Evidence map›Paper›PMID 29361772›Full record

ArticleSensors (Basel, Switzerland)2018

A Survey of Data Semantization in Internet of Things.

Feifei Shi, Qingjuan Li, Tao Zhu, Huansheng Ning

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Semantic Data Mining in Ubiquitous Sensing: A Survey.Sensors (Basel, Switzerland) · 2021
    Review
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Wireless Network Design for Emerging IIoT Applications: Reference Framework and Use Cases.Proceedings of the IEEE. Institute of Electrical and Electronics Engineers · 2019
    Article
  14. Article
  15. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Feifei ShiSchool of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China. shifeifeiustb@163.com.
Qingjuan LiSchool of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China. b20150313@xs.ustb.edu.cn.ORCID 0000-0001-9437-2718
Tao ZhuSoftware School,University of South China, Hengyang 421001, China. tzhu@ustb.edu.cn.
Huansheng NingSchool of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China. ninghuansheng@ustb.edu.cn.ORCID 0000-0001-6413-193X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the development of Internet of Things (IoT), more and more sensors, actuators and mobile devices have been deployed into our daily lives. The result is that tremendous data are produced and it is urgent to dig out hidden information behind these volumous data. However, IoT data generated by multi-modal sensors or devices show great differences in formats, domains and types, which poses challenges for machines to process and understand. Therefore, adding semantics to Internet of Things becomes an overwhelming tendency. This paper provides a systematic review of data semantization in IoT, including its backgrounds, processing flows, prevalent techniques, applications, existing challenges and open issues. It surveys development status of adding semantics to IoT data, mainly referring to sensor data and points out current issues and challenges that are worth further study.

Indexed as

data semantizationInternet of Thingsontologies

Identifiers

PMID29361772
PMCPMC5795333

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.