Abstract: An embodiment of the present invention discloses an index recommendation method and apparatus based on data features. The method includes following steps: acquiring multiple dimensions from query historical data of a user, and constructing an aggregation group according to the dimensions; creating initial indexes according to the aggregation group, which are divided into multiple levels according to dimension combinations; pre-screening the initial indexes of each level based on a sampling algorithm to obtain a candidate index set; and searching an index subset with the minimum cost value from the candidate index set through a genetic algorithm or a greedy algorithm to serve as a recommended index. The present invention can significantly improve the efficiency of pre-computation and save the storage and computation cost.

Abstract: The present invention discloses a method and a device for processing information by batch-stream fusion, and a storage medium. The method comprises the following steps: Obtaining an index based on an input query statement; extracting a pre-computed index data segment based on the index as a query result; and extracting a re-computed index data segment to update the query result. The present invention solves the technical problem that real-time data and offline data are difficult to fuse and analyze.

Abstract: The present invention discloses a method and a device for processing information by batch-stream fusion, and a storage medium. The method comprises the following steps: Obtaining an index based on an input query statement; extracting a pre-computed index data segment based on the index as a query result; and extracting a re-computed index data segment to update the query result. The present invention solves the technical problem that real-time data and offline data are difficult to fuse and analyze.

Abstract: The present invention discloses a method and a system for hybrid query based on a cloud analysis scene, and a storage medium. The method comprises the following steps: obtaining query information, and obtaining an index thereof based on the query information; obtaining meta-information of the index based on pre-computation, and comparing the obtained meta-information with the meta-information of an aggregate index; and determining a query mode corresponding to the meta-information based on a comparison result, the query mode including a query mode of storage-computation separation or a MPP architecture. The present invention solves the technical problem that in an ultra-high-dimensional environment, how to enable a pre-computation query system to most efficiently and more stably utilize the pre-computation result to respond to the query of a client most quickly and avoid generating a large amount of redundant data.

Abstract: The automatic modeling method and a classifier for OLAP data model includes acquiring a SQL query statement; parsing the SQL query statement and judging whether any syntax error or lexical error exists in the SQL query statement; extracting query structures from the SQL query statement to obtain at least N sets of query patterns, if no syntax error or lexical error exists; classifying the at least N sets of query patterns; merging the query patterns in each class to obtain a corresponding pattern relation tree; and converting the pattern relation tree into a corresponding data model. The classifier includes a static rule classification module, and a statistical rule and preset learning rule classification module. A data model generated with the method and classifier provided in the present invention supports all inputted SQL query statements and has certain generic capabilities to full support Cube operations and quick queries.

Abstract: The present application relates to an OLAP pre-calculation model, an automatic modeling method and an automatic modeling system. The model includes a dimension module, an aggregation group module and a measure module. The method includes collecting data statistics on all data sources to obtain data statistics result, conducting query dryrun based on data model and sample queries given by a user to determine a business model, conducting query dryrun on the sample queries and collecting query statistics, carrying out physical modeling and defining dimension, measure and aggregation group of a pre-calculation model, and obtaining a business modeling result and a pre-calculation model. The system includes data statistics module, business model module, query statistics module, and model establishing modules. A more efficient combination of pre-calculated dimensions can be produced and redundant calculation and data storage can be reduced.