CN110347676A - Uncertain temporal data management and querying method based on relationship R tree - Google Patents
Uncertain temporal data management and querying method based on relationship R tree Download PDFInfo
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- 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
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Abstract
The invention discloses a kind of uncertain temporal data management based on relationship R tree and querying method, the method is applied to database field, and management is realized in extendible Moving objects database SECONDO and inquires uncertain temporal data.The present invention is managed by the section that uncertain but length determines to given a large amount of starting point and ending points, and a relationship R tree is built up into these sections, manage uncertain temporal data, it is that weight attribute has also been managed while having managed time attribute uncertainty, combine relationship R tree, in query process, can the influence according to weight size to query result be indexed, to improve search efficiency, and intersection probability is calculated, finally return that the k number evidence for the maximum weight that maximum possible intersects with inquiry data.
Description
Technical field
The invention belongs to data processing techniques, and in particular to a kind of uncertain temporal data management based on relationship R tree
With querying method.
Background technique
With application development, the storage object of data becomes to become increasingly complex, and the data of not only some determinations are also deposited
In some uncertain data, such as in project planning, it is contemplated that the target date is often loose definition, such as " project will
It is completed in three to six months hereafter ".Some tense variable descriptions at uncertain temporal information, the data generated in this way are more
Close to the intuitive of human knowledge, the case where being also more in line with real world.For these there are probabilistic temporal data,
How to carry out managing well is that we can efficiently utilize vital a part of these data.
For not knowing temporal data, be made of uncertain starting point and end point: the form of expression is as follows:
<<x1,x2>,<y1,y2>>。
When therefore establishing temporal data index, spatial index algorithm is utilized mostly, general<x1, x2>,<y1, y2>be mapped to sky
Between 4 fixed points of rectangle handled.It is most common be by R tree technology in conjunction with uncertain Temporal Index.But due to this
Kind of space manner indicates that effective time can have an invalid region, and when inquiry can have some impact on result, and
It has only managed time attribute and has not managed weight, therefore we have proposed a kind of uncertain tense numbers based on relationship R tree
According to management and querying method, weight has also been managed while managing uncertain temporal data.
Summary of the invention
Goal of the invention: the influence of inactive area is eliminated when in order to manage uncertain temporal data, the present invention is intended to provide one
Uncertain temporal data management and querying method of the kind based on relationship R tree.
Technical solution: a kind of uncertain temporal data management and querying method based on relationship R tree, the method is first
The uncertain temporal data of starting point and ending point is managed based on determining siding-to-siding block length, relationship R tree is then constructed, presses
It is inquired according to the weight size order of temporal data；Include the following steps:
(1) generate uncertain temporal data and opening relationships R tree: given interval parameter generates original uncertain tense
Then these data intervals are built up a R tree according to the rectangle frame that time attribute and weight construct by data interval；
(2) relation management of relationship R tree: the R tree constructed in traversal step (1) obtain pressed in present node weight from greatly to
The relationship of the entry index of small sequence, and supplementary structure is combined to store；
(3) do not know temporal data top-k inquiry: the relationship R tree as obtained in step (2) and the value to be inquired are as defeated
Enter, be compared since root point with each node, when the child node of present node to be accessed, is stored according in relationship R tree
Node nodeid and the relationship of weight size select next access target.
Further, step (1) includes, using range as x-axis, being weighed according to the interval range and weight for not knowing interval data
Value is y-axis, and is extended up and down to weight, uncertain interval data is built into a rectangle frame, then basis obtains
One R tree of rectangle frame construction.
It further, is to choose one to will not influence inquiry when carrying out extension up and down to the weight of uncertain interval data
As a result minimum is extended calculating.
Further, supplementary structure described in step (2) is B-tree and array；
In step (2) entry in a present node and present node is obtained and managed according to the R tree that traversal has constructed
The relationship of call number, can be stored in relationship by the weight sequential access node from big to small of the entry in present node
Be weight and call number；When with B-tree come administrative relationships, we are to traverse the nodeid in the resulting relation table of R tree
Keyword is contribute, and is found its position in B-tree according to No. id of current R tree node and is obtained the corresponding son of present node
The call number and weight of node；When with array come administrative relationships, a size and the same number of array of R tree node are created,
Using the nodeid of node as array index, the tupleid in relationship is that array content is mapped, and the rule of mapping is that will traverse
The nodeid of node is corresponding with its access order when R tree.
Further, it is preferentially accessed when access R tree interior joint according to the big node of uncertain data weight in step (3).
Preferably, supplementary structure described in step (2) is B-tree and array；The R constructed in step (2) according to traversal
Tree obtains and manages the relationship of the entry index in a present node and present node, by the power of the entry in present node
The sequential access node of value from big to small, relationship are expressed as follows:
Rel (tuple (int:tupleid, int:nodeid, list:entries)), list=< (w1,
index1),……,wn,indexn)>；
Wherein the node of B-tree is denoted as n (nodeid, L), L=<(w1, index1) ..., (wn, indexn)>；
Based on above-mentioned array administrative relationships, a size and the same number of array of R tree node are created, with node
Nodeid is array index, and the tupleid in relationship is that array content is mapped, and the rule of mapping is will to traverse the R tree time
The nodeid of point is corresponding with its access order, is expressed as follows:
R-Array [nodeid]=tupleid.
It is preferentially accessed when access R tree interior joint according to the big node of uncertain data weight in step (3).
Uncertain temporal data management of the present invention based on relationship R tree and querying method realize respectively,
Uncertain temporal data is built into rectangle frame according to time attribute and weight to be built into a R tree first, then is passed through time
The relationship that this R tree obtains the entry index sorted from large to small in present node by weight is gone through, and combines supplementary structure (B-
Tree and array) this relationship is stored, it in this way can be by when inquiring data-oriented and intersecting with uncertain temporal data
Weight accessed node from big to small.
The utility model has the advantages that compared with prior art, the uncertain temporal data management of the present invention based on relationship R tree
With querying method, except the uncertainty for having managed data has also managed the weight attribute of data, and inactive area is eliminated；?
When doing top-k inquiry, for the child node of the node of current accessed, it can be accessed from big to small by the weight of data, and can
To calculate the probability that inquiry data intersect with current data.
Detailed description of the invention
Fig. 1 is the data representation format of uncertain temporal data of the present invention；
Fig. 2 is the two-dimensional representation that embodiment does not know temporal data；
Fig. 3 is three kinds of situations of two interval datas of embodiment intersection；
Fig. 4 is the basic block diagram of relationship R tree of the present invention；
Fig. 5 is the relational graph that relationship R tree of the present invention need to safeguard；
Fig. 6 is to safeguard relational graph using B-tree in embodiment；
Relational graph is safeguarded using array in Fig. 7 embodiment.
Specific embodiment
In order to which technical solution disclosed in this invention is described in detail, with reference to the accompanying drawings of the specification and specific embodiment is done
It is further elucidated above.
Disclosed in this invention is a kind of uncertain temporal data management based on relationship R tree and querying method, is used for
Realize the management that temporal data is not known in extendible Moving objects database SECONDO.First by uncertain temporal data
Rectangle frame is built into according to time attribute and weight to be built into a R tree, then obtains present node by traversing this R tree
In the relationship of entry index that is sorted from large to small by weight, and store this pass in conjunction with supplementary structure (B-tree and array)
System, in this way can be by weight accessed node from big to small when inquiring data-oriented and intersecting with uncertain temporal data.
(1) temporal data is not known to generate and contribute；
The present invention considers data-oriented special case, in order to test and actual needs, need to generate uncertain tense in advance
Data.We indicate temporal data with section, and section is system automatically generated, but need to give some interval parameters:
1) section minimum value and maximum value, this be it is controllable in order to limit interval value, be in the present invention, as defined in us
Section minimum value is 1, and maximum value is 100000；
2) siding-to-siding block length and interval right weight, length are the definite length for providing each section, are provided in the present invention
Siding-to-siding block length is the random value in interval range；3) section quantity, section quantity specified in this experiment is 2000000.More than
Value can adjust at any time merely to experiment needs according to experimental conditions.Then according to the temporal data of generation, a R is established
Tree.
(2) relation management of relationship R tree；
For the R tree generated, we begun stepping through from root node and record each node nodeid and it
The id and weight of entries will be obtained in a present node and present node after having traversed this tree by power in this way
The relationship for the entry index that value sorts from large to small.In order to manage this relationship, we have proposed two methods, and one is utilizations
B-tree management, we are contribute using traversing the nodeid in the resulting relation table of R tree as keyword；One is combine array management, wound
A size and the same number of array of R tree node are built, using the nodeid of node as array index, the tupleid in relationship is
Array content is mapped one by one.
(3) temporal data top-k inquiry is not known；
Given uncertain temporal data is searched and most may be used with this section when arbitrarily giving a query range
K section of the maximum weight that can intersect.We search according to the relationship R tree built up, first since root node,
Judge whether to intersect with section to be checked, if intersection, continues to access its child node, at this point, no longer indexing by R tree interior joint suitable
Sequence access, but sequential access of the child node by being stored in relation table by weight from big to small.
Specifically, the uncertain temporal data management and querying method of the present invention based on relationship R tree, is managing
Can also manage weight while time attribute, and can the influence according to weight size to query result be indexed, therefore
The relationship of weight size between we need to know node in inquiry, thus our marriage relation R trees manage a node
Relationship between nodeid and the id and weight of its entries.Key step is as follows:
(1) temporal data is not known to generate and contribute；
The present invention needs to construct a series of satisfactory uncertain temporal datas, and the temporal data of construction is saved in
In extensible database system SECONDO, for the convenience of experiment, we set some basic parameters of data, the kind of parameter
Class and meaning are described in detail before.
Fig. 1 is that a series of uncertain temporal datas generated indicate form, and the building of rectangle frame is as shown in Fig. 2, dotted line generation
The movable range of table uncertain data, y-axis indicate weight, and weight is extended a very small range up and down with will be one-dimensional by us
Line segment be extended to two-dimensional rectangle frame, these rectangle frames are then configured to a R tree again, as shown in Figure 4.
(2) relation management of relationship R tree；
For the R tree generated, we begun stepping through from root node and record each node nodeid and it
The id and weight of entries will be obtained in a present node and present node after having traversed this tree by power in this way
The relationship for the entry index that value sorts from large to small, as shown in Figure 5.In order to manage this relationship, we have proposed two methods,
One is B-tree management is utilized, we are contribute using traversing the nodeid in the resulting relation table of R tree as keyword, can be according to current R
No. id of the node of tree finds its position in B-tree and obtains the call number and weight of the corresponding child node of present node,
As shown in Figure 6.One is array management is combined, a size and the same number of array of R tree node are created, with node
Nodeid is array index, and the tupleid in relationship is that array content is mapped, and the rule of mapping is will to traverse the R tree time
The nodeid of point is corresponding with its access order, as shown in Figure 7.
(3) temporal data Top-k inquiry is not known；
Another free-revving engine using relationship R tree is exactly to find and intersect with query range most probable for quick search
K section of maximum weight.It looks into the present invention provides the intersection method for calculating probability of uncertain temporal data and weight are preferential
Look for method.Two interval data intersections are divided into four kinds of situations, as shown in figure 3, the probability function of the two interval datas intersection can
It is denoted as:
Wherein (a, b) is polling interval, and (s, e) is uncertain interval, and L is uncertain data range, and l is uncertain data
Length.We access since root node when inquiry, judge whether to intersect with section to be checked, if intersection, continues to access it
Child node, at this point, no longer press R tree interior joint indexed sequential access method, but pass through in relation table the child node that stores by weight from
Small sequential access is arrived greatly.If Nodeid is the node of x in Fig. 4, the access order for standard R tree its child node is E1, E2,
We are based on relationship R tree, have managed the relationship of inode number Yu weight size, at this time can be according to the B-tree or number constructed in (2)
Sequential access node of the information stored in group by weight from big to small, i.e. E2, E1。
Claims (6)
1. a kind of uncertain temporal data management and querying method based on relationship R tree, it is characterised in that: the method is first
The uncertain temporal data of starting point and ending point is managed based on determining siding-to-siding block length, relationship R tree is then constructed, presses
It is inquired according to the weight size order of temporal data；Include the following steps:
(1) generate uncertain temporal data and opening relationships R tree: given interval parameter generates original uncertain temporal data
Then these data intervals are built up a R tree according to the rectangle frame that time attribute and weight construct by section；
(2) relation management of relationship R tree: the R tree constructed in traversal step (1) obtains is arranged in present node by weight from big to small
The relationship of the entry index of sequence, and supplementary structure is combined to store；
(3) do not know temporal data top-k inquiry: the relationship R tree as obtained in step (2) and the value to be inquired are used as input,
It is compared since root point with each node, when the child node of present node to be accessed, according to the section stored in relationship R tree
The relationship of point nodeid and weight size selects next access target.
2. the uncertain temporal data management and querying method, feature according to claim 1 based on relationship R tree exists
In: step (1) includes according to the interval range and weight for not knowing interval data, and using range as x-axis, weight is y-axis, and to power
Value is extended up and down, and uncertain interval data is built into a rectangle frame, then according to obtained one R of rectangle frame construction
Tree.
3. the uncertain temporal data management and querying method, feature according to claim 2 based on relationship R tree exists
In: step (1) be extended to up and down to the weight of uncertain interval data and be calculated according to its minimum.
4. the uncertain temporal data management and querying method, feature according to claim 1 based on relationship R tree exists
In: supplementary structure described in step (2) is B-tree and array.
5. the uncertain temporal data management and querying method, feature according to claim 1 based on relationship R tree exists
In: in step (2) entry index in a present node and present node is obtained and managed according to the R tree that traversal has constructed
Relationship, by the weight sequential access node from big to small of the entry in present node, what is stored in relationship is weight and rope
Quotation marks；
When with B-tree come administrative relationships, contribute using traversing the nodeid in the resulting relation table of R tree as keyword, according to working as
No. id of preceding R tree node finds its position in B-tree and obtains the call number and power of the corresponding child node of present node
Value；
When with array come administrative relationships, a size and the same number of array of R tree node are created, is with the nodeid of node
Array index, the tupleid in relationship are that array content is mapped, and the rule of mapping is node when will traverse R tree
Nodeid is corresponding with its access order.
6. the uncertain temporal data management and querying method, feature according to claim 1 based on relationship R tree exists
In: it is preferentially accessed when access R tree interior joint in step (3) according to the big node of uncertain data weight.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111723093A (en) * | 2020-06-17 | 2020-09-29 | 江苏海平面数据科技有限公司 | Uncertain interval data query method based on data division |
| CN115098616A (en) * | 2022-07-25 | 2022-09-23 | 北京国科恒通科技股份有限公司 | Multi-temporal spatial data storage and query methods, devices and storage medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100036865A1 (en) * | 2008-08-07 | 2010-02-11 | Yahoo! Inc. | Method For Generating Score-Optimal R-Trees |
| CN102810118A (en) * | 2012-07-05 | 2012-12-05 | 上海电力学院 | K nearest neighbor search method for variable weight network |
| CN103455531A (en) * | 2013-02-01 | 2013-12-18 | 深圳信息职业技术学院 | Parallel indexing method supporting real-time biased query of high dimensional data |
| US20180246896A1 (en) * | 2017-02-24 | 2018-08-30 | Microsoft Technology Licensing, Llc | Corpus Specific Generative Query Completion Assistant |
| CN108829804A (en) * | 2018-06-05 | 2018-11-16 | 洛阳师范学院 | Based on the high dimensional data similarity join querying method and device apart from partition tree |
-
2019
- 2019-06-11 CN CN201910504660.6A patent/CN110347676B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100036865A1 (en) * | 2008-08-07 | 2010-02-11 | Yahoo! Inc. | Method For Generating Score-Optimal R-Trees |
| CN102810118A (en) * | 2012-07-05 | 2012-12-05 | 上海电力学院 | K nearest neighbor search method for variable weight network |
| CN103455531A (en) * | 2013-02-01 | 2013-12-18 | 深圳信息职业技术学院 | Parallel indexing method supporting real-time biased query of high dimensional data |
| US20180246896A1 (en) * | 2017-02-24 | 2018-08-30 | Microsoft Technology Licensing, Llc | Corpus Specific Generative Query Completion Assistant |
| CN108829804A (en) * | 2018-06-05 | 2018-11-16 | 洛阳师范学院 | Based on the high dimensional data similarity join querying method and device apart from partition tree |
Non-Patent Citations (1)
| Title |
|---|
| 鲍金玲等: ""路网环境下的最近邻查询技术"", 《软件学报》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111723093A (en) * | 2020-06-17 | 2020-09-29 | 江苏海平面数据科技有限公司 | Uncertain interval data query method based on data division |
| CN115098616A (en) * | 2022-07-25 | 2022-09-23 | 北京国科恒通科技股份有限公司 | Multi-temporal spatial data storage and query methods, devices and storage medium |
| CN115098616B (en) * | 2022-07-25 | 2022-12-02 | 北京国科恒通科技股份有限公司 | Multi-temporal spatial data storage and query methods, devices and storage medium |
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