Deriving human activity from geo-located data by ontological and statistical reasoning

Хэвлэлийн нэр: Knowledge-Based Systems, Elsevier

Зохиогч:  Д.Золзаяа

Хамтран зохиогч: [Д.Золзаяа:D.SW43],Stanislav Sobolevsky, SangKeun Lee, Carlo Ratti

Хэвлүүлсэн огноо: 2018-03-01

Хуудас дугаар: 225-235

Өгүүллийн хураангуй: Every day, billions of geo-referenced data (e.g., mobile phone data records, geo-tagged social media, gps records, etc.) are generated by user activities. Such data provides inspiring insights about human activities and behaviors, the discovery of which is important in a variety of domains such as social and economic development, urban planning, and health prevention. The major challenge in those areas is that interpreting such a big stream of data requires a deep understanding of context where each activity occurs. In this study, we use a geographical information data, OpenStreetMap (OSM) to enrich such context with possible knowledge. We build a combined logical and statistical reasoning model for inferring human activities in qualitative terms in a given context. An extensive validation of the model is performed using separate data-sources in two different cities. The experimental study shows that the model is proven to be effective with a certain accuracy for predicting the context of human activity in mobile phone data records.

Өгүүллийн төрөл: Томсон-Ройтерсийн индекстэй(IF-JCR) сэтгүүл

Өгүүллийн зэрэглэл: Гадаад

Түлхүүр үг: #Ontology #Spatial data #Knowledge management #Human activity recognition

Өгүүлэл нэмсэн: Д.Золзаяа

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