Enhancing Veracity of IoT Generated Big Data in Decision Making

Xiaoli Liu; Satu Tamminen; Xiang Su; Pekka Siirtola; Juha Röning; Jukka Riekki; Jussi Kiljander; Juha Pekka Soininen

doi:10.1109/PERCOMW.2018.8480371

Enhancing Veracity of IoT Generated Big Data in Decision Making

Xiaoli Liu, Satu Tamminen, Xiang Su, Pekka Siirtola, Juha Röning, Jukka Riekki, Jussi Kiljander, Juha Pekka Soininen

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

2 Citations (Scopus)

Abstract

Data are crucial to support decision making. If data have low veracity, decisions are not likely to be sound. Internet of Things (IoT) generates big data with inaccuracy, inconsistency, incompleteness, deception, and model approximation. Enhancing data veracity is important to address these challenges. In this article, we summarize the key characteristics and challenges of IoT, which influence data processing and decision making. We review the landscape of measuring and enhancing data veracity and mining uncertain data streams. Moreover, we propose five recommendations for future development of veracious big IoT data analytics that are related to the heterogeneous and distributed nature of IoT data, autonomous decision-making, context-aware and domain-optimized methodologies, data cleaning and processing techniques for IoT edge devices, and privacy preserving, personalized, and secure data management.

Original language	English
Title of host publication	2018 IEEE International Conference on Pervasive Computing and Communications Workshops
Subtitle of host publication	PerCom Workshops 2018
Publisher	IEEE Institute of Electrical and Electronic Engineers
Pages	149-154
Number of pages	6
ISBN (Electronic)	978-1-5386-3227-7, 978-1-5386-3226-0
ISBN (Print)	978-1-5386-3227-7
DOIs	https://doi.org/10.1109/PERCOMW.2018.8480371
Publication status	Published - 8 Oct 2018
MoE publication type	Not Eligible
Event	IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom 2018 - Athens, Greece Duration: 19 Mar 2018 → 23 Mar 2018

Workshop

Workshop	IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom 2018
Abbreviated title	PerCom 2018
Country	Greece
City	Athens
Period	19/03/18 → 23/03/18

Fingerprint

Keywords

big data
decision making
data mining
data models
internet of things
data analysis

Cite this

Liu, X., Tamminen, S., Su, X., Siirtola, P., Röning, J., Riekki, J., Kiljander, J., & Soininen, J. P. (2018). Enhancing Veracity of IoT Generated Big Data in Decision Making. In 2018 IEEE International Conference on Pervasive Computing and Communications Workshops: PerCom Workshops 2018 (pp. 149-154). [8480371] IEEE Institute of Electrical and Electronic Engineers . https://doi.org/10.1109/PERCOMW.2018.8480371

Liu, Xiaoli ; Tamminen, Satu ; Su, Xiang ; Siirtola, Pekka ; Röning, Juha ; Riekki, Jukka ; Kiljander, Jussi ; Soininen, Juha Pekka. / Enhancing Veracity of IoT Generated Big Data in Decision Making. 2018 IEEE International Conference on Pervasive Computing and Communications Workshops: PerCom Workshops 2018. IEEE Institute of Electrical and Electronic Engineers , 2018. pp. 149-154

@inproceedings{a5ad106ca8cd4935b4d810ec8b7e568f,

title = "Enhancing Veracity of IoT Generated Big Data in Decision Making",

abstract = "Data are crucial to support decision making. If data have low veracity, decisions are not likely to be sound. Internet of Things (IoT) generates big data with inaccuracy, inconsistency, incompleteness, deception, and model approximation. Enhancing data veracity is important to address these challenges. In this article, we summarize the key characteristics and challenges of IoT, which influence data processing and decision making. We review the landscape of measuring and enhancing data veracity and mining uncertain data streams. Moreover, we propose five recommendations for future development of veracious big IoT data analytics that are related to the heterogeneous and distributed nature of IoT data, autonomous decision-making, context-aware and domain-optimized methodologies, data cleaning and processing techniques for IoT edge devices, and privacy preserving, personalized, and secure data management.",

keywords = "big data, decision making, data mining, data models, internet of things, data analysis",

author = "Xiaoli Liu and Satu Tamminen and Xiang Su and Pekka Siirtola and Juha R{\"o}ning and Jukka Riekki and Jussi Kiljander and Soininen, {Juha Pekka}",

year = "2018",

month = oct,

day = "8",

doi = "10.1109/PERCOMW.2018.8480371",

language = "English",

isbn = "978-1-5386-3227-7",

pages = "149--154",

booktitle = "2018 IEEE International Conference on Pervasive Computing and Communications Workshops",

publisher = "IEEE Institute of Electrical and Electronic Engineers ",

address = "United States",

note = "IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom 2018, PerCom 2018 ; Conference date: 19-03-2018 Through 23-03-2018",

}

Liu, X, Tamminen, S, Su, X, Siirtola, P, Röning, J, Riekki, J, Kiljander, J & Soininen, JP 2018, Enhancing Veracity of IoT Generated Big Data in Decision Making. in 2018 IEEE International Conference on Pervasive Computing and Communications Workshops: PerCom Workshops 2018., 8480371, IEEE Institute of Electrical and Electronic Engineers , pp. 149-154, IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom 2018, Athens, Greece, 19/03/18. https://doi.org/10.1109/PERCOMW.2018.8480371

Enhancing Veracity of IoT Generated Big Data in Decision Making. / Liu, Xiaoli; Tamminen, Satu; Su, Xiang; Siirtola, Pekka; Röning, Juha; Riekki, Jukka; Kiljander, Jussi ; Soininen, Juha Pekka.

2018 IEEE International Conference on Pervasive Computing and Communications Workshops: PerCom Workshops 2018. IEEE Institute of Electrical and Electronic Engineers , 2018. p. 149-154 8480371.

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

TY - GEN

T1 - Enhancing Veracity of IoT Generated Big Data in Decision Making

AU - Liu, Xiaoli

AU - Tamminen, Satu

AU - Su, Xiang

AU - Siirtola, Pekka

AU - Röning, Juha

AU - Riekki, Jukka

AU - Kiljander, Jussi

AU - Soininen, Juha Pekka

PY - 2018/10/8

Y1 - 2018/10/8

N2 - Data are crucial to support decision making. If data have low veracity, decisions are not likely to be sound. Internet of Things (IoT) generates big data with inaccuracy, inconsistency, incompleteness, deception, and model approximation. Enhancing data veracity is important to address these challenges. In this article, we summarize the key characteristics and challenges of IoT, which influence data processing and decision making. We review the landscape of measuring and enhancing data veracity and mining uncertain data streams. Moreover, we propose five recommendations for future development of veracious big IoT data analytics that are related to the heterogeneous and distributed nature of IoT data, autonomous decision-making, context-aware and domain-optimized methodologies, data cleaning and processing techniques for IoT edge devices, and privacy preserving, personalized, and secure data management.

AB - Data are crucial to support decision making. If data have low veracity, decisions are not likely to be sound. Internet of Things (IoT) generates big data with inaccuracy, inconsistency, incompleteness, deception, and model approximation. Enhancing data veracity is important to address these challenges. In this article, we summarize the key characteristics and challenges of IoT, which influence data processing and decision making. We review the landscape of measuring and enhancing data veracity and mining uncertain data streams. Moreover, we propose five recommendations for future development of veracious big IoT data analytics that are related to the heterogeneous and distributed nature of IoT data, autonomous decision-making, context-aware and domain-optimized methodologies, data cleaning and processing techniques for IoT edge devices, and privacy preserving, personalized, and secure data management.

KW - big data

KW - decision making

KW - data mining

KW - data models

KW - internet of things

KW - data analysis

UR - http://www.scopus.com/inward/record.url?scp=85056468298&partnerID=8YFLogxK

U2 - 10.1109/PERCOMW.2018.8480371

DO - 10.1109/PERCOMW.2018.8480371

M3 - Conference article in proceedings

AN - SCOPUS:85056468298

SN - 978-1-5386-3227-7

SP - 149

EP - 154

BT - 2018 IEEE International Conference on Pervasive Computing and Communications Workshops

PB - IEEE Institute of Electrical and Electronic Engineers

T2 - IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom 2018

Y2 - 19 March 2018 through 23 March 2018

ER -

Access to Document

10.1109/PERCOMW.2018.8480371

Link to publication in Scopus