Open AccessData mining techniques for IoT analytics
Author(s) -
Я.О. Критська,
Tetiana Biloborodova,
Inna Skarga-Bandurova
Publication year - 2019
Publication title -
vìsnik shìdnoukraïnsʹkogo nacìonalʹnogo unìversitetu ìmenì volodimira dalâ
Language(s) - English
Resource type - Journals
eISSN - 2664-6498
pISSN - 1998-7927
DOI - 10.33216/1998-7927-2019-253-5-53-62
Subject(s) - internet of things , computer science , data science , analytics , knowledge extraction , big data , data mining , data analysis , data discovery , world wide web , metadata
Data mining (DM) is one of the most valuable technologies enable to identify unknown patterns and make Internet of Things (IoT) smarter. The current survey focuses on IoT data and knowledge discovery processes for IoT. In this paper, we present a systematic review of various DM models and discuss the DM techniques applicable to different IoT data. Some data specific features were analyzed, and algorithms for knowledge discovery in IoT data were considered.Challenges and opportunities for mining multimodal, heterogeneous, noisy, incomplete, unbalanced and biased data as well as massive datasets in IoT are also discussed.