CN105183836B - A kind of algorithm that event big data information is obtained based on symbolic feature - Google Patents
A kind of algorithm that event big data information is obtained based on symbolic feature Download PDFInfo
- Publication number
- CN105183836B CN105183836B CN201510553189.1A CN201510553189A CN105183836B CN 105183836 B CN105183836 B CN 105183836B CN 201510553189 A CN201510553189 A CN 201510553189A CN 105183836 B CN105183836 B CN 105183836B
- Authority
- CN
- China
- Prior art keywords
- decimal
- event
- big data
- symbolic
- time series
- 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.)
- Active
Links
- 238000005070 sampling Methods 0.000 claims abstract description 11
- 230000000694 effects Effects 0.000 claims description 4
- 230000001568 sexual effect Effects 0.000 claims description 2
- 238000000034 method Methods 0.000 description 6
- 229910002056 binary alloy Inorganic materials 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 2
- 238000012512 characterization method Methods 0.000 description 2
- 230000002123 temporal effect Effects 0.000 description 2
- 230000003442 weekly effect Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 235000013399 edible fruits Nutrition 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000010304 firing Methods 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 230000035939 shock Effects 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
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
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2455—Query execution
- G06F16/24568—Data stream processing; Continuous queries
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- Databases & Information Systems (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Complex Calculations (AREA)
- Compression, Expansion, Code Conversion, And Decoders (AREA)
Abstract
The invention discloses a kind of algorithms that event big data information is obtained based on symbolic feature：Step 1：Decimal system time series { the x of acquisition eventmAnd sampling total length is set；Step 2：Binary character length L to be encoded and sampling delay, τ are set；Step 3：Calculate decimal system time series { xmMean μ；Step 4：Using μ as the dividing line P of 0 and 1 two symbol field0, threshold function table is set；Step 5：To decimal system time series { xmAll over threshold function table is applied, build binary symbol sequence { sn}；Step 6：To binary character code sequence { snDecimal coded is carried out, it is converted into decimal symbol code sequence { Sn}；Step 7：Count { SnIn each decimal symbol code SnThe frequency P of appearancen, form decimal symbol code SnFrequency PnNogata figure.The domination of big data feature is realized, the decimal system time series { x of event is represented convenient for judgementmWhether there is big data feature.
Description
Technical field
The present invention relates to a kind of algorithms that event big data information is obtained based on symbolic feature.
Background technology
Such definition is given for " big data " (Big data) research institution Gartner：" big data " is desirable
New tupe could have stronger decision edge, see clearly the magnanimity for finding power and process optimization ability, high growth rate and various
The information assets of change.
For describing the decimal system time series of a certain generalized event as shown in Figure 1, what kind of big data is characterized inSuch as
There are big data features for fruit, then how to obtain the big data featureThe method for obtaining big data in the prior art is not unique, this
Patent proposes a kind of algorithm that event big data information is obtained based on symbolic feature.
Invention content
In view of the above-mentioned problems, the present invention provides a kind of algorithm that event big data information is obtained based on symbolic feature, realize
The domination of big data feature corresponds to some decimal symbol code S convenient for judgementnParticular event whether have big data
Feature；Further, correspond to decimal symbol code sequence { S convenient for judgementn(namely corresponding to decimal system time series { xm})
A certain generalized event whether have randomness or certainty.
To realize above-mentioned technical purpose and the technique effect, the invention is realized by the following technical scheme：
A kind of algorithm that event big data information is obtained based on symbolic feature, which is characterized in that include the following steps：
Step 1：Decimal system time series { the x of acquisition eventmAnd sampling total length is set；
Step 2：Binary character length L to be encoded and sampling delay, τ are set；
Step 3：Calculate decimal system time series { xmMean μ；
Step 4：Using μ as the dividing line P of 0 and 1 two symbol field0, threshold function table is set
Step 5：To decimal system time series { xmAll over threshold function table is applied, according to binary character length L and sampling time delay
τ, by decimal system time series { xmElement xmIt is transformed to binary symbol sequence { tmIn element tm, structure binary system symbol
Number sequence { sn}；
Step 6：To binary character code sequence { snDecimal coded is carried out, it is converted into decimal symbol code sequence
{Sn}；
Step 7：Count { SnIn each decimal symbol code SnThe frequency P of appearancen, form decimal symbol code SnFrequency
PnNogata figure.
It is preferred that further include step 8：According to decimal symbol code SnFrequency PnNogata figure computed improved entropy Hs(L)。
The beneficial effects of the invention are as follows：
To the time series { xm" coarse " --- symbolism is implemented, turn the time series of original numerical value change multiterminal
It changes into only have the symbol sebolic addressing of several numerical value.It is processed by " coarse ", obtains decimal symbol code SnFrequency PnFigure,
In, the symbolic code of big frequency corresponds to strong information, and the symbolic code of small frequency corresponds to Weak Information, it is achieved thereby that big data
The domination of feature.
It further, can be to decimal symbol code SnFrequency PnHistogram calculation improves entropy Hs(L), the H of randomness events
(L) >=0.9, the H of deterministic cases(L)≤0.1, correspond to decimal symbol code sequence { S so as to can determine thatn(namely it is corresponding
In decimal system time series { xm) a certain generalized event whether have randomness or certainty.
Description of the drawings
Fig. 1 is the decimal system time series { x of a certain generalized eventm}；
Fig. 2 is decimal system time series { xmBe converted to binary character code sequence { snSchematic diagram；
Fig. 3 is certain stock index variation { xmWeekly figure and it is converted into binary character code sequence { snSchematic diagram；
When Fig. 4 is binary character length L=3, certain stock index variation { xnWeekly figure decimal symbol code SnFrequency
PnHistogram；
Fig. 5 is certain four-cylinder diesel engine fuselage shaking { xmSchematic diagram；
When Fig. 6 is binary character length L=6, certain four-cylinder diesel engine fuselage shaking { xmDecimal symbol code SnFrequency
Spend PnHistogram.
Specific embodiment
Technical solution of the present invention is described in further detail with specific embodiment below in conjunction with the accompanying drawings, so that ability
The technical staff in domain can be better understood from the present invention and can be practiced, but illustrated embodiment is not as the limit to the present invention
It is fixed.
A kind of algorithm that event big data information is obtained based on symbolic feature, which is characterized in that include the following steps：
Step 1：Decimal system time series { the x of acquisition eventmAnd sampling total length is set；
Step 2：Binary character length L to be encoded and sampling delay, τ are set；
Step 3：Calculate decimal system time series { xmMean μ；
Step 4：Using μ as the dividing line P of 0 and 1 two symbol field0, threshold function table is set
Step 5：To decimal system time series { xmAll over threshold function table is applied, according to binary character length L and sampling time delay
τ, by decimal system time series { xmElement xmIt is transformed to binary symbol sequence { tmIn element tm, structure binary system symbol
Number sequence { sn}；
Step 6：To binary character code sequence { snDecimal coded is carried out, it is converted into decimal symbol code sequence
{Sn}；
Step 7：Count { SnIn each decimal symbol code SnThe frequency P of appearancen, form decimal symbol code SnFrequency
PnNogata figure.In symbolic code SnFrequency PnIn figure, some symbolic code SnCharacterize a certain particular event, corresponding frequency Pn
It is the intensity that the particular event occurs.Wherein, the symbolic code of big frequency corresponds to strong information, and the symbolic code of small frequency corresponds to
Weak Information, if frequency PnNumerical value then can determine that the particular event has big data feature with respect to other symbolic code biggers,
It is achieved thereby that the domination of big data feature.By some decimal system for corresponding to a certain particular event (i.e. " individual events ")
Symbolic code SnThe frequency P of appearancen, can judge whether the particular event has regular big data feature.
One threshold value can be rule of thumb set, as some decimal symbol code SnFrequency PnMore than setting threshold value when,
Judge that the particular event has big data feature.
It further, can be according to decimal symbol code SnFrequency PnNogata graphics calculations " improve entropy Hs(L) " it, calculates
Formula such as formula (1)：
In formula (1)：NseqIt is the decimal symbol code total number with non-zero frequency；I is a number sequence of decimal symbol code
Number；pi,LIt is the frequency for i-th of decimal symbol code that length is L.
Due to the H of randomness events(L) >=0.9, the H of deterministic cases(L)≤0.1, so as to can determine that correspond to ten into
Symbolic code sequence { S processedn(namely corresponding to decimal system time series { xn) a certain generalized event (i.e. " whole event ") whether
With randomness or certainty.
By determining binary symbol sequence { snLength L and delay, τ, determine decimal system time series { xmMean value
μ, setting threshold function table, can be by decimal system time series { xmBe converted to binary temporal sequence { tm, and then build binary system
Symbolic code sequence { sn, to { snAs decimal coded it is converted into decimal symbol code sequence { Sn}.Wherein, each parameter is excellent
It is selected as：Total length >=50 point are sampled, the value range of L is that the value range of 3~6, τ is 1~3.It should be noted that the value of τ
Range 1~3 refers in binary character domain to binary temporal sequence { tmAt interval of 1~3 data take next element.
Fig. 2 is the decimal system time series { x to corresponding to a certain generalized event in Fig. 1m, it is converted into binary character code sequence
{snProcess, for express it is simple clear for the sake of, take symbol lengths L=3, delay, τ=1.
The stock index changing rule of economic field is analyzed, seeks the relationship between more empty two sides.Fig. 3 is economical
Field stock index variation { xmWeekly (5 days) figure and it is converted into binary character code sequence { snProcess,
For the intensive that expression Fig. 3 stock index change, symbol lengths L=3, delay, τ=1 are taken, corresponding stock index changes { xm
Decimal symbol code SnFrequency PnHistogram is as shown in Figure 4.
From fig. 4, it can be seen that the frequency that symbolic code " 101 " occurs is maximum (6 times), the frequency that symbolic code " 010 " occurs takes second place (4
It is secondary).In figure 3, " 101 " characterization stock index depth ∨ rebounds, the big ∧ drops of " 010 " characterization stock index.The empty both sides more than the Zhou Zhong stock markets
The severity of game, to the greatest extent by the decimal symbol code S of weekly stock index variation figurenFrequency PnHistogram quantitative expression, in many ways
Occupy windward compared with short side.And the improvement entropy H of Fig. 4s(L)=0.68, illustrate in weekly stock index change procedure simultaneously by determining
The effect of sexual factor and random factor.
The diesel vibration of engineering field is analyzed, seeks the function and effect in relation to influence factor.Fig. 5 is engineering neck
Domain four-cylinder diesel engine fuselage shaking { xmFigure, for expression Fig. 5 it is of short duration it is big vibration and its between small vibration large-spacing spy
Point takes symbol lengths L=6, delay, τ=3, corresponding four-cylinder diesel engine fuselage shaking { xnDecimal symbol code SnFrequency
PnHistogram is as shown in Figure 6.
As seen from Figure 5, fuselage shaking time history { xnThere is big vibration of short duration several times, this is in firing top centre respectively
With bottom dead center-nearby, exhaust top dead center and bottom dead center-nearby piston excited target shock cylinder sleeve as a result, with diesel load, work
Plug-steel-jacket gap, piston ring sticking state etc. are related.In figure 6, these possible influence factors can be by the ten of fuselage shaking
Hex notation code SnFrequency PnHistogram quantitative expression, wherein the decimal symbol code for having several frequency larger.It can be converted
For the binary character of symbol lengths L=6, the opportunity of its appearance is observed and found in Figure 5, can judge which factor is drawn
Send out diesel engine vibration most very.And the improvement entropy H of Fig. 6s(L)=0.9754, illustrate that diesel engine vibration has on the whole
The attribute of randomness event.
It these are only the preferred embodiment of the present invention, be not intended to limit the scope of the invention, it is every to utilize this hair
The equivalent structure that bright specification and accompanying drawing content are made either equivalent process transformation or be directly or indirectly used in other correlation
Technical field, be included within the scope of the present invention.
Claims (6)
1. a kind of algorithm that event big data information is obtained based on symbolic feature, which is characterized in that include the following steps：
Step 1：Decimal system time series { the x of acquisition eventmAnd sampling total length is set；
Step 2：Binary character length L to be encoded and sampling delay, τ are set；
Step 3：Calculate decimal system time series { xmMean μ；
Step 4：Using μ as the dividing line P of 0 and 1 two symbol field0, threshold function table is set
Step 5：To decimal system time series { xmAll over threshold function table is applied, according to binary character length L and sampling delay, τ, by ten
System time series { xmElement xmIt is transformed to binary symbol sequence { tmIn element tm, build binary symbol sequence
{sn}；
Step 6：To binary character code sequence { snDecimal coded is carried out, it is converted into decimal symbol code sequence { Sn}；
Step 7：Count { SnIn each decimal symbol code SnThe frequency P of appearancen, form decimal symbol code SnFrequency PnDirectly
Square figure.
2. a kind of algorithm that event big data information is obtained based on symbolic feature according to claim 1, which is characterized in that
Sample total length >=50 point.
3. a kind of algorithm that event big data information is obtained based on symbolic feature according to claim 1, which is characterized in that
The value range of L is 3~6.
4. a kind of algorithm that event big data information is obtained based on symbolic feature according to claim 1, which is characterized in that
The value range of τ is 1~3.
5. a kind of algorithm that event big data information is obtained based on symbolic feature according to claim 1, which is characterized in that
As some decimal symbol code SnFrequency PnMore than setting threshold value when, judgement corresponds to a certain particular event of the symbolic code
With big data feature.
6. a kind of algorithm that event big data information is obtained based on symbolic feature according to claim 1, which is characterized in that
According to decimal symbol code SnFrequency PnNogata figure computed improved entropy Hs(L), work as Hs(L) >=0.9 when, judgement corresponds to symbol
Code sequence { SnGeneralized event have randomness；Work as Hs(L)≤0.1 when, judgement corresponds to symbolic code sequence { SnBroad sense thing
Part has certainty；When 0.1<Hs(L)<When 0.9, judgement corresponds to symbolic code sequence { SnGeneralized event simultaneously by determine
The effect of sexual factor and random factor.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510553189.1A CN105183836B (en) | 2015-09-01 | 2015-09-01 | A kind of algorithm that event big data information is obtained based on symbolic feature |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510553189.1A CN105183836B (en) | 2015-09-01 | 2015-09-01 | A kind of algorithm that event big data information is obtained based on symbolic feature |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN105183836A CN105183836A (en) | 2015-12-23 |
| CN105183836B true CN105183836B (en) | 2018-06-15 |
Family
ID=54905918
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510553189.1A Active CN105183836B (en) | 2015-09-01 | 2015-09-01 | A kind of algorithm that event big data information is obtained based on symbolic feature |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105183836B (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103942300A (en) * | 2014-04-15 | 2014-07-23 | 大连海事大学 | Dynamic solution method of center time series |
| CN104636325A (en) * | 2015-02-06 | 2015-05-20 | 中南大学 | Document similarity determining method based on maximum likelihood estimation |
| CN104679991A (en) * | 2015-01-27 | 2015-06-03 | 吉林大学 | Ordered proposition-oriented novel method of information fusion |
| CN104866929A (en) * | 2015-06-11 | 2015-08-26 | 陈虹 | International investment index data processing and analysis method and international investment index data processing and analysis system |
-
2015
- 2015-09-01 CN CN201510553189.1A patent/CN105183836B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103942300A (en) * | 2014-04-15 | 2014-07-23 | 大连海事大学 | Dynamic solution method of center time series |
| CN104679991A (en) * | 2015-01-27 | 2015-06-03 | 吉林大学 | Ordered proposition-oriented novel method of information fusion |
| CN104636325A (en) * | 2015-02-06 | 2015-05-20 | 中南大学 | Document similarity determining method based on maximum likelihood estimation |
| CN104866929A (en) * | 2015-06-11 | 2015-08-26 | 陈虹 | International investment index data processing and analysis method and international investment index data processing and analysis system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN105183836A (en) | 2015-12-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Guez et al. | Influence of autocorrelation on the topology of the climate network | |
| CN105824880A (en) | Webpage grasping method and device | |
| Abd El Hady | Exponentiated transmuted weibull distribution a generalization of the weibull distribution | |
| CN103324888B (en) | Based on virus characteristic extraction method and the system of family's sample | |
| CN105183836B (en) | A kind of algorithm that event big data information is obtained based on symbolic feature | |
| Wu et al. | Chaos criteria design based on modified sign functions with one or three‐threshold | |
| CN101576872B (en) | Chinese text processing method and device thereof | |
| CN104253685B (en) | Symmetric key generation and the dynamic quantization method of distribution based on radio channel characteristic | |
| Pham et al. | Enhance exploring temporal correlation for data collection in WSNs | |
| CN101060411B (en) | A multi-mode matching method for improving the detection rate and efficiency of intrusion detection system | |
| Jiang et al. | Application of improved median filtering algorithm to image de-noising | |
| CN104091123A (en) | Community network level virus immunization method | |
| CN103714346A (en) | People quantity estimation method based on video monitoring | |
| Karimi Firozjaei et al. | Monitoring and prediction of land use changes and physical expansion of Babol city during 1985-2040 using multi-temporal Landsat imagery | |
| CN107144874A (en) | A kind of method and system based on to ENPEMF signals progress BSWT DDTFA time frequency analysis | |
| Chen | Solvers with a bit-encoding phase selection policy and a decision-depth-sensitive restart policy | |
| Cai et al. | Score2SAT: solver description | |
| Cheng et al. | A fuzzy decision tree model for airport terminal departure passenger traffic forecasting | |
| Ki et al. | Addressing water pollution hotspots in the tributary monitoring network using a non-linear data analysis tool | |
| Bhat et al. | Steganalysis of YASS using Huffman Length statistics | |
| Ke et al. | Grey-exponential smoothing prediction model of mining safety based on forecasting validity | |
| Sun et al. | Metallographical image segmentation and compression | |
| CN113917428B (en) | Complex multi-spread signal and jitter signal separation method | |
| Jiang et al. | Quantum correlations in the dimerized spin chain at zero and finite temperatures | |
| Zhang et al. | Lower approximation reduction in ordered information system with fuzzy decision |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| CB03 | Change of inventor or designer information |
Inventor after: Zhang Yu Inventor after: Zhang Chi Inventor after: Shi Huanran Inventor after: Zou Jianping Inventor before: Zhang Yu Inventor before: Zhang Chi |
|
| CB03 | Change of inventor or designer information | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20170707 Address after: 211803 Star Industrial Park, Pukou District, Jiangsu, Nanjing Applicant after: JIANGSU RUNBANG INTELLIGENT PARKING EQUIPMENT CO., LTD. Address before: 1 No. 211167 Jiangsu city of Nanjing province Jiangning Science Park Hongjing Road Applicant before: Nanjing Institute of Technology |
|
| TA01 | Transfer of patent application right | ||
| CB02 | Change of applicant information | ||
| CB02 | Change of applicant information |
Address after: 211803 Pukou District Star Industrial Park in Nanjing, Jiangsu Applicant after: Jiangsu run state intelligent garage Limited by Share Ltd Address before: 211803 Pukou District Star Industrial Park in Nanjing, Jiangsu Applicant before: JIANGSU RUNBANG INTELLIGENT PARKING EQUIPMENT CO., LTD. |
|
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| PE01 | Entry into force of the registration of the contract for pledge of patent right |
Denomination of invention: An algorithm for obtaining event big data information based on symbol features Effective date of registration: 20211125 Granted publication date: 20180615 Pledgee: Bank of China Limited Nanjing Jiangbei New Area Branch Pledgor: JIANGSU RUNBANG INTELLIGENT GARAGE CO.,LTD. Registration number: Y2021980013224 |
|
| PE01 | Entry into force of the registration of the contract for pledge of patent right |