-
Call Us400-6240-800
-
Email Usbusiness@percent.cn
Product Overview
PERCENT Data Science Generic Platform (DeepMatrix) is a one-stop data value realization platform, which covers the full stack tool set for data application construction, which can effectively support data collection, data governance, data processing, data analysis, data visualization and data product development; built-in industry data standards, algorithm models, knowledge graphs and other fields of knowledge, enabling all aspects of data value realization, and helping to improve the efficiency and depth of data applications construction.
Product Advantages
Specialization
Covers all aspects from multi-source heterogeneous data to information (data assets) to meet the professional needs of data engineers.
Integration
Each module can be deployed and used independently, and the modules can form a whole through data exchange protocols to meet the needs of different scenarios.
Intelligent
Use semantic analysis, speech analysis and visual analysis systems to process unstructured data; combine semantic analysis to achieve data semantic drive, automatically plan and generate governance rules and solutions.
Low-code
Through visualization and low-code interaction, reduce the technical threshold of data management and improve data processing efficiency.
Extensive ecological adaptation
Adapt to mainstream storage and computing infrastructure, such as MySQL, Oracle, Huawei Guass and other databases, HDP, CDH, Huawei Cloud MRS and other big data platforms, as well as domestic operating systems and chips.
Specialization
Covers all aspects from multi-source heterogeneous data to information (data assets) to meet the professional needs of data engineers.
Integration
Each module can be deployed and used independently, and the modules can form a whole through data exchange protocols to meet the needs of different scenarios.
Intelligent
Use semantic analysis, speech analysis and visual analysis systems to process unstructured data; combine semantic analysis to achieve data semantic drive, automatically plan and generate governance rules and solutions.
Low-code
Through visualization and low-code interaction, reduce the technical threshold of data management and improve data processing efficiency.
Extensive ecological adaptation
Adapt to mainstream storage and computing infrastructure, such as MySQL, Oracle, Huawei Guass and other databases, HDP, CDH, Huawei Cloud MRS and other big data platforms, as well as domestic operating systems and chips.
Specialization
Covers all aspects from multi-source heterogeneous data to information (data assets) to meet the professional needs of data engineers.
Integration
Each module can be deployed and used independently, and the modules can form a whole through data exchange protocols to meet the needs of different scenarios.
Intelligent
Use semantic analysis, speech analysis and visual analysis systems to process unstructured data; combine semantic analysis to achieve data semantic drive, automatically plan and generate governance rules and solutions.
Low-code
Through visualization and low-code interaction, reduce the technical threshold of data management and improve data processing efficiency.
Extensive ecological adaptation
Adapt to mainstream storage and computing infrastructure, such as MySQL, Oracle, Huawei Guass and other databases, HDP, CDH, Huawei Cloud MRS and other big data platforms, as well as domestic operating systems and chips.
Product Functions
Lakehouse architecture
Support physical and virtual entry of external data sources into the lake; support structured, semi-structured and unstructured data, and provide unified metadata, data permissions and security management; support Hive, Spark, Flink and other big data processing engines, as well as R, TensorFlow, PyTorch and other machine learning tools; support batch flow integration computing and federated queries; support accessing to lake warehouse data through standard SQL.
Data fusion governance
Support the efficient integration of multiple data sources and the exploration of relational databases and real-time CDC capture; support the development and scheduling of multiple data processing tasks; support a comprehensive data governance system including data standards, metadata, data lifecycle, data quality, master data, etc., support the management and operation of data assets, and provide multiple data services such as databases, files, APIs.
Model and knowledge production
Provide unified multi-modal data modeling capabilities, including data annotation, machine learning, model management, etc., support online Notebook and low-code model building, and built-in multiple machine learning algorithms and AI models. The platform can use these models to build business knowledge, support three forms of knowledge representation: labels, indicators and knowledge graphs, and provide corresponding knowledge production and management capabilities.
Knowledge application
The platform provides a variety of data analysis capabilities such as unified knowledge search, agile BI, knowledge graph analysis and inference, and supports a variety of retrieval and analysis means. The platform also unifies the management of various data and knowledge models in the field and uses these knowledge to enhance the entire process of data governance, data modeling, knowledge production and knowledge application.
Product Functions
Lakehouse architecture
Support physical and virtual entry of external data sources into the lake; support structured, semi-structured and unstructured data, and provide unified metadata, data permissions and security management; support Hive, Spark, Flink and other big data processing engines, as well as R, TensorFlow, PyTorch and other machine learning tools; support batch flow integration computing and federated queries; support accessing to lake warehouse data through standard SQL.
Data fusion governance
Support the efficient integration of multiple data sources and the exploration of relational databases and real-time CDC capture; support the development and scheduling of multiple data processing tasks; support a comprehensive data governance system including data standards, metadata, data lifecycle, data quality, master data, etc., support the management and operation of data assets, and provide multiple data services such as databases, files, APIs.
Model and knowledge production
Provide unified multi-modal data modeling capabilities, including data annotation, machine learning, model management, etc., support online Notebook and low-code model building, and built-in multiple machine learning algorithms and AI models. The platform can use these models to build business knowledge, support three forms of knowledge representation: labels, indicators and knowledge graphs, and provide corresponding knowledge production and management capabilities.
Knowledge application
The platform provides a variety of data analysis capabilities such as unified knowledge search, agile BI, knowledge graph analysis and inference, and supports a variety of retrieval and analysis means. The platform also unifies the management of various data and knowledge models in the field and uses these knowledge to enhance the entire process of data governance, data modeling, knowledge production and knowledge application.
Product Functions
Lakehouse architecture
Support physical and virtual entry of external data sources into the lake; support structured, semi-structured and unstructured data, and provide unified metadata, data permissions and security management; support Hive, Spark, Flink and other big data processing engines, as well as R, TensorFlow, PyTorch and other machine learning tools; support batch flow integration computing and federated queries; support accessing to lake warehouse data through standard SQL.
Data fusion governance
Support the efficient integration of multiple data sources and the exploration of relational databases and real-time CDC capture; support the development and scheduling of multiple data processing tasks; support a comprehensive data governance system including data standards, metadata, data lifecycle, data quality, master data, etc., support the management and operation of data assets, and provide multiple data services such as databases, files, APIs.
Model and knowledge production
Provide unified multi-modal data modeling capabilities, including data annotation, machine learning, model management, etc., support online Notebook and low-code model building, and built-in multiple machine learning algorithms and AI models. The platform can use these models to build business knowledge, support three forms of knowledge representation: labels, indicators and knowledge graphs, and provide corresponding knowledge production and management capabilities.
Knowledge application
The platform provides a variety of data analysis capabilities such as unified knowledge search, agile BI, knowledge graph analysis and inference, and supports a variety of retrieval and analysis means. The platform also unifies the management of various data and knowledge models in the field and uses these knowledge to enhance the entire process of data governance, data modeling, knowledge production and knowledge application.
Application Scenario
Based on DeepMatrix, build a one-stop enterprise data middle office with master data management and data service as the core, form a complete multi-source heterogeneous data collection capability, multi-modal data storage and governance capability, data lifecycle management and control capability, data analysis and service capability, and support enterprises to establish a data-driven operation mode.
Utilize the DeepMatrix to integrate the multi-modal data in emergency operations and form an emergency knowledge graph, promote the intelligent, refined, scientific and professional development in the fields of supervision and management, monitoring and early warning, commanding and rescue, auxiliary decision-making, government management, and social mobilization, and help the emergency management department improve its ability and modernize its governance system.
Using the DeepMatrix, combining the network open source data, network log data and manual import data, a unified, flexible and dynamically extensible knowledge graph including the elements of people, places, events, things, and organizations is constructed. Based on this, we can build advanced research and judgment tools such as entity analysis, association analysis, spatiotemporal analysis, and graph mining. Help the public security management department improve the ability of prediction, early warning and prevention, and crack down on network criminal activities.
Based on DeepMatrix, build a one-stop enterprise data middle office with master data management and data service as the core, form a complete multi-source heterogeneous data collection capability, multi-modal data storage and governance capability, data lifecycle management and control capability, data analysis and service capability, and support enterprises to establish a data-driven operation mode.
Utilize the DeepMatrix to integrate the multi-modal data in emergency operations and form an emergency knowledge graph, promote the intelligent, refined, scientific and professional development in the fields of supervision and management, monitoring and early warning, commanding and rescue, auxiliary decision-making, government management, and social mobilization, and help the emergency management department improve its ability and modernize its governance system.
Using the DeepMatrix, combining the network open source data, network log data and manual import data, a unified, flexible and dynamically extensible knowledge graph including the elements of people, places, events, things, and organizations is constructed. Based on this, we can build advanced research and judgment tools such as entity analysis, association analysis, spatiotemporal analysis, and graph mining. Help the public security management department improve the ability of prediction, early warning and prevention, and crack down on network criminal activities.
Inquiry