step from discovering enterprise data assets to understanding, requesting and using them. Integrated metadata, quality, catalog and lineage information improves accessibility and usability, while data flows and relationships help users clearly identify the data they need.

SPORTAL benefits

Convenient search and exploration connecting all enterprise data assets
Unified search and related-asset discovery across distributed columns, tables, dashboards and reports help users find the data they need quickly and easily.
Clear decisions based on metadata, quality and lineage
Combine technical and business metadata, standards, quality and lineage into a single view so users can immediately understand data meaning, provenance and reliability and determine whether it is fit for use.
Faster data use through seamless search, request and approval
Handle requests and approvals consistently within the portal, allowing users to move quickly from discovery to use in a single system.

SPORTAL features	Details
A data catalog connecting enterprise assets at a glance	Manage distributed databases, ETL, reports and dashboards by type for an enterprise-wide view Understand data meaning and reliability through technical and business metadata, quality and lineage
Unified search and related exploration that expand context from a single search	A proprietary search engine supports keyword and natural-language searches for fast, accurate discovery Follow visual lineage, related data and impact relationships to expand exploration naturally
A data map offering an intuitive enterprise-wide overview	Treemap charts visualize asset composition and scale to reveal distribution and characteristics Drill down by system, resource and asset type to inspect the required assets step by step
Role-based personalized dashboards with tailored UI/UX	Role-based dashboards with recent views, favorites, owned and recommended assets Manage requests, approvals, comments, searches and registrations in My Page for workflow visibility
Systematic permissions and processes for data publication, use and improvement	Department-, user- and asset-level permissions balance usability and security Standardize error reporting and feedback to manage issues systematically and establish an improvement cycle
Standardized ecosystem integration connecting data management, analytics and AI	Integrate closely with SMETA, SDQ and other management solutions to keep catalog information consistent An extensible portal architecture supports ETL, BI, analytics and AI integration for ongoing expansion

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HYPERGLORY

(AI Knowledge Management Platform)

An enterprise knowledge platform that integrates and manages vast, varied data such as documents, reports, emails and relational databases. Retrieval-Augmented Generation (RAG) retrieves relevant information so AI can understand context and provide recommendations, interpretations and judgments. This simplifies workflows, improves efficiency and helps businesses respond quickly to change.

HYPERGLORY benefits

High-quality answers grounded in industry knowledge
AI trained on specialized knowledge understands industry context and provides accurate, natural answers.
AI tailored to your business
Flexible system design supports Q&A chatbots, knowledge management, training assistance and other scenarios.
Automate knowledge-based decision support
Automate policy reviews, summaries, insights and context-aware recommendations to reduce risk and improve productivity.
Role-based access control
RBAC security policies and data-level filtering provide granular permissions and stronger security.

HYPERGLORY features	Details
Support for on-premises AI	Process sensitive data internally to minimize external leakage risks Safely deploy and operate AI in regulated, security-sensitive sectors such as finance and government
Support for diverse data and business scenarios	Connect documents, images and relational databases to democratize access to the data each person needs Build optimized processing pipelines tailored to each department and workflow Scalable architecture that responds flexibly to complex, diverse business needs
RAG management for non-specialists	Intuitive interface without complex coding Easily manage and operate data without specialist knowledge Improve efficiency through visualization and automation
Custom pipelines and endpoints	Create and run multiple pipelines simultaneously for diverse processing needs Apply custom functions to optimize processing logic for each workflow Independent API endpoints for each pipeline simplify system integration