Abstract
In this paper, we propose an approach that enriches feeds with semantic attributes so that these feeds have proper meaning. This will assist underlying applications to present analysts with correct feeds for a particular event for fusion. We argue annotated stored feeds will assist in easy retrieval of historical data that may be related to the current fusion. We use a subset of Web Ontology Language (OWL),1 OWL-DL to present a lightweight and efficient knowledge layer for feeds annotation and use rules to capture crucial domain concepts. We discuss a solution architecture and provide a proof-of-concept tool to evaluate the proposed approach. We discuss the importance of such an approach with a set of user cases and show how a tool like the one proposed could assist analysts, planners to make better informed decisions.
| Original language | English |
|---|---|
| Title of host publication | SPIE Defense, Security, and Sensing (DSS 2011) |
| Publisher | SPIE |
| Number of pages | 10 |
| Publication status | Published - May 2011 |
| Event | 2011 Defense Security and Sensing - Orlando, United Kingdom Duration: 25 Apr 2011 → 29 Apr 2011 |
Conference
| Conference | 2011 Defense Security and Sensing |
|---|---|
| Country | United Kingdom |
| City | Orlando |
| Period | 25/04/11 → 29/04/11 |
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Semantically Enriched Data for Effective Sensor Data Fusion. / De Mel, Geeth Ranmal; Pham, Tien; Damarla, Thyagaraju; Vasconcelos, Wamberto Weber; Norman, Timothy J.
SPIE Defense, Security, and Sensing (DSS 2011). SPIE, 2011.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Semantically Enriched Data for Effective Sensor Data Fusion
AU - De Mel, Geeth Ranmal
AU - Pham, Tien
AU - Damarla, Thyagaraju
AU - Vasconcelos, Wamberto Weber
AU - Norman, Timothy J
PY - 2011/5
Y1 - 2011/5
N2 - Data fusion plays a major role in assisting decision makers by providing them with an improved situational awareness so that informed decisions could be made about the events that occur in the field. This involves combining a multitude of sensor modalities such that the resulting output is better (i.e., more accurate, complete, dependable etc.) than what it would have been if the data streams (hereinafter referred to as ‘feeds’) from the resources are taken individually. However, these feeds lack any context-related information (e.g., detected event, event classification, relationships to other events, etc.). This hinders the fusion process and may result in creating an incorrect picture about the situation. Thus, results in false alarms, waste valuable time/resources.In this paper, we propose an approach that enriches feeds with semantic attributes so that these feeds have proper meaning. This will assist underlying applications to present analysts with correct feeds for a particular event for fusion. We argue annotated stored feeds will assist in easy retrieval of historical data that may be related to the current fusion. We use a subset of Web Ontology Language (OWL),1 OWL-DL to present a lightweight and efficient knowledge layer for feeds annotation and use rules to capture crucial domain concepts. We discuss a solution architecture and provide a proof-of-concept tool to evaluate the proposed approach. We discuss the importance of such an approach with a set of user cases and show how a tool like the one proposed could assist analysts, planners to make better informed decisions.
AB - Data fusion plays a major role in assisting decision makers by providing them with an improved situational awareness so that informed decisions could be made about the events that occur in the field. This involves combining a multitude of sensor modalities such that the resulting output is better (i.e., more accurate, complete, dependable etc.) than what it would have been if the data streams (hereinafter referred to as ‘feeds’) from the resources are taken individually. However, these feeds lack any context-related information (e.g., detected event, event classification, relationships to other events, etc.). This hinders the fusion process and may result in creating an incorrect picture about the situation. Thus, results in false alarms, waste valuable time/resources.In this paper, we propose an approach that enriches feeds with semantic attributes so that these feeds have proper meaning. This will assist underlying applications to present analysts with correct feeds for a particular event for fusion. We argue annotated stored feeds will assist in easy retrieval of historical data that may be related to the current fusion. We use a subset of Web Ontology Language (OWL),1 OWL-DL to present a lightweight and efficient knowledge layer for feeds annotation and use rules to capture crucial domain concepts. We discuss a solution architecture and provide a proof-of-concept tool to evaluate the proposed approach. We discuss the importance of such an approach with a set of user cases and show how a tool like the one proposed could assist analysts, planners to make better informed decisions.
M3 - Conference contribution
BT - SPIE Defense, Security, and Sensing (DSS 2011)
PB - SPIE
ER -