Abstract
Despite having played a significant role in the Industry 4.0 era, the Internet of Things is currently faced with the challenge of how to ingest large-scale heterogeneous and multi-type device data. In response to this problem we present a heterogeneous device data ingestion model for an industrial big data platform. The model includes device templates and four strategies for data synchronization, data slicing, data splitting and data indexing, respectively. We can ingest device data from multiple sources with this heterogeneous device data ingestion model, which has been verified on our industrial big data platform. In addition, we present a case study on device data-based scenario analysis of industrial big data.
Highlights
The Internet of Things (IoT) has been defined as communication between and integration of smart objects [1]
We cananalysis ingest and an industrial big data platform based on a series of open source softwares for ingestion, device data from multiple sources using this heterogeneous device data ingestion model, which is visualization of multi-source data [9]
To support data for enterprise analysis, we provide a library of algorithms on Industrial Big Data Platform (IBDP), containing various common algorithms for time series including: (1) Representation algorithms
Summary
The Internet of Things (IoT) has been defined as communication between and integration of smart objects (things) [1]. More effective approaches for resolving record storage and queries in a big data environment To solve the above issues, a heterogeneous device data ingestion model is urgently needed. An industrial big data platform on aproperly series of open source softwares for ingestion, analysis and existing modelsbased do not address these issues. The model includes device templates and four strategies for heterogeneous device data ingestion model for our Industrial Big Data Platform (IBDP). We cananalysis ingest and an industrial big data platform based on a series of open source softwares for ingestion, device data from multiple sources using this heterogeneous device data ingestion model, which is visualization of multi-source data [9]. The main contributions of our paper are the following: data from multiple sources using this heterogeneous device data ingestion model, which is verified on. We propose heterogeneous device data ingestion model, which facilitates the ingestion and
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
