CN107943848A - A kind of ubiquitous space time information association and polymerization - Google Patents
A kind of ubiquitous space time information association and polymerization Download PDFInfo
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Abstract
The invention discloses a kind of ubiquitous space time information association and polymerization, ubiquitous space time information is cleaned first, screens and integrates；Then standardization, the descriptive model of structuring are established；Then the descriptive model by ubiquitous space time information with chemical conversion structuring；The correlation rule between model element is finally established, the information of structuring is associated according to rule.The present invention can effectively solve the problem that under ubiquitous space-time existing for massive spatial data the problem of fragmentation, diversity, discretization, and new method is provided for Internet resources are accumulated associated data network.
Description
Technical field
The invention belongs to geographical spatial data association process field, and in particular to a kind of ubiquitous space time information association is with polymerizeing
Method.
Background technology
Under the overall background in big data epoch, the interoperability of data and mutually shared it is of great significance and very big
Application value and researching value.However, due to mobile interchange data, there is the characteristics of uncompleted structure so that data and number
According to association and shared receive resistance.Increasing a large amount of foreign peoples, isomery, distribution, multi-source on internet
Data message, how according to the knowledge relation in information resources and object, to merge, there are the interior of incidence relation between each other
Hold, find and organize so as to reach the efficient of knowledge, be urgent problem under current mobile internet.Both at home and abroad over the ground
The research of reason space correlation data reaps rich fruits, wherein geographical link data (GeoLinkedData) have become ground
Spatial Data Sharing and the important research and practice content of integration field are managed, such as OpenStreetMap possesses by oneself
Magnanimity geographical spatial data new semantic dimension is added according to LinkedData principles.
The content of the invention
In order to solve the above technical problem, the present invention provides a kind of ubiquitous space time information to associate the method with polymerizeing, and leads to
Cross and ubiquitous space time information is expressed using a kind of descriptive model of structuring, to people, activity, object in ubiquitous space-time data
Information parsing and understand, establish their association modes geographical location between, realize ubiquitous information position polymerize.
The technical solution adopted in the present invention is：A kind of ubiquitous space time information association and polymerization, it is characterised in that bag
Include following steps：
Step 1：Ubiquitous space time information is cleaned, screens and integrates；
Step 2：Establish standardization, the descriptive model of structuring；
Step 3：Descriptive model by ubiquitous space time information with chemical conversion structuring；
Step 4：The correlation rule between model element is established, the information of structuring is associated according to rule.
Relative to the prior art, the beneficial effects of the invention are as follows：The present invention can effectively solve the problem that magnanimity is empty under ubiquitous space-time
Between existing for data the problem of fragmentation, diversity, discretization, provided for Internet resources are accumulated associated data network
New method.
Brief description of the drawings
Fig. 1 is the ubiquitous space-time data pretreatment module figure of the embodiment of the present invention；
Fig. 2 is the five-tuple model schematic of the embodiment of the present invention；
Fig. 3 is flow chart of the ubiquitous space time information with the descriptive model for being melted into structuring of the embodiment of the present invention.
Embodiment
Understand for the ease of those of ordinary skill in the art and implement the present invention, with reference to the accompanying drawings and embodiments to this hair
It is bright to be described in further detail, it will be appreciated that implementation example described herein is merely to illustrate and explain the present invention, not
For limiting the present invention.
Referring to Fig.1, a kind of ubiquitous space time information association provided by the invention and polymerization, comprise the following steps：
Step 1：Ubiquitous space time information is cleaned, screens and integrates；
Duplicate checking inspection is carried out to ubiquitous space time information and availability is examined, it is mutually complementary to ubiquitous space time information attribute integrality
Fill.
In duplicate checking inspection, if the corresponding five-tuple element information that a plurality of data include is identical, only retain it
Middle a data.Otherwise, all retain.
In availability inspection, judge data whether containing geographical location information and main body first (people, event, object) letter
Breath, if including positional information and main body metamessage in data, retains the data, otherwise, removes the data.
In attribute integrality supplement, the benefit between the supplement between same source data attribute and different source data attribute is included
Fill.Attribute supplements between same source data, i.e., attribute of some in same source data with fixed character data belongs to missing
The data of property are supplemented, and (GPS data from taxi in a such as period has excalation, can be according to this whole section of data
Feature is supplemented)；Attribute supplements between different source data, i.e., attribute is complementary to one another between a variety of source datas.
Step 2：Establish standardization, the descriptive model of structuring；
Initially set up using people, activity, object, the time, position as the five-tuple of basic element descriptive model.Model is shown in attached
Fig. 2.It is described using two layers of nested XML structure：First layer is diadactic structure, including main body member and space-time element.This binary
All it is essential option, i.e. nonempty entry.This layer of binding character is stronger.The second layer is five meta structures based on diadactic structure, wherein space-time element
Expand to time, position, main body member extension is people, activity, object, the Rotating fields be expressed as { people, activity, object }, the time,
Position } }, i.e.,<<P,A,O>,<T,L>>.In the structure shown here, position is essential option, and people, activity, object three must have one.
This layer of binding character is weaker.XML structure is described as：
Then specification is carried out to the XML data format of proposition using XML Schame.Norm structure is described as：
Step 3：Descriptive model by ubiquitous space time information with chemical conversion structuring；
Operating process as shown in Figure 3, specifically includes following sub-step：
Step 3.1 establishes unified space reference and time reference, establishes position, activity, the ontology library of object.Establishing
In unified space reference, using 2000 national earth coordinates, in unified time reference is established, time format is defined as
Form " the YYYY-MM-DD HH on " date+time ":MM:SS " is for example, " 2017-01-01 00:00:01”.
Ubiquitous space time information is decomposed and parsed by step 3.2, forms individual element.I.e. from pretreated data
People, activity, object, time, positional information are extracted, including the word segmentation processing to text message.Such as shown in table 1, in band
Have in the microblog data of positional information, extract coordinate and address information and temporal information, can from microblogging text extraction activity,
Object, people information.
Table 1
Isomeric data after processing is mapped to five-tuple data model by step 3.3, as shown in table 2.
(a) in the mapping block of five-tuple, if there are coordinate information, whether the coordinate information for judging to obtain is defined
Coordinate under coordinate system, if not regulation coordinate, then carry out coordinate conversion.
(b) in the mapping block of five-tuple, if existence time information, whether the temporal information for judging to obtain is defined
Standard time, if not stipulated time form, then carry out time conversion.
Table 2
Step 4：The correlation rule between model element is established, the information of structuring is associated according to rule；
Specific implementation includes following sub-step：
Step 4.1：The correlation rule between each basic element of five-tuple is established, establishes person to person, activity respectively with living
Dynamic, object and the correlation rule of object, time and time, position and position；
(1) relation of person to person (Person)
Judge that P1 and P2 is same people according to id, or be not same people (i.e. other social relation)；
(2) relation of object and object (Object)；
Between O1 and O2, existing relation be same, same class, substitute or Complements relation；
(3) activity and the relation of movable (Activity)；
Existing relation is same part, same class, causality or association relationship between A1 and A2；
(4) relation of position and position (Location)；
Between L1 and L2 existing relation be same position, neighbouring or differently relation；
(5) relation of time and time (Time)；
Existing relation is at the same time or not simul relation between T1 and T2；
Existing correlation rule is between each basic element in step 4.1：
(1) correlation rule of person to person
Judge whether P1.id==P2.id is true, if true, be then shown to be same people, otherwise, utilize Sp(P1.id,
P2.id the relation similarity of two people) is calculated, judges whether to belong to other social relation.Wherein P1, P2 refer in two datas
People, id refer to the identification number of people, SpRefer to the computational methods of the similarity of people.
(2) correlation rule of object and object
Judge whether Obj1.name=Obj2.name is true, if true, be then shown to be same object, otherwise utilize
SoThe similarity of (Obj1.kind, Obj2.kind) computing object, determines whether that same class, substitute or Complements close
System.Wherein Obj1, Obj2 refer to the object in two datas, and name refers to the title of object, and kind refers to the species of object, SoRefer to object
Similarity computational methods.
(3) correlation rule of activity and activity
Judge whether Act1.name=Act2.name is true, if true, be then shown to be same activity, otherwise utilize
SaThe similarity of (Act1.kind, Act1.kind) calculating activity, determines whether that same class, causality or association are closed
System.Wherein Act1, Act2 refer to the activity in two datas, and name refers to the title of activity, and kind refers to the species of activity, SaRefer to object
Similarity computational methods.
(4) correlation rule of time and time
1. judging time granularity tLod1==tLod2, if true, then calculated according to following formula, t=|
TPoint1-tPoint2 |, if t is 0, same time point is shown to be, if t<εt, be shown to be the same period, otherwise for it is other when
Between relation.TPoint1, tPoint2 refer to the specific time in two datas；εtRefer to a certain specific time threshold, according to demand
Specific setting；What tLod1, tLod2 were represented respectively is the granularity of time in two datas.
2. judging time granularity tLod1==tLod2, if vacation, L is usedt(tLod1, tLod2) carries out time granularity
Conversion, is converted into after same time granularity, uses the rule of step 1..Wherein, LtFor time granularity conversion method.
(5) correlation rule of position and position
1. judging position granularity locLod1==locLod2, if vacation, L is usedl(locLod1, locLod2) is carried out
Position granularity transform, if true, progress next step.Wherein, LlFor position granularity transform method, locLod1, locLod2 divide
What is do not represented is the granularity of position in two datas.
2. judge whether location type is identical, if it is different, using Cl(locValue1, locValue2) carries out position class
Type is changed, if identical, is carried out in next step.Wherein, ClFor location type conversion method.Wherein locValue1, locValue2
For two position datas.
3. in the case of same position type, same position granularity, S is usedl(locValue, locValue2) is calculated
The similarity of two positions, in the threshold value of setting, judges to obtain two positions and indicates whether as same position, phase according to similarity
Neighbour or differently relation；Wherein, SlFor the method for calculation position similarity.
Exemplified by specifically being associated by position, the location type that the description in geographical location is common has：Normal address, longitude and latitude, postal
Political affairs coding, telephone number, IP address, self-centeredness position, linear reference position, three-dimensional position, dynamic position etc..Ubiquitous information
In the association process of position, scale, the granularity the level of detail and ability of spatial dimension (represent description) it is not necessarily consistent, it is necessary to
The mapping graph between diverse location type and different grain size is established, each location type can be with the fine journey of expression of space scope
Degree and ability are as shown in table 3.
Table 3
Accordingly position association is divided into the association of same particle sizes information and the association of different grain size information.Wherein phase
The information association of one-size particularly may be divided into same particle sizes same type, same particle sizes different type two parts.With same particle sizes
Similar, the association between different grain size information can also be divided into different grain size same type, different grain size different type two is divided.
(a) relation of the granule size of two kinds of positions is judged；If identical, carry out in next step；If it is different, then will be compared with
The information of small grain size is converted to the positional representation of larger granularity；
(b) judge whether the type of two kinds of positions is identical；If identical, carry out in next step；If it is different, then carry out class
Type is changed, and is converted into same type and is represented；
Specific similarity calculating method, and the similarity of calculation position are selected according to the location type of selected conversion；
In the threshold value of setting, according to similarity, the whether same position of two positions is determined；If same position, associated with position, otherwise make its elsewhere
Reason.
Step 4.2：Respiratory sensation is extended to based on the associated principle of individual element, tuple is multi-level between establishing a plurality of record
Correlation rule.
It should be appreciated that the part that this specification does not elaborate belongs to the prior art.
It should be appreciated that the above-mentioned description for preferred embodiment is more detailed, can not therefore be considered to this
The limitation of invention patent protection scope, those of ordinary skill in the art are not departing from power of the present invention under the enlightenment of the present invention
Profit is required under protected ambit, can also be made replacement or deformation, be each fallen within protection scope of the present invention, this hair
It is bright scope is claimed to be determined by the appended claims.
Claims (9)
1. a kind of ubiquitous space time information association and polymerization, it is characterised in that comprise the following steps：
Step 1：Ubiquitous space time information is cleaned, screens and integrates；
Step 2：Establish standardization, the descriptive model of structuring；
Step 3：Descriptive model by ubiquitous space time information with chemical conversion structuring；
Step 4：The correlation rule between model element is established, the information of structuring is associated according to rule.
2. ubiquitous space time information association according to claim 1 and polymerization, it is characterised in that：In step 1, to ubiquitous
Space time information carries out duplicate checking inspection and availability is examined, and the attribute integrality of ubiquitous space time information is complementary to one another.
3. ubiquitous space time information association according to claim 2 and polymerization, it is characterised in that：In duplicate checking inspection,
If the corresponding five-tuple element information that a plurality of data include is identical, only retain wherein a data；Otherwise, all protect
Stay；
In availability inspection, judge whether data contain geographical location information and main body metamessage, if including position in data
Information and main body metamessage, retain the data；Otherwise, the data are removed；
In attribute integrality is complementary to one another, the benefit between the supplement between same source data attribute and different source data attribute is included
Fill；Attribute supplements between same source data, is the number according to the attribute in same source data with fixed character data to missing attribute
According to being supplemented；Attribute supplements between different source data, i.e., attribute is complementary to one another between a variety of source datas.
4. ubiquitous space time information association according to claim 1 and polymerization, it is characterised in that：Built described in step 2
Legislate generalized, the descriptive model of structuring, be using people, activity, object, the time, position as the five-tuple of basic element description
Model, expresses the model of foundation using XML language form, and the XML data format using XML Schema to proposition
Carry out specification.
5. ubiquitous space time information association according to claim 4 and polymerization, it is characterised in that：The five-tuple is retouched
Model is stated, is described using two layers of nested XML structure；First layer is diadactic structure, including main body member and space-time element；The
Two layers are five meta structures based on diadactic structure, and wherein space-time element expands to time, position, and main body member extension is people, activity, right
As.
6. ubiquitous space time information association according to claim 1 and polymerization, it is characterised in that the specific reality of step 3
Now include following sub-step：
Step 3.1：Unified space reference and time reference are established, establishes position, activity, the ontology library of object；
Step 3.2：Ubiquitous space time information is decomposed and parsed, forms individual element, i.e., people, work are extracted from source data
Dynamic, object, time, positional information；
Step 3.3：Isomeric data after processing is mapped to five-tuple data model.
7. ubiquitous space time information association according to claim 6 and polymerization, it is characterised in that：In step 3.3, five
In the mapping of tuple, if there are coordinate information, the coordinate information that judges to obtain whether be as defined in coordinate under coordinate system, if not
Provide coordinate, then carry out coordinate conversion；
In the mapping of five-tuple, if existence time information, whether the temporal information for judging to obtain is the defined standard time, if
Non- stipulated time form, then carry out time conversion.
8. ubiquitous space time information association according to claim 1 and polymerization, it is characterised in that the specific reality of step 4
Now include following sub-step：
Step 4.1：Establish the correlation rule between each basic element of five-tuple, establish respectively person to person, it is movable with it is movable, right
As the correlation rule with object, time and time, position and position；
Step 4.2：Respiratory sensation is extended to based on the associated principle of individual element, establishes tuple multilayer secondary association between a plurality of record
Rule.
9. ubiquitous space time information association according to claim 8 and polymerization, it is characterised in that each in step 4.1
Existing incidence relation includes between basic element：
(1) human relationship；
Judge that P1 and P2 is same people according to id, or be not same people；
(2) relation of object and object；
Existing relation is same, same class, substitute or Complements relation between O1 and O2；
(3) relation of activity and activity；
Existing relation is same part, same class, causality or association relationship between A1 and A2；
(4) relation of time and time；
Existing relation is at the same time or not simul relation between T1 and T2；
(5) relation of position and position；
Between L1 and L2 existing relation be same position, neighbouring or differently relation；
Existing correlation rule is between each basic element in step 4.1：
(1) correlation rule of person to person；
Judge whether P1.id==P2.id is true, if true, be then shown to be same people, otherwise, utilize Sp(P1.id,
P2.id the relation similarity of two people) is calculated, judges whether to belong to other social relation；Wherein P1, P2 refer in two datas
People, id refer to the identification number of people, SpRefer to the computational methods of the similarity of relationship；
(2) correlation rule of object and object；
Judge whether Obj1.name=Obj2.name is true, if true, be then shown to be same object, otherwise utilize So
The similarity of (Obj1.kind, Obj2.kind) computing object, determines whether same class, substitute or Complements relation；
Wherein Obj1, Obj2 refer to the object in two datas, and name refers to the title of object, and kind refers to the species of object, SoRefer to object
The computational methods of similarity；
(3) correlation rule of activity and activity；
Judge whether Act1.name=Act2.name is true, if true, be then shown to be same activity, otherwise utilize Sa
The similarity of (Act1.kind, Act1.kind) calculating activity, determines whether same class, causality or association relationship；
Wherein Act1, Act2 refer to the activity in two datas, and name refers to the title of activity, and kind refers to the species of activity, SaRefer to object
The computational methods of similarity；
(4) correlation rule of time and time；
1. judging time granularity tLod1==tLod2, if true, then calculated according to following formula, t=| tPoint1-
TPoint2 |, if t is 0, same time point is shown to be, if t<εt, the same period is shown to be, is otherwise other time relation；
TPoint1, tPoint2 refer to the specific time in two datas, εtRefer to a certain specific time threshold；TLod1, tLod2 distinguish
What is represented is the granularity of time in two datas；
2. judging time granularity tLod1==tLod2, if vacation, L is usedt(tLod1, tLod2) carries out time granularity conversion,
It is converted into after same time granularity, uses rule 1.；Wherein, LtFor time granularity conversion method；
(5) correlation rule of position and position；
1. judging position granularity locLod1==locLod2, if vacation, L is usedl(locLod1, locLod2) is into row position
Granularity transform, if true, progress next step；Wherein, LlFor position granularity transform method；LocLod1, locLod2 distinguish table
What is shown is the granularity of position in two datas；
2. judge whether location type is identical, if it is different, using Cl(locValue1, locValue2) carries out location type and turns
Change, if identical, carry out in next step；Wherein, ClFor location type conversion method, locValue1, locValue2 are two positions
Put data；
3. in the case of same position type, same position granularity, S is usedl(locValue, locValue2) calculates two positions
Similarity, in the threshold value of setting, according to similarity judge to obtain two positions indicate whether as same position, it is adjacent or
Differently relation；Wherein, SlFor the method for calculation position similarity.
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| CN102291435A (en) * | 2011-07-15 | 2011-12-21 | 武汉大学 | Mobile information searching and knowledge discovery system based on geographic spatiotemporal data |
| CN103390057A (en) * | 2013-07-26 | 2013-11-13 | 国家测绘地理信息局卫星测绘应用中心 | Spatialized modeling and storing method of historical information |
| US20150032762A1 (en) * | 2013-07-28 | 2015-01-29 | Tiberiu Stef-Praun | System and Method for Filtering One's Personal Availability, Based on Preferences and Matching |
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2017
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102291435A (en) * | 2011-07-15 | 2011-12-21 | 武汉大学 | Mobile information searching and knowledge discovery system based on geographic spatiotemporal data |
| CN103390057A (en) * | 2013-07-26 | 2013-11-13 | 国家测绘地理信息局卫星测绘应用中心 | Spatialized modeling and storing method of historical information |
| US20150032762A1 (en) * | 2013-07-28 | 2015-01-29 | Tiberiu Stef-Praun | System and Method for Filtering One's Personal Availability, Based on Preferences and Matching |
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