CN105117483A - Ontology-driven mass data event decision-making method - Google Patents
Ontology-driven mass data event decision-making method Download PDFInfo
- Publication number
- 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
- Authority
- CN
- China
- Prior art keywords
- ontology
- event
- supply chain
- decision
- making
- 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.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 37
- 238000012545 processing Methods 0.000 claims abstract description 18
- 238000013507 mapping Methods 0.000 claims abstract description 11
- 238000004519 manufacturing process Methods 0.000 claims abstract description 9
- 230000010365 information processing Effects 0.000 claims abstract description 7
- 238000009826 distribution Methods 0.000 claims abstract description 6
- 238000006243 chemical reaction Methods 0.000 claims description 6
- 239000000463 material Substances 0.000 claims description 2
- 230000003068 static effect Effects 0.000 claims description 2
- 230000008878 coupling Effects 0.000 claims 1
- 238000010168 coupling process Methods 0.000 claims 1
- 238000005859 coupling reaction Methods 0.000 claims 1
- 230000007547 defect Effects 0.000 claims 1
- 230000009466 transformation Effects 0.000 abstract description 6
- 238000005516 engineering process Methods 0.000 abstract description 5
- 230000008447 perception Effects 0.000 abstract description 3
- 238000013480 data collection Methods 0.000 description 3
- 230000010354 integration Effects 0.000 description 2
- 238000003860 storage Methods 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 230000002123 temporal effect Effects 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
- G06F16/27—Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor
Landscapes
- Engineering & Computer Science (AREA)
- Databases & Information Systems (AREA)
- Theoretical Computer Science (AREA)
- Computing Systems (AREA)
- Data Mining & Analysis (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
一种本体驱动的海量数据事件决策方法。它通过建立构建供应链事件本体库，以供应链事件本体为上层本体，采用两层语义映射技术，实现局部本体到目标本体的统一语义转换，最终形成事实库。采用SWRL语言来定义供应链事件决策的处理规则构成事件决策规则库。以事实库和规则库为基础，采用分布式的Rete算法实现面向海量数据的事实分发和规则匹配过程，从而推理出最佳匹配结果，实现面向海量数据的高效处理，辅助事件决策。本发明采用事实和规则相结合的供应链分布式信息处理机制，有效解决制造企业感知信息处理中信息异构、海量性等难题，提高企业决策的敏捷性和准确性。
An ontology-driven decision-making method for massive data events. It builds the supply chain event ontology library, takes the supply chain event ontology as the upper ontology, and adopts two-layer semantic mapping technology to realize the unified semantic transformation from the local ontology to the target ontology, and finally forms the fact library. Using SWRL language to define the processing rules of supply chain event decision-making constitutes event decision-making rule base. Based on the fact base and rule base, the distributed Rete algorithm is used to realize the fact distribution and rule matching process for massive data, so as to infer the best matching results, realize efficient processing for massive data, and assist event decision-making. The present invention adopts a supply chain distributed information processing mechanism that combines facts and rules, effectively solves the problems of information heterogeneity and massiveness in the perception information processing of manufacturing enterprises, and improves the agility and accuracy of enterprise decision-making.
Description
技术领域 technical field
本发明涉及一种语义信息处理技术，特别是本体驱动的海量数据信息集成和处理方法。 The invention relates to a semantic information processing technology, in particular to an ontology-driven massive data information integration and processing method.
背景技术 Background technique
随着制造企业的供应链的全球化以及物联网技术的发展和普及，通过物联网技术采集的庞大数据来自诸多源头，包括所谓的智能设备，它们可通过传感器自动监控各种环境因素并生成大量关于性能、通讯、环境及位置等数据。制造企业在进行业务决策时涉及的数据量越来越大、数据源越来越复杂，制造企业的大数据时代已经到来。IBM（InternationalBusinessMachinesCorporation，国际商业机器公司）将“大数据”定义为无法使用传统流程或工具进行处理或分析的信息，大数据给各行各业带来了大量的挑战和潜在机遇。 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 processed or analyzed using traditional processes or tools. Big data brings a lot of challenges and potential opportunities to all walks of life.
在制造业中利用大数据的关键在于，企业不再单纯地仅以结构性数据为制定业务决策的依据。通过对非结构化数据进行分析所收集的信息同样具有重要意义。企业需要解决这些结构化以及非结构化信息间的异构问题。同时系统采集的数据通常具有周期性，市场环境对企业的实时反应能力提出的了更高的要求，制造企业需要解决对这些实时采集的海量数据进行高效的处理和应用的问题。传统的方法主要依靠提高计算系统处理器的处理能力和单个计算机的CPU数量来提高效率，但这一方面引起处理成本的增加，造成大量计算能耗，也使得整个系统缺乏扩展性和灵活性；另一方面对降低处理时间效果并不显著，仍然导致时耗过大，从而降低决策的及时性，影响企业的运作效率。 The key to utilizing big data in manufacturing is that companies no longer simply use structured data as the basis for making business decisions. Information gathered through analysis of unstructured data is equally important. Enterprises need to solve these heterogeneous problems between structured and unstructured information. At the same time, the data collected by the system is usually cyclical, and the market environment puts forward higher requirements for the real-time response capabilities of enterprises. Manufacturing enterprises need to solve the problem of efficient processing and application of these massive data collected in real time. The traditional method mainly relies on improving the processing power of the computing system processor and the number of CPUs of a single computer to improve efficiency, but this aspect causes an increase in processing costs, resulting in a large amount of computing energy consumption, and also makes the entire system lack of scalability and flexibility; On the other hand, the effect of reducing processing time is not significant, and it still leads to excessive time consumption, thereby reducing the timeliness of decision-making and affecting the operational efficiency of enterprises.
本发明针对供应链业务过程中面向实时的、大规模的感知数据的业务决策处理，提出一种本体驱动的海量数据事件决策方法，采用事实和规则相结合的供应链分布式信息处理机制，通过事实和规则在分布式网络中的循环流动匹配来实现海量数据的高效决策。 Aiming at the real-time, large-scale perception data-oriented business decision-making process in the supply chain business process, the present invention proposes an ontology-driven massive data event decision-making method, adopts a supply chain distributed information processing mechanism combining facts and rules, and passes The circular flow matching of facts and rules in the distributed network realizes the efficient decision-making of massive data.
发明内容 Contents of the invention
本发明要解决的技术问题是为了克服现有决策过程中语义集成不足的问题，提出一种本体驱动的海量数据事件决策方法。 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.
解决上述技术问题采用如下技术方案：本体驱动的海量数据事件决策方法包括以下步骤： To solve the above technical problems, the following technical solutions are adopted: the ontology-driven massive data event decision-making method includes the following steps:
（1）构建供应链事件本体库：事件本体相对于传统本体而言，除了充分描述静态的物质概念及相互关系之外，还表达了由实体参与的动态事件概念及相互关系，更能精确定义和表达企业业务处理粒度，弥补了传统本体表达的缺陷。领域专家在事件处理的通用概念模型的基础上构建供应链事件本体库，包括具象类、时象类、抽象类、时间类、地点类； (1) Construct a supply chain event ontology library: Compared with traditional ontologies, event ontology not only fully describes static material concepts and interrelationships, but also expresses dynamic event concepts and interrelationships participated by entities, which can be more precisely defined And express the granularity of enterprise business processing, making up for the shortcomings of traditional ontology expression. Domain experts build a supply chain event ontology library based on the general conceptual model of event processing, including concrete, temporal, abstract, time, and location categories;
（2）异构数据源到目标本体统一语义信息的转换：实现供应链业务决策中异构数据源到局部本体的自动获取方法，并在此基础上进行本体映射处理，最终实现局部本体到目标本体的统一语义转换； (2) Transformation of unified semantic information from heterogeneous data sources to target ontology: realize the automatic acquisition method from heterogeneous data sources to local ontology in supply chain business decision-making, and carry out ontology mapping processing on this basis, and finally realize local ontology to target Unified semantic transformation of ontology;
（3）构建供应链事件决策规则库：根据SCOR标准定义的供应链事件过程：包括计划、采购、生产、配送、退货五个主要的供应链过程，以构建的供应链事件本体库为基础，采用 (3) Build a supply chain event decision rule library: supply chain event process defined according to the SCOR standard: including five main supply chain processes of planning, procurement, production, distribution, and return, based on the constructed supply chain event ontology library, use
SWRL语言来定义供应链事件决策的处理规则，形成事件决策规则库； SWRL language is used to define the processing rules of supply chain event decision-making, forming an event decision-making rule base;
（4）基于Rete算法的事件决策推理：以步骤2得到的目标本体作为事实库，以步骤3得到事件决策规则作为规则库，采用分布式的算法实现面向海量数据的事实分发和规则匹配过程，从而推理出最佳匹配结果，辅助事件决策。 (4) Event decision-making reasoning based on the Rete algorithm: use the target ontology obtained in step 2 as the fact library, use the event decision rules obtained in step 3 as the rule library, and use a distributed algorithm to realize the fact distribution and rule matching process for massive data. In this way, the best matching result can be deduced to assist event decision-making.
本发明的有益效果是：The beneficial effects of the present invention are:
实现供应链业务决策中异构数据源到局部本体的自动获取方法，以供应链事件本体为上层本体，采用两层本体映射处理，最终实现局部本体到目标本体的统一语义转换。 Realize the automatic acquisition method from heterogeneous data sources to local ontology in supply chain business decision-making, take the supply chain event ontology as the upper ontology, adopt two-layer ontology mapping processing, and finally realize the unified semantic conversion from local ontology to target ontology.
采用事实和规则相结合的供应链分布式信息处理机制，以供应链事件本体库作为上层本体，支持事实和规则的定义，通过事实和规则在分布式网络中的循环流动匹配来实现海量数据的高效决策。 The supply chain distributed information processing mechanism that combines facts and rules is adopted, and the supply chain event ontology library is used as the upper ontology, which supports the definition of facts and rules, and realizes the massive data collection through the circular flow matching of facts and rules in the distributed network. Efficient decision-making.
附图说明 Description of drawings
附图是业务决策处理流程图。 The accompanying drawing is a flow chart of business decision processing.
具体实施方式 Detailed ways
下面结合附图和实施例对本发明作进一步说明： Below in conjunction with accompanying drawing and embodiment the present invention will be further described:
参见附图，本发明的具体实施过程按以下步骤进行： Referring to the accompanying drawings, the specific implementation process of the present invention is carried out according to the following steps:
（1）构建供应链事件本体库：领域专家在ABC(Antecedent-Behavior-Consequences，前因-行为-后果)事件本体模型和SCOR（Supply-ChainOperationsReference-model，供应链运作参考模型）供应链模型的基础上，构建基于感知数据处理的供应链事件本体PSEO（Perception-basedSupply-chainEventOntology）模型；该本体包括具象类、时象类、抽象类、时间类、地点类，对供应链协作中的各种事件、数据等概念和内涵进行了规范化和形式化的描述；该本体在供应链业务事件处理过程中相当于上层本体模型，是业务决策处理过程中的核心部分；在具体应用时可根据供应链事件对象进行本体实例的创建和填充。 (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 process; specific applications can be based on supply chain Event objects are used to create and populate ontology instances.
（2）异构数据源到目标本体的统一语义信息的转换：供应链业务决策的数据主要来源于数据采集系统，采集来的异构数据源主要分为关系数据表、XML文档以及HTML文档等格式的存储文档；这些异构数据需要通过统一语义转换来解决数据的异构问题；这包括两个分步骤： (2) Conversion of heterogeneous data sources to unified semantic information of target ontology: The data for supply chain business decision-making mainly comes from the data collection system, and the collected heterogeneous data sources are mainly divided into relational data tables, XML documents, and HTML documents, etc. Format storage documents; these heterogeneous data need to solve the data heterogeneity problem through unified semantic transformation; this includes two sub-steps:
①实现异构数据源到局部本体的自动获取，即对不同异构信息源中数据的存储结构进行语义描述，形成局部本体（LocalOntology，LO）；对于关系数据库主要采取以下四个子步骤实现异构数据源到局部本体的转换：A、通过提取数据存储结构及相应信息；B、概念分类处理；C、创建和描述类、子类、属性、实例本体元素；D、存储生成的入库信息局部本体。对于非关系数据库类型的异构数据，首先通过对象-关系映射技术，实现非关系数据库到关系数据库的映射，然后通过上述方法实现局部本体的生成； ① Realize the automatic acquisition of heterogeneous data sources to local ontology, that is, semantically describe the storage structure of data in different heterogeneous information sources to form a local ontology (Local Ontology, LO); for relational databases, the following four sub-steps are mainly adopted to realize heterogeneity Transformation of data source to local ontology: A. By extracting data storage structure and corresponding information; B. Concept classification processing; C. Creating and describing classes, subclasses, attributes, and instance ontology elements; D. Storing the generated warehousing information locally ontology. For heterogeneous data of non-relational database type, first realize the mapping from non-relational database to relational database through object-relational mapping technology, and then realize the generation of local ontology through the above method;
②局部本体到目标本体的统一语义转换：以供应链事件本体库作为上层本体，通过将局部本体与其进行语义相似度匹配，形成语义映射关系；同样上层本体与目标本体间也形成语 ②Uniform semantic transformation from local ontology to target ontology: take the supply chain event ontology library as the upper ontology, and form a semantic mapping relationship by matching the semantic similarity between the local ontology and the target ontology;
义映射关系，通过两级语义映射，将局部本体的概念描述转化为目标本体对应的概念描述，最终完成局部本体到目标本体的统一语义转换； The semantic mapping relationship, through two-level semantic mapping, transforms the concept description of the local ontology into the corresponding concept description of the target ontology, and finally completes the unified semantic conversion from the local ontology to the target ontology;
（3）构建供应链事件决策规则库： (3) Build supply chain event decision rule base:
根据SCOR标准定义的供应链事件过程：包括计划、采购、生产、配送、退货五个主要的供应链过程，对每个供应链事件进行分解，在已构建的供应链事件本体库的基础上，采用SWRL语言来定义供应链事件决策的处理规则；事件决策规则定义了事件规则的定义、事件规则的标准构成、业务决策原则、事件规则的集合定义、事件规则的转换方法等；这一系列事件决策规则构成了事件决策规则库，为进行业务过程自动匹配和推理提供了基础； The supply chain event process defined according to the SCOR standard: includes five main supply chain processes of planning, procurement, production, distribution, and return, and decomposes each supply chain event. On the basis of the constructed supply chain event ontology library, The SWRL language is used to define the processing rules of supply chain event decision-making; the event decision-making rules define the definition of event rules, the standard composition of event rules, business decision-making principles, the set definition of event rules, the conversion method of event rules, etc.; this series of events Decision rules constitute the event decision rule library, which provides a basis for automatic matching and reasoning of business processes;
（4）基于Rete算法的事件决策推理： (4) Event decision-making reasoning based on Rete algorithm:
Rete算法是一种高效匹配的推理算法，让事实集在网络中传播和过滤，如果事实元素能够满足规则的所有条件部分，则会被传送到网络终端即终结点，并触发该条规则的行为部分；以步骤2得到的目标本体作为事实库，以步骤3得到事件决策规则作为规则库；采用分布式的Master/Worker模式，事实库通过Master进行事实分发，规则库通过Master进行规则的分解；然后通过Worker进行事实的分配和过滤，当事实与子规则匹配成功时则获得了中间匹配结果；最后将这些中间匹配结果汇总给Master进行最终的判断，根据结果激活相应规则并且执行决策。 The Rete algorithm is an efficient matching reasoning algorithm, which allows the fact set to propagate and filter in the network. If the fact element can satisfy all the conditional parts of the rule, it will be transmitted to the network terminal, that is, the endpoint, and trigger the behavior of the rule. part; use the target ontology obtained in step 2 as the fact base, and use the event decision rules obtained in step 3 as the rule base; adopt the distributed Master/Worker mode, the fact base distributes facts through the Master, and the rule base decomposes rules through the Master; Then the distribution and filtering of facts is carried out by the Worker. When the facts and the sub-rules are successfully matched, the intermediate matching results are obtained; finally, these intermediate matching results are summarized to the Master for final judgment, and the corresponding rules are activated according to the results and decisions are executed.
实现供应链业务决策中异构数据源到局部本体的自动获取方法，以供应链事件本体为上层本体，采用两层本体映射处理，最终实现局部本体到目标本体的统一语义转换。 Realize the automatic acquisition method from heterogeneous data sources to local ontology in supply chain business decision-making, take the supply chain event ontology as the upper ontology, adopt two-layer ontology mapping processing, and finally realize the unified semantic conversion from local ontology to target ontology.
采用事实和规则相结合的供应链分布式信息处理机制，以供应链事件本体库作为上层本体，支持事实和规则的定义，通过事实和规则在分布式网络中的循环流动匹配来实现海量数据的高效决策。 The supply chain distributed information processing mechanism that combines facts and rules is adopted, and the supply chain event ontology library is used as the upper ontology, which supports the definition of facts and rules, and realizes the massive data collection through the circular flow matching of facts and rules in the distributed network. Efficient decision-making.
Claims (4)
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510592253.7A CN105117483A (en) | 2015-09-17 | 2015-09-17 | Ontology-driven mass data event decision-making method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510592253.7A CN105117483A (en) | 2015-09-17 | 2015-09-17 | Ontology-driven mass data event decision-making method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN105117483A true CN105117483A (en) | 2015-12-02 |
Family
ID=54665471
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510592253.7A Pending CN105117483A (en) | 2015-09-17 | 2015-09-17 | Ontology-driven mass data event decision-making method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105117483A (en) |
Cited By (2)
| 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 |
| 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 |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103699663A (en) * | 2013-12-27 | 2014-04-02 | 中国科学院自动化研究所 | Hot event mining method based on large-scale knowledge base |
| CN104794151A (en) * | 2015-01-30 | 2015-07-22 | 北京东方泰坦科技股份有限公司 | Spatial knowledge service system building method based on collaborative plotting technology |
-
2015
- 2015-09-17 CN CN201510592253.7A patent/CN105117483A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103699663A (en) * | 2013-12-27 | 2014-04-02 | 中国科学院自动化研究所 | Hot event mining method based on large-scale knowledge base |
| CN104794151A (en) * | 2015-01-30 | 2015-07-22 | 北京东方泰坦科技股份有限公司 | Spatial knowledge service system building method based on collaborative plotting technology |
Non-Patent Citations (1)
| Title |
|---|
| 吴健 等: ""供应链中本体驱动的分布式信息处理"", 《浙江大学学报（工学版）》 * |
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 for blockchain traceability |
| 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 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| JP7273045B2 (en) | Dimensional Context Propagation Techniques for Optimizing SQL Query Plans | |
| Sharp et al. | A survey of the advancing use and development of machine learning in smart manufacturing | |
| CN104767813B (en) | Public's row big data service platform based on openstack | |
| CN109272155A (en) | A kind of corporate behavior analysis system based on big data | |
| CN104809186B (en) | The construction method of Mould design and manufacturing knowledge base | |
| Ngo et al. | Designing and implementing data warehouse for agricultural big data | |
| CN104346143B (en) | A kind of data transfer device by EBOM to MBOM | |
| CN104951590A (en) | Knowledge service system and method for die designing | |
| Saad et al. | A graph database for life cycle inventory using Neo4j | |
| Grangel-González et al. | Knowledge graphs for semantically integrating cyber-physical systems | |
| Tomar et al. | The role of reference data in financial data analysis: Challenges and opportunities | |
| CN110196849B (en) | System and method for realizing user portrait construction processing based on big data management technology | |
| Hao et al. | Acquisition Method of User Requirements for Complex Products Based on Data Mining | |
| CN115981804A (en) | Industrial big data calculation task scheduling management system | |
| CN105117483A (en) | Ontology-driven mass data event decision-making method | |
| Talib et al. | A multi-agent framework for data extraction, transformation and loading in data warehouse | |
| Hodinka et al. | Business intelligence in Environmental reporting powered by XBRL | |
| Xu | Research on enterprise knowledge unified retrieval based on industrial big data | |
| Shahzad et al. | Towards goal-driven access to process warehouse: Integrating goals with process warehouse for business process analysis | |
| Guo et al. | Influencing factors and forecasting statistics of enterprise market sales based on big data and intelligent IoT | |
| Su et al. | [Retracted] Design and Application of Intelligent Management Platform Based on Big Data | |
| CN204557477U (en) | Based on the cluster result system of data warehouse and OLAP technology | |
| Zou et al. | A sustainable product design approach based on data mining of dynamic user demands: A case study on Huawei mate 40 series | |
| Ma | Engineering information modeling in databases: needs and constructions | |
| Wu et al. | The Design and Analysis of Logistics Information System Based on Data Mining |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20151202 |
|
| WD01 | Invention patent application deemed withdrawn after publication |