WO2009042552A4 - Temporally-aware evaluative score - Google Patents

Temporally-aware evaluative score Download PDF

Info

Publication number
WO2009042552A4
WO2009042552A4 PCT/US2008/077263 US2008077263W WO2009042552A4 WO 2009042552 A4 WO2009042552 A4 WO 2009042552A4 US 2008077263 W US2008077263 W US 2008077263W WO 2009042552 A4 WO2009042552 A4 WO 2009042552A4
Authority
WO
WIPO (PCT)
Prior art keywords
grain
value
finer grain
values
finer
Prior art date
Application number
PCT/US2008/077263
Other languages
French (fr)
Other versions
WO2009042552A3 (en
WO2009042552A2 (en
Inventor
Todd O. Dampier
Original Assignee
Merced Systems, Inc.
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Merced Systems, Inc. filed Critical Merced Systems, Inc.
Publication of WO2009042552A2 publication Critical patent/WO2009042552A2/en
Publication of WO2009042552A3 publication Critical patent/WO2009042552A3/en
Publication of WO2009042552A4 publication Critical patent/WO2009042552A4/en

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/02—Knowledge representation; Symbolic representation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G06F16/2455—Query execution
    • G06F16/24553—Query execution of query operations
    • G06F16/24554—Unary operations; Data partitioning operations
    • G06F16/24556—Aggregation; Duplicate elimination
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
    • G06F16/2477—Temporal data queries
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/28—Databases characterised by their database models, e.g. relational or object models
    • G06F16/283—Multi-dimensional databases or data warehouses, e.g. MOLAP or ROLAP

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Databases & Information Systems (AREA)
  • Physics & Mathematics (AREA)
  • Data Mining & Analysis (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Computational Linguistics (AREA)
  • Software Systems (AREA)
  • Mathematical Physics (AREA)
  • Evolutionary Computation (AREA)
  • Computing Systems (AREA)
  • Artificial Intelligence (AREA)
  • Fuzzy Systems (AREA)
  • Probability & Statistics with Applications (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
  • Image Generation (AREA)

Abstract

A method includes processing a performance query to a dimensional data model by processing dimension coordinates that exist within the dimensional data model, wherein the dimension coordinates have a first particular grain ('finer grain') that is finer than a second particular grain ('coarser grain'), the method to determine an evaluative score for a particular finer grain value based on performance facts for dimension coordinates associated with the particular finer grain value. Performance parameters are determined relative to a particular coarser grain value, against which to measure the performance facts associated with the finer grain value, including processing the temporal relationships of finer grain values to coarser grain values for the dimension coordinates. The evaluative score is determined for the particular finer grain value based on performance facts of dimension coordinates having the particular finer grain value, in view of the determined performance parameters.

Claims

AMENDED CLAIMS received by the International Bureau on 25 November 2009 (25.11.2009)What is claimed is:
1. A method of processing a performance query to a dimensionally-modeled fact collection by processing dimension coordinates that exist within a dimensional data model, wherein facts of the dimensionally-modeled fact collection are accessible according to the dimensional data model and wherein the dimension coordinates have a first particular grain ("finer grain") that is finer than a second particular grain ("coarser grain"), the method to determine an evaluative score for a particular value at the finer grain ("finer grain value") based on performance facts for dimension coordinates associated with the particular finer grain value, the method comprising: determining at least one performance parameter, relative to a particular value at the coarser grain ("coarser grain value"), against which to measure the performance facts associated with the finer grain value, including processing temporal relationships of finer grain values to coarser grain values for the dimension coordinates, wherein processing the temporal relationships of finer grain values to coarser grain values includes processing data indicative of how relationships of the finer grain values to coarser grain values change over time and. based thereon, processing performance facts based on performance facts for dimension coordinates having the particular coarser grain value; determining the evaluative score for the particular finer grain value based on performance facts of dimension coordinates having the particular finer grain value, in view of the at least one determined performance parameter.
2. The method of claim 1 , wherein: processing data indicative of how the relationships of finer grain values to coarser grain values changes over time includes, for the particular coarser grain value, determining a set of finer grain values for dimension coordinates having the particular coarser grain value; determining a set of dimension coordinates having finer grain values in the determined set of finer grain values, including dimension coordinates having finer grain values in the determined set of finer grain values but not having the particular coarser grain value.
46
3. The method of cl aim I . wherein : for the dimension coordinates having the particular finer grain value, on which the determination of evaluative score for the particular finer grain value is based, at least some of those dimension coordinates do not have the particular coarser grain value.
4. The method of claim 3, wherein: determining a set of dimension coordinates having finer grain values in the determined set of finer grain values, including dimension coordinates having finer grain values in the determined set of finer grain values but not having the particular coarser grain value includes: processing a query constraint that is expressed in terms of the particular coarser grain value to determine an initial set of dimension coordinates; and rewriting and reapplying the query constraint in terms of finer grain values of the determined initial set of dimension coordinates, to determine the set of dimension coordinates,
5. The method of claim 1 , wherein : the particular finer grain value is a first particular finer grain value of a plurality of particular finer grain values; and the method further comprises processing a temporal mode to determine a time extent descriptor; and determining the plurality of finer grain values based at least in part on processing the determined time extent descriptor.
6. A computer program product for processing a performance query to a dimensionally-modclcd fact collection by processing dimension coordinates that exist within a dimensional data model, wherein facts of the dimensionally-modeled fact collection are accessible according to the dimensional data model and wherein the dimension coordinates have a first particular grain ("finer grain") that is finer than a second particular grain ("coarser grain"), the method to determine an evaluative score for a particular value at the finer grain ("finer grain value'') based on performance
47 facts for dimension coordinates associated with the particular finer grain value, the computer program product comprising at least one computer-readable medium having computer program instructions stored therein which are operable to cause at least one computing device to: determine at least one performance parameter, relative to a particular value at the coarser grain ("coarser grain value"), against which to measure the performance facts associated with the finer grain value, including processing [[the]] temporal relationships of finer grain values to coarser grain values for the dimension coordinates, wherein processing the temporal relationships of finer grain values to coarser grain values includes processing data indicative of how relationships of the finer grain values to coarser grain values change over time and, based thereon, processing performance facts based on performance facts for dimension coordinates having the particular coarser grain value; and determine the evaluative score for the particular finer grain value based on performance facts of dimension coordinates having the particular finer grain value, in view of the at least one determined performance parameter.
7. The computer pτogτam product of claim 6, wherein: processing data indicative of how the relationships of finer grain values to coarser grain values changes over time includes, for the particular coarser grain value, includes determining a set of finer grain values for dimension coordinates having the particular coarser grain value; determining a set of dimension coordinates having finer grain values in the determined set of finer grain values, including dimension coordinates having finer grain values in the determined set of finer grain values but not having the particular coarser grain value.
S. The computer program product of claim 6, wherein: for the dimension coordinates, having the particular finer grain value, on which the determination of performance score for the particular finer grain value is based, at least some of those dimension coordinates do not have the particular coarser grain value.
48
9. The computer program product of claim 8, wherein: determining a set of dimension coordinates having finer grain values in the determined set of finer grain values, including dimension coordinates having finer grain values in the determined set of finer grain values but not having the particular coarser grain value includes: processing a query constraint that is expressed in terms of the particular coarser grain value to determine an initial set of dimension coordinates; and rewriting and reapplying the query constraint in terms of fineτ grain values of the determined initial set of dimension coordinates, to determine the set of dimension coordinates.
10. The computer program product of claim 6, wherein: the particular finer grain value is a first particular finer grain value of a plurality of particular finer grain values; and the computer program instructions axe further operable to cause at least one computing device to: process a temporal mode to determine a time extent descriptor; and determine the plurality of finer grain values based at least in part on processing the determined time extent descriptor.
11. A computer system configured to process a performance query to a dimensionally-modeled fact collection by processing dimension coordinates that exist within a dimensional data model, wherein facts of the dimensionally-modeled fact collection are accessible according to the dimensional data model and wherein the dimension coordinates have a first particular grain ("finer grain") that is finer than a second particular grain ("coarser grain"), the method to determine an evaluative score for a particular value at the finer grain ("finer grain value") based on performance facts for dimension coordinates associated with the particular finer grain value, th£ computer system configured to: determine performance parameters, relative to a particular coarser grain value, against which to measure the performance facts associated with the finer grain value, including processing the temporal relationships of finer grain values to coarser grain values for the dimension coordinates; and determine the evaluative score for the particular finer grain value based on perforrnance facts of dimension coordinates having the particular finer grain value, in view of the determined performance parameters.
12. The computer system of claim 1 1 , wherein: processing data indicative of how the relationships of finer grain values to coarser grain values changes over time includes, for the particular coarser .grain value, includes determining a set of finer grain values for dimension coordinates having the particular coarser grain value; determining a set of dimension coordinates having finer grain values in the determined set of finer grain values, including dimension coordinates having finer grain values in the determined set of finer grain values but not having the particular coarser grain value.
13. The computer system of claim \ 1 , wherein: for the dimension coordinates, having the particular finer grain value, on which the determination of perforrnance score for the particular finer grain value is based, at least some of those dimension coordinates do not have the particular coarser grain value.
14. The computer system of claim 11 , wherein: determining a set of dimension coordinates having finer grain values in the determined set of finer grain values, including dimension coordinates having finer grain values in the determined set of finer grain values but not having the particular coarser grain value includes: processing a query constraint that is expressed in terms of the particular coarser grain value to determine an initial set of dimension coordinates; and rewriting and reapplying the query constraint in terms of finer grain values of the determined initial set of dimension coordinates, to determine the set of dimension coordinates.
15, The computer system of claim 11 , wherein: the particular finer grain value is a first particular finer grain value of a plurality of particular finer grain values; and the computer system is further configured to: process a temporal mode to determine a time extent descriptor; and determine the plurality of finer grain values based at least in part on processing the determined time extent descriptor.
51
PCT/US2008/077263 2007-09-24 2008-09-22 Temporally-aware evaluative score WO2009042552A2 (en)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US11/860,275 2007-09-24
US11/860,275 US8051075B2 (en) 2007-09-24 2007-09-24 Temporally-aware evaluative score

Publications (3)

Publication Number Publication Date
WO2009042552A2 WO2009042552A2 (en) 2009-04-02
WO2009042552A3 WO2009042552A3 (en) 2009-11-19
WO2009042552A4 true WO2009042552A4 (en) 2010-01-14

Family

ID=40394309

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/US2008/077263 WO2009042552A2 (en) 2007-09-24 2008-09-22 Temporally-aware evaluative score

Country Status (2)

Country Link
US (2) US8051075B2 (en)
WO (1) WO2009042552A2 (en)

Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8473519B1 (en) * 2008-02-25 2013-06-25 Cisco Technology, Inc. Unified communication audit tool
WO2010148326A1 (en) * 2009-06-19 2010-12-23 Ingenix, Inc. System and method for generation of attribute driven temporal clustering
US8719217B1 (en) * 2013-03-15 2014-05-06 Decisyon, Inc. Systems, devices, and methods for generation of contextual objects mapped by dimensional data to data measures
US11697260B2 (en) 2016-06-30 2023-07-11 Bridgestone Americas Tire Operations, Llc Methods for treating inner liners, inner liners resulting therefrom and tires containing such inner liners

Family Cites Families (40)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4918603A (en) 1988-08-01 1990-04-17 Michael B. Hughes Computerized statistical football game
US5832496A (en) 1995-10-12 1998-11-03 Ncr Corporation System and method for performing intelligent analysis of a computer database
JP3952518B2 (en) 1996-03-29 2007-08-01 株式会社日立製作所 Multidimensional data processing method
US5999924A (en) 1997-07-25 1999-12-07 Amazon.Com, Inc. Method and apparatus for producing sequenced queries
GB2336007B (en) 1998-04-01 2003-01-29 Mitel Corp Agent-based data mining and warehousing
US6161103A (en) 1998-05-06 2000-12-12 Epiphany, Inc. Method and apparatus for creating aggregates for use in a datamart
US6735593B1 (en) 1998-11-12 2004-05-11 Simon Guy Williams Systems and methods for storing data
US6763353B2 (en) 1998-12-07 2004-07-13 Vitria Technology, Inc. Real time business process analysis method and apparatus
FR2806183B1 (en) 1999-12-01 2006-09-01 Cartesis S A DEVICE AND METHOD FOR INSTANT CONSOLIDATION, ENRICHMENT AND "REPORTING" OR BACKGROUND OF INFORMATION IN A MULTIDIMENSIONAL DATABASE
US6356900B1 (en) 1999-12-30 2002-03-12 Decode Genetics Ehf Online modifications of relations in multidimensional processing
US6434557B1 (en) 1999-12-30 2002-08-13 Decode Genetics Ehf. Online syntheses programming technique
US20040230461A1 (en) 2000-03-30 2004-11-18 Talib Iqbal A. Methods and systems for enabling efficient retrieval of data from data collections
US6831668B2 (en) 2000-04-03 2004-12-14 Business Objects, S.A. Analytical reporting on top of multidimensional data model
US7451389B2 (en) 2000-06-06 2008-11-11 Microsoft Corporation Method and system for semantically labeling data and providing actions based on semantically labeled data
WO2002003251A2 (en) 2000-06-29 2002-01-10 Alphablox Corporation Caching scheme for multi-dimensional data
US6371855B1 (en) 2000-09-08 2002-04-16 Winamax.Com Limited Fantasy internet sports game
US20020099563A1 (en) 2001-01-19 2002-07-25 Michael Adendorff Data warehouse system
US7720842B2 (en) 2001-07-16 2010-05-18 Informatica Corporation Value-chained queries in analytic applications
WO2003014867A2 (en) 2001-08-03 2003-02-20 John Allen Ananian Personalized interactive digital catalog profiling
KR100656528B1 (en) 2001-09-10 2006-12-12 한국과학기술원 Dynamic Update Cube and Hybrid Queries Search Method for Range-Sum Queries
US6826568B2 (en) 2001-12-20 2004-11-30 Microsoft Corporation Methods and system for model matching
CA2418753A1 (en) 2002-02-12 2003-08-12 Cognos Incorporated Method and system for database join disambiguation
US7366730B2 (en) 2002-04-26 2008-04-29 Oracle International Corporation Registration of solved cubes within a relational database management system
BR0312989A (en) 2002-07-26 2008-03-04 Ron Everett database and knowledge operation system
US7152073B2 (en) 2003-01-30 2006-12-19 Decode Genetics Ehf. Method and system for defining sets by querying relational data using a set definition language
CA2419502A1 (en) 2003-02-21 2004-08-21 Cognos Incorporated Time-based partitioned cube
US7634478B2 (en) 2003-12-02 2009-12-15 Microsoft Corporation Metadata driven intelligent data navigation
US7809678B2 (en) 2004-07-09 2010-10-05 Microsoft Corporation Fact dimensions in multidimensional databases
US7653528B2 (en) 2005-03-08 2010-01-26 Microsoft Corporation Resource authoring incorporating ontology
US7699707B2 (en) 2005-05-09 2010-04-20 Hotbox Sports Llc Fantasy sports system and method thereof
US7587388B2 (en) 2005-07-28 2009-09-08 Microsoft Corporation Separating uploads into aggregate and raw data storage
US7516124B2 (en) 2005-12-20 2009-04-07 Yahoo! Inc. Interactive search engine
US20070060325A1 (en) 2005-08-25 2007-03-15 Microsoft Corporation Fantasy sports live
US7415485B2 (en) 2005-09-13 2008-08-19 International Business Machines Corporation Workflow application having linked workflow components
US20070233651A1 (en) 2006-03-31 2007-10-04 International Business Machines Corporation Online analytic processing in the presence of uncertainties
US8206214B2 (en) 2006-04-18 2012-06-26 Yahoo! Inc. Player roster selection interface
US8392358B2 (en) 2006-06-29 2013-03-05 Nice Systems Technologies Inc. Temporal extent considerations in reporting on facts organized as a dimensionally-modeled fact collection
US7856431B2 (en) 2006-10-24 2010-12-21 Merced Systems, Inc. Reporting on facts relative to a specified dimensional coordinate constraint
US7685119B2 (en) 2006-12-20 2010-03-23 Yahoo! Inc. System and method for query expansion
US8036859B2 (en) 2006-12-22 2011-10-11 Merced Systems, Inc. Disambiguation with respect to multi-grained dimension coordinates

Also Published As

Publication number Publication date
US20110161275A1 (en) 2011-06-30
US8051075B2 (en) 2011-11-01
WO2009042552A3 (en) 2009-11-19
WO2009042552A2 (en) 2009-04-02
US8166050B2 (en) 2012-04-24
US20090083216A1 (en) 2009-03-26

Similar Documents

Publication Publication Date Title
WO2009042552A4 (en) Temporally-aware evaluative score
EP1840552A3 (en) Tire transient response data calculating method, data processing method, tire designing method, vehicle motion predicting method, and tire cornering characteristic evaluation method and evaluation device therefor
WO2008142423A3 (en) Improvements in and relating to payment cards and fuel cards
WO2008036550A3 (en) Self-calibrating fraud detection
CA2635089A1 (en) Filtering and detection of telemetry
WO2005101028A3 (en) System and method for monitoring a mobile computing product/arrangement
WO2007071883A3 (en) Digital data processing method and system
WO2005066660A3 (en) Processing seismic data representing a physical system
WO2002091232A3 (en) Method, system, and program for querying data in a personal information manager database
EP1703416A3 (en) Method and Computer-Readable Medium for Providing Spreadsheet-Driven Key Performance Indicators
HK1060428A1 (en) Method for converting a music signal into a note-based description and for referencing a music signal in a data bank.
CA2379452A1 (en) Pattern collation device and pattern collating method thereof, and pattern collation program
EP1562152A3 (en) Credit-points managing apparatus, vehicle, credit-points calculating method, and a computer product
WO2005013627A3 (en) Compliance rules for dynamic bidding
CN106778476A (en) Human posture recognition method and human body attitude identifying device
CN102890738A (en) Method for encapsulating vulnerable elements determined in printed board component under effect of dynamic load
CN105574471A (en) Uploading method of user behavior data, and user behavior identification method and device
CN112109715A (en) Method, device, medium and system for generating vehicle power output strategy
CN115655740B (en) Method and device for detecting vehicle braking performance, electronic equipment and storage medium
Chen Combination determining weights method for multiple attribute decision making based on maximizing deviations
WO2005094301A3 (en) Methods and systems for optimization of economic value from asset sets
CN114426025B (en) Driving assistance method, driving assistance device, computer device, and storage medium
CN102508990A (en) Online game data updating method
KR101936132B1 (en) Apparatus and Method for Control of Wheel
WO2005052821A3 (en) System, method, and computer program product for determining wall thickness in graphic model

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 08833036

Country of ref document: EP

Kind code of ref document: A2

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 08833036

Country of ref document: EP

Kind code of ref document: A2