在线分析处理相关的专利数据

序号	专利名	申请号	申请日	公开（公告）号	公开（公告）日	发明人
181	데이터 웨어하우스 상의 온라인 분석 처리(OLAP) 연산의 최적화된 시퀀싱을 위한 하이퍼 격자 모델	KR1020137002253	2010-11-30	KR1020130083899A	2013-07-23	차키,나벤두; 센,소우미야
데이터 웨어하우스 내에 포함되는 데이터의 온라인 분석 처리를 위한 시스템 및 방법이 제공된다. 일 예시에 따르면, 차원을 하이퍼 격자 구조에 추가하기 위한 방법이 제공되며, 방법은 새로운 기저 큐보이드를 데이터 웨어하우스의 기능을 설명하는 하이퍼 격자에 추가하는 단계를 포함한다. 다른 예시에 따르면, 기존의 하이퍼 격자를 통한 최적의 경로를 결정함으로써 소스 큐보이드로부터 목적 큐보이드를 생성하기 위한 방법이 또한 제공된다.
182	HYPER-LATTICE MODEL FOR OPTIMIZED SEQUENCING OF ONLINE ANALYTICAL PROCESSING (OLAP) OPERATIONS ON DATA WAREHOUSES	PCT/IB2010/003058	2010-11-30	WO2012001455A1	2012-01-05	CHAKI, Nabendu; SEN, Soumya
Systems and methods are provided for Online Analytical Processing of data included within data warehouses. According to one example, a method for adding a dimension to a hyper-lattice structure is provided and includes adding a new base cuboid to a hyper-lattice that describes functionality of the data warehouses. According to another example, a method for determining an optimal path through an existing hyper-lattice by which to generate a destination cuboid from a source cuboid is also provided.
183	데이터 웨어하우스 상의 온라인 분석 처리(OLAP) 연산의 최적화된 시퀀싱을 위한 하이퍼 격자 모델	KR1020137002253	2010-11-30	KR101526514B1	2015-06-05	차키,나벤두; 센,소우미야
데이터웨어하우스내에포함되는데이터의온라인분석처리를위한시스템및 방법이제공된다. 일예시에따르면, 차원을하이퍼격자구조에추가하기위한방법이제공되며, 방법은새로운기저큐보이드를데이터웨어하우스의기능을설명하는하이퍼격자에추가하는단계를포함한다. 다른예시에따르면, 기존의하이퍼격자를통한최적의경로를결정함으로써소스큐보이드로부터목적큐보이드를생성하기위한방법이또한제공된다.
184	INPUT/OUTPUT EFFICIENCY FOR ONLINE ANALYSIS PROCESSING IN A RELATIONAL DATABASE	US13430897	2012-03-27	US20120254252A1	2012-10-04	Yi Jin; Lei Li; Li Li Wang; Wan Chuan Zhang
Embodiments of the invention relate to improved input/output efficiency for online analysis processing in a relational database. An aspect of the invention includes selecting a table from a relational database. The table is split by column into sub-tables, with at least one of the sub-tables including at least two columns. Each sub-table is written into a corresponding table of a row-based storage database.
185	System and method for automatic transmission of audible on-line analytical processing system report output	US09460708	1999-12-14	US07082422B1	2006-07-25	Michael Zirngibl; Anurag Patnaik; Christopher S. Leon; Ki Sung Yoon; Wolf Mosle; Kyle N. Yost; Peter G. Wilding; Robert G. Trenkamp
A system and method for creation and automatic deployment of audible personalized, dynamic and interactive services, including information derived from on-line analytical processing (OLAP) systems, to a two-way communication device, including electronic mail, two-way pagers, phones, personal digital assistants, and telephones, based on subscriber-specified criteria.
186	System for Performing On-Line Transaction Processing and On-Line Analytical Processing on Runtime Data	US15178280	2016-06-09	US20160283573A1	2016-09-29	Marc-Philip Werner; Wolfgang Auer
An in-memory computing system for conducting on-line transaction processing and on-line analytical processing includes system tables in main memory to store runtime information. A statistics service can access the runtime information using script procedures stored in the main memory to collect monitoring data, generate historical data, and other system performance metrics while maintaining the runtime data and generated data in the main memory.
187	Use of online analytical processing (OLAP) in a rules based decision management system	US09217016	1998-12-21	US06430545B1	2002-08-06	Laurence Honarvar; Steve Campbell; Traci Showalter
A rules based decision management system using online analytical processing (OLAP) technology for dynamic assessment of strategy results. Generally, a rules based decision management system applies strategies which produce results. The results are aggregated over time, typically in accordance with values of a discrete dimension and ranges of a continuous dimension, to prepare for the application of OLAP technology. Date stamping can be used so that, when aggregating the results, different values and different ranges can be valid for different periods of time. OLAP technology is then applied to the aggregated results, to evaluate the applied strategies.
188	SYSTEM AND METHOD FOR CROSS ATTRIBUTE ANALYSIS AND MANIPULATION IN ONLINE ANALYTICAL PROCESSING (OLAP) AND MULTI-DIMENSIONAL PLANNING APPLICATIONS BY DIMENSION SPLITTING	PCT/US2004/026122	2004-08-12	WO2005019997A2	2005-03-03	MORRIS, Richard, Adrian
A system for cross attribute analysis for sales data in a multi-dimensional planning system. The system includes a set of processing modules that performs cross attribute analysis and manipulation in online analytical processing (OLAP) and multi-dimensional planning applications dimension splitting. A number of processing module are utilized to perform the required processing. The system includes a hierarchy processing module for aggregating data up a hierarchical data structure, a dimension splitting module for creating pseudo-hierarchical data structures from data within the hierarchical data structure, and a multi-dimensional data viewing module for displaying a set of multi-dimensional data set according to the hierarchical data structure in a multi-dimensional spreadsheet. A single dimension corresponds to an attribute of the data contained within the hierarchical data structure.
189	Creation of neuro-fuzzy expert system from online analytical processing (OLAP) tools	US12434952	2009-05-04	US08392352B2	2013-03-05	Gene I. Kofman; Serguei A. Lyssenkov; Rouslan V. Lobachev
A method for automatic generation of a Neuro-Fuzzy Expert System (Fuzzy Logic Expert System implemented as a Neural Network) from data. The method comprising a Data Interface allowing description of location, type, and structure of the Data. The Interface also allows designation of input attributes and output attributes in the Data Structure; automatic Neuro-Fuzzy Expert System generation driven by the Data; Training of the Expert System's Neural Network on the Data and the presentation of results which include new knowledge embedded in the parameters and structure of the trained Neuro-Fuzzy Expert System to a user.
190	Analysis of massive data accumulations using patient rule induction method and on-line analytical processing	US09797752	2001-03-01	US20020124002A1	2002-09-05	Hua-Ching Su; Taiki Sakata; Charles Herman; Steven Dolins
A method of analyzing data contained in a very large database or data warehouse includes applying first a patient rule induction method with selected input variables and an output variable to develop a region containing a subset of database records having a highest average output value. Then, the subset of records may be aggregated, sorted, compared, or new measures computed, preferably using an on line analytical processing function, for an pattern analysis of the subset records. An alternative is to apply a weighted item set function to the subset to produce a reduced subset, and then applying the reduced subset to on line analytical processing for pattern analysis.
191	SYSTEM AND METHOD FOR PROVIDING CROSS-DIMENSIONAL COMPUTATION AND DATA ACCESS IN AN ON-LINE ANALYTICAL PROCESSING (OLAP) ENVIRONMENT	PCT/US0106824	2001-03-01	WO0173608A2	2001-10-04	REDDY VENUGOPAL P; MATHARU HARMINDAR S
A system (10) for generating a value for a first attribute includes a database (16) having one or more dimensions (30) that each include one or more members (32). The database (16) includes one or more storage locations (22) that are each associated with one member (32) from each dimension (30) in a set of one or more of the dimensions (30). A server (14) evaluates an expression including at least one second attribute that depends on a set of one or more of the dimensions (30), the expression mapping at least one member (32) of a first dimension (30) on which the first attribute depends to at least one member (32) of a second dimension (30) on which the second attribute depends. The value for the first attribute is generated according to the expression. The server (14) and database (16) may operate in an on-line analytical processing (OLAP) environment.
192	線上分析處理之方法及系統 METHOD AND SYSTEM FOR ONLINE ANALYTICAL PROCESSING (OLAP)	TW092128546	2003-10-15	TW200413969A	2004-08-01	派崔克亞拉斯 PATRICK ARRAS; 亞方斯約翰尼史汀霍夫 ALFONS JOHANN STEINHOFF
本發明揭示一種用於產生被包含在一資料庫中之資料記錄的使用者定義之樞紐分析檢視(pivotview)的方法及系統，在該資料庫中(如圖2a所示)由連續的索引值(indexvalue)(200)來擴展一基礎真實事實(realfact)資料表，該索引值自「1」至「x」(在本實例中x＝20)提供該等事實之連續編號。在圖2b所展示之所得樞紐分析檢視中，在每一單元格(210)中，該等事實之索引被表示為：必須在對應的單元格中求和的銷售值。藉由一序列向量而產生該樞紐分析檢視。圖2c中描繪圖2b中之樞紐分析檢視的基礎序列向量且由兩行(260，270)組成，左邊行(260)包含又是自「1」至「x」之連續數字，右邊行(270)包含圖2a中描繪之已提到的索引值(275)，該等索引值係已排序的排列形式而得以按順序地建立圖2b之樞紐分析檢視。
193	Automatic creation of neuro-fuzzy expert system from online anlytical processing (OLAP) tools	US11020542	2004-12-22	US07529722B2	2009-05-05	Gene I. Kofman; Serguei A. Lyssenkov; Rouslan V. Lobachev
A method for automatic generation of a Neuro-Fuzzy Expert System (Fuzzy Logic Expert System implemented as a Neural Network) from data. The method comprising a Data Interface allowing description of location, type, and structure of the Data. The Interface also allows designation of input attributes and output attributes in the Data Structure; automatic Neuro-Fuzzy Expert System generation driven by the Data; Training of the Expert System's Neural Network on the Data and the presentation of results which include new knowledge embedded in the parameters and structure of the trained Neuro-Fuzzy Expert System to a user.
194	데이터베이스 시스템에서 온라인 분석 처리를 위한 데이터를 유지하는 방법 및 장치	KR1020167018873	2014-01-02	KR101805561B1	2017-12-07	비라라가반비노쓰; 라마무르티프라사나벤카테시; 첸지비아오
본발명의실시예는데이터베이스시스템에서온라인분석처리를위한데이터를유지하는방법및 장치를개시한다. 본방법은: 메인프로세스에서변경된페이지를추적하는단계; 및온라인분석처리를위한자식프로세스에변경된페이지를동기화하는단계를포함한다. 온라인분석처리를위한데이터를유지하는방법및 장치에서는, 변경된페이지가추적되고자식프로세스가변경된페이지와동기화된다. 따라서주기적인포크가회피되고, 종래기술에서주기적포크에의해발생하는포크오버헤드가제거되며, 변경된페이지만이동기화되므로동기화가빨라지고, 온라인데이터처리의성능이향상된다.
195	SYSTEM AND METHOD FOR IMPLEMENTING ONLINE ANALYTICAL PROCESSING (OLAP) SOLUTION USING MAPREDUCE	US14559642	2014-12-03	US20150178367A1	2015-06-25	Shyam Kumar Doddavula; Arun Viswanathan
The technique relates to a system and method for implementing petabyte scale online analytical processing solutions using MapReduce. The technique involves receiving an OLAP query from a user through an OLAP-QL Driver. After receiving the query it is parsed through the compiler. Then the metadata information is retrieved from the parsed query through the metadata manager. Validating the parsed query using plan generator module for generating a MapReduce job execution plan based on the retrieved metadata information. The next step is to identify the scope for optimization in the generated MapReduce job execution plan and optimizing the MapReduce job execution plan using the identified scope. Then executing the optimized MapReduce job plan using the execution engine and finally storing the output data in the cube specific distributed file system directory.
196	Method And Apparatus For Reconstructing Cube In Multidimensional Online Analytical Processing System	US15264294	2016-09-13	US20170004170A1	2017-01-05	Yong Zhang; Bian Yin; Dandan Tu
A method and an apparatus for reconstructing a cube in a multidimensional online analytical processing (MOLAP) system, where a cube is reconstructed based on a received reconstruction request and data stored in an old cube, and there is no need to acquire, from a database, data required for updating the cube, thereby ensuring data integrity when model reconstruction and data reconstruction are performed in the MOLAP system.
197	System and method for online analysis processing using dimension attribute and a plurality of hierarchies per dimension	JP2004185648	2004-06-23	JP2005018778A	2005-01-20	PETCULESCU CRISTIAN; NETZ AMIR; PASUMANSKY MOSHA; DUMITRU MARIUS; BERGER ALEXANDER; SANDERS PAUL JONATHON
PROBLEM TO BE SOLVED: To provide an improved mechanism accessing to a database in an OLAP system. SOLUTION: A data model for accessing data in the relational database 312 in the OLAP system utilizes a multiple-hierarchy dimensions 326 and 328. The dimensions 326 and 328 include a set of attributes. Each attribute is bound to columns 314-324 in the relational database 312. A logical structure is defined, indicating the relationships between the attributes. Hierarchies are defined. Each hierarchy includes a sequence of attributes. A hierarchy provides a common drill-down path that a database user can utilize to access the database. A hierarchy can include a single attribute or a combination of attributes. Both the relationships between the attributes and the sequence of attributes in a hierarchy are defined independent of any restrictions associated with the database. COPYRIGHT: (C)2005,JPO&NCIPI
198	METHOD AND APPARATUS OF MAINTAINING DATA FOR ONLINE ANALYTICAL PROCESSING IN A DATABASE SYSTEM	US15201044	2016-07-01	US20160314177A1	2016-10-27	Vinoth Veeraraghavan; Prasanna Venkatesh Ramamurthi; Zhibiao Chen
A method and an apparatus of maintaining data for online analytical processing in a database system. The method includes: tracking a changed page in a main process; and synchronizing the changed page to a child process for online analytical processing. In the method and apparatus of maintaining data for online analytical processing, the changed pages are tracked and then the child process is synchronized with the changed pages. Therefore, periodic forking is avoided, fork overhead due to periodic forking in the prior art is removed, the synchronization is faster since only the changed pages are synchronized, and the performance of online data processing is enhanced.
199	線上分析處理之方法及系統 METHOD AND SYSTEM FOR ONLINE ANALYTICAL PROCESSING (OLAP)	TW092128546	2003-10-15	TWI230344B	2005-04-01	派崔克亞拉斯 PATRICK ARRAS; 亞方斯約翰尼史汀霍夫 ALFONS JOHANN STEINHOFF
本發明揭示一種用於產生被包含在一資料庫中之資料記錄的使用者定義之樞紐分析檢視(pivot view)的方法及系統，在該資料庫中(如圖2a所示)由連續的索引值(index value)(200)來擴展一基礎真實事實(real fact)資料表，該索引值自「1」至「x」(在本實例中x=20)提供該等事實之連續編號。在圖2b所展示之所得樞紐分析檢視中，在每一單元格(210)中，該等事實之索引被表示為：必須在對應的單元格中求和的銷售值。藉由一序列向量而產生該樞紐分析檢視。圖2c中描繪圖2b中之樞紐分析檢視的基礎序列向量且由兩行(260，270)組成，左邊行(260)包含又是自「1」至「x」之連續數字，右邊行(270)包含圖2a中描繪之已提到的索引值(275)，該等索引值係已排序的排列形式而得以按順序地建立圖2b之樞紐分析檢視。
200	Accelerated query operators for high-speed, in-memory online analytical processing queries and operations	US13336962	2011-12-23	US08892586B2	2014-11-18	Christian Lemke; Tobias Mindnich; Christoph Weyerhaeuser; Franz Faerber; Kai-Uwe Sattler
An additional data structure can be initialized for a column of compressed data to include a prefix storing, for each block of values in the column, a total number of bits set in previous blocks in the bit vector. A block number can be determined for a target block of the plurality of blocks, for example by checking whether or not a specified row number is located in the prefix. If the specified row number is located in the prefix, the prefix value of the prefix is returned, the most frequently occurring value is returned if a corresponding bit in the bit vector in the specified row number is not located in the prefix, or a position of the specified row in an index vector for the column is returned.

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