CN103678617A - Processing system and method for sensing context by moving based on stream calculation - Google Patents
Processing system and method for sensing context by moving based on stream calculation Download PDFInfo
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Abstract
The invention discloses a processing system and method for sensing a context by moving based on stream calculation. A context filtering module based on a rule is provided by the processing system. The filtering module will match context information according to the Rete rule matching algorithm, thus the context information is rapidly and effectively filtered. The processing system provides a context converging and optimizing module which is characterized in that a document type internal memory database is called based on a document object model and a converging computation unit in a stream calculation platform is organically combined with the document type internal memory database. The number of times of reading and writing a disc in operation can be effectively reduced and the converging efficiency of the context information is improved. In addition, the processing system provides an incident capturing module based on scheduling. The incident capturing module organically combines cluster sensing and individual sensing, individual behavior can be obtained in real time, thus the situation of a certain individual in a cluster can be effectively known.
Description
Technical field
The present invention relates to mobile cloud computing technology field, be specifically related to a kind of mobile context-aware disposal system and method for calculating based on flowmeter.
Background technology
Mobile context-aware refers to the heat transfer agent that mobile terminal device gets from surrounding environment by its sensor.Flowmeter is calculated, and Data Stream Processing, is in the middle of huge and various continuous data stream, to extract a kind of computation schema of effective knowledge and information.
Due to what the present invention relates to, be the processing of Large-scale Mobile context-aware, if do not done specified otherwise, in the present invention, mobile aware application is refered in particular to mobile colony aware application.In mobile aware application, sensed activation is a continuous events, sensor image data is a lasting process, the data that mobile device is uploaded are so continuous, transient, uncertain a kind of stream datas fast, to the processing of these class data, be exactly that a kind of stream is processed, this application is that the stream of a quasi-representative is processed application.When the Large-scale Mobile context-aware information flow coming from sensor collection is processed in real time, stream is processed the characteristic that application must possess low delay, especially importing data rate into unsettled time, in order to solve this class problem, calculating must be to complete beyond the clouds, and the feature of cloud computing to be data be that extensive and computing is distributed, therefore the processing that the mode that adopts beyond the clouds distributed stream to calculate is carried out large-scale data stream is a core work that improves application real-time, and academia and industry member have also all been carried out a large amount of correlative studys to this.
For the batch processing of large-scale data, at present maximum is based on MapReduce model construction distributed processing system(DPS), or makes improvements, and makes treatment effeciency become higher.MapReduce framework is applicable to calculating and analytical work for processing big data quantity, but in practical application, many tasks can not be expressed as single MapReduce Job, such as K mean cluster, the algorithm that SVM etc. are iterative, only carries out a Job and can not complete calculating, and MapReduce framework, comprise that Hadoop initial design become to solve batch processing task, while realizing this iterative algorithm with it, can only, after each iteration, by reducer, interim result be write to the file of HDFS; In next iteration, by mapper, read in.Need like this to carry out a large amount of IO operations, expense is very large, programmes also more cumbersome.If MapReduce framework can support to be similar to the mode of pipeline, the output of a upper Job can be directly inputted in the mapper of next Job, not only can save the expense of a large amount of IO, for the realization of numerous iterative machine learning algorithms, will be greatest Gospel.Tyson doctor Condie of UC Berkly, in his one piece of paper MapReduce Online, has proposed the idea of Pipelining Hadoop, and has realized a prototype HOP (Hadoop Online Prototype project).Yet, the scheme of carrying out Stream Processing based on MapReduce as this class of HOP remains Shortcomings part, they become the Interval data of input the fragment of fixed size, then by MapReduce, process, because the delay of Data processing is directly proportional to the length of data slot itself, the expense of initialization process task, so little can the reduction of segmentation postpones to increase additional overhead, and the degree of coupling between segmentation is larger, segmentation more can increase delay significantly.In addition, in order to support stream to process, MapReduce is transformed into the pattern of Pipeline, and not Reduce directly exports, simultaneously in order to raise the efficiency, the intermediate result of processing must be kept in internal memory, these characteristics all make the complexity of framework increase, and it is difficult that the maintenance and expansion of system become.Therefore,, in order better large-scale data stream to be processed, must build a distributed stream computing platform that retractility is stronger.
The processing of large-scale data stream is the emphasis of Distributed Calculation area research in recent years, no matter is business circles or academia, and several representative data-flow computation systems that have all been born, for solving actual production problem and carrying out academic research.Storm meets Twitter actual demand by simple realization, but Restoration Mechanism based on resetting is difficult to guarantee high availability and calculates consistance, and based on functional language Clojure, exploitation also makes its be difficult to extensively increase income use in addition; Borealis is extensive and deep to the each side research of data-flow computation, has good reference value, but owing to being academia's product, not yet in business circles, uses on a large scale; And although the critical functions such as S4 dynamic load leveling and online service migration are all not yet realized, version updating is slower, but it uses Java, exploitation provides simple programming interface, status of support persistence failover capability is high, add its use decentralization and symmetrical architecture, can meet easy development, Dynamical Deployment, symmetrical extendible demand completely.
In sum, in order to tackle the real-time processing of extensive contextual information in mobile colony aware application, the present invention adopts towards extensive contextual stream computation schema, built a kind of context transaction module based on distributed stream computing platform S4, based on this modelling, realized a mobile context-aware disposal system, this system is mainly for the context data in Large-scale Mobile colony aware application, beyond the clouds based on S4 how to its filtration, how to obtain the contextual information that current application or required by task are wanted, how the context data after filtering is carried out to efficient polymerization according to demand, how in whole context polymerization process, polymerization intermediate result to be caught in real time, thereby meet different application demand, for user provides better services.
Summary of the invention
In order to solve the deficiencies in the prior art, the invention provides a kind of mobile context-aware disposal system and method for calculating based on flowmeter, the contextual information stream scale that continues collection for mobile device is large, the problem that kind is many, the described mobile context-aware disposal system of calculating based on flowmeter provides a rule-based context filtering module, this filtering module will be realized contextual information will be mated based on Rete rule matching algorithm, thereby can be effectively to carrying out fast filtering with contextual information, on the other hand, for how efficiently in real time contextual information stream to be carried out to polymerization, the described mobile context-aware disposal system of calculating based on flowmeter provides a context optimizing polymerization module, this optimizing polymerization module is mainly to adopt and based on DOM Document Object Model, document-type memory database is called, polymerization arithmetic element and document-type memory database in stream computing platform are organically combined, the read-write operation number of times of disk in the time of can effectively reducing operation, thereby improve the polymerization efficiency of contextual information, in addition, for how obtaining in real time certain individual behavioral problem in colony, the described mobile context-aware disposal system of calculating based on flowmeter provides an event capturing module based on scheduling, this event capturing module organically combines the individual perception of colony's perception, can obtain in real time individual behavior, thereby can effectively understand certain individual situation in colony.
The invention provides a kind of mobile context-aware disposal system of calculating based on flowmeter, it comprises context transceiver module, rule-based context filtering module, context optimizing polymerization module, event capturing module and memory module based on scheduling, wherein,
Described context transceiver module is the contextual information from mobile device in order to reception, and contextual information is converted into the data stream form that S4 platform is supported;
Described rule-based context filtering module is in order to mate contextual information and to filter, it comprises filtration operation unit and regulation engine, wherein, described regulation engine reads filtering rule from rule base, described filtration treatment unit mates contextual information based on Rete rule matching algorithm with filtering rule, to transfer contextual information to flow of event form, contextual information is filtered;
Described context optimizing polymerization module is carried out polymerization processing in order to the contextual information to after filtering, it comprises polymerization arithmetic element, database access component and serviced component, wherein, described serviced component provides the method that builds polymerizing component, and the polymerizing component having built is managed, described database access component provides the access method based on OO document-type memory database, and the data that described arithmetic element is called in polymerizing component and document-type memory database are carried out polymerization processing to the contextual information after filtering;
The described event capturing module based on scheduling is in order to catch the information in described context optimizing polymerization module.
Further, described database access component adopts the object/Document mapping mechanism based on annotation, and it provides a simple and easy query interface;
The invention provides a kind of mobile context-aware disposal route of calculating based on flowmeter, it comprises the following steps:
Step S01: context transceiver module receives the contextual information from mobile device, and contextual information is converted into the data stream form that S4 platform is supported;
Step S02: the contextual information of data stream form and the filtering rule that reads via regulation engine are inputed to filtration treatment unit, described filtration treatment unit, by Rete rule matching algorithm, mates the contextual information of data stream form to filter the contextual information that forms flow of event form;
Step S03: the contextual information of flow of event form is distributed to each polymerization arithmetic element, polymerization arithmetic element is called serviced component, and by corresponding data in the direct access document type of database access component memory database, the contextual information of flow of event form is carried out to polymerization processing;
Step S04: polymerization result is stored to memory module for client-access;
Step S05: adopt the event capturing module based on scheduling to obtain the information in polymerization process;
Particularly, step S05 specifically comprises the following steps:
(1), scheduler starts request monitor after receiving request, meanwhile send request to the queue of request monitor initialization requests;
(2), actuator obtains event from request queue;
(3), actuator thread dispatching grabber sends event capturing request toward S4;
(4), grabber captures S4 by the event information returning, and returns to actuator simultaneously;
(5), actuator returns results and waits for next step processing.
Based on disclosing of technique scheme, the invention provides a kind of mobile context-aware disposal system and method for calculating based on flowmeter and there is following beneficial effect:
The invention provides a kind of mobile context-aware disposal system and method for calculating based on flowmeter, the contextual information stream scale that continues collection for mobile device is large, the problem that kind is many, the described mobile context-aware disposal system of calculating based on flowmeter provides a rule-based context filtering module, this filtering module will be realized contextual information will be mated based on Rete rule matching algorithm, thereby can be effectively to carrying out fast filtering with contextual information, on the other hand, for how efficiently in real time contextual information stream to be carried out to polymerization, the described mobile context-aware disposal system of calculating based on flowmeter provides a context optimizing polymerization module, this optimizing polymerization module is mainly to adopt and based on DOM Document Object Model, document-type memory database is called, polymerization arithmetic element and document-type memory database in stream computing platform are organically combined, the read-write operation number of times of disk in the time of can effectively reducing operation, thereby improve the polymerization efficiency of contextual information, in addition, for how obtaining in real time certain individual behavioral problem in colony, the described mobile context-aware disposal system of calculating based on flowmeter provides an event capturing module based on scheduling, this event capturing module organically combines the individual perception of colony's perception, can obtain in real time individual behavior, thereby can effectively understand certain individual situation in colony.
Accompanying drawing explanation
Fig. 1 is the framework schematic diagram of a kind of mobile context-aware disposal system of calculating based on flowmeter of proposing of the present invention;
Fig. 2 is the framework schematic diagram of the context transceiver module of a kind of mobile context-aware disposal system of calculating based on flowmeter of proposing of the present invention;
Fig. 3 is the schematic diagram of the rule-based context filtering module matching process of a kind of mobile context-aware disposal system of calculating based on flowmeter of proposing of the present invention;
Fig. 4 is that the database access component of the context optimizing polymerization module of a kind of mobile context-aware disposal system of calculating based on flowmeter of proposing of the present invention is XMongoAccess object map core classes figure;
Fig. 5 is the process flow diagram of a kind of mobile context-aware disposal route of calculating based on flowmeter of proposing of the present invention;
Fig. 6 is the process flow diagram of step S06 in a kind of mobile context-aware disposal route of calculating based on flowmeter of proposing of the present invention.
Embodiment
As shown in Figure 1, what the mobile device of carry sensors (smart mobile phone) was inputted is undressed raw sensory data (original context information), the mobile context-aware disposal system of calculating based on flowmeter is responsible for processing the original context information of self terminal, mainly draw together the mapping comprising the conversion of the text formatting of original context information and data structure, the seizure to the intermediate result producing in the filtration of contextual information, polymerization, interpretation of result processing and polymerization process.
Refer to Fig. 1, the described mobile context-aware disposal system of calculating based on flowmeter comprises context transceiver module 100, rule-based context filtering module 200, context optimizing polymerization module 300, event capturing module 400 and memory module 500 based on scheduling.
Refer to Fig. 2, described context transceiver module 100 is in order to receive contextual information and contextual information is converted into the data stream form that S4 platform is supported, described context transceiver module comprises text code module, format converting module and structure mapping module, first, original context information arranges in encoded module, text conversion module conversion becomes JSON formatted data, then according to the structure usage data structure mapping module of system desired data stream, shine upon accordingly, stream processing system based on S4 is the data-mapping of JSON structure to be become to the flow of event of POJOs form.
Refer to Fig. 1, described rule-based context filtering module 200 is in order to mate contextual information and to filter, it comprises filtration operation unit and regulation engine, wherein, described regulation engine reads filtering rule from rule base (being equipped with corresponding rule base for specific application), described filtration treatment unit mates contextual information based on Rete rule matching algorithm with filtering rule, to transfer contextual information to flow of event form, contextual information is filtered, concrete filter process, as shown in Figure 3, PE_Split is exactly first Logical processing unit that S4 flows the definition while processing, according to different services, application or the different rule of task definition, the contextual information that terminal is uploaded is by mating with rule condition, in first stream processing unit, just they are filtered, in figure, Context_0 representative is some events that comprises original context information, it is after flowing into PE_Split, first mate with " rule _ 1 ", if eligible, complete to process and send corresponding event Event_1, if not mating, " rule _ 1 " do not read " rule _ 2 ", mate and meet the event of sending, otherwise continue down, wherein " rule _ 1 " is combined by certain logical relation with regard to being equivalent to a lot of conditions, and original context information is through after a series of condition judgment filtration, scale can greatly reduce, and output result be also the needed input of this polymerization, in context filtering rule matching algorithm as follows:
Input: regular collection Rules={Rule}, set of context Contexts={Context}
Output: filter result set Results={Result}
Step 1: // according to a tree construction of regular collection structure of definition, its leaf node is each rule, non-leaf node is regular kind
RuleTree?ruleTree=constructRuleTree(Rules)
Step 2: // initialization filter result set
Results?results={}
Step 3: // each data in data acquisition are mated with rule tree
For?all?context?in?Contexts
Result?result=ruleTree.match(context);
results.add(result);
Step 4: // return to filter result set,
Return?results
Described context optimizing polymerization module 300 is carried out polymerization processing in order to the contextual information to after filtering, it comprises polymerization arithmetic element, database access component and serviced component, wherein, described serviced component provides the method that builds polymerizing component, and the polymerizing component having built is managed, described database access component provides the access method based on OO document-type memory database, and the data that described arithmetic element is called in polymerizing component and document-type memory database are carried out polymerization processing to the contextual information to after filtering.
It is the core logic calculating process in whole context processing procedure that polymerization is processed, its development efficiency and execution efficiency directly affect convenience and the real-time that context stream processing system is used, angle from computing, a lot of context polymerization computings relate to historical data, and traditional relevant database is for the processing of present ultra-large concurrent data, particularly as the concurrent processing of context data on a large scale in mobile colony perception events environment, it is unable to do what one wishes, in efficiency, can not meet our demand, therefore introduce satisfactory document-type memory database MongoDB, the present invention has built database access component, for the bottom data of described polymerization arithmetic element, manage and provide support, defining this database access component is XMongoAccess, the core of XmongoAccess realizes thought: directly do not use the Java of MongoDB to drive, but adopt higher level object-oriented thought to carry out operating database.Particularly, its adopts the object/Document mapping mechanism based on annotation, and it provides a large amount of frequently-used data access object methods, and it also provides a simple and easy query interface in addition.
Fig. 4 has described XMongoDBAccess object map core classes figure, in XMongoDBAccess, in order to realize the mapping of annotation and document, has defined tool-class MapperUtil, and its key interface is described below shown in table:
/**
* database object DBObject converts entity object Entity to
*/
public?static<T>T?fromDBObject(Class<T>clazz,DBObject?dbo);
/**
* entity object Entity converts database object DBObject to
*/
public?static?DBObject?toDBObject(Object?obj);
/**
* obtain the name attribute that@Entity has annotated.If name attribute is not set up, return to so class title.
*/
public?static?String?getEntityName(Class<?>clazz);
The realization of MapperUtil, key is again the realization of the automatic conversion between object (Object, i.e. entity Entity), document (Document), this is also the most crucial function of XMongoDBAccess.8 annotations (i.e. eight classes in annotations bag ,@Entity ,@Id ,@Property ,@Embed ,@EmbedList ,@Ref ,@RefList ,@Ignore) and 1 interface (XMongoEntity) are provided in assembly.In transfer process, most crucial is again the realization of encoding and decoding, i.e. Encoder and Decoder, and it is the same that they realize principle, just a positive and negative process.This simple and easy query interface is to make the class of XMongoDBQuery realize by one, wherein, comprises redefining as 23 kinds of operations of the logics such as and, or, not, lessthen and relational calculus.
Described memory module 400 in order to store aggregated result for client-access.
The described event capturing module 500 based on scheduling is in order to catch the information in described context optimizing polymerization module.
Refer to Fig. 5, the invention provides a kind of mobile context-aware disposal route of calculating based on flowmeter, it comprises the following steps:
Step S01: context transceiver module receives the contextual information from mobile device, and contextual information is converted into the data stream form that S4 platform is supported, described context transceiver module comprises text code module, format converting module and structure mapping module, first, original context information arranges in encoded module, text conversion module conversion becomes JSON formatted data, then according to the structure usage data structure mapping module of system desired data stream, shine upon accordingly, stream processing system based on S4 is the data-mapping of JSON structure to be become to the flow of event of POJOs form,
Step S02: the contextual information of data stream form and the filtering rule that reads via regulation engine are inputed to filtration treatment unit, described filtration treatment unit is by Rete rule matching algorithm, the contextual information of data stream form is mated and filters the contextual information that forms flow of event form, particularly, refer to Fig. 3, the contextual information of data stream form mates with " rule _ 1 ", if eligible, complete to process and sends corresponding event Event_1; If not mating, " rule _ 1 " do not read " rule _ 2 ", mate and meet the event of sending, otherwise continue down, wherein " rule _ 1 " is combined by certain logical relation with regard to being equivalent to a lot of conditions, and original context information is through after a series of condition judgment filtration, scale can greatly reduce, and the result of output is also the needed input of this polymerization;
Step S03: the contextual information of flow of event form is distributed to each polymerization arithmetic element, polymerization arithmetic element is called serviced component, and by corresponding data in the direct access document type of database access component memory database, the contextual information of flow of event form is carried out to polymerization processing, polymerization arithmetic element needs the support of serviced component and data when computing, wherein, described serviced component directly can provide via S4 platform, and for database, the present invention introduces satisfactory document-type memory database MongoDB, and built database access component, for the bottom data of described polymerization arithmetic element, manage and provide support, defining this database access component is XMongoAccess, the core of XmongoAccess realizes thought: directly do not use the Java of MongoDB to drive, but adopt higher level object-oriented thought to carry out operating database.Particularly, its adopts the object/Document mapping mechanism based on annotation, and it provides a large amount of frequently-used data access object methods, and it also provides a simple and easy query interface in addition, therefore when access document type memory database MongoDB more fast directly;
Step S04: polymerization result is stored to memory module for client-access;
Step S05: adopt the event capturing module based on scheduling to obtain the information in polymerization process, particularly, refer to Fig. 6, step S05 specifically comprises the following steps:
(1), Scheduler starts RequestMaster after receiving request, meanwhile sends request initialization RequestQueue to RequestMaster;
(2), Workers obtains event from RequestQueue;
(3), Workers thread dispatching Capturer sends event capturing request toward S4;
(4), Capturer captures S4 by the event information returning, and returns to Workers simultaneously;
(5), Workers returns results and waits for next step processing;
Wherein, Scheduler is scheduler, and RequestMaster is request monitor, and RequestQueue is request queue, and Workers is actuator, and Capturer is grabber.
Should be noted that and understand; in the situation that not departing from the desired the spirit and scope of the present invention of accompanying claim; can make various modifications and improvement to the present invention of foregoing detailed description; therefore, the scope of claimed technical scheme is not subject to the restriction of given any specific exemplary teachings.
Claims (4)
1. a mobile context-aware disposal system of calculating based on flowmeter, is characterized in that, it comprises context transceiver module, rule-based context filtering module, context optimizing polymerization module, event capturing module and memory module based on scheduling, wherein,
Described context transceiver module is the contextual information from mobile device in order to reception, and contextual information is converted into the data stream form that S4 platform is supported;
Described rule-based context filtering module is in order to mate contextual information and to filter, it comprises filtration operation unit and regulation engine, wherein, described regulation engine reads filtering rule from rule base, described filtration treatment unit mates contextual information based on Rete rule matching algorithm with filtering rule, to transfer contextual information to flow of event form, contextual information is filtered;
Described context optimizing polymerization module is carried out polymerization processing in order to the contextual information to after filtering, it comprises polymerization arithmetic element, database access component and serviced component, wherein, described serviced component provides the method that builds polymerizing component, and the polymerizing component having built is managed, described database access component provides the access method based on OO document-type memory database, and the data that described arithmetic element is called in polymerizing component and document-type memory database are carried out polymerization processing to the contextual information after filtering;
Described memory module in order to store aggregated result for client-access;
The described event capturing module based on scheduling is in order to catch the information in described context optimizing polymerization module.
2. the mobile context-aware disposal system of calculating based on flowmeter according to claim 1, is characterized in that, described database access component adopts the object/Document mapping mechanism based on annotation, and it provides simple and easy query interface.
3. a mobile context-aware disposal route of calculating based on flowmeter, is characterized in that, it comprises the following steps:
A mobile context-aware disposal route of calculating based on flowmeter, it comprises the following steps:
Step S01: context transceiver module receives the contextual information from mobile device, and contextual information is converted into the data stream form that S4 platform is supported;
Step S02: the contextual information of data stream form and the filtering rule that reads via regulation engine are inputed to filtration treatment unit, described filtration treatment unit, by Rete rule matching algorithm, mates the contextual information of data stream form to filter the contextual information that forms flow of event form;
Step S03: the contextual information of flow of event form is distributed to each polymerization arithmetic element, polymerization arithmetic element is called serviced component, and by corresponding data in the direct access document type of database access component memory database, the contextual information of flow of event form is carried out to polymerization processing;
Step S04: polymerization result is stored to memory module for client-access;
Step S05: adopt the event capturing module based on scheduling to obtain the information in polymerization process.
4. the mobile context-aware disposal route of calculating based on flowmeter according to claim 3, is characterized in that, step S05 specifically comprises the following steps:
(1), scheduler starts request monitor after receiving request, meanwhile send request to the queue of request monitor initialization requests;
(2), actuator obtains event from request queue;
(3), actuator thread dispatching grabber sends event capturing request toward S4;
(4), grabber captures S4 by the event information returning, and returns to actuator simultaneously;
(5), actuator returns results and waits for next step processing.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109271261A (en) * | 2018-08-29 | 2019-01-25 | 中国建设银行股份有限公司 | Method, equipment and storage medium is uniformly processed in a kind of event |
| CN110175676A (en) * | 2019-04-28 | 2019-08-27 | 中国科学院软件研究所 | A kind of high-performance rule matching method towards memory constrained environment |
| CN110472230A (en) * | 2019-07-11 | 2019-11-19 | 平安科技（深圳）有限公司 | The recognition methods of Chinese text and device |
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2013
- 2013-12-17 CN CN201310695630.0A patent/CN103678617A/en active Pending
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109271261A (en) * | 2018-08-29 | 2019-01-25 | 中国建设银行股份有限公司 | Method, equipment and storage medium is uniformly processed in a kind of event |
| CN109271261B (en) * | 2018-08-29 | 2022-03-11 | 中国建设银行股份有限公司 | Event unified processing method, device and storage medium |
| CN110175676A (en) * | 2019-04-28 | 2019-08-27 | 中国科学院软件研究所 | A kind of high-performance rule matching method towards memory constrained environment |
| CN110472230A (en) * | 2019-07-11 | 2019-11-19 | 平安科技（深圳）有限公司 | The recognition methods of Chinese text and device |
| CN110472230B (en) * | 2019-07-11 | 2023-09-05 | 平安科技（深圳）有限公司 | Chinese text recognition method and device |
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