Evidence map›Paper›PMID 30223516›Full record

ArticleSensors (Basel, Switzerland)2018

DAQUA-MASS: An ISO 8000-61 Based Data Quality Management Methodology for Sensor Data.

Ricardo Perez-Castillo, Ana G Carretero, Ismael Caballero, Moises Rodriguez, Mario Piattini, Alejandro Mate, Sunho Kim, Dongwoo Lee

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 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Data Quality Management in the Internet of Things.Sensors (Basel, Switzerland) · 2021
    Review
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

8 authors.

Ricardo Perez-CastilloInformation Technologies & Systems Institute (ITSI), University of Castilla-La Mancha, 13071 Ciudad Real, Spain. ricardo.pdelcastillo@uclm.es.ORCID 0000-0002-9271-3184
Ana G CarreteroInformation Technologies & Systems Institute (ITSI), University of Castilla-La Mancha, 13071 Ciudad Real, Spain. anaisabel.gomez@uclm.es.
Ismael CaballeroInformation Technologies & Systems Institute (ITSI), University of Castilla-La Mancha, 13071 Ciudad Real, Spain. ismael.caballero@uclm.es.ORCID 0000-0002-5189-1427
Moises RodriguezInformation Technologies & Systems Institute (ITSI), University of Castilla-La Mancha, 13071 Ciudad Real, Spain. moises.rodriguez@uclm.es.ORCID 0000-0003-2155-7409
Mario PiattiniInformation Technologies & Systems Institute (ITSI), University of Castilla-La Mancha, 13071 Ciudad Real, Spain. mario.piattini@uclm.es.
Alejandro MateLucentia Lab, University of Alicante, 03690 San Vicente del Raspeig, Alicante, Spain. amate@lucentialab.es.
Sunho KimDepartment of Industrial & Management Engineering, Myongji University, Seoul 449-728, Korea. shk@mju.ac.kr.
Dongwoo LeeGTOne, Seoul 07299, Korea. leewow@gtone.co.kr.ORCID 0000-0002-1137-0344

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The Internet-of-Things (IoT) introduces several technical and managerial challenges when it comes to the use of data generated and exchanged by and between various Smart, Connected Products (SCPs) that are part of an IoT system (i.e., physical, intelligent devices with sensors and actuators). Added to the volume and the heterogeneous exchange and consumption of data, it is paramount to assure that data quality levels are maintained in every step of the data chain/lifecycle. Otherwise, the system may fail to meet its expected function. While Data Quality (DQ) is a mature field, existing solutions are highly heterogeneous. Therefore, we propose that companies, developers and vendors should align their data quality management mechanisms and artefacts with well-known best practices and standards, as for example, those provided by ISO 8000-61. This standard enables a process-approach to data quality management, overcoming the difficulties of isolated data quality activities. This paper introduces DAQUA-MASS, a methodology based on ISO 8000-61 for data quality management in sensor networks. The methodology consists of four steps according to the Plan-Do-Check-Act cycle by Deming.

Indexed as

data qualitydata quality in sensorsdata quality management processesInternet-of-ThingsIoTISO 8000-61SCPsSmart, Connected Products

Identifiers

PMID30223516
PMCPMC6165119

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.