The invention discloses a multi-dimensional analysis method for multi-level storage data. The multi-dimensional analysis method comprises the following steps: step 1, building a unified data model; step 2, building a multi-dimensional analysis model; step 3, performing multi-dimensional analytic calculation and demonstration: a calculation engine automatically generates an aggregate calculating sentence, corresponding to a next-level calculation node, of the multi-dimensional analysis model according to routing information, a calculation agent executes aggregation operation to generate a pre-aggregation result and uploads the pre-aggregation result onto a main-control server, and then secondary aggregation calculation is executed to generate a multi-dimensional calculation result set; the calculation engine forms a dimension member inquiry algorithm according to the multi-dimensional analysis model and the unified data model, inquires a dimension member, generates cell objects of a demonstration report and fills the multi-dimensional analysis result into the cell objects to realize multi-dimensional demonstration of the multi-dimensional analysis result. The multi-dimensional analysis method can realize real-time multi-dimensional analysis of hierarchical storage data without centralizing data in advance.

Multi-dimensional analysis method for multi-level storage data

The invention relates to a memory database OLTP and OLAP concurrency query optimization method. The method includes the steps that (1) by means of two query processing engines, independent storage engines are adopted to a dimension table and a fact table; (2) the dimension table is updated through an embedded concurrency control mechanism of the independent storage engines, the fact table is equivalent to multiple continuous arrays in logic and maintains two dynamic data structures, namely a read record pointer and a write record pointer, the read record pointer records the position of the last record inquired through OLPA currently, and the write record pointer records the insert position of a new record; (3) an OLTP transactional queue and an OLAP transactional queue are independently executed with the read pointer and the write pointer as boundaries, the fact table adopts a column storage horizontal fragmentation model based on the fixed number of columns, N columns of storage records serve as an independent column storage container, and each column storage container adopts an independent data compression mechanism; (4) an access function on compressed data or non-compressed data is provided through access interfaces of the column storage containers when OLAP query has access to the column storage containers.

Memory database OLTP and OLAP concurrency query optimization method

The invention provides an information processing method and device, and relates to the technical field of data services. The method and device are used for improving the accuracy of a prediction result provided for marketers, thereby reducing the operating cost. The method includes the steps of obtaining information of a current marketer; obtaining a recommendation model matched with the information of the current marketer according to the information of the current marketer, wherein the recommendation model is obtained through training by means of the information of a preset marketer and theinformation of historical customers corresponding to the preset marketer; obtaining the information of to-be-processed customers; determining a target customer from the to-be-processed customers for the current marketer according to the information of the current marketer, the information of the to-be-processed customers and the recommendation model. The method is mainly used in the business marketing technology.

Information processing method and device

A method for grouping and storing objects across different storage solutions in storage systems according to analysis of multiple dimensions of information may be implemented as or in a storage management module. The module collects information about objects (e.g., data objects) in a storage system (e.g., a data storage system). The objects may be objects already stored in the storage system or may be new objects to be stored in the storage system. The module analyzes the collected information across multiple dimensions to determine groupings of the objects, and determines a storage solution for each determined grouping. The module may then direct storage of the objects in the groupings according to the determined storage solutions. Upon obtaining new information about object(s) in the storage system, the module may direct movement of the object(s) from one storage solution to another storage solution according to an analysis including the new information.

Object storage using multiple dimensions of object information

The invention discloses an OLAP (On-Line Analytical Processing) star connection query optimizing method under a CPU (Central Processing Unit) and GPU (Graphics Processing Unit) mixing framework. The method comprises the following steps: first filtering and optimizing an OLAP star connection operation through bitmap connection indexing, buffering high-frequently accessed connecting bitmap in a GPU buffer memory, next, loading fact table external key attribute group satisfying the connecting bitmap filtering condition into the GPU buffer memory to carry out star connection filtering, and finally switching the whole table scanning of the big fact table of the internal memory into accessing randomly according to the position through filtering bitmap generated by the GPU, thereby improving the querying and treating performances of the OLAP star connection. The method improves the storage efficiency of the GPU buffer memory and the parallel processing efficiency of the GPU, and improves the OLAP querying and treating performance of the hybrid processor platform on the whole.

OLAP star connection query optimizing method under CPU and GPU mixing framework

The invention discloses a multi-dimensional OLAP (On Line Analytical Processing) inquiry processing method facing a column storage data warehouse. In the multi-dimensional OLAP inquiry processing method, the OLAP inquiry is decomposed into a bitmap filtering operation, a grouping operation and an aggregation operation. In the bitmap filtering operation, firstly, the predication is executed on a dimension table and a predicate vector bitmap is generated; and a connection operation is converted into a direct dimension table record access operation by surrogate key address mapping so as to implement access according to the positions. In the grouping operation, the pre-generation of grouping units is carried out on fact table records which meet the filtering conditions according to grouping attributes in an SQL (Structured Query Language) command and increasing IDs (identity) are distributed. In the aggregation operation, the grouping aggregation calculation which is carried out according to a grouping item of a grouping filtering vector of a fact table is implemented by carrying out column scanning on the metric attribute of the fact table for once. According to the invention, all OLAP processing tasks can be completed only by carrying out column scanning on the fact table for once, so that the cost of repeatedly scanning is avoided.

Shan Wang

Papers

Multi-dimensional OLAP (On Line Analytical Processing) inquiry processing method facing column storage data warehouse