Ontology-driven mass data event decision-making method

A technology of massive data and decision-making methods, applied in the fields of electronic digital data processing, structured data retrieval, special data processing applications, etc., can solve problems such as insufficient semantic integration, and achieve the effect of efficient decision-making

Inactive Publication Date: 2015-12-02
ZHEJIANG FORESTRY UNIVERSITY
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AI Technical Summary

Problems solved by technology

[0005] The technical problem to be solved by the present invention is to propose an ontology-driven massive data event decision-making method in order to overcome the problem of insufficient semantic integration in the existing decision-making process

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  • Ontology-driven mass data event decision-making method

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Embodiment Construction

[0016] Below in conjunction with accompanying drawing and embodiment the present invention will be further described:

[0017] Referring to the accompanying drawings, the specific implementation process of the present invention is carried out in the following steps:

[0018] (1) Construct the supply chain event ontology library: domain experts in the ABC (Antecedent-Behavior-Consequences, antecedent-behavior-consequences) event ontology model and SCOR (Supply-ChainOperationsReference-model, supply chain operation reference model) supply chain model On the basis of this, a supply chain event ontology PSEO (Perception-basedSupply-chainEventOntology) model based on perception data processing is constructed; Concepts and connotations such as events and data are described in a standardized and formalized manner; this ontology is equivalent to the upper-level ontology model in the process of supply chain business event processing, and is the core part of the business decision-making...

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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 [0001] The invention relates to a semantic information processing technology, in particular to an ontology-driven massive data information integration and processing method. Background technique [0002] With the globalization of the supply chain of manufacturing companies and the development and popularization of IoT technology, huge data collected through IoT technology come from many sources, including so-called smart devices, which can automatically monitor various environmental factors through sensors and generate a large number of Data about performance, communication, environment, and location. The amount of data involved in business decisions of manufacturing enterprises is increasing, and the data sources are becoming more and more complex. The era of big data for manufacturing enterprises has arrived. IBM (International Business Machines Corporation, International Business Machines Corporation) defines "big data" as information that cannot be proce...

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Application Information

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IPC IPC(8): G06F17/30
CPCG06F16/27
Inventor 倪益华吕艳倪忠进吴健
Owner ZHEJIANG FORESTRY UNIVERSITY
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