| Class / Patent application number | Description | Number of patent applications / Date published |
|---|
| 707604000 | Using a denormalized schema | 16 |
| 20110161280 | SYSTEM, METHOD, AND COMPUTER-READABLE MEDIUM THAT FACILITATE IN-DATABASE ANALYTICS WITH SUPERVISED DATA DISCRETIZATION - A system, method, and computer-readable medium that facilitate in-database supervised discretization mechanisms which improve data classification are provided. The disclosed mechanisms provide an efficient, automatic, and repeatable way to perform data discretization without human intervention. Efficient processing of large and complex unknown data is provided that advantageously does not require the data being analyzed to be processed outside the database. The disclosed mechanisms may use an External Stored Procedure to avoid multiple joins of large tables and minimize the number of full table scans and, consequently, provide better performance than contemporary mechanisms. The disclosed system produces intermediate results in tables which may be conveyed to a visualization subsystem thereby providing users a better understanding of the data distribution in each category. Further, the disclosed system and method introduce a novel similarity-based solution to merge intervals when chi-square testing is not reliable and thereby improves the quality of the interval merge process. | 06-30-2011 |
| 20140201130 | SYSTEM AND METHOD FOR ASSIGNING DATA TO COLUMNAR STORAGE IN AN ONLINE TRANSACTIONAL SYSTEM - A method, apparatus, and computer program product for assigning data to columnar storage is disclosed. In one aspect of the invention, a computer implemented method is provided comprising analyzing, on one or more computers, a database stored in a storage system accessible from the one or more computers. The method also comprises determining, on one or more computers, one or more database elements from the database to be assigned to a columnar storage in the database and assigning, on one or more computers, the one or more database elements to the columnar storage in the database. The one or more database elements to be assigned to the columnar storage is determined based on at least one of a table dimension, entity relationship, compatibility with a specified schema structure, relational data structure of the database, access statistics of the database element or incoming query workload of the database element. | 07-17-2014 |
| 20160048572 | Building a Distributed Dwarf Cube using Mapreduce Technique - Systems and methods for building a distributed dwarf cube comprising dwarf cuboid using mapreduce technique are disclosed. Data comprising cube values and a cube definition may be received. The cube definition comprises dimensions defined for the cube values. The data received is processed. The data may be transformed to a format. Based upon the format of the data, indexes may be generated. The cube values in one or more dimensions may be sorted based on a cardinality of the cube values. The cube values may be sorted in an order of highest cardinality to lowest cardinality. The cardinality indicates distinctiveness of the cube values in the one or more dimensions. The data may be partitioned into data blocks. A dwarf cuboid may be built for one or more data blocks based upon the order of the cardinality of the cube values. | 02-18-2016 |
| 20160378814 | Formula-Encoded Time Stamps for Time Series Data - Time stamps for time series data can be efficiently compressed by grouping rows in a database table such that time stamp values of the rows in the group are ordered and characterizable by an increment and an offset, which can be stored for the set of rows respectively in an increment column and an offset column such that the time stamp values of the set of rows are represented by a single slope and offset. A run-length compression can be applied to the increment column and offset column for the table. | 12-29-2016 |
| 707605000 | Using a star schema | 8 |
| 20100169267 | METHOD AND SYSTEM FOR DATA PROCESSING USING MULTIDIMENSIONAL FILTERING - In one embodiment the present invention includes a method comprising receiving a data filter for filtering a collection of data, wherein the collection of data is configured as a star schema including a fact table and dimension tables. The data filter is applied against the dimension tables to generate a modified dimension table. The modified dimension tables are applied against the fact table to produce a modified fact table. The data filter is then applied against the modified fact table to generate a second modified fact table, which is the output of the process. | 07-01-2010 |
| 20120179644 | Automatic Synthesis and Presentation of OLAP Cubes from Semantically Enriched Data Sources - This system comprises methods that simplify the creation of multidimensional OLAP models from one or more semantically enabled data sources. The system also comprises methods enabling interoperability between existing OLAP end-user interfaces, the system's representation of OLAP and the underlying data sources. This includes web-enabled OLAP interfaces. | 07-12-2012 |
| 20130282650 | OLAP Query Processing Method Oriented to Database and HADOOP Hybrid Platform - An OLAP query processing method oriented to a database and Hadoop hybrid platform is described. When OLAP query processing is performed, the processing is executed first on a main working copy, and a query processing result is recorded in an aggregate result table of a local database; when a working node is faulty, node information of a fault-tolerant copy corresponding to the main working copy is searched for through namenode, and a MapReduce task is invoked to complete the OLAP query processing task on the fault-tolerant copy. The database technology and the Hadoop technology are combined, and the storage performance of the database and the high expandability and high availability of the Hadoop are combined; the database query processing and the MapReduce query processing are integrated in a loosely-coupled mode, thereby ensuring the high query processing performance, and ensuring the high fault-tolerance performance. | 10-24-2013 |
| 20140122415 | GENERATION OF CUBE METADATA AND QUERY STATEMENT BASED ON AN ENHANCED STAR SCHEMA - A method for generating cube metadata based on an enhanced star schema includes extracting dimension references from a factless fact table in an enhanced star schema comprising a fact table, a plurality of dimension tables of the fact table and the factless fact table; constructing a hierarchy reference based on the dimension references; and generating cube metadata by combining the hierarchy reference with measures obtained from the fact table and a hierarchy obtained from the dimension tables in the enhanced star schema. | 05-01-2014 |
| 20140172780 | Data Warehouse Queries Using SPARQL - Disclosed is a system allowing to query data warehouses using SPARQL. An aspect of the system may support the representation of multidimensional data as virtual graphs. Another aspect of the system may provide mapping of SPARQL queries directed against multidimensional data vis-à-vis the graphs to native queries directed against the multidimensional data. Responses from the native queries may then be translated to a SPARQL response format. | 06-19-2014 |
| 20140214755 | EXTENSIBLE MODEL FOR IT RESOURCE CHARGEBACK - The present disclosure provides techniques for chargeback of IT resources. Resource change data may be stored until the data is accessed by a chargeback system. The chargeback system may access the resource change data daily and may convert the resource change data to daily resource usage and cost data. The resource usage and cost data may be stored in a chargeback database and the daily usage and cost data may be reported. | 07-31-2014 |
| 20150088809 | DENSELY GROUPING DIMENSIONAL DATA - Methods, computer systems, and stored instructions are described herein for densely grouping dimensional data and/or aggregating data using a data structure, such as one that is constructed based on dimensional data. When smaller tables are joined with a larger table, a server may analyze the smaller tables first to determine actual value combinations that occur in the smaller tables, and these actual value combinations are used to more efficiently process the larger table. A dense data structure may be generated by processing dimensional data before processing data from fact table. The dense data structure may be generated by compressing ranges of values that are possible in dimensions into a range of values that actually occurs in the dimensions. The compressed range of values may be represented by dense set identifiers rather than the actual compressed range of values. | 03-26-2015 |
| 20160253403 | OBJECT QUERY MODEL FOR ANALYTICS DATA ACCESS | 09-01-2016 |
| 707606000 | Using a snowflake schema | 4 |
| 20110208692 | GENERATION OF STAR SCHEMAS FROM SNOWFLAKE SCHEMAS CONTAINING A LARGE NUMBER OF DIMENSIONS - An aspect of the present invention simplifies generating a star schema from a snowflake schema. In an embodiment, a user first specifies fact tables to be included in a star schema, and a synchronization tool inspects the snowflake schema to determine the dimension tables linked to the specified fact tables. The determined dimension tables are included in the star schema sought to be generated. | 08-25-2011 |
| 20120011097 | METHOD, COMPUTER PROGRAM, AND SYSTEM-MODEL CONVERTER FOR CONVERTING SYSTEM MODEL - [Object] To provide a system model conversion method, a computer program, and a system model converter which facilitate analysis and editing of the system model by using information indicating a hierarchical relationship. | 01-12-2012 |
| 20140244573 | DATA WAREHOUSE WITH CLOUD FACT TABLE - A data warehouse includes plurality of master data tables, a plurality of dimension tables and a fact table. The master data tables including surrogate identifiers. The dimension tables use the surrogate identifiers to link to the master data table domains within the master data tables. The fact table stores dimension identifiers that provide links to the master data tables. A cloud storage area includes a plurality of cloud dimension tables and a cloud fact table. Each cloud dimension table stores summary characteristics. Each cloud dimension table associates a separate cloud identifier with each entry of summary characteristics. The cloud fact table stores aggregated data representing key performance indicators. The cloud fact table includes a plurality of cloud identifier columns in which cloud identifiers are stored. Each cloud identifier column is dedicated to a single associated cloud dimension table from the plurality of cloud dimension tables, so that each cloud identifier column only stores cloud identifiers for a single cloud dimension table. | 08-28-2014 |
| 20160078064 | AUTOMATIC GENERATION OF LOGICAL DATABASE SCHEMAS FROM PHYSICAL DATABASE TABLES AND METADATA - Automatic generation of logical database schemas from physical database tables and metadata is disclosed. One exemplary method for automatic generation of logical database schemas from physical database tables and metadata includes identifying physical fact tables in a data repository. The method further includes identifying physical dimension tables in the data repository. The method includes mapping the physical fact tables to logical fact tables. The method further includes mapping the physical dimension tables to logical dimension tables. The method further includes determining relationships between the physical fact and dimension tables. The method further includes logically joining the logical tables based on the identified relationships between the physical tables to form a logical database schema. The method further includes outputting the logical database schema to the user. | 03-17-2016 |
