CN105915224A - Parallelization track compression method based on Mapreduce - Google Patents
Parallelization track compression method based on Mapreduce Download PDFInfo
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- CN105915224A CN105915224A CN201610215007.4A CN201610215007A CN105915224A CN 105915224 A CN105915224 A CN 105915224A CN 201610215007 A CN201610215007 A CN 201610215007A CN 105915224 A CN105915224 A CN 105915224A
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- H—ELECTRICITY
- H03—ELECTRONIC CIRCUITRY
- H03M—CODING; DECODING; CODE CONVERSION IN GENERAL
- H03M7/00—Conversion of a code where information is represented by a given sequence or number of digits to a code where the same, similar or subset of information is represented by a different sequence or number of digits
- H03M7/30—Compression; Expansion; Suppression of unnecessary data, e.g. redundancy reduction
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Abstract
The invention discloses a parallelization track compression method based on Mapreduce, comprising steps of dividing a GPS track sequence to be compressed into segments according to two modes, wherein a first mode includes steps of dividing the GPS track sequence to be compressed into N segments and marking the segmented compression result as 1, a second mode includes steps of dividing the GPS track sequence to be compressed into N-1 segments and marking the segmented compression result as 2, and all triple track segmented recordings are shown in a triple form; using all the triple track segmented recordings as inputs of an Map function on a parallel mapping node after Hash processing on all the triple track segmented recordings; performing compression processing on the segmented track sequence and outputting an input of a Reduce function by the Map function, adopting a single Reduce function to re-organize the segmented compression results which are marked as 1 and 2 into a track sequence S1 and a track sequence S2, intercepting N-1 track subsequences positioned on the S2 to replace the corresponding track sequences near segmented points on the S1, and finally obtaining the sequence S1 which is a GPS track which is generated after compression. The parallelization track compression method based on Mapreduce adopts parallelization processing, shortens compression processing time, matches and combines the two segmented compression results and eliminates an error caused by incontinuous track points because of segmentation.
Description
Technical field
The present invention relates to GIS-Geographic Information System and cloud computing technology application thereof, a kind of based on MapReduce also
Rowization trace compression method.
Background technology
In recent years, along with the fast development of information technology, people are to the computing capability of the system of calculating and wanting of data-handling capacity
Ask and day by day improve.And along with computer and the fast development of information technology and popularization and application, the scale of sector application system is also
Expanding rapidly, data produced by sector application are explosive increase, and thus cloud computing technology arises at the historic moment.Cloud computing is one
Plant distributed system, distribution of computation tasks can be processed to multiple stage computer so that various application systems can be as required
Obtain computing capability, memory space and information service.MapReduce is exactly a kind of that process towards large-scale data and racks
Computation model and method, it is researched and proposed by Google company the earliest.Up to the present, have in Goolge company thousands of
Ten thousand various different algorithmic issues and program all use MapReduce process.
Meanwhile, along with location equipment application on mobile terminals such as GPS, user can obtain the position of individual very easily
Information.The data of the positional information the most more and more moving object are collected and are stored in data base, but this
The hugest data volume is unfavorable for the storage of data, inquires about, analyzes and process, so, the compression to location information data is deposited
One of storage focus becoming current research.Current track compression divides according to the mode of compression that can be divided into two classes, a class be off-line
Batch processing is compressed, and a class is online trace compression.
Off-line batch processing compression algorithm typical case is exactly Douglas-Pu Ke trace compression algorithm (The Douglas-Peucker
Algorithm), the initial form of this algorithm respectively by Wu Ersi-Rameau (Urs Ramer) in 1972 and Douglas
(David Douglas) and Thomas-Pu Ke (Thomas Peucker) proposed in 1973, and many decades later
In given perfect by other scholar.The thinking of this algorithm is: the first and last point of each trajectory is connected a dotted line, asks
Point between first and last point is to the vertical dimension of dotted line, and finds out maximum range value dmax, use dmaxCompared with limit difference D；If dmax< D,
Intermediate point on this curve can all reduce；If dmax> D, then retain dmaxCorresponding coordinate, and with this point as boundary,
Curve is divided into two parts, and this two parts track is reused the method, until cannot compress.
Online trace compression algorithm typical case is exactly open window algorithm (The Open Window Algorithm), this class of algorithms
It is similar to sliding window algorithm.It is to use didactic Douglas algorithm to select in the position of the internal max-thresholds of sliding window
Put a little as starting point.Its basic thought is: first store the starting point of this track, selects first point of this track as cunning
Starting point start of dynamic window, the 3rd the terminating point end as sliding window, calculate and all in sliding window be positioned at starting point
Tracing point between start and terminating point end to the vertical dimension of dotted line determined by starting point start and terminating point end, if
The threshold value that distance is the most previously given, then the terminating point end of sliding window moves to next tracing point, then calculates initial
Put the vertical dimension to these 2 dotted lines determined of the tracing point between terminating point, repeat previous step；Otherwise, if sliding window
First tracing point more than threshold value occurs, then just stored by this tracing point in Kou, and will change the time and be labeled as sliding window
Starting point start of mouth, the step before double counting, until all tracing points all calculate complete.Both the above algorithm but because of
Segmentation can produce error before and after waypoint.
Summary of the invention
The technical problem to be solved be to overcome the deficiencies in the prior art and provide a kind of based on MapReduce also
Rowization trace compression method, the present invention be by by trace compression Algorithm parallelization process, shorten compression process time and
Improve compression speed, two kinds of sectional compression results are mutually matched merging simultaneously, eliminate the tracing point caused because of segmentation and do not connect
The continuous error produced.
The present invention solves above-mentioned technical problem by the following technical solutions:
A kind of based on MapReduce the parallelization trace compression method proposed according to the present invention, comprises the following steps:
Step one, by GPS track sequence segmentation in two ways to be compressed, the 1st kind of mode is divided into N section, is labeled as 1,
2nd kind of mode is divided into N-1 section, is labeled as 2, and represents all tlv triple trajectory segment records with triple form；
Step 2, all tlv triple trajectory segment records are carried out after Hash process as Map function on parallel mapping node
Input；Segmentation track sets in input is compressed processing and exporting the input as Reduce function by Map function；
Step 3, use single Reduce function will to be labeled as the sectional compression result of 1 and 2 the most respectively
Composition track sets S1And S2；
Step 4, intercepting N-1 are positioned at S2On track subsequence, and by these track subsequences replace S1Upper waypoint
Neighbouring corresponding track sets, sequence S finally given1Be exactly compression after generate GPS track.
As a kind of parallelization trace compression further prioritization scheme of method based on MapReduce of the present invention, described
Step one is specific as follows:
Step (1): definition GPS track sequence T={Pi, wherein, PiFor i-th tracing point, i is according to the time of track
Sequentially, i=1,2,3...n, n are tracing point sum；
Step (2): by track sets T segmentation, with triple form represent all tlv triple trajectory segment records < label,
K, Tk>, wherein, label represents the mode of segmentation, and k represents the sequence number of segmentation, TkRepresent the track sets of kth section, order
Tk={ Pj|j∈[bk, ek], wherein, bk, ekRepresent the starting and ending subscript of kth section tracing point；
Use two ways by track sets segmentation:
Track sets T is divided into N section, label=1 by (1) the 1st kind of mode, and division rule is as follows:
Track sets T is divided into N-1 section, label=2 by (2) the 2nd kinds of modes, and division rule is as follows:
Wherein:
As a kind of parallelization trace compression further prioritization scheme of method based on MapReduce of the present invention, described
Step 2 is specific as follows:
By all in step (2) tlv triple trajectory segment record < label, k, Tk> according to key, (label k) carries out Hash process
Afterwards as the input of Map function on parallel mapping node；Map function is for each tlv triple trajectory segment record of input
< label, k, Tk>, to track sets TkTrack sets T is obtained after being compressed processingk', and export record < label, k,
Tk' > as the input of Reduce function.
As a kind of parallelization trace compression further prioritization scheme of method based on MapReduce of the present invention, described
Step 3 is specific as follows:
Single Reduce function is used to read in the output record of all parallel Map functions, by the segmentation pressure of all label=1
Sheepshank fruit reformulates a track sets S in chronological order1, meanwhile, by the sectional compression result group the most again of label=2
Become a track sets S2。
As a kind of parallelization trace compression further prioritization scheme of method based on MapReduce of the present invention, described
Step 4 is specific as follows:
By track sets S1And S2Carrying out intercepting and merge, intercepting the rule merged is: from track sets S2Upper intercepting N-1
Track subsequence Ql, l ∈ [1, N-1], Ql={ Pj|j∈[cl, dl], and meet following condition:
(1)cl≤el, dl≥bl+1, wherein el, bl+1Be respectively in the 1st kind of segmented mode the end subscript of l section tracing point and
The initial subscript of l+1 section tracing point；
(2) j=c is worked aslOr j=dl, Pj∈S1And work as cl<j<dl,
Then by all track subsequence QlReplace track sets S1On corresponding track subsequence { Pj|j∈[cl, dl],
The track sets S obtained eventually1Be exactly compression after generate track.
As a kind of parallelization trace compression further prioritization scheme of method based on MapReduce of the present invention, described
GPS track point sum n >=1000000.
As a kind of parallelization trace compression further prioritization scheme of method based on MapReduce of the present invention, described
In step (2), N is equal to the number of Map node.
As a kind of parallelization trace compression further prioritization scheme of method based on MapReduce of the present invention, described
Hash in step (3) processes and uses division Hash.
As a kind of parallelization trace compression further prioritization scheme of method based on MapReduce of the present invention, step
(3) the trace compression method in uses open window compression algorithm.
The present invention uses above technical scheme compared with prior art, has following technical effect that
(1) trace compression parallelization is processed by this method, shortens the time that compression processes；
After the compression of (2) two kinds of segmented modes and be mutually matched merging, eliminate because segmentation causes tracing point discontinuously to produce
Error.
Accompanying drawing explanation
Fig. 1 is the general flow chart of the present invention.
Detailed description of the invention
Below in conjunction with the accompanying drawings technical scheme is described in further detail:
It is the general flow chart of the present invention as shown in Figure 1, a kind of parallelization trace compression method based on MapReduce, including
Following steps:
Step one, by GPS track sequence segmentation in two ways to be compressed, the 1st kind of mode is divided into N section, is labeled as 1,
2nd kind of mode is divided into N-1 section, is labeled as 2, and represents all tlv triple trajectory segment records with triple form；
Step 2, all tlv triple trajectory segment records are carried out after Hash process as Map function on parallel mapping node
Input；Segmentation track sets in input is compressed processing and exporting the input as Reduce function by Map function；
Step 3, use single Reduce function will to be labeled as the sectional compression result of 1 and 2 the most respectively
Composition track sets S1And S2；
Step 4, intercepting N-1 are positioned at S2On track subsequence, and by these track subsequences replace S1Upper waypoint
Neighbouring corresponding track sets, sequence S finally given1Be exactly compression after generate GPS track.
Concrete scheme comprises the following steps:
Step (1): definition GPS track sequence T={Pi, wherein, PiFor i-th tracing point, i is according to the time of track
Sequentially, i=1,2,3...n, n are tracing point sum；
Step (2): by track sets T segmentation, with triple form represent all tlv triple trajectory segment records < label,
K, Tk>, wherein, label represents the mode of segmentation, and k represents the sequence number of segmentation, TkRepresent the track sets of kth section, order
Tk={ Pj|j∈[bk, ek], wherein, bk, ekRepresent the starting and ending subscript of kth section tracing point；
Use two ways by track sets segmentation:
Track sets T is divided into N section, label=1 by (1) the 1st kind of mode, and division rule is as follows:
Track sets T is divided into N-1 section, label=2 by (2) the 2nd kinds of modes, and division rule is as follows:
Wherein:
Step (3): by all in step 2 tlv triple trajectory segment record < label, k, Tk> according to key, (label k) is carried out
As the input of Map function on parallel mapping node after Hash process；Map function is for each tlv triple track of input
Segmentation record < label, k, Tk>, to track sets TkTrack sets T is obtained after being compressed processingk', and export record < label,
K, Tk' > as the input of Reduce function；
Step (4): use single Reduce function to read in the output record of all parallel Map functions, by all label=1
Sectional compression result reformulate a track sets S in chronological order1, meanwhile, by the sectional compression result of label=2
Also a track sets S is reformulated2；By track sets S1And S2Carrying out intercepting and merge, intercepting the rule merged is: from
Track sets S2N-1 track subsequence Q of upper interceptingl, l ∈ [1, N-1], Ql={ Pj|j∈[cl, dl], and meet following condition:
(1)cl≤el, dl≥bl+1, wherein el, bl+1Be respectively in the 1st kind of segmented mode the end subscript of l section tracing point and
The initial subscript of l+1 section tracing point；
(2) j=c is worked aslOr j=dl, Pj∈S1And work as cl<j<dl,
Then by all track subsequence QlReplace track sets S1On corresponding track subsequence { Pj|j∈[cl, dl],
The track sets S obtained eventually1Be exactly compression after generate track.
Below in conjunction with instantiation, technical scheme is described in further detail:
(1) assume there is GPS track sequence T={P1, P2, P3, P4, P5, P6, P7, P8, P9, P10, P11, P12,
P13, P14, P15, P16}
(2) by track sets T segmentation, with tlv triple < label, k, Tk> represent,
Track sets is divided into 3 sections and is expressed as follows by the 1st kind of mode:
First paragraph is: < 1,1, { P1, P2, P3, P4, P5}>
Second segment is: < 1,2, { P6, P7, P8, P9, P10}>
3rd section is: < 1,3, { P11, P12, P13, P14, P15, P16}>
Track sets is divided into 2 sections and is expressed as follows by the 2nd kind of mode:
First paragraph is: < 2,1, { P4, P5, P6, P7, P8}>
Second segment is: < 2,2, { P9, P10, P11, P12, P13}>
(3) by all above tlv triple trajectory segment record, (label, as mapping joint parallel after k) carrying out Hash process for button
The input of Map function on point.Map function is for each tlv triple record of input, to track sets TkIt is compressed
Obtain track sets after process, and be expressed as follows with triple form:
< 1,1, { P1, P3, P4, P5}>
< 1,2, { P6, P8, P9, P10}>
< 1,3, { P11, P13, P16}>
< 2,1, { P4, P8}>
< 2,2, { P9, P13}>
Output tlv triple record is as the input of Reduce function.
(4) single Reduce function is used to read in the output record of all parallel Map functions, by all label=1
Sectional compression result reformulate a track sets S in chronological order1:
S1={ P1, P3, P4, P5, P6, P8, P9, P10, P11, P13, P16}
The sectional compression result of label=2 is also reformulated a track sets S2:
S2={ P4, P8, P9, P13}。
Further, by track sets S1And S2Carry out intercepting and merge.Because track sets S1Waypoint respectively at P5And P6,
P10And P11Between, and for track sets S2On track subsequence Q1={ P4, P8And Q2={ P9, P13From the point of view of }, rail
Mark point P4、P8、P9、P13It is all located at track sets S1On, P4To P8And P9To P13Between tracing point not at S1Rail
In mark sequence, meet 4<5 and 8>6,9<10 and 13 simultaneously>11, so from track sets S2Upper locus intercepting subsequence is:
Q1={ P4, P8}
Q2={ P9, P13}
Then by track subsequence Q1、Q2Replace track sets S respectively1On corresponding track subsequence:
{P4, P5, P6, P8}
{P9, P10, P11, P13}
The track sets generated after finally giving compression is:
{P1, P3, P4, P8, P9, P13, P16}。
Specific embodiments described above, has been carried out the purpose of the present invention, technical scheme and beneficial effect the most in detail
Describe in detail bright, be it should be understood that and the foregoing is only specific embodiments of the present invention, be not limited to the present invention
Scope, any those skilled in the art, the equivalent made on the premise of without departing from the design of the present invention and principle becomes
Change and amendment, the scope of protection of the invention all should be belonged to.
Claims (9)
1. a parallelization trace compression method based on MapReduce, it is characterised in that comprise the following steps:
Step one, by GPS track sequence segmentation in two ways to be compressed, the 1st kind of mode is divided into N section, is labeled as 1,
2nd kind of mode is divided into N-1 section, is labeled as 2, and represents all tlv triple trajectory segment records with triple form；
Step 2, all tlv triple trajectory segment records are carried out after Hash process as Map function on parallel mapping node
Input；Segmentation track sets in input is compressed processing and exporting the input as Reduce function by Map function；
Step 3, use single Reduce function will to be labeled as the sectional compression result of 1 and 2 the most respectively
Composition track sets S1And S2；
Step 4, intercepting N-1 are positioned at S2On track subsequence, and by these track subsequences replace S1Upper waypoint
Neighbouring corresponding track sets, sequence S finally given1Be exactly compression after generate GPS track.
A kind of parallelization trace compression method based on MapReduce the most according to claim 1, it is characterised in that
Described step one is specific as follows:
Step (1): definition GPS track sequence T={Pi, wherein, PiFor i-th tracing point, i is according to the time of track
Sequentially, i=1,2,3...n, n are tracing point sum；
Step (2): by track sets T segmentation, with triple form represent all tlv triple trajectory segment records < label,
K, Tk>, wherein, label represents the mode of segmentation, and k represents the sequence number of segmentation, TkRepresent the track sets of kth section, order
Tk={ Pj|j∈[bk, ek], wherein, bk, ekRepresent the starting and ending subscript of kth section tracing point；
Use two ways by track sets segmentation:
Track sets T is divided into N section, label=1 by (1) the 1st kind of mode, and division rule is as follows:
Track sets T is divided into N-1 section, label=2 by (2) the 2nd kinds of modes, and division rule is as follows:
Wherein:
A kind of parallelization trace compression method based on MapReduce the most according to claim 2, it is characterised in that
Described step 2 is specific as follows:
By all in step (2) tlv triple trajectory segment record < label, k, Tk> according to key, (label k) carries out Hash process
Afterwards as the input of Map function on parallel mapping node；Map function is for each tlv triple trajectory segment record of input
< label, k, Tk>, to track sets TkTrack sets T is obtained after being compressed processingk', and export record < label, k,
Tk' > as the input of Reduce function.
A kind of parallelization trace compression method based on MapReduce the most according to claim 3, it is characterised in that
Described step 3 is specific as follows:
Single Reduce function is used to read in the output record of all parallel Map functions, by the segmentation pressure of all label=1
Sheepshank fruit reformulates a track sets S in chronological order1, meanwhile, by the sectional compression result group the most again of label=2
Become a track sets S2。
A kind of parallelization trace compression method based on MapReduce the most according to claim 4, it is characterised in that
Described step 4 is specific as follows:
By track sets S1And S2Carrying out intercepting and merge, intercepting the rule merged is: from track sets S2Upper intercepting N-1
Track subsequence Ql, l ∈ [1, N-1], Ql={ Pj|j∈[cl, dl], and meet following condition:
(1)cl≤el, dl≥bl+1, wherein el, bl+1Be respectively in the 1st kind of segmented mode the end subscript of l section tracing point and
The initial subscript of l+1 section tracing point；
(2) j=c is worked aslOr j=dl, Pj∈S1And work as cl<j<dl,
Then by all track subsequence QlReplace track sets S1On corresponding track subsequence { Pj|j∈[cl, dl],
The track sets S obtained eventually1Be exactly compression after generate track.
A kind of parallelization trace compression method based on MapReduce the most according to claim 2, it is characterised in that
Described GPS track point sum n >=1000000.
A kind of parallelization trace compression method based on MapReduce the most according to claim 2, it is characterised in that
In described step (2), N is equal to the number of Map node.
A kind of parallelization trace compression method based on MapReduce the most according to claim 3, it is characterised in that
Hash in described step (3) processes and uses division Hash.
A kind of parallelization trace compression method based on MapReduce the most according to claim 3, it is characterised in that
Trace compression method in step (3) uses open window compression algorithm.
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| CN106643734A (en) * | 2016-12-30 | 2017-05-10 | 中国科学院电子学研究所 | Grading processing method for space-time track data |
| CN106790468A (en) * | 2016-12-10 | 2017-05-31 | 武汉白虹软件科技有限公司 | A kind of distributed implementation method for analyzing user's WiFi event trace rules |
| CN108470146A (en) * | 2018-02-11 | 2018-08-31 | 西南电子技术研究所（中国电子科技集团公司第十研究所） | The similar flight path recognition methods of classical flight path |
| CN110730001A (en) * | 2019-08-27 | 2020-01-24 | 浙江海洋大学 | Track compression method for ship berthing |
| CN111309977A (en) * | 2020-02-24 | 2020-06-19 | 北京明略软件***有限公司 | ID space-time trajectory matching method and device |
| CN114328784A (en) * | 2021-12-27 | 2022-04-12 | 中科星图股份有限公司 | Track multidimensional distributed compression method based on space-time database |
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| CN106790468A (en) * | 2016-12-10 | 2017-05-31 | 武汉白虹软件科技有限公司 | A kind of distributed implementation method for analyzing user's WiFi event trace rules |
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| CN108470146B (en) * | 2018-02-11 | 2022-07-08 | 西南电子技术研究所（中国电子科技集团公司第十研究所） | Similar track identification method of classic track |
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| CN111309977A (en) * | 2020-02-24 | 2020-06-19 | 北京明略软件***有限公司 | ID space-time trajectory matching method and device |
| CN114328784A (en) * | 2021-12-27 | 2022-04-12 | 中科星图股份有限公司 | Track multidimensional distributed compression method based on space-time database |
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