WO2005002243A2 - Multidimensional data object searching using bit vector indices - Google Patents
Multidimensional data object searching using bit vector indices Download PDFInfo
- Publication number
- WO2005002243A2 WO2005002243A2 PCT/US2004/014115 US2004014115W WO2005002243A2 WO 2005002243 A2 WO2005002243 A2 WO 2005002243A2 US 2004014115 W US2004014115 W US 2004014115W WO 2005002243 A2 WO2005002243 A2 WO 2005002243A2
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- interval
- hyper
- data
- feature space
- dimension
- Prior art date
Links
Classifications
-
- 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
- G06F16/22—Indexing; Data structures therefor; Storage structures
- G06F16/2228—Indexing structures
- G06F16/2264—Multidimensional index structures
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/40—Information retrieval; Database structures therefor; File system structures therefor of multimedia data, e.g. slideshows comprising image and additional audio data
- G06F16/43—Querying
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y10—TECHNICAL SUBJECTS COVERED BY FORMER USPC
- Y10S—TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y10S707/00—Data processing: database and file management or data structures
- Y10S707/953—Organization of data
- Y10S707/957—Multidimensional
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y10—TECHNICAL SUBJECTS COVERED BY FORMER USPC
- Y10S—TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y10S707/00—Data processing: database and file management or data structures
- Y10S707/99931—Database or file accessing
- Y10S707/99933—Query processing, i.e. searching
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y10—TECHNICAL SUBJECTS COVERED BY FORMER USPC
- Y10S—TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y10S707/00—Data processing: database and file management or data structures
- Y10S707/99941—Database schema or data structure
- Y10S707/99942—Manipulating data structure, e.g. compression, compaction, compilation
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y10—TECHNICAL SUBJECTS COVERED BY FORMER USPC
- Y10S—TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y10S707/00—Data processing: database and file management or data structures
- Y10S707/99941—Database schema or data structure
- Y10S707/99943—Generating database or data structure, e.g. via user interface
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y10—TECHNICAL SUBJECTS COVERED BY FORMER USPC
- Y10S—TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y10S707/00—Data processing: database and file management or data structures
- Y10S707/99941—Database schema or data structure
- Y10S707/99944—Object-oriented database structure
- Y10S707/99945—Object-oriented database structure processing
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y10—TECHNICAL SUBJECTS COVERED BY FORMER USPC
- Y10S—TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y10S707/00—Data processing: database and file management or data structures
- Y10S707/99941—Database schema or data structure
- Y10S707/99948—Application of database or data structure, e.g. distributed, multimedia, or image
Definitions
- (MD) objects in the database For example, a sample of a song having multiple
- characteristics may be compared to a number of songs stored in a
- epsilon used is significantly less than the average interpoint distance.
- linear scanning is a very time intensive process.
- each dimension in the MD feature space is divided
- MD data objects in the feature space is then used to determine matches for the
- Fig. 1 illustrates an exemplary data mapping and searching system.
- Fig. 2 illustrates an exemplary feature space of the data mapping
- Fig. 3 is an exemplary operational flow diagram illustrating various aspects of searching system of Fig. 1.
- Fig. 3 is an exemplary operational flow diagram illustrating various aspects of searching system of Fig. 1.
- Fig. 4 is another exemplary operational flow diagram illustrating various aspects of the present disclosure.
- Fig. 5 is an exemplary operational flow diagram illustrating various aspects of the present disclosure.
- Fig. 6 is another exemplary operational flow diagram illustrating various aspects of the present disclosure.
- Fig. 7 illustrates one embodiment of a computing system in which the data
- mapping and searching system of Fig. 1 and the operations flows of Figs. 4 - 6 may
- MD multidimensional
- MD data objects are
- the MD data objects are first mapped to hyper-
- each dimension in the feature space is first divided into a number of predetermined
- a bit vector index is then created for each interval in each dimension.
- Each bit vector index indicates whether each of the hyper-rectangles in the feature
- the result bit vector index identifies a reduced set of hyper-
- FIG. 1 illustrated therein is one embodiment of an exemplary
- the searching system 100 As shown, the searching system 100
- a data store 102 includes a mapping module 104, a search module 106, a shape
- space 110 are a number of MD data objects of a first type 114 (Si through S n ) and a
- the MD feature space 110 is a type
- MD data points in the MD feature space 110 are vectors of values.
- the MD data objects 114 and 116 are sets of MD data points.
- MD data objects 114 and 115 may be defined as functions or algorithms that
- an MD data object is said to be “coupled to” an MD data object
- object manipulates vectors whose type corcesponds to the MD feature space.
- MD data point vectors are considered coordinates in a high-dimensional
- MD data objects are sets of MD data points, hence may be considered to be
- An MD feature space is said to "include” an MD data point or object.
- An MD data object is
- This set membership can be determined by
- an MD data object is said to match the query point if the MD data object is
- searching a feature space is used herein to describe
- the search module 106 is operable to determine which of the data
- the search module 106 does not search the data items 112
- the search module 104 extracts from the feature space 110 by the mapping module 104.
- mapping module 104 maps the
- module 104 maps the data items 112 to MD data objects of the first type 114. In this
- the shape approximater module 108 then converts or maps the MD
- search module 106 then conducts the search with respect to the MD data objects of
- the data store 102 is composed of or
- the data store 102 is a database having data
- a computer-readable media such as magnetic or optical media.
- computer-readable media may be any available media that can store
- modulated data non-removable media, and modulated data signals.
- modulated data modulated data
- signal refers to a signal that has one or more of its characteristics set or changed in
- data store 102 is a data sample or file.
- data sample or file For example, and without limitation, in
- each of the data items 112 is a media sample
- the data items 112 may be other types of samples or files.
- mapping module 104 is operable to map data items 112 in the
- items 112 may be mapped either as MD data objects of the first type 114 or as MD
- mapping module 104 will typically map data items 112 to MD data objects of
- hyper-ellipsoids or polytopes are examples of polytopes.
- the MD data objects of a first type 114 are hyper-
- the MD data objects of a second type 116 are hyper-rectangles.
- hyper-rectangles referred to herein as hyper-rectangles.
- MD data objects of the first type are other varieties of MD data objects.
- a hyper-rectangle may be defined as a set of all points in an MD feature
- sphere may be defined as a set of all points in an MD feature space such that each
- FIG. 2 illustrated therein is a generalized exemplary
- the feature space 110 has a first dimension (diml) 210
- feature space 110 can attain a range of possible values. This range of possible
- dimension may also include negative values and floating point values. Likewise, it
- rectangles having floating point value ranges are also possible.
- FIG 2 illustrates one
- feature space 110 includes an identifier (Rl, R2, . . . , etc.) and two coordinate
- the first coordinate pair identifies the location of the lower left
- the lower left most hyper-rectangle 214 in the feature space is designated
- Rl indicates the hyper-rectangle identifier
- ⁇ 1,1 ⁇ indicates the lower left corner of the hyper-rectangle 214
- ⁇ 4,2 ⁇ indicates
- mapping module 104 the mapping module 104, and the shape approximater module 108 are
- the search module 106 the mapping module 104, the shape
- the search module 106 the mapping module 104, the shape
- the shape approximater module 108 is
- approximater module 108 may vary, based on the type of hyper-sphere 114 that is
- each hyper-sphere 114 is mapped to a hyper-rectangle
- a size that completely encloses the hyper-sphere 114 For example, a
- hyper-sphere 114 may be mapped to a hyper-rectangle 116 having dimensions such
- the hyper-sphere 114 would be completely contained within the hyper-
- rectangle 116 As such, it will be appreciated that the overall size or volume of a
- hyper-rectangle will be dependent on the overall size or volume of the hyper-sphere
- each hyper-rectangle 116 will be the smallest
- each hyper-rectangle 116 may be the smaller than the smallest possible
- hyper-rectangle that would completely enclose the hyper-sphere 114 from which it
- the hyper- rectangles 116 in the feature space may be created in, or mapped to, the feature
- the search module 106 performs searches of the feature space 110
- hyper-rectangle is an MD data object, the definition of overlapping and matching a
- search module 106 performs the operations illustrated in FIGS. 3, 4, 5, and/or 6, as
- FIG. 3 illustrated therein is an exemplary operational flow
- the zooming searching the feature space 110.
- the zooming searching the feature space 110.
- operations 300 may be performed at various times. Typically, however, the
- operation 310 partitions each dimension in the feature space 110 into a number of disjoint intervals. For example, as shown in Fig. 2, both dimensions 210 and 212
- hyper-rectangles are determined may vary, and may be dependent on such things as hyper-rectangle
- interval dividers are selected between the intervals. For example, as shown in
- the first and last interval in each dimension are the first and last interval in each dimension
- interval one 210 is
- interval two 212 is bounded on one side by value 8, but remains unbounded at its
- each interval divider the position of each interval divider
- the divider 230 between interval one 224 and interval two 226 occurs at the
- m is the desired number of intervals
- a/b is used to represent
- Equation (2) gives the IDs of
- the remaining dividers For instance, if FirstEDs ⁇ , then the first divider is at the
- a restricted space is where the dividers are located.
- a restricted space is where the dividers are located.
- set of rectangle boundaries is used based upon prior knowledge of query point
- bit vector index is created that specifies
- a hyper-rectangle 116 may be said to overlap an interval in a
- R4 220, and R5 222 overlaps interval one 224; each of hyper-rectangles R2 216, R4
- each bit vector index includes the same
- bit in the bit vector index is associated with a single one of the hyper-rectangles in
- bit vectors may include
- hyper-rectangles when a hyper-rectangle is removed from the feature space, its associated bit may
- Each bit in a bit vector index indicates whether or not the hyper-rectangle to
- a bit having a value of "1" might indicate that its associated hyper-
- a value of "0" might indicate that its associated hyper-rectangle does not overlap
- a first bit vector index associated with Interval one 224 includes five bits and may
- the second bit (0) indicates that R2 does not overlap interval one
- the third bit
- bit vector index associated with interval two 226 is
- a partition dimension operation 412 partitions the dimension variable (dim) to a value of 1.
- dimension operation 412 will partition the first dimension of the given feature
- rectangle operation 416 sets or initializes a hyper-rectangle variable rect to a value
- operation 418 sets a bit associated with the specified hyper-rectangle in a bit vector
- the set bit operation 418 sets a bit
- an increment hyper-rectangle operation 420 increments the hyper-
- rectangle variable rect A rectangle number determination operation 422 then
- the hyper-rectangle variable rect determines if the hyper-rectangle variable rect is equal to the number of hyper- rectangles in the feature space plus 1. If the hyper-rectangle variable rect is not
- rect is equal to the number of hyper-rectangles in the feature space plus 1, the
- operation 426 determines if the interval variable intvl equals the number of
- interval variable intvl does not equal the number of intervals in the dimension
- dimension variable dim is incremented.
- determination operation 430 determines if the dimension variable dim equals the
- dimension variable dim does equal the number of dimensions in the feature space
- operational flow 500 may be used in searching the feature space 110 after bit vector
- receive query operation 514 receives a query item.
- a map query operation 515 receives a query item.
- a dimension may be said to overlap a query point if the value of the query point in
- ANDing operation 518 logically ANDs all of the bit vector indices corresponding to
- bit vector index that specifies a set
- a matching operation 520 compares
- FIG. 6 illustrated therein is another, more detailed exemplary
- operational flow 600 including operations that may be used for searching a feature
- the operational flow 600 may be carried
- a receive query operation 610 receives a query point.
- a set dimension operation 612 sets a dimension
- variable dim 1
- interval operation 614 determines an interval in the
- select bit vector index operation 616 then selects the bit vector index corresponding
- determination operation 618 determines if the dimension variable dim is equal to 1.
- a set result bit vector index operation 620 sets the result bit vector index equal to the bit vector index selected in the select bit
- variable increment operation 624 if the dimension determination
- index selected in the select bit vector index operation 616 is logically ANDed with
- determination operation 626 determines if the dimension variable dim equals the
- variable dim does equal the number of dimensions in the feature space
- compare data object operation 630 compares the received query point to all of the
- a return data object operation 630 then returns all MD
- FIG. 7 illustrates one operating environment 710 in which the various components
- the exemplary operating environment 710 of Fig. 7 includes a general purpose
- computing device in the form of a computer 720, including a processing unit 721, a
- system memory 722 and a system bus 723 that operatively couples various system
- components include the system memory to the processing unit 721. There may be
- Computer 720 comprises a single central-processing unit (CPU), or a plurality of
- computer 720 may be a conventional computer, a distributed computer, or any other
- the system bus 723 may be any of several types of bus structures including a
- memory bus or memory controller a peripheral bus, and a local bus using any of a
- system memory may also be referred to as simply
- the memory includes read only memory (ROM) 724 and random access
- RAM random access memory
- BIOS basic input/output system
- the computer 720 is stored in ROM 724.
- the computer 720 furthermore, is stored in ROM 724.
- the computer 720 furthermore, is stored in ROM 724.
- a hard disk drive 727 for reading from and writing to a hard disk, not
- a magnetic disk drive 728 for reading from or writing to a removable
- removable optical disk 731 such as a CD ROM or other optical media.
- the hard disk drive 727, magnetic disk drive 728, and optical disk drive 730 are the hard disk drive 727, magnetic disk drive 728, and optical disk drive 730.
- a hard disk drive interface 732 is connected to the system bus 723 by a hard disk drive interface 732, a magnetic disk drive interface 733, and an optical disk drive interface 734, respectively.
- RAMs random access memories
- ROMs read only memories
- a number of program modules may be stored on the hard disk, magnetic disk
- application programs 736 one or more application programs 736, other program modules 737, and program
- a user may enter commands and information into the personal computer
- input devices may include a microphone, joystick, game pad, satellite
- bus but may be connected by other interfaces, such as a parallel port, game port, or
- USB universal serial bus
- a video adapter 748 connected to the system bus 723 via an interface, such as a video adapter 748.
- computers typically include other peripheral output devices
- the computer 720 may operate in a networked environment using logical
- remote computer 749 connections to one or more remote computers, such as remote computer 749.
- logical connections may be achieved by a communication device coupled to or a
- the remote computer 749 may be
- Another computer a server, a router, a network PC, a client, a peer device or other
- FIG. 7 The logical connections depicted in Fig. 7 include a local-
- LAN local area network
- WAN wide-area network
- the computer 720 When used in a LAN-networking environment, the computer 720 is
- the computer 720 typically includes a modem 754, a type of
- communications device or any other type of communications device for
- the modem 754 establishing communications over the wide area network 752.
Abstract
Description
Claims
Priority Applications (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
JP2006517105A JP4516071B2 (en) | 2003-06-23 | 2004-05-06 | Multidimensional data object search using bit vector index |
EP04751482A EP1629397A4 (en) | 2003-06-23 | 2004-05-06 | Multidimensional data object searching using bit vector indices |
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US10/602,438 | 2003-06-23 | ||
US10/602,438 US6941315B2 (en) | 2003-06-23 | 2003-06-23 | Multidimensional data object searching using bit vector indices |
Publications (2)
Publication Number | Publication Date |
---|---|
WO2005002243A2 true WO2005002243A2 (en) | 2005-01-06 |
WO2005002243A3 WO2005002243A3 (en) | 2005-06-30 |
Family
ID=33518093
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
PCT/US2004/014115 WO2005002243A2 (en) | 2003-06-23 | 2004-05-06 | Multidimensional data object searching using bit vector indices |
Country Status (7)
Country | Link |
---|---|
US (3) | US6941315B2 (en) |
EP (1) | EP1629397A4 (en) |
JP (1) | JP4516071B2 (en) |
KR (1) | KR101015324B1 (en) |
CN (1) | CN1809826A (en) |
TW (1) | TWI360756B (en) |
WO (1) | WO2005002243A2 (en) |
Families Citing this family (33)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US7082394B2 (en) * | 2002-06-25 | 2006-07-25 | Microsoft Corporation | Noise-robust feature extraction using multi-layer principal component analysis |
US20080015870A1 (en) * | 2003-05-30 | 2008-01-17 | Lawrence Benjamin Elowitz | Apparatus and method for facilitating a search for gems |
US7831615B2 (en) * | 2003-10-17 | 2010-11-09 | Sas Institute Inc. | Computer-implemented multidimensional database processing method and system |
US20070198494A1 (en) * | 2005-07-08 | 2007-08-23 | Vadon Mark C | Apparatus and method for facilitating a search for sets of gems |
US8271521B2 (en) * | 2006-03-20 | 2012-09-18 | Blue Nile, Inc. | Computerized search technique, such as an internet-based gemstone search technique |
US20070239675A1 (en) * | 2006-03-29 | 2007-10-11 | Microsoft Corporation | Web search media service |
US20080086493A1 (en) * | 2006-10-09 | 2008-04-10 | Board Of Regents Of University Of Nebraska | Apparatus and method for organization, segmentation, characterization, and discrimination of complex data sets from multi-heterogeneous sources |
US20080263010A1 (en) * | 2006-12-12 | 2008-10-23 | Microsoft Corporation | Techniques to selectively access meeting content |
US9992648B2 (en) * | 2007-11-17 | 2018-06-05 | S. Sejo Pan | Apparatus, method and system for subsequently connecting people |
US8688723B2 (en) * | 2007-12-21 | 2014-04-01 | Hewlett-Packard Development Company, L.P. | Methods and apparatus using range queries for multi-dimensional data in a database |
US8024288B2 (en) * | 2008-08-27 | 2011-09-20 | Oracle International Corporation | Block compression using a value-bit format for storing block-cell values |
US8055687B2 (en) * | 2009-01-20 | 2011-11-08 | Hewlett-Packard Development Company, L.P. | System and method for determining intervals of a space filling curve in a query box |
US8738354B2 (en) * | 2009-06-19 | 2014-05-27 | Microsoft Corporation | Trans-lingual representation of text documents |
US8229716B2 (en) * | 2010-01-05 | 2012-07-24 | The United States Of America As Represented By The Secretary Of The Navy | Fast tracking methods and systems for air traffic modeling using a Monotonic Lagrangian Grid |
CN102255788B (en) * | 2010-05-19 | 2014-08-20 | 北京启明星辰信息技术股份有限公司 | Message classification decision establishing system and method and message classification system and method |
CN101866358B (en) * | 2010-06-12 | 2012-09-05 | 中国科学院计算技术研究所 | Multidimensional interval querying method and system thereof |
US20120102453A1 (en) * | 2010-10-21 | 2012-04-26 | Microsoft Corporation | Multi-dimensional objects |
US8676801B2 (en) | 2011-08-29 | 2014-03-18 | Sas Institute Inc. | Computer-implemented systems and methods for processing a multi-dimensional data structure |
JP2014006613A (en) * | 2012-06-22 | 2014-01-16 | Dainippon Screen Mfg Co Ltd | Neighborhood search method and similar image search method |
CN103049296B (en) * | 2012-12-28 | 2016-01-20 | 北界创想(北京)软件有限公司 | For the method and apparatus of download equipment Auto-matching intended application |
US9298757B1 (en) * | 2013-03-13 | 2016-03-29 | International Business Machines Corporation | Determining similarity of linguistic objects |
CN104935504B (en) * | 2014-03-17 | 2018-05-22 | 中国移动通信集团河北有限公司 | A kind of method and device of the corresponding data rule of definite data packet |
US10565198B2 (en) | 2015-06-23 | 2020-02-18 | Microsoft Technology Licensing, Llc | Bit vector search index using shards |
US10733164B2 (en) | 2015-06-23 | 2020-08-04 | Microsoft Technology Licensing, Llc | Updating a bit vector search index |
US10467215B2 (en) | 2015-06-23 | 2019-11-05 | Microsoft Technology Licensing, Llc | Matching documents using a bit vector search index |
US11392568B2 (en) | 2015-06-23 | 2022-07-19 | Microsoft Technology Licensing, Llc | Reducing matching documents for a search query |
US10242071B2 (en) | 2015-06-23 | 2019-03-26 | Microsoft Technology Licensing, Llc | Preliminary ranker for scoring matching documents |
US11281639B2 (en) | 2015-06-23 | 2022-03-22 | Microsoft Technology Licensing, Llc | Match fix-up to remove matching documents |
US10229143B2 (en) | 2015-06-23 | 2019-03-12 | Microsoft Technology Licensing, Llc | Storage and retrieval of data from a bit vector search index |
JP6638484B2 (en) * | 2016-03-10 | 2020-01-29 | 富士通株式会社 | Information processing apparatus, similarity search program, and similarity search method |
US10650012B1 (en) * | 2016-07-13 | 2020-05-12 | United States Of America As Represented By Secretary Of The Navy | Multi-dimensional range-index searching using search box approximation and splitting |
CN107992503B (en) * | 2016-10-26 | 2022-05-24 | 微软技术许可有限责任公司 | Query processing in data analysis |
JP6666312B2 (en) | 2017-08-03 | 2020-03-13 | 株式会社日立製作所 | Multidimensional data management system and multidimensional data management method |
Family Cites Families (16)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US1647992A (en) * | 1923-10-08 | 1927-11-08 | Walter F Stimpson | Adjustable leveling foot for scale bases |
US1690408A (en) * | 1926-10-25 | 1928-11-06 | Champion Hardware Company | Hasp |
US2583806A (en) * | 1950-11-08 | 1952-01-29 | Joseph H Batzle | Garment carrying rack for automobiles |
JPS4823376Y1 (en) * | 1969-10-04 | 1973-07-07 | ||
US3912315A (en) * | 1972-12-13 | 1975-10-14 | Aisin Seiki | Door latch device |
DE8715925U1 (en) * | 1987-12-02 | 1988-02-11 | Kiekert Gmbh & Co Kg, 5628 Heiligenhaus, De | |
US5499360A (en) * | 1994-02-28 | 1996-03-12 | Panasonic Technolgies, Inc. | Method for proximity searching with range testing and range adjustment |
US5761652A (en) * | 1996-03-20 | 1998-06-02 | International Business Machines Corporation | Constructing balanced multidimensional range-based bitmap indices |
US5781906A (en) * | 1996-06-06 | 1998-07-14 | International Business Machines Corporation | System and method for construction of a data structure for indexing multidimensional objects |
US6122628A (en) * | 1997-10-31 | 2000-09-19 | International Business Machines Corporation | Multidimensional data clustering and dimension reduction for indexing and searching |
US6134541A (en) * | 1997-10-31 | 2000-10-17 | International Business Machines Corporation | Searching multidimensional indexes using associated clustering and dimension reduction information |
US6490532B1 (en) * | 1999-01-25 | 2002-12-03 | Mount Sinai Hospital | Method to construct protein structures |
US6871201B2 (en) * | 2001-07-31 | 2005-03-22 | International Business Machines Corporation | Method for building space-splitting decision tree |
KR100483321B1 (en) * | 2001-10-17 | 2005-04-15 | 한국과학기술원 | The Device and Method for Similarity Search Using Hyper-rectangle Based Multidimensional Data Segmentation |
JP2003330943A (en) * | 2002-05-17 | 2003-11-21 | Fujitsu Ltd | Multidimensional index creating device and method, approximate information creating device and method, and search device |
US6928445B2 (en) * | 2002-06-25 | 2005-08-09 | International Business Machines Corporation | Cost conversant classification of objects |
-
2003
- 2003-06-23 US US10/602,438 patent/US6941315B2/en not_active Expired - Fee Related
-
2004
- 2004-05-06 CN CNA2004800172021A patent/CN1809826A/en active Pending
- 2004-05-06 KR KR1020057024563A patent/KR101015324B1/en not_active IP Right Cessation
- 2004-05-06 JP JP2006517105A patent/JP4516071B2/en not_active Expired - Fee Related
- 2004-05-06 WO PCT/US2004/014115 patent/WO2005002243A2/en active Application Filing
- 2004-05-06 EP EP04751482A patent/EP1629397A4/en not_active Withdrawn
- 2004-05-11 TW TW093113244A patent/TWI360756B/en not_active IP Right Cessation
-
2005
- 2005-04-07 US US11/101,120 patent/US7325001B2/en not_active Expired - Fee Related
- 2005-06-24 US US11/166,627 patent/US7430567B2/en not_active Expired - Fee Related
Non-Patent Citations (1)
Title |
---|
See references of EP1629397A4 * |
Also Published As
Publication number | Publication date |
---|---|
US20050171972A1 (en) | 2005-08-04 |
KR20060033733A (en) | 2006-04-19 |
US7325001B2 (en) | 2008-01-29 |
JP2007521565A (en) | 2007-08-02 |
US6941315B2 (en) | 2005-09-06 |
KR101015324B1 (en) | 2011-02-15 |
JP4516071B2 (en) | 2010-08-04 |
WO2005002243A3 (en) | 2005-06-30 |
EP1629397A4 (en) | 2012-03-21 |
US7430567B2 (en) | 2008-09-30 |
TWI360756B (en) | 2012-03-21 |
US20040260727A1 (en) | 2004-12-23 |
US20060041541A1 (en) | 2006-02-23 |
TW200508911A (en) | 2005-03-01 |
CN1809826A (en) | 2006-07-26 |
EP1629397A2 (en) | 2006-03-01 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
US7430567B2 (en) | Multidimensional data object searching using bit vector indices | |
US7966327B2 (en) | Similarity search system with compact data structures | |
EP1025514B1 (en) | Multidimensional data clustering and dimension reduction for indexing and searching | |
Beckmann et al. | A revised R*-tree in comparison with related index structures | |
US20070005556A1 (en) | Probabilistic techniques for detecting duplicate tuples | |
US20040024738A1 (en) | Multidimensional index generation apparatus, multidimensional index generation method, approximate information preparation apparatus, approximate information preparation method, and retrieval apparatus | |
CN106503223B (en) | online house source searching method and device combining position and keyword information | |
Cha et al. | The GC-tree: a high-dimensional index structure for similarity search in image databases | |
CN107784110B (en) | Index establishing method and device | |
EP1174804A2 (en) | Method for searching multimedia using progressive histogram | |
Skopal et al. | Nearest Neighbours Search using the PM-tree | |
Cui et al. | Indexing high-dimensional data for efficient in-memory similarity search | |
Chua et al. | Relevance feedback techniques for color-based image retrieval | |
Al Aghbari et al. | Efficient KNN search by linear projection of image clusters | |
Egas et al. | Adapting kd trees to visual retrieval | |
Abdelrahim et al. | Image retrieval based on content and image compression | |
EP1160690A1 (en) | Method of indexing and similarity search in a feature vector space | |
CN112860734A (en) | Seismic data multi-dimensional range query method and device | |
JP2001052024A (en) | Method and device for retrieving similar feature amount and storage medium storing retrieval program for similar feature amount | |
Zhu et al. | Using keyblock statistics to model image retrieval | |
Digout et al. | Similarity search and dimensionality reduction: Not all dimensions are equally useful | |
Celentano et al. | Multiple feature indexing in image retrieval systems | |
Kurasawa et al. | Optimal pivot selection method based on the partition and the pruning effect for metric space indexes | |
CN114791966A (en) | Index construction method and device, vector search method and retrieval system | |
CN115408545A (en) | Hard disk and memory combined neighbor graph vector retrieval method and device |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
AK | Designated states |
Kind code of ref document: A2 Designated state(s): AE AG AL AM AT AU AZ BA BB BG BR BW BY BZ CA CH CN CO CR CU CZ DE DK DM DZ EC EE EG ES FI GB GD GE GH GM HR HU ID IL IN IS JP KE KG KP KR KZ LC LK LR LS LT LU LV MA MD MG MK MN MW MX MZ NA NI NO NZ OM PG PH PL PT RO RU SC SD SE SG SK SL SY TJ TM TN TR TT TZ UA UG US UZ VC VN YU ZA ZM ZW |
|
AL | Designated countries for regional patents |
Kind code of ref document: A2 Designated state(s): BW GH GM KE LS MW MZ NA SD SL SZ TZ UG ZM ZW AM AZ BY KG KZ MD RU TJ TM AT BE BG CH CY CZ DE DK EE ES FI FR GB GR HU IE IT LU MC NL PL PT RO SE SI SK TR BF BJ CF CG CI CM GA GN GQ GW ML MR NE SN TD TG |
|
121 | Ep: the epo has been informed by wipo that ep was designated in this application | ||
DPEN | Request for preliminary examination filed prior to expiration of 19th month from priority date (pct application filed from 20040101) | ||
WWE | Wipo information: entry into national phase |
Ref document number: 2004751482 Country of ref document: EP |
|
WWE | Wipo information: entry into national phase |
Ref document number: 5866/DELNP/2005 Country of ref document: IN |
|
WWE | Wipo information: entry into national phase |
Ref document number: 20048172021 Country of ref document: CN |
|
WWE | Wipo information: entry into national phase |
Ref document number: 1020057024563 Country of ref document: KR |
|
WWE | Wipo information: entry into national phase |
Ref document number: 2006517105 Country of ref document: JP |
|
WWP | Wipo information: published in national office |
Ref document number: 2004751482 Country of ref document: EP |
|
WWP | Wipo information: published in national office |
Ref document number: 1020057024563 Country of ref document: KR |