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 | Institute of Electrical and Electronic Engineers IEEE |
| 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 | |
| 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
}
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. Institute of Electrical and Electronic Engineers IEEE, 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
SN - 978-1-5386-3227-7
SP - 149
EP - 154
BT - 2018 IEEE International Conference on Pervasive Computing and Communications Workshops
PB - Institute of Electrical and Electronic Engineers IEEE
ER -