CN106777092A - The intelligent medical calling querying method of dynamic Skyline inquiries under mobile cloud computing environment - Google Patents
The intelligent medical calling querying method of dynamic Skyline inquiries under mobile cloud computing environment Download PDFInfo
- Publication number
- CN106777092A CN106777092A CN201611150537.1A CN201611150537A CN106777092A CN 106777092 A CN106777092 A CN 106777092A CN 201611150537 A CN201611150537 A CN 201611150537A CN 106777092 A CN106777092 A CN 106777092A
- Authority
- CN
- China
- Prior art keywords
- data
- skyline
- hospital
- dynamic
- space
- 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.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 50
- 238000013138 pruning Methods 0.000 claims abstract description 30
- 238000001914 filtration Methods 0.000 claims abstract description 14
- 230000008569 process Effects 0.000 claims abstract description 14
- 238000011156 evaluation Methods 0.000 claims abstract description 8
- 230000006870 function Effects 0.000 claims description 14
- 238000012545 processing Methods 0.000 claims description 13
- 238000004458 analytical method Methods 0.000 claims description 11
- 238000003745 diagnosis Methods 0.000 claims description 6
- 238000012216 screening Methods 0.000 claims description 6
- 238000005457 optimization Methods 0.000 claims description 4
- 241001269238 Data Species 0.000 claims description 3
- 238000004364 calculation method Methods 0.000 claims description 3
- 238000013508 migration Methods 0.000 claims description 3
- 230000005012 migration Effects 0.000 claims description 3
- 230000002441 reversible effect Effects 0.000 claims description 2
- 230000011218 segmentation Effects 0.000 claims description 2
- 238000012795 verification Methods 0.000 claims description 2
- 230000000903 blocking effect Effects 0.000 claims 1
- 201000010099 disease Diseases 0.000 claims 1
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 claims 1
- 239000004744 fabric Substances 0.000 claims 1
- 238000012544 monitoring process Methods 0.000 description 14
- 238000005516 engineering process Methods 0.000 description 8
- 238000004891 communication Methods 0.000 description 5
- 230000002045 lasting effect Effects 0.000 description 5
- 238000009826 distribution Methods 0.000 description 4
- 238000000605 extraction Methods 0.000 description 4
- 239000000284 extract Substances 0.000 description 3
- 230000036541 health Effects 0.000 description 3
- 230000007246 mechanism Effects 0.000 description 3
- 239000000047 product Substances 0.000 description 3
- 238000003860 storage Methods 0.000 description 3
- 238000003556 assay Methods 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 238000007405 data analysis Methods 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 239000003814 drug Substances 0.000 description 2
- 230000007613 environmental effect Effects 0.000 description 2
- 238000007726 management method Methods 0.000 description 2
- 239000000203 mixture Substances 0.000 description 2
- 230000007935 neutral effect Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- 230000004913 activation Effects 0.000 description 1
- 230000003044 adaptive effect Effects 0.000 description 1
- 230000002776 aggregation Effects 0.000 description 1
- 238000004220 aggregation Methods 0.000 description 1
- 230000032683 aging Effects 0.000 description 1
- 239000012491 analyte Substances 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 238000007418 data mining Methods 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 238000000354 decomposition reaction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000004880 explosion Methods 0.000 description 1
- 239000002360 explosive Substances 0.000 description 1
- 238000009472 formulation Methods 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 230000000474 nursing effect Effects 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000012827 research and development Methods 0.000 description 1
- 238000010845 search algorithm Methods 0.000 description 1
- 238000012916 structural analysis Methods 0.000 description 1
- 239000013589 supplement Substances 0.000 description 1
- 238000003786 synthesis reaction Methods 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
- G06F16/2457—Query processing with adaptation to user needs
-
- 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
- G06F16/22—Indexing; Data structures therefor; Storage structures
- G06F16/2228—Indexing structures
- G06F16/2255—Hash tables
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9537—Spatial or temporal dependent retrieval, e.g. spatiotemporal queries
-
- G06F19/32—
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Databases & Information Systems (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Software Systems (AREA)
- Computational Linguistics (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
- Mobile Radio Communication Systems (AREA)
Abstract
A kind of intelligent medical calling querying method of dynamic Skyline inquiries under mobile cloud computing environment, belongs to intelligent medical and space big data process field；Inquiry request with the attribute selected by terminal and each attribute thresholds is sent to cloud center service system, the Spark cloud platforms that cloud center service system is made up of multiple calculate nodes, undertake the main evaluation work of dynamic Skyline inquiries stream, and the continuous medical call spatial filtering result inquired about is returned to terminal, untill all results are exported；In the query process of medical multidimensional big data, space lattice beta pruning is used and network medical treatment data has persistently been monitored and carried out Medical space big data control, so as to more efficiently obtain corresponding medical institutions' data of user's needs.
Description
Technical field
It is based on dynamic Skyline under a kind of mobile Internet and cloud computing environment the invention belongs to Spark technical fields
The intelligent medical calling querying method of inquiry, is directed to space lattice Pruning strategy and network medical treatment data monitoring.
Background technology
With the innovation of modern technologies, most intelligent equipment and system are known gradually.From intelligence
Medical call query communication system, smart home, intelligent transportation, intelligent grid, intelligent building.Intelligent medical treatment and wisdom logistics
Deng, the concept of " the wisdom earth " was proposed by IBM by 2009, intellectual technology is changing our life., while also enriching me
The world, intelligent medical calls query communication system by existing large hospital and small towns middle and small hospital, and community
Equipment, the collection of environment and user's information, monitoring, management and control, realize medical treatment in the related medical institutions in resident clinic
The Combinatorial Optimization of information environment, so as to meet hospital's thing design function demand and modern information technologies application need for user provides
Ask.For example, University Of Dalian and Dalian University Of Communications are exactly the domestic relatively early well-known colleges and universities for being engaged in Internet of Things research, by with enterprise
Product and the projects such as internet of things sensors, Internet of Things module, smart home of having released one after another are researched and developed with great concentration in cooperation for many years, cause wide
Big scholar, the concern of colleague and user.In recent years, we and Shuo Jia medical institutions of the country and enterprise's combination among the strong ones, resource are mutual
Mend, release application product and the cases such as " tele-medicine of wisdom electrocardio ", " intelligent old man's household care ", by vast medical treatment and family
The welcome of front yard user.
For example：The new model of wisdom family endowment utilizes technology of Internet of things, makes intelligent, digitlization, virtualization community,
For community-dwelling elder provides round-the-clock, comprehensive, one-stop service " theory, with 96*** call centers as integrated platform, old
Technological meanses are continually introduced in year people's love service, the service network of omnibearing stereo is made, and by actively research and development suitably
The sci-tech product of different type old man, for the elderly provides maximized facility.
For community resident provides comprehensive one-stop daily life service, make great efforts to provide a kind of brand-new house for them
Endowment new model.With the service aim of " for the convenience of the people ", the service theory of " people-oriented "；The service goal of " responding to every plea ",
" three complete ", the i.e. service measures of " round-the-clock, comprehensive, tracking control of full process supervisory service " are taken, with " love, patient, heat
The attitude of the heart ", highlights the theory of " community is entered at center, and service is at one's side ".Using multiple channels such as phone, short message, websites
It is citizen, especially the elderly, there is provided one-stop service.The special the elderly's emergency aid system, solitary the elderly made is closed
Love system, family endowment service system, senior health and fitness's archives economy, rehabilitation nursing system, e-commerce system, disbursement and sattlement
System, the exclusive front yard love system of mistake, aging education system, DFA health care system etc..It is just true existing old according to wisdom is supported
Always.
But at this stage, the hospital data of explosion type has been well beyond the limit that people are born, various features are formed
Medical data, the single hospital data before people can not meet instantly, so their broken exigences are changed.Cause
This, present traditional Data Analysis Services technology has increasingly been not suitable with the demand of current big data analysis and treatment.For
It is cost-effective, for the storage and calculating of large-scale data provide distributed treatment framework, cloud computing, big data, cloud storage,
The correlation techniques such as MapReduce, BigTable are suggested.Used as emerging Distributed sharing calculating platform, it can be with for cloud computing
Set up on extensive cheap PC, the storage and calculating of mass data are carried out using the resource in network.Due to cloud computing skill
Art is especially suitable for processing mass data, and many companies study and develop cloud computing processing platform, including searching for Google
Index is held up cloud platform, " Lan Yun " platform of IBM, the elastic calculation cloud of Amazon and Hadoop and is increased income cloud platform etc..Mass data
Treatment is a research direction for focus, and numerous scholars rely on cloud computing platform to propose at many new efficient mass datas
Adjustment method, Skyline algorithms are a kind of efficient data query therein and extracting method, can rapidly from mass data
The information of key is extracted, greatly reduces data volume, the requirement in reduction mass data processing to software and hardware improves data processing
Efficiency.Skyline algorithms are extracted and processing method as a kind of effective data, primary concern is that how from huge number
The information useful to people is found out according to concentrating, is had a wide range of applications in terms of massive spatial data analyzing and processing, such as multiple target
Decision-making, hospital's addressing, environmental monitoring, image retrieval, personalized recommendation, data mining etc..Space S kyline inquires about total concept master
It is divided into the anti-skyline inquiries of space S kyline and space, multiattribute judge original can be provided in decision process for user
Then, evaluation function can also use different measuring method (such as Euclidean distance, space length according to different applications
Deng) lifting the Quality of experience of user；Magnanimity commerce transactions data is recorded, Skyline is calculated and can be helped Market Analyst
Carry out the positioning of price and market strategy；In environmental monitoring, the mass data accumulated by analyte sensors network can
Potential natural calamity and risk are gone out with assay.Additionally, Skyline inquiries are also applied to medical image retrieval, hospital
The fields such as information.Traditional medical data treatment be towards small-scale, static, the extraction of routine information data and point
Analysis, these methods have been not applied for the with obvious space of nowadays explosive increase, higher-dimension, many complicated factors doctor
Big data is treated, based on the starting point, we have designed and Implemented the invention.
The content of the invention
Defect and deficiency according to present in above-mentioned background technology, the present invention are adopted the following technical scheme that：A kind of mobile cloud
The intelligent medical calling querying method of dynamic Skyline inquiries under computing environment, cloud center service system is performed based on distribution
Grid Pruning strategy and Network Data Control, and run the dynamic Skyline and/or anti-Skyline algorithms of Spark cloud platforms；
Inquiry request with the attribute selected by terminal and each attribute thresholds is sent to cloud center service system, cloud center service system
The Spark cloud platforms that system is made up of multiple calculate nodes, undertake the main evaluation work of dynamic Skyline inquiry stream, and to
Terminal returns to the medical call spatial filtering result of continuous inquiry, untill all results are exported.
Beneficial effect：Under the invention provides mobile cloud computing environment based on dynamic Skyline (including dynamics
Skyline and anti-Skyline two classes) inquiry intelligent medical calling inquiry system, it is existing to a large amount of hospital spaces to improve
Characteristic is extracted and multiplicity method, greatly reduces data volume, using space lattice Pruning strategy and network medical treatment number
According to the efficiency that the synthesis of monitoring method, the medical big data further to improve space characteristics are processed.
Brief description of the drawings
Fig. 1 is flow chart of data processing figure；
Fig. 2 is HDFS system assumption diagrams；
Fig. 3 is MapReduce index building file maps；
Fig. 4 is BNL algorithm patterns；
Fig. 5 is the partial enlarged drawing of Fig. 1.
Specific embodiment
Embodiment 1：Dynamic space Skyline inquiry method under mobile cloud computing environment, including cloud center service system and
Intelligent mobile FTP client FTP, cloud center service system therein provides space lattice Pruning strategy and constant network medical data
Monitor to perform dynamic Skyline and anti-Skyline algorithms, intelligent mobile client is input into each just to attribute desirability
The threshold value of attribute and send Query Result and improve the attribute of hospital.That is system execution step is as follows：
S1. dynamic Skyline provides space lattice beta pruning plan to cloud center service system with anti-Skyline algorithms in a distributed manner
Slightly, extensive medical institutions' data are screened.
S2. intelligent mobile client is positioned by GPS first on the terminal device, it is determined that the sky where inquiry user
Between and individual demand, then run medical call program, communicated by cloud server, send query statement, and with
The information that the spatial filtering result that cloud center service system feedback is returned is carried out under user participates in lasting space monitoring data is handed over
Mutually.
In one embodiment, the above method is introduced in more detail：
Step is as follows:
S1. cloud center service system provides the dynamic memory and the formulation of grid Pruning strategy and medical and health network of Medical space data
The lasting monitoring in network spatial data region.And operation is exactly the anti-skyline inquiries of dynamic space Skyline and space under Spark
The execution mechanism of algorithm, we using Spark to process distributed space between big data treatment, performed with Skyline it is many because
Plain space lattice filters beta pruning and pretreatment work.
Application program (GPRS, BDS) on S2.ipad, computer, mobile phone and mobile data house keeper's terminal device, these
Terminal interacts communication using the mobile networks such as internet or 3G/4G and server, inquiry request is sent in good time and is received
Query Result.User can carry out the query point that positioning initiates medical call in each terminal first, it is then determined that the need of client
Ask and examine doctor's behavior hobby with daily, carry out corresponding selection to the fancy grade of attribute and be input into each medical diagnosis and treat data association attributes
Threshold value, is finally sent to server end by query statement in the way of multifactor inquiry.
As the supplement of technical scheme, being inquired about with anti-Skyline based on dynamic Skyline under the movement cloud computing environment
The intelligent medical cloud center-side service system that is used of calling inquiry system taken by the network no less than a cloud data center
What business device or cloud computing cluster or multiple fictitious host computers were constituted, calculate to process extensive number using this parallelization of cloud computing
According to tackling in the substantial amounts of user for requiring to look up hospital's diagnosis and treatment data, and space lattice Pruning strategy and lasting net in a distributed manner
The monitoring of network medical data is extracted and is analyzed existing hospital data, has initiated to ask by mobile Internet, and will choose
Data is activation space lattice beta pruning task is completed to high in the clouds, beyond the clouds and lasting space querying result returns to mobile client
End, and manually confirm the result after beta pruning by user oneself and further selected.
Distributed parallel decomposition of the space big data with calculating task and high in the clouds are performed by cloud center service system
Skyline query processings, beyond the clouds it is important that entering Mobile state using space lattice beta pruning and lasting network medical treatment data monitoring
Skyline is inquired about with anti-Skyline, and the optimal one group result (knot of to be one group meet filter condition that Skyline is performed
Really, rather than one, including one group of optimal solution and suboptimal solution) and finally return that user carries out repeatedly band and calculates.
The process step of mesh space Pruning strategy is specially：(including previously described three kinds of the medical institutions of one d dimension
The medical institutions of type) data space S, S be all of hospital's related data in cloud center service system, including hospital is three-dimensional
The hobby of address hospital grade star number patients, and its evaluation to hospital, the spatial migration behavior record of patient (are such as transferred from one hospital to another
Record), spatial relation during the medical cooperation diagnosis and treatment of multiple diverse locations (such as large-scale central hospital is community
Medical treatment provide remote service) etc. data, P is the data subset in hospital's related data space S, and each data point p ∈ P are expressed
It is { x1,x2,...,xd}.Width means per one-dimensional location grid are λi.The grid of Arbitrary Digit strong point p is sat in data set
Mark can be by being calculated, i.e.,Appoint
The mark of meaning grid is expressed as Intkeyj, its coordinate representation in mesh space is (Intkeyj.x1,Intkeyj.x2,...,
Intkeyj.xd), i.e. each data point of medical institutions in the position data sets P in space can be mapped to corresponding space
In grid, one grid cell coordinates of correspondence.The process step of Network Data Control is specially：Cloud center service system is pressed first
Intrinsic spatial filtering principle is filtered to former data S, and the data under filtering are q, and then the data q under intercepting is carried out
Data under intercepting finally are carried out distributed analysis and make optimizing decision by data convert.
The dynamic Skyline and anti-Skyline monitored based on space lattice Pruning strategy and constant network medical data is looked into
The method of inquiry is：
Define 1.3 (Dynamic Skyline Query)：The hospital related data space S={ s of one d dimension1,s2,...,
sd, P is the data acquisition system i.e. P={ p in medical data space S1,p2,...,pn, a medical user query object ref
Locus coordinate according to itself and the multifactor dynamic dominance relation between Medical space point or point set are carried out to vector
The calculating of dynamic domination, is calculated the filter result collection of space S kyline and the anti-skyline in space.If medical data pair
As b dynamically arranges a, and if only if, and b is remote apart from ref unlike a on all properties, and at least dimension is nearer than a, i.e. medical treatment
User is inquired about in data by the medical treatment that mobile Internet is submitted to the original medical treatment met in medical cloud center service system
Data, then cloud center service system can analyze and extract the data of correlation and return to optimal one group of result set, i.e., final feedback
To the result set of user, and carry out next round and change band.
Embodiment 2：With technical scheme same as Example 1.Using distributed algorithm therein, by the ground of hospital
Location, patient user's custom, the collection of demand and comment data and user's information, monitoring, manage and domination set is integrated, effectively
Realize combined integratedization.In large hospital, by a series of huge database of hospital be all presented to dynamic Skyline with it is anti-
Skyline algorithms, carry out detecting hospital data related information parameters, so that needed for user faster obtains by this algorithm
Information, if middle-size and small-size cities and towns are medical, these data can be gathered as mobile terminal, be transmitted in the way of the data flow of batch
To medium-and-large-sized hospital, if community's mini clinic, the data of medium-and-large-sized hospital can be transferred in the way of real-time stream
Center, final all of data should be transferred to high in the clouds, be shared with tripartite medical institutions and certification client, long-range with mobile terminal respectively
The mode of access carries out that high in the clouds is shared to be used.
Embodiment 3：With technical scheme same as Example 2.For present hospital person, they may need
More efficiently method obtains the corresponding information for hospital for oneself needing.So, we can connect backstage by front mobile device
Cloud service center, with Data Centre in Hospital display systems as platform, emphasizes that intellectualizing system design is matched somebody with somebody with information for hospital method
Close and coordinate, it is equipment management system (BAS) that such as intelligent parameter of all kinds of medical models, hospital can remotely use, large-scale urgent
Medical alarm system (BFAS) etc., in the remittance strictly according to the facts of hospital's situation in itself, will finally integrate multilayer technique in specific sheet
In the application of patent, by user it is multifactor the need for come carry out space S kyline filter inquiry with constantly decision-making so that
The demand of user is better met, when a user is when optimal hospital's positional information is selected, can be according to many of user preferences
Weight factor, on the basis of extensive medical data, they can be just used on the intelligent movable mobile phone in hand, ipad, computer etc.
Result that inquiry quickly and consistently needs oneself simultaneously carries out further filtering and screening, so as to realize the essence of user self-help formula
Quasi- medical decision making.
Embodiment 4：With technical scheme same as Example 3, wherein intelligent medical calling query communication system possesses
The related Based Intelligent Control effect of Internet of Things, simultaneously as add mobile enquiry and high in the clouds high speed processing, so greatly reinforce in
Common control mode, compared with traditional automatic control system, based on the large-scale distributed dynamic that movement is combined with high in the clouds
The characteristics of Multiobjective Intelligent decision system of Skyline and anti-Skyline has quick space medical treatment big data structural analysis, energy
Totally from optimizing, with mobile universality, intelligent adaptive, self-organizing, self study and self-coordinating ability, it can utilize network
Medical data monitoring is automatically completed it and solves the query process that medical treatment calculates target, and the intelligent machine of its mobile terminal can be familiar with
Or, it is further to reduce the mankind because medical context. in unfamiliar environment automatically or people-machine alternatively completes anthropomorphic task
The error and the random inaccuracy of medical decision making of not enough subjective selection.Can be counted using distribution on the basis of this algorithm
Neutral net or artificial intelligence process with Internet of Things with medical big data storage are calculated, from the medical complex object of analysis, structure
Logical sum neutral net is intended in modeling, on this basis, good medical advantages is passed on to the next generation, and it is intelligent algorithm, a step to be
Hospital's large scale network intelligent information system is improved to step, the huge and business data of complexity is at full speed calculated and process, when
Right search algorithm based on Skyline, can only one of part at last, primarily focus on it is multifactor, space, on a large scale
Intelligent medical calling system in use.
Embodiment 5：Based in cloud computing distributed processing system(DPS), we take continuing for medical data between distributed space
The method of monitoring, it is characterised in that we carry out space characteristics during the Map of Spark using network medical treatment data monitoring
Medical multidimensional data extract, after extraction to do not meet user needs Medical space address data pre-processed, have
, because the excessive extraction of skyline key values can not be inconsistent yet, we are in the Map stages finally according to keyword for partial results
Key carries out the Hash matching of value values, is further monitored and extracts if matching is unsuccessful, then by shuffling
Journey is sent to Reduce ends and carries out further aggregation process.This monitoring method make use of the distribution process mechanism of Spark.So as to big
Screening time of the user to hospital data is reduced greatly, distribution process mechanism is more efficiently utilized.
Embodiment 6：A kind of intelligent medical calling querying method of dynamic Skyline inquiries under mobile cloud computing environment, cloud
Center service system is performed and is based on distributed grid Pruning strategy and Network Data Control, and runs the dynamic of Spark cloud platforms
Skyline and/or anti-Skyline algorithms；Inquiry request with the attribute selected by terminal and each attribute thresholds is sent to
Cloud center service system, the Spark cloud platforms that cloud center service system is made up of multiple calculate nodes, undertakes dynamic
The main evaluation work of Skyline inquiry streams, and the medical call spatial filtering result of continuous inquiry is returned to terminal, until
Untill all results are exported.
Cloud center service system receives inquiry request, and extraction sets a property related to user from existing hospital's addressing
The data of connection, and it is ranked up according to the attribute difference in the request, obtain preferably hospital's addressing new data relatively
Collection, then system is scanned screening, then compared with the attribute set with user, obtains the comparing knot after a series of optimization
Really, then this result returns to terminal, and returns to Network Data Control center and be monitored, and further result is screened.
The process step of distributed grid Pruning strategy is specially：Medical institutions the data space S, S of one d dimension are by cloud
The data set of all of hospital's related data composition in center service system, including hospital's three-dimensional address, hospital grade, patient
Hobby (distance that hospital leaves home, the scale of hospital, diagnosis and treatment condition etc.), patient are to the evaluation of hospital, the spatial migration row of patient
Spatial relation during for record (record of such as transferring from one hospital to another), the medical cooperation diagnosis and treatment of multiple diverse location is (such as large-scale
Central hospital provides remote service for community medicine), P is the data subset on data space S, and each data point p ∈ P are expressed
It is { x1,x2,...,xd, wherein xdRefer to d-th manifold in p data sets；
Width means per one-dimensional location grid are λiThe mesh coordinate of Arbitrary Digit strong point p is represented in data subset P
ForThe mark of arbitrary mess is represented
It is Intkeyj, its coordinate in mesh space is the position in medical institutions space, is represented as (Intkeyj.x1,
Intkeyj.x2,...,Intkeyj.xd), each data point in data subset P is mapped in corresponding space lattice,
One grid cell coordinates of correspondence.(grid Pruning strategy is coordinate of the Mapping of data points in the data set P that will be obtained to grid
In).
The process step of the Network Data Control is specially：Cloud center service system first with the three-dimensional coordinate of hospital,
Hospital grade, patient's hobby carry out distributed filtering to data space S for principle, and the data under filtering are q, then under interception
Data q carry out data convert, finally to intercept under data carry out distributed analysis, optimization is progressively made according to analysis result
Decision references, until obtaining final optimizing decision untill；(former data referred to traditional treatment side of data center here originally
Method and initial data (initial data is static, and we form flow data and are processed now)
Distributed analysis step is specially：User realizes treatment logic by Map functions and Reduce functions, and (logic can
It is input into come wizard-like with interaction man-machine interface, such as GPS is automatically positioned, artificially icon point selection, list selection etc.),
Each function with<key,value>To used as input and output, Map functions are used for processing<key,value>It is right, produce interim
's<key,value>Result set is simultaneously stored in distributed cache；Reduce functions are used for merging with identical interim key values
Value values.
Using dynamic Skyline inquiries, it is defined as dynamic Skyline algorithms：It is assumed that a hospital data space for d dimensions
S={ s1,s2,...,sd, P={ p1,p2,...,pnBe user application inquiry hospital's addressing, each user application inquiry
Hospital data point pi∈ P are the d dimension hospital data points on data space S, during F is the dynamic i.e. cloud in hospital data space S
The dynamic, | F |=k and k≤d, d are one by one that be divided into for data space S using distributed grid Pruning strategy by central server system
Total space data dimension k subspaces data dimension, it is both that k is space that user's field (dimension) interested is extracted in total space F
Dimension, such as the space of d dimensions refers to just that the whole space of hospital is S, it has a many dimensions, such as position coordinates, title,
Medical condition etc., user is in hospital data space S application inquiry data object pi, piThe user's application that is projected as on dynamic F is looked into
The hospital data of inquiry dynamically overlaps with the data after beta pruning, is hospital's number of the cloud service center screening system required for user
According to being represented as pi', pi' it is k tuples, do not exist the unwanted hospital data point p of user on and if only if dynamic Fj' Zhi Peiyong
The hospital data p that family needsi', pi' it is result that cloud center service system is obtained by dynamic Skyline.
The data that Network Data Control will meet user give application function, cause that useful information content to the greatest extent may be used by filtering
Can lack, and realize progressively simplifying for data processing amount by the continuous flow filters of multilayer for network data.
The efficiency of analysis dynamic Skyline algorithms and grid Pruning strategy a, it is assumed that data set for d dimensions, the worst
In the case of, the complexity of BNL algorithms is Ο (d α2), d dimension datas space is by mesh segmentation into e1×e2×...×edIndividual grid,D is dimension, 1~d of span；X is certain of p set
Individual data element, the 1~α of span of x；
Because generation grid is carried out in pretreatment stage, so disregarding in inquiry about the time overhead of grid computing
Between in, it is assumed that data point is generally evenly distributed in all grids, if Pruning strategy return number of grid for γ (1≤γ≤
e1×e2×...×ed), then the dynamic Skyline algorithm complexes after beta pruning are γ ＜ ＜ e1×e2×...×ed,Algorithm after beta pruning can
The comparing of dominance relation is efficiently reduced, so as to reduce the computation complexity of algorithm.
Anti- Skyline algorithms are inquired about using anti-Skyline, are the invers verification processes of dynamic Skyline algorithms, instead
Skyline inquires about the result (i.e. overall situation Skyline points) for inquiring about Skyline as the query point q of Query, first by original
The point in all of data acquisition system d in grid is ranked up as inlet flow according to the minimum range with new query point, and
Set up most rickle；When heap is not space-time, heap top is recorded and is released, because being most rickle, the point for first accessing can not possibly be rear
The point of access is arranged, and after obtaining heap top record, judges whether the record is arranged by global Skyline, if arranged,
Filter out；Not by branch timing, it is inserted into heap；By the way of stream, constantly repeatedly band calculates reverse Skyline, constantly
Checking partial result is obtained, until all of anti-Skyline result of calculations checking is completed.Intrinsically, anti-Skyline is looked into
Whether the essence of inquiry is exactly in fact, using result points as query point, reversely to assay former query point in range of results, its algorithm and
The pseudo-code of Skyline is identical, and simply object is different.
The above, the only present invention preferably specific embodiment, but protection scope of the present invention is not limited thereto,
Any one skilled in the art in the technical scope of present disclosure, technology according to the present invention scheme and its
Inventive concept is subject to equivalent or change, should all be included within the scope of the present invention.
Claims (8)
1. the intelligent medical of dynamic Skyline inquiries calls querying method under a kind of mobile cloud computing environment, it is characterised in that:Cloud
Center service system is performed and is based on distributed grid Pruning strategy and Network Data Control, and runs the dynamic of Spark cloud platforms
Skyline and/or anti-Skyline algorithms；Inquiry request with the attribute selected by terminal and each attribute thresholds is sent to
Cloud center service system, the Spark cloud platforms that cloud center service system is made up of multiple calculate nodes, undertakes dynamic
The main evaluation work of Skyline inquiry streams, and the medical call spatial filtering result of continuous inquiry is returned to terminal, until
Untill all results are exported.
2. the intelligent medical of dynamic Skyline inquiries calls querying method under mobile cloud computing environment as claimed in claim 1,
It is characterized in that：Cloud center service system receives inquiry request, is extracted from existing hospital's addressing and is set a property with user
Associated data, and it is ranked up according to the attribute difference in the request, obtain preferably hospital's addressing relatively new
Data set, then system is scanned screening, then compared with the attribute set with user, obtains the comparing after a series of optimization
As a result, then this result returns to terminal, and returns to Network Data Control center and be monitored, and further result is sieved
Choosing.
3. the intelligent medical of dynamic Skyline inquiries calls querying method under mobile cloud computing environment as claimed in claim 1,
It is characterized in that：The process step of distributed grid Pruning strategy is specially：Medical institutions the data space S, S of one d dimension be
The data set being made up of all of hospital's related data in cloud center service system, including hospital's three-dimensional address, hospital grade, disease
The hobby of people, patient to the evaluation of hospital, the spatial migration behavior record of patient, multiple diverse locations medical cooperation diagnosis and treatment
When spatial relation, P is the data subset on data space S, and each data point p ∈ P are represented as { x1,x2,...,
xd, wherein xdRefer to d-th manifold in p data sets；
Width means per one-dimensional location grid are λiThe mesh coordinate of Arbitrary Digit strong point p is expressed as in data subset PThe mark of arbitrary mess is expressed as
Intkeyj, its coordinate in mesh space is the position in medical institutions space, is represented as (Intkeyj.x1,
Intkeyj.x2,...,Intkeyj.xd), each data point in data subset P is mapped in corresponding space lattice,
One grid cell coordinates of correspondence.
4. the intelligent medical of dynamic Skyline inquiries calls querying method under mobile cloud computing environment as claimed in claim 3,
It is characterized in that：The process step of the Network Data Control is specially：Cloud center service system is sat with the three-dimensional of hospital first
Mark, hospital grade, patient's hobby carry out distributed filtering to data space S for principle, and the data under filtering are q, then to blocking
The data q for cutting down carries out data convert, finally carries out distributed analysis to the data under intercepting, and is progressively made according to analysis result
Optimal Decision-making refer to, until obtaining final optimizing decision untill；
Distributed analysis step is specially：User realizes treatment logic by Map functions and Reduce functions, each function with
<key,value>To used as input and output, Map functions are used for processing<key,value>It is right, produce interim<key,value
>Result set is simultaneously stored in distributed cache；Reduce functions are used for merging the value values with identical interim key values.
5. the intelligent medical calling inquiry of the dynamic Skyline inquiries under mobile cloud computing environment according to claim 4
Method, it is characterised in that：Using dynamic Skyline inquiries, it is defined as dynamic Skyline algorithms：It is assumed that a hospital for d dimensions
Data space S={ s1,s2,...,sd, P={ p1,p2,...,pnBe user application inquiry hospital's addressing, each user Shen
The hospital data point p that please be inquire abouti∈ P are the d dimension hospital data points on data space S, and F is the utilization point of cloud center service system
The dynamic one by one that be divided into for data space S by cloth grid Pruning strategy, | F |=k and k≤d, d are total space data dimension k
Spatial data dimension, it is k that user's field interested is extracted in total space F, and user inquires about data in the application of hospital data space S
Object pi, piThe hospital data for being projected as user's application inquiry on dynamic F dynamically overlaps with the data after beta pruning, is user
The hospital data of required cloud service center screening system, is represented as p 'i, p 'iIt is k tuples, on and if only if dynamic F not
There is the unwanted hospital data point p ' of userjThe hospital data p ' that domination user needsi, p 'iIt is that cloud center service system passes through
The result that dynamic Skyline is obtained.
6. under mobile Internet as claimed in claim 5 and cloud computing environment dynamic Skyline inquiries intelligent medical calling
Querying method, it is characterised in that：The data that Network Data Control will meet user give application function, are caused by filtering useful
Information content it is few as far as possible, and realize data processing amount by the continuous flow filters of multilayer for network data
Progressively simplify.
7. the intelligent medical of dynamic Skyline inquiries calls querying method under mobile cloud computing environment as claimed in claim 5,
It is characterized in that：The efficiency of analysis dynamic Skyline algorithms and grid Pruning strategy a, it is assumed that data set for d dimensions, the worst
In the case of, the complexity of BNL algorithms is Ο (d α2), d dimension datas space is by mesh segmentation into e1×e2×...×edIndividual net
Lattice, D is dimension, 1~d of span；X is p set
Certain data element, the 1~α of span of x；
Time overhead about grid computing is disregarded in query time, it is assumed that data point is generally evenly distributed in all grids
, if the number of grid that Pruning strategy is returned is γ (1≤γ≤e1×e2×...×ed), then the dynamic after beta pruning
Skyline algorithm complexes areγ ＜ ＜ e1×
e2×...×ed,
8. the intelligent medical of dynamic Skyline inquiries calls querying method under mobile cloud computing environment as claimed in claim 7,
It is characterized in that：Anti- Skyline algorithms are inquired about using anti-Skyline, are the invers verification processes of dynamic Skyline algorithms, instead
Skyline inquiry using Skyline inquire about result as Query query point q, first by all of number in former grid
According to the point in set d as inlet flow, it is ranked up according to the minimum range with new query point, and sets up most rickle；Work as heap
It is not space-time, heap top is recorded and is released, after obtaining heap top record, judges whether the record is arranged by global Skyline, if
Arranged, then filtered out；Not by branch timing, it is inserted into heap；By the way of stream, constantly repeatedly band calculates reverse
Skyline, is continuously available checking partial result, until all of anti-Skyline result of calculations checking is completed.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611150537.1A CN106777092B (en) | 2016-12-14 | 2016-12-14 | Intelligent medical call query method for dynamic Skyline query in mobile cloud computing environment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611150537.1A CN106777092B (en) | 2016-12-14 | 2016-12-14 | Intelligent medical call query method for dynamic Skyline query in mobile cloud computing environment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106777092A true CN106777092A (en) | 2017-05-31 |
| CN106777092B CN106777092B (en) | 2020-04-03 |
Family
ID=58876928
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201611150537.1A Active CN106777092B (en) | 2016-12-14 | 2016-12-14 | Intelligent medical call query method for dynamic Skyline query in mobile cloud computing environment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106777092B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107480435A (en) * | 2017-07-31 | 2017-12-15 | 广东精点数据科技股份有限公司 | A kind of automatic searching machine learning system and method applied to clinical data |
| CN111383748A (en) * | 2020-03-09 | 2020-07-07 | 武汉比邻软件有限公司 | Medical integrated platform system based on elastic calculation and 5G technology |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103150404A (en) * | 2013-03-28 | 2013-06-12 | 北京大学 | Hybrid relational-extensible markup language (XML) data keyword searching method |
| CN105760465A (en) * | 2016-02-05 | 2016-07-13 | 大连大学 | Medical calling method based on large-scale reverse nearest neighbor query in mobile environment |
| CN105760470A (en) * | 2016-02-05 | 2016-07-13 | 大连大学 | Medical calling system based on spatial reverse nearest neighbor query in cloud computing environment |
-
2016
- 2016-12-14 CN CN201611150537.1A patent/CN106777092B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103150404A (en) * | 2013-03-28 | 2013-06-12 | 北京大学 | Hybrid relational-extensible markup language (XML) data keyword searching method |
| CN105760465A (en) * | 2016-02-05 | 2016-07-13 | 大连大学 | Medical calling method based on large-scale reverse nearest neighbor query in mobile environment |
| CN105760470A (en) * | 2016-02-05 | 2016-07-13 | 大连大学 | Medical calling system based on spatial reverse nearest neighbor query in cloud computing environment |
Non-Patent Citations (3)
| Title |
|---|
| YUANYUAN LI ET.AL.: ""Skyline Query Based on User Preference with MapReduce"", 《IEEE》 * |
| 单观敏: ""基于MapReduce的移动对象的Skyline查询"", 《万方》 * |
| 孙晓曦: ""协同过滤推荐在医疗领域的应用研究"", 《万方》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107480435A (en) * | 2017-07-31 | 2017-12-15 | 广东精点数据科技股份有限公司 | A kind of automatic searching machine learning system and method applied to clinical data |
| CN107480435B (en) * | 2017-07-31 | 2020-12-08 | 广东精点数据科技股份有限公司 | Automatic search machine learning system and method applied to clinical data |
| CN111383748A (en) * | 2020-03-09 | 2020-07-07 | 武汉比邻软件有限公司 | Medical integrated platform system based on elastic calculation and 5G technology |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106777092B (en) | 2020-04-03 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107046557A (en) | The intelligent medical calling inquiry system that dynamic Skyline is inquired about under mobile cloud computing environment | |
| Zhang et al. | An incremental CFS algorithm for clustering large data in industrial internet of things | |
| Zheng et al. | Construction of the ontology-based agricultural knowledge management system | |
| WO2021233342A1 (en) | Neural network construction method and system | |
| CN107193967A (en) | A kind of multi-source heterogeneous industry field big data handles full link solution | |
| CN104820708B (en) | A kind of big data clustering method and device based on cloud computing platform | |
| CN104809244B (en) | Data digging method and device under a kind of big data environment | |
| Li et al. | Research on QoS service composition based on coevolutionary genetic algorithm | |
| Xia et al. | SW-BiLSTM: a Spark-based weighted BiLSTM model for traffic flow forecasting | |
| CN103412903A (en) | Method and system for interested object prediction based real-time search of Internet of Things | |
| Gouineau et al. | PatchWork, a scalable density-grid clustering algorithm | |
| Saravanakumar et al. | Clustering big data for novel health care system | |
| CN106503271A (en) | The intelligent shop site selection system of subspace Skyline inquiry under mobile Internet and cloud computing environment | |
| CN106777092A (en) | The intelligent medical calling querying method of dynamic Skyline inquiries under mobile cloud computing environment | |
| Karim et al. | Spatiotemporal Aspects of Big Data. | |
| Kekevi et al. | Real-time big data processing and analytics: Concepts, technologies, and domains | |
| Yang | Research on integration method of AI teaching resources based on learning behaviour data analysis | |
| Zhu et al. | DSCPL: A deep cloud manufacturing service clustering method using pseudo-labels | |
| Liang et al. | The graph embedded topic model | |
| Wang et al. | A novel visual analytics approach for clustering large-scale social data | |
| WO2023122854A1 (en) | Data processing method and apparatus | |
| Zhang | The design of regional medical cloud computing information platform based on deep learning | |
| Lu et al. | Framework of industrial networking sensing system based on edge computing and artificial intelligence | |
| Ju et al. | Innovation trend of edge computing technology based on patent perspective | |
| CN106599188A (en) | Smart store location method employing sub-space Skyline query under mobile internet and cloud computing environment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| OL01 | Intention to license declared | ||
| OL01 | Intention to license declared |