67 results about "Big data architecture" patented technology

The invention discloses a security and high-efficiency vulnerability management system, and relates to the technical field of vulnerability management,, in particular to a security and high-efficiencyvulnerability management system, a threat intelligence system and an internet intelligence intelligent collection system based on big data architecture, which are mainly used for uniformly integrating intelligence searched by company self-research, friend-merchant sharing, industry sharing and the internet, and sharing related intelligence to a third-party system or product according to industrycharacteristics. Te local threat and vulnerability management platform is mainly used for realizing Internet asset auditing, scanning engine centralized management and vulnerability management supportfunctions. According to the safe and efficient vulnerability management system, except for collecting and analyzing the risk data of the scanning missing product, other risk data can be imported forunified analysis, and the management capacity of quick response, ordered repair and continuous optimization is provided for the customer vulnerability management process in combination with local riskcontinuous monitoring and vulnerability management full-process management and control capacity.

A system and method for rapid development and deployment of reusable analytic code for use in computerized data modeling and analysis is provided. The system includes a centralized, continually updated environment to capture pre-processing steps used in analyzing big data, such that the complex transformations and calculations become continually fresh and accessible to those investigating business opportunities. The system incorporates deep domain expertise as well as ongoing expertise in data science, big data architecture, and data management processes. In particular, the system allows for rapid development and deployment of analytic code that can easily be re-used in various data analytics applications, and on multiple computer systems.

The invention relates to a data storage system and a data storage method of large data. The system comprises a four-layer structure. The metadata structure layer defines and stores a plurality of metadata types and attributes of the metadata types. The metadata layer defines and stores all the association relations between metadata object information and metadata object according to the attributesof metadata type. The data structure layer defines and stores the structural relationship of statistical data according to the metadata object information. The data layer stores statistical data according to the structural relationship of the statistical data. The invention is based on DDI metadata standard, Defines a four-tier structure for statistical data storage: metadata structure, Metadata,data structure and data, so as to unify the storage and management of massive micro-data according to the big data structure, break down the barriers of data resource integration, realize the flexible and efficient storage and use of statistical data, and then largely solve the problems of large amount of data, complex structure, rapid growth rate, and data analysis and use difficulties and so on.

A semiconductor fabrication scheduling method includes creating a load scheduling data schema including facility data of product lots to be dispatched to a plurality of workstations; generating a load schedule profile using a load-balancing model and based on the load scheduling data schema, wherein the load-balancing model includes one or more objective functions and there is at least one weight factor in an objective function; generating a current load schedule based on the load schedule profile; dispatching the product lots to the plurality of workstations using the current load schedule to complete fabrication of the product lots; obtaining a set of current key performance indicators (KPIs) of the completed fabrication of the product lots; and automatically adjusting the weight factors of the objective functions of the load-balancing model based on the current KPIs using a big-data architecture to generate a next load schedule for next cycle of fabrication.