CN105117483A - Ontology-driven mass data event decision-making method - Google Patents
Ontology-driven mass data event decision-making method Download PDFInfo
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- CN105117483A CN105117483A CN201510592253.7A CN201510592253A CN105117483A CN 105117483 A CN105117483 A CN 105117483A CN 201510592253 A CN201510592253 A CN 201510592253A CN 105117483 A CN105117483 A CN 105117483A
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Abstract
Disclosed is an ontology-driven mass data event decision-making method. According to the method, a supply chain event ontology base is established, a supply chain event ontology is taken as the upper ontology, the two-layer semantic mapping technology is adopted, uniform semantic translation from a local ontology to a target ontology is achieved, and finally a factbase is formed. SWRL language is adopted to define the processing rule of supply chain event decision-making so that an event decision-making rule base can be formed. Based on the factbase and the rule base, mass data-oriented fact distribution and rule matching are achieved by means of the distributed Rete algorithm, and then an optimum matching result is reasoned out, high-efficiency processing of mass data is achieved and event decision-making is assisted. The fact and rule combined supply chain distributed information processing mechanism is adopted, the problems including information heterogeneity and massiveness existing during manufacturing enterprise perceptual information processing are effectively solved, and the agility and accuracy of enterprise decision-making are improved.
Description
Technical field
The present invention relates to a kind of mass data information integerated and disposal route of Semantic Information Processing technology, particularly ontology-driven.
Background technology
Along with the globalization of the supply chain of manufacturing enterprise and the development of technology of Internet of things and universal, the huge data gathered by technology of Internet of things are from many sources, comprise so-called smart machine, they are by the various environmental factor of sensor automatic monitoring and generation is a large amount of about data such as performance, communication, environment and positions.The data volume that manufacturing enterprise relates to when carrying out operational decision making is increasing, data source becomes increasingly complex, and the large data age of manufacturing enterprise arrives.IBM(InternationalBusinessMachinesCorporation, International Business Machine Corporation (IBM)) " large data " are defined as traditional process or instrument cannot be used to carry out the information processing or analyze, large data bring a large amount of challenges and potential opportunity to all trades and professions.
In manufacturing industry, utilize the key of large data to be, enterprise is no longer merely only with the foundation of structural data for formulation operational decision making.Significant equally by the information collected by analyzing unstructured data.Enterprise needs to solve the Heterogeneity between these structurings and unstructured information.The data of simultaneity factor collection have periodically usually, the higher requirement that market environment proposes the real time reaction ability of enterprise, problem that manufacturing enterprise needs solution to process efficiently the mass data of these Real-time Collections and apply.Traditional method mainly relies on the CPU quantity of the processing power and single computing machine improving computing system processor to raise the efficiency, but this causes the increase of processing cost on the one hand, causes and calculates energy consumption in a large number, also makes whole system lack extendability and dirigibility; Not remarkable to reduction processing time effect on the other hand, consume excessive when still causing, thus reduce the promptness of decision-making, affect the operational paradigm of enterprise.
The present invention is directed to the operational decision making process towards real-time, large-scale perception data in supply chain business procedure, a kind of mass data event decision method of ontology-driven is proposed, the supply chain distributed information processing mechanism adopting true Sum fanction to combine, realizes the efficient decision-making of mass data by the coupling that circulates of true Sum fanction in distributed network.
Summary of the invention
The technical problem to be solved in the present invention is the problem in order to overcome semantic intergration deficiency in existing decision process, proposes a kind of mass data event decision method of ontology-driven.
Solve the problems of the technologies described above and adopt following technical scheme: the mass data event decision method of ontology-driven comprises the following steps:
(1) supply chain event ontology storehouse is built: event ontology is for conventional bulk, except fully describing static material concept and mutual relationship, also have expressed the dynamic event concept and mutual relationship that are participated in by entity, more can explication and express business event process granularity, compensate for the defect that conventional bulk is expressed.Domain expert builds supply chain event ontology storehouse on the basis of the generic concept model of event handling, comprise tool resemble class, time resemble class, abstract class, time class, place class;
(2) heterogeneous data source is to the conversion of target body Uniform semantic information: to realize in supply chain operational decision making heterogeneous data source to the automatic obtaining method of local ontology, and carry out Ontology Mapping process on this basis, finally realize local ontology and change to the Uniform semantic of target body;
(3) supply chain event decision rule base is built: the supply chain event procedure according to the definition of SCOR standard: comprise the supply chain process that plan, buying, production, dispensing, the return of goods five are main, based on the supply chain event ontology storehouse built, employing
SWRL language defines the processing rule of supply chain event decision, forms event decision rule base;
(4) based on the event decision reasoning of Rete algorithm: the target body obtained using step 2 is as factbase, event decision rule is obtained as rule base using step 3, adopt distributed algorithm realization towards the fact distribution Sum fanction matching process of mass data, thus infer best matching result, ancillary events decision-making.
the invention has the beneficial effects as follows:
Realize heterogeneous data source in supply chain operational decision making and, to the automatic obtaining method of local ontology, with supply chain event ontology for upper strata body, adopt two-layer Ontology Mapping process, finally realize local ontology and change to the Uniform semantic of target body.
The supply chain distributed information processing mechanism adopting true Sum fanction to combine, using supply chain event ontology storehouse as upper strata body, support the definition of true Sum fanction, realize the efficient decision-making of mass data by the coupling that circulates of true Sum fanction in distributed network.
Accompanying drawing explanation
Accompanying drawing is operational decision making processing flow chart.
Embodiment
Below in conjunction with drawings and Examples, the invention will be further described:
See accompanying drawing, specific embodiment of the invention process is carried out according to the following steps:
(1) supply chain event ontology storehouse is built: domain expert is at ABC (Antecedent-Behavior-Consequences, cause-behavior-consequence) event ontology model and SCOR(Supply-ChainOperationsReference-model, supply-chain operations reference model) Supply Chain Model basis on, build the supply chain event ontology PSEO(Perception-basedSupply-chainEventOntology based on perception data process) model; This body comprise tool resemble class, time resemble class, abstract class, time class, place class, carried out standardizing and formal description to the definition and connotation such as various events, data in supply chain collaboration; This body is equivalent to upper strata ontology model in supply chain business event processing procedure, is the core in operational decision making processing procedure; Establishment and the filling of instances of ontology can be carried out according to supply chain event object when embody rule.
(2) heterogeneous data source is to the conversion of the Uniform semantic information of target body: the data of supply chain operational decision making are mainly derived from data acquisition system (DAS), gathers the storage document that the heterogeneous data source come mainly is divided into the forms such as relation database table, XML document and html document; These isomeric datas need the Heterogeneity being solved data by Uniform semantic conversion; This comprises two step by step:
1. realize the automatic acquisition of heterogeneous data source to local ontology, namely semantic description is carried out to the storage organization of data in different isomerization information source, form local ontology (LocalOntology, LO); Following four sub-steps are mainly taked to realize the conversion of heterogeneous data source to local ontology for relational database: A, by extracting data store organisation and corresponding information; B, concept classification process; C, establishment and description class, subclass, attribute, example ontology element; What D, storage generated enters library information local ontology.For the isomeric data of non-relational database type, first by Object-Relation Mapping Technology, realize the mapping of non-relational database to relational database, then realized the generation of local ontology by said method;
2. local ontology is changed to the Uniform semantic of target body: using supply chain event ontology storehouse as upper strata body, mates by local ontology and its are carried out semantic similarity, forms Semantic mapping relation; Also language is formed between same upper strata body and target body
Justice mapping relations, by two-stage Semantic mapping, are converted into conceptual description corresponding to target body, finally complete local ontology and change to the Uniform semantic of target body by the conceptual description of local ontology;
(3) supply chain event decision rule base is built:
Supply chain event procedure according to the definition of SCOR standard: comprise plan, buying, production, dispensing, the return of goods five main supply chain processes, each supply chain event is decomposed, on the basis in the supply chain event ontology storehouse built, SWRL language is adopted to define the processing rule of supply chain event decision; Event decision rule defines the definition of event rules, the standard formation of event rules, the set definition of operational decision making principle, event rules, the conversion method etc. of event rules; This sequence of events decision rule constitutes event decision rule base, for carrying out business procedure Auto-matching and reasoning provides the foundation;
(4) based on the event decision reasoning of Rete algorithm:
Rete algorithm is a kind of reasoning algorithm of efficient matchings, allows true collection propagate in a network and to filter, if true element can meet all conditions part of rule, then can be sent to the network terminal and destination node, and trigger the behavior part of this rule; The target body obtained using step 2, as factbase, obtains event decision rule as rule base using step 3; Adopt distributed Master/Worker pattern, factbase carries out fact distribution by Master, and rule base carries out the decomposition of rule by Master; Then carry out true distribution and filtration by Worker, then obtain intermediate match result with sub-rule when the match is successful when true; Finally these intermediate match results are gathered and carry out final judgement to Master, activate respective rule according to result and perform decision-making.
Realize heterogeneous data source in supply chain operational decision making and, to the automatic obtaining method of local ontology, with supply chain event ontology for upper strata body, adopt two-layer Ontology Mapping process, finally realize local ontology and change to the Uniform semantic of target body.
The supply chain distributed information processing mechanism adopting true Sum fanction to combine, using supply chain event ontology storehouse as upper strata body, support the definition of true Sum fanction, realize the efficient decision-making of mass data by the coupling that circulates of true Sum fanction in distributed network.
Claims (4)
1. a mass data event decision method for ontology-driven, is characterized in that carrying out as follows:
(1) supply chain event ontology storehouse is built: event ontology is for conventional bulk, except fully describing static material concept and mutual relationship, also have expressed the dynamic event concept and mutual relationship that are participated in by entity, more can explication and express business event process granularity, compensate for the defect that conventional bulk is expressed.
2. domain expert builds supply chain event ontology storehouse on the basis of the generic concept model of event handling, comprise tool resemble class, time resemble class, abstract class, time class, place class;
(2) heterogeneous data source is to the conversion of target body Uniform semantic information: to realize in supply chain operational decision making heterogeneous data source to the automatic obtaining method of local ontology, and carry out Ontology Mapping process on this basis, finally realize local ontology and change to the Uniform semantic of target body;
(3) supply chain event decision rule base is built: the supply chain event procedure according to the definition of SCOR standard: comprise the supply chain process that plan, buying, production, dispensing, the return of goods five are main, based on the supply chain event ontology storehouse built, adopt SWRL language to define the processing rule of supply chain event decision, form event decision rule base;
(4) based on the event decision reasoning of Rete algorithm: the target body obtained using step 2 is as factbase, event decision rule is obtained as rule base using step 3, adopt distributed algorithm realization towards the fact distribution Sum fanction matching process of mass data, thus infer best matching result, ancillary events decision-making.
3. the mass data event decision method of ontology-driven according to claim 1, it is characterized in that: to realize in supply chain operational decision making heterogeneous data source to the automatic obtaining method of local ontology, with supply chain event ontology for upper strata body, adopt two-layer Ontology Mapping process, finally realize local ontology and change to the Uniform semantic of target body.
4. the mass data event decision method of ontology-driven according to claim 1, it is characterized in that: the supply chain distributed information processing mechanism adopting true Sum fanction to combine, using supply chain event ontology storehouse as upper strata body, support the definition of true Sum fanction, realize the efficient decision-making of mass data by the coupling that circulates of true Sum fanction in distributed network.
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| CN110490605A (en) * | 2018-05-14 | 2019-11-22 | 上海交通大学 | The supply chain data normalization system traced to the source towards block chain based on ontology |
| CN115605894A (en) * | 2020-09-03 | 2023-01-13 | 京东方科技集团股份有限公司（Cn） | Intelligent management system, intelligent management method and computer program product |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110490605A (en) * | 2018-05-14 | 2019-11-22 | 上海交通大学 | The supply chain data normalization system traced to the source towards block chain based on ontology |
| CN110490605B (en) * | 2018-05-14 | 2023-12-01 | 上海交通大学 | Ontology-based supply chain data standardization system oriented to blockchain tracing |
| CN115605894A (en) * | 2020-09-03 | 2023-01-13 | 京东方科技集团股份有限公司（Cn） | Intelligent management system, intelligent management method and computer program product |
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