A map reduce frame work for large scale data processing is optimized by the method of the invention that can be implemented by a master node. The method comprises reception of data from worker nodes on read pointer locations pointing to input data of tasks executed by these worker nodes and stealing of work from these tasks, the work being stolen being applied to input data that have not yet been processed by the task from which work is stolen.

DEVICE AND METHOD FOR OPTIMIZATION OF DATA PROCESSING IN A MapReduce FRAMEWORK

The invention discloses an intelligent health management system. The intelligent health management system is characterized by comprising an intelligent hospital system, a regional health system and a family health system. Through the intelligent health management system, intercommunication and sharing of medical information and/or health information between families, communities, hospitals, medical scientific research institutions and health supervision and management departments can be achieved, and the intelligent health management system can deal with mass data processing.

Intelligent health management system

The invention discloses an adaptive parallel processing method aiming at variable length characteristic extraction for big data. The method aims at variable-length characteristic data, and big data are processed through a graphic processing unit (GPU) parallel computing power based on compute unified device architecture (CUDA). During processing of the big data, an adaptive parallel matrix array processing mode is used for performing multithreading concurrent execution processing on the data according to own hardware characteristics and lengths of the characteristic data, so that the characteristic extraction speed is accelerated. According to the method, the data are processed in batches according to the hardware own processing capacity and the lengths of the characteristic data, and characteristics of certain length are extracted every time, and matching results are recorded; after the whole characteristics are extracted, all matching results are processed according to the allowed error-tolerant rate of data sampling to obtain requested characteristic extraction results. Good parallelism of matrix arrays is used, and the method aims at extracting variable-length characteristics, so that data can be parallelized effectively and sufficiently, and the method is particularly applicable to big-data rapid characteristic extraction with certain fault tolerance.

Adaptive parallel processing method aiming at variable length characteristic extraction for big data

The invention discloses a migration method based on PostgreSQL block storage equipment The migration method comprises the following steps: performing reading/writing on a PostgreSQL block by using PostgreSQL block storage equipment reading/writing module, managing the PostgreSQL block in PostgreSQL block storage equipment by the PostgreSQL block storage equipment reading/writing module according to a mapping relationship table of PostgreSQL block-data table and an idle PostgreSQL block table, copying the mapping relationship table of the PostgreSQL block-data table and the idle PostgreSQL block table into a new database from an original database, and migrating the PostgreSQL block storage equipment into a computer where the new database is located from a computer where the original database is located By adopting the migration method based on the PostgreSQL block storage equipment, databases can be very easily migrated among different systems, and special knowledge of operators is not highly required The checking speed and the reading/writing speed can be greatly increased, and the speed can be increased by 10-20% under the condition of identical rear end storage The increase is very considerable for the databases

Migration method based on PostgreSQL block storage equipment

Embodiments are directed to isolating a cloud computing node using network-or some other type of isolation. In one scenario, a computer system determines that a cloud computing node is no longer responding to monitoring requests. The computer system isolates the determined cloud computing node to ensure that software programs running on the determined cloud computing node are no longer effectual (either the programs no longer produce outputs, or those outputs are not allowed to be transmitted). The computer system also notifies various entities that the determined cloud computing node has been isolated. The node may be isolated by powering the node down, by preventing the node from transmitting and/or receiving data, and by manually isolating the node. In some cases, isolating the node by preventing the node from transmitting and/or receiving data includes deactivating network switch ports used by the determined cloud computing node for data communication.

Automated failure handling through isolation

The invention discloses a massive satellite data processing system and a massive satellite data processing method based on the cluster and parallel technology in the technical field of computer data processing. The massive satellite data processing system comprises a storage module, a network transmission module and a processing module which are connected in sequence, wherein the processing module comprises a global blocking unit, a projection lookup table reading and writing unit, a task allocation unit, a dispatch manner configuring unit and computing node units. The massive satellite data processing system includes: declustering by 10 degrees according to longitude and latitude of the globe to obtain 10-degree blocks; determining valid data in the projection lookup table and valid data in five-minute data segments corresponding to each 10-degree block; transmitting one 10-degree block to any one of idle computing node units by the task allocation unit according to the task allocation manner; and processing data in the 10-degree block by the idle computing node unit. Massive data can be processed parallelly by the cluster system, and satellite data processing capability is improved.

Massive satellite data processing system and massive satellite data processing method based on cluster and parallel technology

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Massive satellite data processing system and massive satellite data processing method based on cluster and parallel technology