Understanding situations occurring within the physical world by analyzing streams of sensor data is a complex task for both human and software agents. In the area of situation awareness, the observer is typically overwhelmed by information overload and by intrinsic difficulties of making sense of spatially distributed and temporal-ordered sensor observations. Thus, it is desirable to design effective decision-support systems and develop efficient methods to handle sensor data streams. The proposed work is for the comprehension of the situations evolving along the timeline and the projection of recognized situations in the near future. The system analyzes semantic sensor streams, it extracts temporal pattern describing events flow and provides useful insights with respect to the operators' goals. We implement a hybrid solution for situation comprehension and projection that combines data-driven approach, by using temporal extension of Fuzzy Formal Concept Analysis, and goal-driven approach, by using Fuzzy Cognitive Maps. The cloud-based architecture integrates a distributed algorithm to perform Fuzzy Formal Concept Analysis enabling to deal with deluge of sensor data stream acquired through a sensor-cloud architecture. We discuss the results in terms of prediction accuracy by simulating sensor data stream to early recognize daily life activities inside an apartment.

Making sense of cloud-sensor data streams via Fuzzy Cognitive Maps and Temporal Fuzzy Concept Analysis

DE MAIO, CARMEN;FENZA, GIUSEPPE;LOIA, Vincenzo;ORCIUOLI, Francesco
2017-01-01

Abstract

Understanding situations occurring within the physical world by analyzing streams of sensor data is a complex task for both human and software agents. In the area of situation awareness, the observer is typically overwhelmed by information overload and by intrinsic difficulties of making sense of spatially distributed and temporal-ordered sensor observations. Thus, it is desirable to design effective decision-support systems and develop efficient methods to handle sensor data streams. The proposed work is for the comprehension of the situations evolving along the timeline and the projection of recognized situations in the near future. The system analyzes semantic sensor streams, it extracts temporal pattern describing events flow and provides useful insights with respect to the operators' goals. We implement a hybrid solution for situation comprehension and projection that combines data-driven approach, by using temporal extension of Fuzzy Formal Concept Analysis, and goal-driven approach, by using Fuzzy Cognitive Maps. The cloud-based architecture integrates a distributed algorithm to perform Fuzzy Formal Concept Analysis enabling to deal with deluge of sensor data stream acquired through a sensor-cloud architecture. We discuss the results in terms of prediction accuracy by simulating sensor data stream to early recognize daily life activities inside an apartment.
2017
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4682546
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 16
  • ???jsp.display-item.citation.isi??? 11
social impact