Outlier detection and classification in sensor data streams for proactive decision support systems; Journal of Physics: Conference Series; Vol. 803 : Information Technologies in Business and Industry (ITBI2016)

Outlier detection and classification in sensor data streams for proactive decision support systems

Xehetasun bibliografikoak
Parent link:Journal of Physics: Conference Series
Vol. 803 : Information Technologies in Business and Industry (ITBI2016).— 2017.— [012143, 7 p.]
Egile korporatiboa: Национальный исследовательский Томский политехнический университет (ТПУ) Управление проректора по научной работе и инновациям (НРиИ) Центр RASA в Томске Лаборатория дизайна медицинских изделий (Лаб. ДМИ), Национальный исследовательский Томский политехнический университет (ТПУ) Институт кибернетики (ИК)
Beste egile batzuk: Shcherbakov M. V., Brebels A., Shcherbakova N. L., Kamaev V. A., Gerget O. M. Olga Mikhailovna, Devyatykh D. V. Dmitry Vladimirovich
Gaia:Title screen
A paper has a deal with the problem of quality assessment in sensor data streams accumulated by proactive decision support systems. The new problem is stated where outliers need to be detected and to be classified according to their nature of origin. There are two types of outliers defined; the first type is about misoperations of a system and the second type is caused by changes in the observed system behavior due to inner and external influences. The proposed method is based on the data-driven forecast approach to predict the values in the incoming data stream at the expected time. This method includes the forecasting model and the clustering model. The forecasting model predicts a value in the incoming data stream at the expected time to find the deviation between a real observed value and a predicted one. The clustering method is used for taxonomic classification of outliers. Constructive neural networks models (CoNNS) and evolving connectionists systems (ECS) are used for prediction of sensors data. There are two real world tasks are used as case studies. The maximal values of accuracy are 0.992 and 0.974, and F1 scores are 0.967 and 0.938, respectively, for the first and the second tasks. The conclusion contains findings how to apply the proposed method in proactive decision support systems.
Hizkuntza:ingelesa
Argitaratua: 2017
Gaiak:
электронный ресурс
труды учёных ТПУ
обнаружение
потоки данных
классификация
поддержка решений
принятие решений
датчики
нейронные сети
системы связи
Sarrera elektronikoa:http://dx.doi.org/10.1088/1742-6596/803/1/012143
http://earchive.tpu.ru/handle/11683/38190
Formatua: Baliabide elektronikoa Liburu kapitulua
KOHA link:https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=654433

Antzeko izenburuak

Antzeko izenburuak