Graph DataBase
Reasoning ability
Multiple data models processing function
High quality
Ultra-large volume graph data maintenance function
Insight
Graph data analysis function
Prediction ability
Multiple data models support function and data diagnosis, prediction, analysis function
saltlux technology
Graph DataBase Technology
Automatically converts correlations between data
Create and store big data as knowledge graph structures
Integrated management, analysis, and direct application of big data
Enables the generation, management, and intelligent analysis (prediction and reasoning) of the best knowledge graph data
Also includes data integration, risk management, content recommendation, data opening, large-scale reasoning, and selective use of add-on packages
main features
Graph Database provides all functions corresponding to the lifecycle of graph data, such as the conversion functions to convert data into graph data, graph data storage, graph data-based Geodata analysis, knowledge network analysis, graph data open functions, and visualization and operation management.
Converge and commercialize Asia's first Al and Graph Database technologies
Verification functionality of 108 ultra-large volume graph data processing
Powerful built-in commercial reasoning engine functionality
Powerful built-in knowledge extraction and conversion functionality
Guarantee stability according to experience in securing maximum Graph Database references and execution
why do we need graph database technology
Graph Database Technology selects and applies an optimized solution according to the business environment, which depends significantly on data inflow speed and data volume possessed by a company or an organization. Graph Database Technology is the best solution that creates a new market based on graph data. It finds a customer and raises the customer’s economic value by satisfying the customer’s needs and providing optimized services.
Our Graph Database engines
Discover Saltlux Technology's tools that apply Graph Database technology
Graph DB Conversion Engine
This tool generates data corresponding to the knowledge graph, through mapping data sources (DBMS, CSV, RDF,...). It also provides structured data like RDB, through W3C's R2RML language, and RML language (internal data transformation rules). Moreover, the tool can convert and process user data into a virtual data view (Data View). Users can perform data transformation quickly and easily through the graph database conversion tool. Main functions:- Supports multiple formats for data conversion
- Enhances and transforms extremely large graph data
- Manages data transformation tools
Graph DB Storing Engine
Graph Database storing engine provides RDF-based graph data storage and Property Graph storage, providing Property Graph storage and analysis through Apache server linkage TinkerPop and Gremlin. Graph data based on RDF is represented as triples, Property Graph is represented as Vertex, Edge and Verte, Edge can carry properties. Main functions:- Secure large-scale graph data storing management efficiency and data interoperability
- Distinct analysis based on SPARQL query
- Scalable through large distributed environments
- Access easily through management tools
Graph DB Reasoning Engine
Graph Database reasoning engine allows transforming and storing new facts from transformed data through reasoning. In particular, this tool can provide schema axiom and instance axiom for high-low-level reasoning and relational reasoning between concepts in the data model. Main functions:- Embedded axiom and rule-based reasoning
- Support various knowledge expression languages
- Graph data augmentation and verification
Graph DB Analysis Engine
Graph database analysis engine acts as a knowledge finder, including a knowledge graph generator for external information (social media, email, knowledge management systems, etc.). It generates graph index for knowledge network analysis, knowledge network analyzer by user, by unit time and by topic, topic network analyzer for topic analysis of contents distributed on the network, interested topic tendency analyzer by unit time, and knowledge retriever. Main functions:- Robust built-in graph data analysis
- Large-scale graph data analysis performance
- Extended graph data analysis
graph database technology for saltlux inc. customers
Discover Graph Database technology-based products that Salltux Inc. developed for major customers around the world.
Project Information
- Establish a scientific crime analysis base system: - Establish structured and unstructured data convergence infrastructure for five violent crimes (murder, sexual assault, robbery, arson, death resulting from bodily injury through assault)- Text analysis of approximately 800 semi-structured and unstructured documents- Design various scenarios that support crime analysis- Analyze criminal evidence, such as voice, video, or image- Technical methodology research (external POC execution)
- Establish an intelligent crime analysis knowledge base: - Establish base materials for deep crime analysis- Establish a concept dictionary and crime classification system for deep crime analysis- Set up machine learning data and a knowledge expression system to automate in-depth crime analysis
- Prepare the basis for a crime prevention cooperation system between relevant authorities:- Identify the required information for relevant authorities to establish crime prevention- Prepare a linkage base method with relevant authorities
Applied technologies and solutions
- Apply Graph Database for graph data generation, storing, reasoning, crime dictionary, and information.
- Apply big data storing and the search engine (DISCOVERY) for the intelligent semantic search service, unstructured analysis engine (TMS), and cognitive analysis engine (CAS) for collected unstructured data analysis.
- Apply OpenSource Apache SPARK, STORM, and KAFKA in the intelligent crime prevention analysis system.
Validity of product selection
Customer Happiness Center and all business branches of the NongHyup Bank have improved their knowledge management system to manage and share business knowledge needed for customer consultation. Functions such as knowledge generation, management, and search are improved to increase convenience. When knowledge contents are generated, they are managed by particular business fields and properties. Later, these contents will be stored after being converted into a Knowledge-Graph based knowledge base. This converts natural language content into knowledge data in a machine-readable format. AI-based automatic knowledge extraction is also applied as part of the process.
Project Information
The knowledge management system was formerly designed to generate, store, manage the business knowledge for customer consultation. It is now used to share knowledge between bank consultants and/or branch employees. However, the demand for AI-based services is continuously increasing as bank customers now use many different digital channels. As a result, it is crucial to establish and manage the AI-based knowledge data separately for AI services. The previous knowledge contents and AI-based knowledge data had different shapes and structures, making it difficult to maintain the same information if they were managed separately. Duplicated knowledge generation, management, and use would cause inconvenience to users in many ways. The aim is to improve the convenience of managing and sharing consultation knowledge and information between employees. This can be done by reforming the outdated 10-year knowledge management system into an intelligent one that meets the latest AI technology trends. Users also can improve management efficiency and knowledge utilization by creating and managing knowledge data in AI-based services.
Applied technologies and solutions
Main accomplishments
Project Information
Aiming at “the development of AI-based personalized knowledge consultation services for HACCP intelligent information services”, a knowledge base based on close HACCP certified safe food is established using raw materials and nutritional content data. It also provides a theme-based personalized safe food recommendation service.
Applied technologies and solutions
Validity of Product Selection
Main accomplishments
Project Information
Establish an information channel to link various business information owned by other government departments and public institutions, based on the intellectual property rights information owned by Korean Intellectual Property Office.
- Link business information owned by government departments and public institutions.
- Converge intellectual property rights information owned by Korean Intellectual Property Office and business information, and provide customized information.
- Provide customized reports based on established intellectual property rights information and business information.