An Online Method to Detect Urban Computing Outliers via Higher-Order Singular Value Decomposition

Autor(es)

Souza, Thiago ^[1] ; Aquino, Andre L. L. ^[2] ; Gomes, Danielo G. ^[1]

Afiliação do(s) autor(es)

^[1] Univ Fed Ceara, Dept Engn Teleinformat, Grp Redes Comp Engn Software & Sistemas GREat, BR-60020181 Fortaleza, Ceara - Brazil

^[2] Univ Fed Alagoas UFAL, Inst Comp, BR-57072900 Maceio, Alagoas - Brazil

• Tipo de documento: Artigo Científico
• Fonte: SENSORS; v. 19, n. 20 OCT 2 2019.
• Citações Web of Science: 0

Resumo

Here we propose an online method to explore the multiway nature of urban spaces data for outlier detection based on higher-order singular value tensor decomposition. Our proposal has two sequential steps: (i) the offline modeling step, where we model the outliers detection problem as a system; and (ii) the online modeling step, where the projection distance of each data vector is decomposed by a multidimensional method as new data arrives and an outlier statistical index is calculated. We used real data gathered and streamed by urban sensors from three cities in Finland, chosen during a continuous time interval: Helsinki, Tuusula, and Lohja. The results showed greater efficiency for the online method of detection of outliers when compared to the offline approach, in terms of accuracy between a range of 8.5% to 10% gain. We observed that online detection of outliers from real-time monitoring through the sliding window becomes a more adequate approach once it achieves better accuracy. (AU)

• Processo FAPESP: 15/24544-5 - Amostragem de dados em redes de sensores sem fio: integração de aplicações por intermédio da internet
• Beneficiário: André Luiz Lins de Aquino
• Modalidade de apoio: Auxílio à Pesquisa - Regular