CN102567497B - Inquiring method of best matching with fuzzy trajectory problems - Google Patents
Inquiring method of best matching with fuzzy trajectory problems Download PDFInfo
- Publication number
- CN102567497B CN102567497B CN 201110437137 CN201110437137A CN102567497B CN 102567497 B CN102567497 B CN 102567497B CN 201110437137 CN201110437137 CN 201110437137 CN 201110437137 A CN201110437137 A CN 201110437137A CN 102567497 B CN102567497 B CN 102567497B
- Authority
- CN
- China
- Prior art keywords
- blurring
- matching degree
- trajectorie
- blurring trajectorie
- candidate
- Prior art date
- Legal status (The legal status 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 status listed.)
- Expired - Fee Related
Links
Images
Landscapes
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The invention discloses an inquiring method of best matching with fuzzy trajectory problems, which invents novel matching degree measurement criteria to measure matching degree between fuzzy trajectories. According to the method, range space is first divided into a series of cells, and then a time index is set up in each cell. When dealing with matching inquiries, first, the method visits an index structure, calculates an upper bound and a lower bound of matching degree between each fuzzy trajectory and each inquiring trajectory; then, pruning is performed to unqualified fuzzy trajectories by utilizing the upper bound and the lower bound so that a candidate answer collection is obtained; and last, the method calculates accurate matching degree of each candidate fuzzy trajectory and judges whether the fuzzy trajectory is a true inquiring result or not. The method fully utilizes existing research and achievements of databases and information retrieval, and is capable of quickly and conveniently providing inquiring capability of best matching with fuzzy trajectory problems based on expansion and fusion of an existing space data inquiring method and providing best performance.
Description
Technical field
The present invention relates to Database Systems, information retrieval, spatial index and inquiring technology, particularly relate to a kind of inquiry processing method that mates the blurring trajectorie inquiry most.
Background technology
In actual applications, produced a large amount of track datas.For example, the widespread use of GPS equipment has caused the generation of a large amount of moving vehicles and mobile object track.Ideally, such track data is modeled as a sequence that has the geographic position of timestamp.But this modeling method is too simple, can not consider the uncertainty of positional information.
The uncertainty of object space information has a lot of sources.For example, the positional information that GPS equipment reads itself promptly is not an accurate geographic position, but represent by an accurate location point (precision and dimension) and error range.In addition, because location-based secret protection has been subjected to increasing attention, a lot of positional informations are before issue, promptly by uncertain zone of extensive one-tenth.In this case, the trace information of a mobile object is modeled as a sequence that has the band of position of timestamp.And corresponding each timestamp represents that with probability distribution function (pdf) track is arranged in the probability distribution situation in zone, relevant position constantly at this.
Matching degree inquiry on the blurring trajectorie data has a very wide range of applications in actual life.For the blurring trajectorie data, a key issue handling the matching degree inquiry is how to weigh two matching degrees between the blurring trajectorie.
The matching degree criterion of more existing in the world time series datas, dynamic time warping algorithm (Discrete Time Warping, DTW) for example, Longest Common Substring (Longest Common Subsequences, LCSS) etc.But these methods all are to propose at the time series data of determining type, can not be applied to uncertain track data.In addition, these criterions only are applicable to and have the data that discrete time is stabbed, and can not be applied to the data type of section continuous time between two timestamps of the such consideration of track data.A kind of criterion that can weigh the matching degree of blurring trajectorie data in the continuous time section intuitively is the mathematical expectation of Euclidean distance.But the mathematical expectation of Euclidean distance is very responsive for the ambiguous point of uncertain data.So concerning blurring trajectorie, the mathematical expectation of Euclidean distance is not a reliable criterion.
In this case, can efficiently to handle the querying method that mates the blurring trajectorie problem most be crucial to invention one cover.
Summary of the invention
The object of the present invention is to provide a kind of querying method that mates the blurring trajectorie problem most.
The step that the present invention solves the technical scheme that its technical matters adopts is as follows:
1) utilizes grid method that the codomain spatial division is become a plurality of cells, and utilize the border of all cells that each blurring trajectorie is divided into path segment;
2) set up the time index of an one dimension in the cell of each in step 1);
3) when query processing, accessing step 1 according to this) in all cells, and calculate the upper bound and the lower bound of matching degree between each blurring trajectorie and the inquiry track;
4) utilize the upper bound and the lower bound of matching degree between each blurring trajectorie in the step 3) and the inquiry track, underproof blurring trajectorie is carried out beta pruning, thereby obtain a candidate answers set;
5) calculation procedure 4) in candidate answers set in each candidate's blurring trajectorie and the matching degree between the inquiry track, and judge whether each candidate's blurring trajectorie is real Query Result.
Step 1) utilizes grid method that the codomain spatial division is become a plurality of cells; The border of all cells has been divided into path segment with each blurring trajectorie; Each path segment independently is positioned at a cell; The also corresponding time interval of each path segment is this path segment and is in the interior time period of respective cells.
Step 2) for obtaining each cell in the step 1), sets up the time index of an one dimension, be used for index and be in the time interval of all path segment in this cell.
Accessing step 1 according to this in the step 3)) cell in, and the path segment in the cell calculate the upper bound and the lower bound of matching degree between each blurring trajectorie and the inquiry track.
Find out the minimum value in the upper bound of the matching degree between all blurring trajectories in the step 5) and the inquiry track in the step 4); Then, if the lower bound of the matching degree between blurring trajectorie and the inquiry track greater than this minimum value, then this blurring trajectorie is underproof blurring trajectorie, will be fallen by beta pruning.
Each candidate's blurring trajectorie in the candidate answers set in the step 5) in the calculating step 4) and the matching degree between the inquiry track; If candidate's blurring trajectorie and the matching degree of inquiring about between the track are maximum in all candidate's blurring trajectories, then this candidate's blurring trajectorie becomes real Query Result.
The beneficial effect that the present invention has is:
The present invention has made full use of the existing research of database and information retrieval and has realized achievement, expansion and fusion based on existing space index method and querying method can very conveniently provide the query capability that mates the blurring trajectorie problem most efficiently, and performance offers the best.The present invention is widely used in the mode excavation of vehicular traffic command and management, the daily movement of population in city and excavates based on the excavation of network log and the business data of coupling.
Description of drawings
Fig. 1 is the index structure synoptic diagram.
Fig. 2 is the path segment synoptic diagram.
Fig. 3 is the querying method synoptic diagram that mates the blurring trajectorie problem most.
Embodiment
Now the invention will be further described with specific embodiment in conjunction with the accompanying drawings.
Specific implementation process of the present invention and principle of work, as shown in Figure 3
1) utilizes grid method that the codomain spatial division is become a plurality of cells, and utilize the border of all cells that each blurring trajectorie is divided into path segment;
2) set up the time index of an one dimension in the cell of each in step 1);
3) when query processing, accessing step 1 according to this) in all cells, and calculate the upper bound and the lower bound of matching degree between each blurring trajectorie and the inquiry track;
4) utilize the upper bound and the lower bound of matching degree between each blurring trajectorie in the step 3) and the inquiry track, underproof blurring trajectorie is carried out beta pruning, thereby obtain a candidate answers set;
5) calculation procedure 4) in candidate answers set in each candidate's blurring trajectorie and the matching degree between the inquiry track, and judge whether each candidate's blurring trajectorie is real Query Result.
Step 1) utilizes grid method that the codomain spatial division is become cell.As shown in Figure 2, the border of all cells has been divided into path segment with each blurring trajectorie.In Fig. 2, a blurring trajectorie is divided into 4 path segment.Each path segment independently is positioned at a cell
cIn.The corresponding time interval of each path segment is this path segment and is in cell
cThe interior time period.In Fig. 2, the 2nd path segment time corresponding interval is [t
2 -, t
2 +].Simultaneously, the also corresponding probability equation of each path segment,
P X, c (t), be used for describing this blurring trajectorie
XBe in cell
cInterior probability is along with the situation of change of time.
Step 2) as shown in Figure 1,, sets up the time index of an one dimension in, be used for index and be in all path segment time corresponding intervals in this cell for obtaining each cell in the step 1).Particularly, each cell
cAlso corresponding pointer, all are in cell to point to storage
cIn the bucket (bucket) of path segment.When a bucket is not enough to store a cell
cIn all path segment the time, need use more than one bucket and store these path segment.Especially, the close path segment of time interval can be by cluster together, and be stored in the bucket.Like this, the same corresponding time range of each bucket, this time range is the time interval that covers the minimum of the time interval of all tracks in the bucket.Then, this method adopts an one dimension R-tree as time index, is used for the indexing units lattice
cAll bucket time corresponding scopes.
Accessing step 1 according to this in the step 3)) cell in, and the path segment in the cell.For visit earlier comprises and inquire about the cell of the path segment that track mates most, this querying method is according to each cell and inquire about track
QThe ascending order of distance cell is put into rickle
HAmong.Cell of each visit
c, this querying method is from cell
cBucket in take out the information of path segment, and calculate the upper bound and the lower bound of the matching degree between each blurring trajectorie and the inquiry track.
In the addressed location lattice in the process of path segment, this querying method is preserved the minimum value in the upper bound of matching degree of all blurring trajectories that are accessed in internal memory.When all lower bounds of matching degree that do not have a blurring trajectorie in the accessed cell during greater than this minimum value, the process of addressed location lattice stops.
Step 4) is found out the minimum value in the upper bound of the matching degree between all blurring trajectories in the step 3) and the inquiry track.For any blurring trajectorie that has been accessed to
XIf,
XAnd the lower bound of the matching degree between the inquiry track is greater than this minimum value, blurring trajectorie
XBe underproof blurring trajectorie, can not become the result of inquiry, so fallen by beta pruning; If
XAnd the lower bound of the matching degree between the inquiry track is less than or equal to this minimum value, blurring trajectorie
XMay become the result of inquiry, will be placed in the candidate answers set.
Each candidate's blurring trajectorie in the candidate answers set in the step 5) in the calculating step 4) and the matching degree between the inquiry track.If candidate's blurring trajectorie and the matching degree of inquiring about between the track are maximum in all candidate's blurring trajectories, then this candidate's blurring trajectorie becomes real Query Result.
Claims (5)
1. one kind mates the querying method of blurring trajectorie problem most, and wherein track is produced by mobile object, it is characterized in that adopting following steps to realize:
1) utilizes grid method that the codomain spatial division is become a plurality of cells, and utilize the border of all cells that each blurring trajectorie is divided into path segment;
2) set up the time index of an one dimension in the cell of each in step 1);
3) when query processing, accessing step 1 successively) in all cells, and calculate the upper bound and the lower bound of matching degree between each blurring trajectorie and the inquiry track;
4) utilize the upper bound and the lower bound of matching degree between each blurring trajectorie in the step 3) and the inquiry track, underproof blurring trajectorie is carried out beta pruning, thereby obtain a candidate answers set;
Find out the minimum value in the upper bound of the matching degree between all blurring trajectories in the step 3) and the inquiry track in the step 4); Then, if the lower bound of the matching degree between blurring trajectorie and the inquiry track greater than this minimum value, then this blurring trajectorie is underproof blurring trajectorie, will be fallen by beta pruning;
5) calculation procedure 4) in candidate answers set in each candidate's blurring trajectorie and the matching degree between the inquiry track, and judge whether each candidate's blurring trajectorie is real Query Result.
2. a kind of querying method that mates the blurring trajectorie problem most according to claim 1 is characterized in that: step 1) utilizes grid method that the codomain spatial division is become a plurality of cells; The border of all cells has been divided into path segment with each blurring trajectorie; Each path segment independently is positioned at a cell; The also corresponding time interval of each path segment is this path segment and is in the interior time period of respective cells.
3. a kind of querying method that mates the blurring trajectorie problem most according to claim 1, it is characterized in that: step 2) obtain each cell in the step 1), set up the time index of an one dimension, be used for index and be in the time interval of all path segment in this cell.
4. a kind of querying method that mates the blurring trajectorie problem most according to claim 1, it is characterized in that: the cell accessing step 1 successively in the step 3)), and the path segment in the cell, calculate each blurring trajectorie and inquire about the upper bound and the lower bound of matching degree between the track.
5. a kind of querying method that mates the blurring trajectorie problem most according to claim 1 is characterized in that: each candidate's blurring trajectorie in the candidate answers set in the step 5) in the calculating step 4) and the matching degree between the inquiry track; If candidate's blurring trajectorie and the matching degree of inquiring about between the track are maximum in all candidate's blurring trajectories, then this candidate's blurring trajectorie becomes real Query Result.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN 201110437137 CN102567497B (en) | 2011-12-23 | 2011-12-23 | Inquiring method of best matching with fuzzy trajectory problems |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN 201110437137 CN102567497B (en) | 2011-12-23 | 2011-12-23 | Inquiring method of best matching with fuzzy trajectory problems |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN102567497A CN102567497A (en) | 2012-07-11 |
| CN102567497B true CN102567497B (en) | 2013-07-24 |
Family
ID=46412898
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN 201110437137 Expired - Fee Related CN102567497B (en) | 2011-12-23 | 2011-12-23 | Inquiring method of best matching with fuzzy trajectory problems |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN102567497B (en) |
Families Citing this family (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105910611A (en) * | 2016-06-27 | 2016-08-31 | 江苏迪纳数字科技股份有限公司 | Road matching method based on matching degree feedback |
| CN106528629B (en) * | 2016-10-09 | 2018-04-03 | 深圳云天励飞技术有限公司 | A kind of vector based on geometric space division searches for method and system generally |
| CN107247961B (en) * | 2017-05-10 | 2019-12-24 | 西安交通大学 | Track prediction method applying fuzzy track sequence |
| CN107766406A (en) * | 2017-08-29 | 2018-03-06 | 厦门理工学院 | A kind of track similarity join querying method searched for using time priority |
| CN108920499B (en) * | 2018-05-24 | 2022-04-19 | 河海大学 | Space-time trajectory indexing and retrieval method for periodic retrieval |
| CN109344337B (en) | 2018-08-09 | 2019-11-05 | 百度在线网络技术（北京）有限公司 | Matching process, device and the storage medium of mobile hot spot and mobile point of interest |
| CN110160539A (en) * | 2019-05-28 | 2019-08-23 | 北京百度网讯科技有限公司 | Map-matching method, calculates equipment and medium at device |
| CN112597190A (en) * | 2020-12-28 | 2021-04-02 | 京东城市(北京)数字科技有限公司 | Point neighbor track query method and device, electronic equipment and readable storage medium |
| CN113032391B (en) * | 2021-02-05 | 2022-04-12 | 浙江大学 | Distributed sub-track connection query processing method |
| CN113051360B (en) * | 2021-04-16 | 2024-04-09 | 深圳前海中电慧安科技有限公司 | Method and device for determining similar tracks, computer equipment and storage medium |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101499277A (en) * | 2008-07-25 | 2009-08-05 | 中国科学院计算技术研究所 | Service intelligent navigation method and system |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8572100B2 (en) * | 2004-01-19 | 2013-10-29 | Nigel Hamilton | Method and system for recording search trails across one or more search engines in a communications network |
-
2011
- 2011-12-23 CN CN 201110437137 patent/CN102567497B/en not_active Expired - Fee Related
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101499277A (en) * | 2008-07-25 | 2009-08-05 | 中国科学院计算技术研究所 | Service intelligent navigation method and system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN102567497A (en) | 2012-07-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN102567497B (en) | Inquiring method of best matching with fuzzy trajectory problems | |
| Su et al. | A survey of trajectory distance measures and performance evaluation | |
| CN112182410B (en) | User travel mode mining method based on space-time track knowledge graph | |
| US9880012B2 (en) | Hybrid road network and grid based spatial-temporal indexing under missing road links | |
| CN102289466B (en) | K-nearest neighbor searching method based on regional coverage | |
| CN106570062B (en) | Road network track uncertain moving object range query system and method | |
| CN109215338B (en) | Gridding-based Beidou gps vehicle track management system and method thereof | |
| CN103106280A (en) | Uncertain space-time trajectory data range query method under road network environment | |
| CN111209261A (en) | User travel track extraction method and system based on signaling big data | |
| Gong et al. | High-performance spatiotemporal trajectory matching across heterogeneous data sources | |
| Abbasifard et al. | Efficient indexing for past and current position of moving objects on road networks | |
| CN102012908B (en) | Method for inquiring visible moving neighbours objects in environment with barriers | |
| Myllymaki et al. | High-performance spatial indexing for location-based services | |
| Sasikala et al. | Uncertain data prediction on dynamic road network | |
| Rashid et al. | Challenging issues of spatio-temporal data mining | |
| Rslan et al. | Spatial R-tree index based on grid division for query processing | |
| Wang et al. | A novel dual-index design to efficiently support snapshot location-based query processing in mobile environments | |
| CN109902139B (en) | R-tree-based track data compression method | |
| Ayeelyan et al. | DTNH Indexing Method: Past Present and Future Data Prediction for Spatio-Temporal Data. | |
| Schmiegelt et al. | Continuous queries on trajectories of moving objects | |
| Yang et al. | Distributed grid-based k nearest neighbour query processing over moving objects | |
| Jung et al. | A group based insert manner for storing enormous data rapidly in intelligent transportation system | |
| Abbasifard et al. | Approximate aggregate nearest neighbor search on moving objects trajectories | |
| Guangjun et al. | Supporting Uncertainty in Indexing and Querying of Moving Objects in Networks Databases | |
| Heendaliya et al. | Optimizing predictive queries on moving objects under road-network constraints |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20130724 Termination date: 20211223 |
|
| CF01 | Termination of patent right due to non-payment of annual fee |