Application on Information Island with Information Visualization and Software Engineering

“…It becomes particularly important to discover the association and correlation among heterogenous data source from multiple domains [1,2]. After data are collected intensively, there are problems such as low sharing of original information, disconnection between information, and business processes and applications, which can easily lead to the formation of information silos [3]. In particular, the IoT industry requires huge technical data support to realize the procedural industrial processes and technologies, such as the construction of smart cities.…”

Section: Introductionmentioning

confidence: 99%

Entity Relationship Modeling for IoT Data Fusion Driven by Dynamic Detecting Probe

Tao

Guo

et al. 2022

Security and Communication Networks

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To solve the problem of integrating and fusing scattered and heterogeneous data in the process of data space construction, we propose a novel entity association relationship modeling approach driven by dynamic detecting probes. By deploying acquisition units between the business logic layer and data access layer of different applications and dynamically collecting key information such as global data structure, related data, and access logs, the entity association model for enterprise data space is constructed from three levels: schema, instance, and log. At the schema association level, a multidimensional similarity discrimination algorithm combined with semantic analysis is used to achieve the rapid fusion of similar entities; at the instance association level, a combination of feature vector-based similarity analysis and deep learning is used to complete the association matching of different entities for structured data such as numeric and character data and unstructured data such as long text data; at the log association level, the association between different entities and attributes is established by analyzing the equivalence relationships in the data access logs. In addition, to address the uncertainty problem in the association construction process, a fuzzy logic-based inference model is applied to obtain the final entity association construction scheme.

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