CN103177094A - Cleaning method of data of internet of things - Google Patents
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Abstract
The invention discloses a cleaning method of data of the internet of things, and relates to the field of data process of the internet of things. The method includes the following steps: (1) operators obtains original data of the internet of things through web service, (2) the operators reconstruct the original data and store the original data in a memory, (3) the operators reassemble the reconstructed original data into a UDB tree, and (4) a preset rule is read and data cleaning is carried out. The data cleaning is carried out according to the preset rule so as to reduce input/output (I/O) operations to the largest extent, and therefore traditional performance bottleneck problems can be solved fundamentally.
Description
Technical field
The present invention relates to the Internet of Things data processing field, relate in particular to a kind of Internet of Things Data Cleaning Method.
Background technology
Development along with the various devices such as sensor, radio-frequency (RF) identification (RFID), GPS, infrared inductor, laser scanner, gas sensor and technology, all things in reality may anyly need to monitor by Real-time Collection, connect, in interactive object or process, gather the information of the various needs such as its sound, light, heat, electricity, mechanics, chemistry, biology, position, be combined a huge network that forms with the internet, so a kind of giant grid is referred to as Internet of Things.
Above characteristic due to Internet of Things, in the process of image data, produce the mass data that a large amount of various forms are different, meaning is different, and how from these data the needs according to the user data are cleaned, become the emphasis of Internet of Things information processing to reach purpose that data effectively utilize.
Traditional data cleansing mode, most based on the data in magnetic disk file, be accompanied by a large amount of I/O operations to disk in cleaning process, although query optimization can improve efficient to a certain extent, but when in the face of mass data, the I/O operation still directly becomes the bottleneck that affects performance frequently.The present invention is directed to above problem, invented a kind of new Data Cleaning Method, the Internet of Things data are loaded in server memory, then Uniform data format and data structure (Uniform-Delicate B-Tree), utilize data structure for the cleaning algorithm, on multiple servers, according to predefined rule, data are cleaned, farthest reduce the I/O operation, thereby fundamentally solving traditional performance bottleneck problem.
Summary of the invention
The present invention is directed to above problem, invented a kind of new Data Cleaning Method, the Internet of Things data are loaded in server memory, then Uniform data format and data structure (Uniform-Delicate B-Tree), utilize data structure for the cleaning algorithm, on multiple servers, according to predefined rule, data are cleaned, farthest reduce the I/O operation, thereby fundamentally solving traditional performance bottleneck problem.
For achieving the above object, the present invention takes following technical scheme: a kind of Internet of Things Data Cleaning Method comprises the following steps: the first step: the enforcement personnel obtain the Internet of Things raw data by Web Service; Second step: the enforcement personnel with raw data reconstruct after, it is stored in internal memory; The 3rd step: the raw data of enforcement personnel after with reconstruct is reassembled as the UDB tree; The 4th step: the rule of reading pre-set, carry out data cleansing.
Preferred version: the detailed process of the first step comprises the steps: to utilize the mode of Web Service, database service interface externally is provided, the data unification is transferred to central database, central database adopts traditional relevant database, equipment establishment tables of data separately for every type, device numbering is as unique identifier, and the raw data of all collections will be stored in separately tables of data.
Preferred version: after the detailed process of second step comprised the steps: to obtain from the central database all data, the enforcement personnel were assembled into these raw data and are data block; After completing data reconstruction, then these data blocks are loaded in internal memory.
Preferred version: the detailed process in the 3rd step comprises the steps: to utilize search algorithm UDB, and the data block in second step is begun index according to key word, after finding corresponding position, then according to the UDB insertion algorithm, data block is inserted corresponding node.
Preferred version: the detailed process in the 4th step comprises the steps: when data cleansing, operating personnel can configure by software, perhaps utilize configuration file, cleaning rule is defined, in whole data cleansing process, will clean under the condition of cleaning rule agreement, and when cleaning, can be divided into one and reach with last step, each step can be formulated different cleaning rules.
Preferred version: save as the internal memory that computer cluster forms in described in second step.
Preferred version: when holding all data, raw data will be retained in disk when low memory, and each data assembling that only will part is data block and is loaded on internal memory, after the data block in internal memory is completed processing, then loads remaining data from disk.
Preferred version: described cleaning rule can usage data library inquiry language or script create and edit.
In sum, owing to having adopted technique scheme, concrete beneficial effect of the present invention is: take full advantage of the capacity storage basic data of internal memory, thereby reduced the I/O expense, improved access efficiency; Use unified data layout, be conducive to the data fast resolving, although can consume the more time in the process of Uniform data format, when resolution data, will greatly improve speed, thereby improve bulk velocity; Use utilizes searching algorithm with strong points for the data structure of Internet of Things, improves retrieval rate; Utilize the advantage of computer cluster, walk abreast and carry out the data cleansing process, thereby fundamentally solving traditional performance bottleneck problem.
Description of drawings
Fig. 1 is the intermediate node schematic diagram;
Fig. 2 is the back end schematic diagram;
Fig. 3 is the data block schematic diagram;
Fig. 4 is UDB tree schematic diagram:
Fig. 5 data cleansing process flow diagram;
Fig. 6 is the data cleansing process flow diagram.
Embodiment
Disclosed all features in this instructions, or the step in disclosed all methods or process except mutually exclusive feature and/or step, all can make up by any way.
Disclosed arbitrary feature in this instructions (comprising any accessory claim, summary and accompanying drawing) is unless special narration all can be replaced by other equivalences or the alternative features with similar purpose.That is, unless special narration, each feature is an example in a series of equivalences or similar characteristics.
Owing to needing to use some for the newly-established data structure of the present invention and the algorithm that relates to thereof, therefore the algorithm of first setting forth these data structures and relating in concrete implementation step.
Concept 1.UDB (Uniform-Delicate B-Tree) tree,
Definition: this is a kind B-tree, meets the General Definition of B-tree construction, and because the B-tree is a kind of data structure of reaching common understanding in this area, therefore do not elaborate, the present invention only is described in detail for the special construction of UDB:
The UDB tree includes 2 kinds of nodes, and they are defined as follows:
The first node is intermediate node, and as shown in Figure 1, this node only includes classified information, and the purpose that builds intermediate node is for the convenient search data, and itself does not comprise data.The structure of intermediate node contains N, pointer and Key key word:
N: the node number is the node number that includes in this node.
*: pointer is the data structure that is used for connection between node.
Key: key word is the unique identifier that indicates node.Be type number.
The identification number of intermediate node, need to accomplish overall unique on type, for example, cell phone apparatus and electronic tag, cell phone apparatus and the different equipment of electronic tag 2 classes, must distinguish with unique numbering, numbering can not repeat, as SJ, BQ, and the concrete cell phone apparatus under cell phone apparatus and the concrete equipment under electronic tag, identical numbering can be arranged, as 001 below 001 and BQ below SJ.
The second node is back end, is called again terminal node, leaf node, as shown in Figure 2.Terminal node contains a HASH hash table, and the HASH hash table utilizes the hashed value quick-searching data block of key word.
The structure of data block as shown in Figure 3, wherein:
Key (key word) is the sign of data block, and the data block Key under same HASH hashed value cannot repeat, and uses pointer to connect in order according to the lexicographic order data block, is generally type.
Data represents the True Data of data block store.
* represent pointer, be the data structure that is used for connection between data block.
Above two kinds of nodes have formed the structure of UDB tree, as shown in Figure 4.Specifically be summarized as follows: the UDB tree comprises intermediate node and back end, described intermediate node comprises root node and non-leaf node, described back end is leaf node, described intermediate node comprises the number of the first structure and described the first structure, and described the first structure specifically comprises pointer and the numbering for the presentation class relation; Described back end comprises the data block chain and is used for retrieving the HASH hash table of described data block chain, described data block chain specifically comprises the data block that several have linking relationship, and described data block comprises the data block data, is used for pointer and the numbering of expression data relation at the same level.
Due in the process of setting up the UDB tree, need to use corresponding search algorithm and insertion algorithm, existing that these 2 kinds of arthmetic statements are as follows:
Algorithm 1.UDB tree query algorithm.
1) according to the KEY value of node, in whole tree, from the beginning all nodes in node have begun, and retrieve in intermediate node in these, according to the Query Result in node, enter and carry out interval query in corresponding zone.
2) in obtaining node after the Query Result of subregion, the child node pointer that need to deposit according to the subregion head adds the relative displacement that leaves in the index node data block, and this side-play amount is namely the node serial number of relatively first node.Circulation is until find indexed node.
3) utilize the HASH function that sets, after calculating corresponding HASH value, visit again the data block chain.
Find at last the data block that will look for.
Algorithm 2.UDB sets insertion algorithm.
When inserting new node to the UDB tree.
The leaf node that inquiry KEY should insert.After leaf node is positioned, need to judge whether this node has enough spaces to hold new index entry.If insufficient space needs to divide leaf node.
When leaf node need to divide, enough spaces are arranged when needing to consider father node.If father node P has enough spaces, f is the child node group pointer on some subregions in P.G is the child node group of f indication.During certain node split in g, need all nodes in the larger child node group g' of distribution ratio g all to be copied in g, comprise simultaneously the new node that is produced by division.Then f will point to new child node group g' and the space of g will be released.The index entry that new node is corresponding is inserted in father node P.
If father node does not have redundant space, its ability also will be divided so.Suppose that P is father node, f is the child node group pointer on some subregions in P, and g is the child node group of f indication.During division, distribute new child node group g, and copy all child nodes and new split vertexes from g.P itself also will be divided, and generates new node P ', and copies the index entry of half from P.Simultaneously, the node group at P place also needs to redistribute the space and the replica node data are held P'.If necessary, also will continue to divide father's node of P.Simultaneously, the division of every minor node also needs index in knot modification.
Web Service: be a kind of General Model that builds application program, can implement operation in the operating system of any network enabled communication; It is a kind of new web application branch, is self-contained, self-described, modular application, can issue, locates, call by web.Web Service is an application component, its logicality provide geodata and services for other application programs.Each application program visits Web Service by some standard data formats (Http, XML, Soap) of procotol and regulation, obtains results needed by inner execution of Web Service.Web Service can carry out any function from simple request to complicated business processing.In case after disposing, the service that it is disposed can be found and call to other Web Service application programs.
After the concept that fully understands above-mentioned special use and algorithm, further introduce the specific implementation process that relates in the present invention, overall flow figure such as Fig. 5:
The first step: the enforcement personnel obtain the Internet of Things raw data by Web Service.because the data volume of Internet of Things is huge, the Internet of Things data will be stored in different network nodes, and the data type difference of these data is huge, as relevant database, the non-relational database, non-structured data is (as XML, Excel etc.), these data will be deployed on network node, utilize the mode of Web Service, database service interface externally is provided, the data unification is transferred to central database, central database adopts traditional relevant database, equipment establishment tables of data separately for every type, device numbering is as unique identifier, the raw data of all collections will be stored in separately tables of data.When guaranteeing real-time property, do not bring performance bottleneck to the network bandwidth, the frequency of the transmission of data is for once a day.
Second step: the enforcement personnel with raw data reconstruct after, it is stored in internal memory.After obtaining from the central database all data, the enforcement personnel are assembled into these raw data the data block of mentioning in concept 1, and at this moment, the pointer in data block is empty, and data is data, can become having of Key key word:
1. device type is the type of image data equipment.
2. node serial number is the numbering of network node.
3. data layout, be the data of same data layout as a class.
After completing data reconstruction, then these data blocks are loaded in one, many, internal memory that even computer cluster forms.But the mass data of considering Internet of Things may make low memory, to use the buffering of " disk-internal memory " to process, that is: when low memory when holding all data, raw data will be retained in disk, each be only data block with a certain amount of data assembling and be loaded on internal memory, after data block in internal memory is completed processing, then load remaining data from disk.
The 3rd step: the raw data of enforcement personnel after with reconstruct is reassembled as the UDB tree, and this UDB tree is namely the described data structure of concept 1.Search algorithm UDB who utilizes algorithm 1 to describe, data block in second step is begun index according to key word, after finding corresponding position, the UDB insertion algorithm of describing according to algorithm 2 again, data block is inserted corresponding node, after the data block that needs was inserted into corresponding position, these data blocks just were constructed as the UDB tree.Wherein, root node represents total type, intermediate node presentation class type, and the kind number of the data representation same level of each intermediate node inside, terminal node represents the True Data of this type.
The 4th step: the rule of reading pre-set, carry out data cleansing.When data cleansing, operating personnel can configure by software, perhaps utilize configuration file, and cleaning rule is defined, and this rule can usage data library inquiry language (SQL), script (as JavaScript) creates and edits.In whole data cleansing process, will clean under the condition of cleaning rule agreement, and when cleaning, can be divided into a plurality of steps, each step can be formulated cleaning rule, as Fig. 6.
Above demonstration and described ultimate principle of the present invention, principal character and advantage.Above embodiment is only in order to describe technical scheme of the present invention rather than technical method is limited; the present invention is extensible on using is other modification, variation and application, and thinks that all such modifications, variation and application all fall in the claimed scope of the invention.
Claims (8)
1. Internet of Things Data Cleaning Method is characterized in that: comprise the following steps:
The first step: the enforcement personnel obtain the Internet of Things raw data by Web Service;
Second step: the enforcement personnel with raw data reconstruct after, it is stored in internal memory;
The 3rd step: the raw data of enforcement personnel after with reconstruct is reassembled as the UDB tree;
The 4th step: the rule of reading pre-set, carry out data cleansing.
2. a kind of Internet of Things Data Cleaning Method according to claim 1, it is characterized in that: the detailed process of the first step comprises the steps: to utilize the mode of Web Service, database service interface externally is provided, the data unification is transferred to central database, central database adopts traditional relevant database, be that the equipment of every type creates tables of data separately, device numbering is as unique identifier, and the raw data of all collections will be stored in separately tables of data.
3. a kind of Internet of Things Data Cleaning Method according to claim 1 and 2, it is characterized in that: after the detailed process of second step comprised the steps: to obtain from the central database all data, the enforcement personnel were assembled into these raw data and are data block; After completing data reconstruction, then these data blocks are loaded in internal memory.
4. a kind of Internet of Things Data Cleaning Method according to claim 1 and 2, it is characterized in that: the detailed process in the 3rd step comprises the steps: to utilize search algorithm UDB, data block in second step is begun index according to key word, after finding corresponding position, then according to the UDB insertion algorithm, data block is inserted corresponding node.
5. a kind of Internet of Things Data Cleaning Method according to claim 1 and 2, it is characterized in that: the detailed process in the 4th step comprises the steps: when data cleansing, operating personnel can configure by software, perhaps utilize configuration file, cleaning rule is defined, in whole data cleansing process, to clean under the condition of cleaning rule agreement, and when cleaning, can be divided into one and reach with last step, each step can be formulated different cleaning rules.
6. a kind of Internet of Things Data Cleaning Method according to claim 3, is characterized in that: save as the internal memory that computer cluster forms in described in second step.
7. a kind of Internet of Things Data Cleaning Method according to claim 6, it is characterized in that: when low memory when holding all data, raw data will be retained in disk, each is data block and is loaded on internal memory data assembling partly, after data block in internal memory is completed processing, then load remaining data from disk.
8. a kind of Internet of Things Data Cleaning Method according to claim 5 is characterized in that: described cleaning rule can usage data library inquiry language or script create and edit.
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| CN105913316A (en) * | 2016-04-15 | 2016-08-31 | 中国银行股份有限公司 | Rule configuration method and apparatus |
| CN105913316B (en) * | 2016-04-15 | 2019-12-10 | 中国银行股份有限公司 | Rule configuration method and device |
| CN106294745A (en) * | 2016-08-10 | 2017-01-04 | 东方网力科技股份有限公司 | Big data cleaning method and device |
| CN108153747A (en) * | 2016-12-02 | 2018-06-12 | 航天星图科技（北京）有限公司 | A kind of parallel data cleaning system |
| CN106790491B (en) * | 2016-12-14 | 2019-10-15 | 日照职业技术学院 | The implementation method of the Internet of Things movable termination intelligent platform of data-oriented |
| CN106790491A (en) * | 2016-12-14 | 2017-05-31 | 日照职业技术学院 | The implementation method of the Internet of Things movable termination intelligent platform of data-oriented |
| CN108319609A (en) * | 2017-01-16 | 2018-07-24 | 医渡云（北京）技术有限公司 | ETL data processing methods and system, data cleaning method and device |
| CN106933990A (en) * | 2017-02-21 | 2017-07-07 | 南京朴厚生态科技有限公司 | A kind of sensing data cleaning method |
| CN109241045A (en) * | 2018-08-29 | 2019-01-18 | 宜人恒业科技发展（北京）有限公司 | A kind of method and apparatus of preprocessed data |
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