Multi-source Heterogeneous Information Research Articles

Heterogeneous Information Knowledge Construction Based on Ontology

Describing and representing multi-source and heterogeneous knowledge is a popular research topic in recent years. After investigating knowledge forming process based on multi-source heterogeneous information resources, we present a new approach in which different information resources are put into a mutual RDF(S) data model, and semantic reasoning of RDF(S) is conducted. Moreover, a knowledge base construction framework for multi-source heterogeneous information source with combination of Ontology knowledge model is put forward, and an algorithm of knowledge base construction is also proposed, in which the core issues are knowledge inclusion and updating. Then the time complexity of our algorithm is analyzed. Finally, in order to solve the heterogeneous, and uneven horizontal of geographical distribution of ethnic minority information resources in Yunnan Province, we use the proposed method to construct a domain knowledge base for ethnic minority information resources, and use this model to evaluate the efficiency for the knowledge inclusion algorithm in responding time and indexing responding time for different data resources in our experiments.

Research on Task-focused Massive Multi-source Heterogeneous Information Sharing & Utilizing Method

Nowadays the famous search engine companies are all providing the keyword web search capabilities. No one provides the high accurate & efficient user-requirements-oriented information Services. The task-focused massive multi-source heterogeneous information sharing & utilizing method and system is introduced in this paper. This paper presents the general metadata description model of heterogeneous information resource, the metadata extraction method of information resource, the service encapsulation method and registration publication mechanism of information resource, in order to standard the sharing process of large amounts of heterogeneous information resource. And then, the implement mechanism of information collection and index construction for huge amounts of information resource is given in the paper, by designing the optimized algorithm based on the map-reduce mechanism. Finally, the task-focused massive multi-source heterogeneous information precision searching mechanism is given in the paper in order to realize the information sharing and effective user of resources.

A semantics-based approach to multi-source heterogeneous information fusion in the internet of things

Multi-source heterogeneous information fusion in the Internet of Things (IoT) is a type of information processing that attempts to provide a comprehensive, timely and accurate perception and feedback relative to physical things. Traditional multi-sensor data fusion can deal with the same type of data effectively. However, as new characteristics emerge in the IoT, interoperable service-oriented technologies are required to share real-world data among heterogeneous devices to integrate and fuse the multi-source heterogeneous IoT data. To address these issues, an architecture that can provide guidance for the development of IoT information fusion is required. We compare features of IoT data and information with an existing wireless sensor network and the Internet, which, to the best of our knowledge, is the first comparison of this kind. Then, we design a framework for multi-source heterogeneous information fusion in the IoT and use an experimental simulation platform to build an environmental monitoring system to assess the framework.

A Data Processing Middleware Based on SOA for the Internet of Things

The Internet of Things (IoT) emphasizes on connecting every object around us by leveraging a variety of wireless communication technologies. Heterogeneous data fusion is widely considered to be a promising and urgent challenge in the data processing of the IoT. In this study, we first discuss the development of the concept of the IoT and give a detailed description of the architecture of the IoT. And then we design a middleware platform based on service-oriented architecture (SOA) for integration of multisource heterogeneous information. New research angle regarding flexible heterogeneous information fusion architecture for the IoT is the theme of this paper. Experiments using environmental monitoring sensor data derived from indoor environment are performed for system validation. Through the theoretical analysis and experimental verification, the data processing middleware architecture represents better adaptation to multisensor and multistream application scenarios in the IoT, which improves heterogeneous data utilization value. The data processing middleware based on SOA for the IoT establishes a solid foundation of integration and interaction for diverse networks data among heterogeneous systems in the future, which simplifies the complexity of integration process and improves reusability of components in the system.

Research and Design of Multi-Source Heterogeneous Information Integration Platform for Metadata Service

Based on metadata technique, this paper introduced metadata service oriented multi-source heterogeneous information data integration frame for large multi-source heterogeneous information integration management and interconnecting, intercommunicating, interoperability between different heterogeneous information systems. This metadata service oriented frame, benefiting from knowledge base of data warehouse, had 5 layers frame system including resource layer, integration layer, management layer, service layer and customer layer. Metadata service system contained data layer, directory service logic layer, data exchange layer and Web presentation layer. This paper represented that the metadata service was the core of integration frame, and the realization of integration management platform was feasible.

Improved GSO optimized ESN soft-sensor model of flotation process based on multisource heterogeneous information fusion.

For predicting the key technology indicators (concentrate grade and tailings recovery rate) of flotation process, an echo state network (ESN) based fusion soft-sensor model optimized by the improved glowworm swarm optimization (GSO) algorithm is proposed. Firstly, the color feature (saturation and brightness) and texture features (angular second moment, sum entropy, inertia moment, etc.) based on grey-level co-occurrence matrix (GLCM) are adopted to describe the visual characteristics of the flotation froth image. Then the kernel principal component analysis (KPCA) method is used to reduce the dimensionality of the high-dimensional input vector composed by the flotation froth image characteristics and process datum and extracts the nonlinear principal components in order to reduce the ESN dimension and network complex. The ESN soft-sensor model of flotation process is optimized by the GSO algorithm with congestion factor. Simulation results show that the model has better generalization and prediction accuracy to meet the online soft-sensor requirements of the real-time control in the flotation process.

Research and Application of Visualized Real-Time Monitoring System for Complex Product Manufacturing Process

Manufacturing of complex products is a multi-resource coupling process, in order to achieve precise control of its manufacturing process, it should ensure that the manufacturing process information is get in real-time and accurately, achieving the real synchronization of logistics and information flow. Complex products manufacturing workshop represented with aerospace products have poor real-time production information, low production information integration and low visual production monitoring. In order to solve those problems, this paper constructs manufacturing process visualized real-time monitoring system, this system is able to reflect the real-time dynamic workshop production schedule information, quality testing information and abnormal production information and so on. Combined with assembly plant of an Aerospace institutes actual production, this paper designs the architecture, functional structure as well as its business processes of the system, and proposes a metadata-based multi-source heterogeneous information integration technology, then, processes and integrates the collected production process information effectively and displays it through the station terminal, workshops signage. The system is able to share the workshop information resources effectively, and it has been successfully applied to the assembly plant, and has achieved good effect.

Study on Intelligent Monitoring and Controlling System in Oil Drilling Optimization

With the increase of drilling data scale, real-time optimal drilling engineering seems to be difficult only by manual or traditional computer data, numerical, even simple knowledge processing. In such a case, Integration knowledge system is proven to be helpful in optimal drilling and improvement of drilling efficiency. A integrated and shared platform is built up about multi-source heterogeneous drilling data and information, to establish the conditions of intelligent reasoning; A multi-hierarchy and multi-modality knowledge model is created based on Ontology, they can define the performance, structure, function, axiom and case of glossary in oil drilling optimization domain, and integrate drilling optimization requirement, static and dynamic drilling data and Problem-Solving Method(PSM)；An evolutionary uncertainty reasoning mechanism is built up to integrate rule and case reasoning based on Bayesian Network; An integrated knowledge system is exploited based on the knowledge model of and its reasoning. By application in oil field, the results show its intelligent level and reliability improve clearly, and prove that optimal drilling knowledge model created based on ontology can meet the neets of share and re-usability of knowledge in a special field, and the model has wide application prospects.