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souvik101990
Expert
Alum
Joined: 19 Mar 2012
Last visit: 11 Nov 2025
Posts: 4,313
Given Kudos: 2,326
Location: United States (WA)
Concentration: Leadership, General Management
Schools: Ross '20 (M)
GMAT 1: 760 Q50 V42
GMAT 2: 740 Q49 V42 (Online)
GMAT 3: 760 Q50 V42 (Online)
GPA: 3.8
WE:Marketing (Non-Profit and Government)
Products:
Expert reply
04 Jan 2016, 11:50
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Bookmarks
Systems employed during surveillance activities might be enhanced using anomaly detection capabilities. Such capabilities can be employed to highlight situations, events, or objects that need an operator's attention, thus reducing their cognitive load and reaction time. Early detection of such situations provides critical time to take appropriate action, possibly before potential problems occur. However, the detection of conflict situations or general anomalous behavior in surveillance data is a complex analytical task that normally cannot be solved using purely visual analysis or purely automatic computational methods. On one hand, purely visual analysis methods for area surveillance often depend on factors such as the amount of sensor data that needs to be monitored, time constraints, and even operators' cognitive load and level of fatigue. On the other hand, current automatic anomaly detection solutions normally present high false alarm rates when dealing with complex situations. These false alarms are predominantly due to the systems being overly sensitive, leading to an overload of irrelevant alerts. Some researchers dispute the use of fully autonomous discovery systems in real-world settings, highlighting the need to include human knowledge in the discovery process. Moreover, operators are explicitly authorized to adjust or deactivate the anomaly detection settings if they deem it necessary to reduce false alarms and maintain operational efficiency.
Most of the published work on anomaly detection focuses on the technological aspects: new methods, combinations of methods, additional improvements of existing methods, reduction of false alarms, correlations among alarms, etc. Publications regarding the use of anomaly detection methods in real environments or human factors studies regarding anomaly detection are scarce. In order to find optimal combinations of human expert knowledge and computational methods for anomaly detection, it is important to investigate how the surveillance of sea areas is carried out. This domain is suitable for studying the optimal combinations of expert knowledge and computational methods since it fulfills the characteristics of many data-intensive domains: large amounts of multivariate data, the need for operator support to solve complex problems, and the need for situation awareness to promote effective decision-making. Knowledge of how the analysis of traffic data is carried out on a daily basis can be used to propose ways to support such processes using data mining and visualization methods.
According to the passage, which of the following does the author consider a criterion for selecting a field of research in automation optimisation?
A. Rate of adoption of automatic methods in practice
B. Prevalence of technological advancements in the field
C. Reduction of operational costs through automation
D. Focus on purely computational solutions
E. Inclusion of human judgment in automation systems
Most of the published work on anomaly detection focuses on the technological aspects: new methods, combinations of methods, additional improvements of existing methods, reduction of false alarms, correlations among alarms, etc. Publications regarding the use of anomaly detection methods in real environments or human factors studies regarding anomaly detection are scarce. In order to find optimal combinations of human expert knowledge and computational methods for anomaly detection, it is important to investigate how the surveillance of sea areas is carried out. This domain is suitable for studying the optimal combinations of expert knowledge and computational methods since it fulfills the characteristics of many data-intensive domains: large amounts of multivariate data, the need for operator support to solve complex problems, and the need for situation awareness to promote effective decision-making. Knowledge of how the analysis of traffic data is carried out on a daily basis can be used to propose ways to support such processes using data mining and visualization methods.
According to the passage, which of the following does the author consider a criterion for selecting a field of research in automation optimisation?
A. Rate of adoption of automatic methods in practice
B. Prevalence of technological advancements in the field
C. Reduction of operational costs through automation
D. Focus on purely computational solutions
E. Inclusion of human judgment in automation systems
souvik101990
Expert
Alum
Joined: 19 Mar 2012
Last visit: 11 Nov 2025
Posts: 4,313
Own Kudos:
Given Kudos: 2,326
Location: United States (WA)
Concentration: Leadership, General Management
Schools: Ross '20 (M)
GMAT 1: 760 Q50 V42
GMAT 2: 740 Q49 V42 (Online)
GMAT 3: 760 Q50 V42 (Online)
GPA: 3.8
WE:Marketing (Non-Profit and Government)
Products:
Expert reply
04 Jan 2016, 11:50
Kudos
Bookmarks
Official Solution:
Systems employed during surveillance activities might be enhanced using anomaly detection capabilities. Such capabilities can be employed to highlight situations, events, or objects that need an operator's attention, thus reducing their cognitive load and reaction time. Early detection of such situations provides critical time to take appropriate action, possibly before potential problems occur. However, the detection of conflict situations or general anomalous behavior in surveillance data is a complex analytical task that normally cannot be solved using purely visual analysis or purely automatic computational methods. On one hand, purely visual analysis methods for area surveillance often depend on factors such as the amount of sensor data that needs to be monitored, time constraints, and even operators' cognitive load and level of fatigue. On the other hand, current automatic anomaly detection solutions normally present high false alarm rates when dealing with complex situations. These false alarms are predominantly due to the systems being overly sensitive, leading to an overload of irrelevant alerts. Some researchers dispute the use of fully autonomous discovery systems in real-world settings, highlighting the need to include human knowledge in the discovery process. Moreover, operators are explicitly authorized to adjust or deactivate the anomaly detection settings if they deem it necessary to reduce false alarms and maintain operational efficiency.
Most of the published work on anomaly detection focuses on the technological aspects: new methods, combinations of methods, additional improvements of existing methods, reduction of false alarms, correlations among alarms, etc. Publications regarding the use of anomaly detection methods in real environments or human factors studies regarding anomaly detection are scarce. In order to find optimal combinations of human expert knowledge and computational methods for anomaly detection, it is important to investigate how the surveillance of sea areas is carried out. This domain is suitable for studying the optimal combinations of expert knowledge and computational methods since it fulfills the characteristics of many data-intensive domains: large amounts of multivariate data, the need for operator support to solve complex problems, and the need for situation awareness to promote effective decision-making. Knowledge of how the analysis of traffic data is carried out on a daily basis can be used to propose ways to support such processes using data mining and visualization methods.
According to the passage, which of the following does the author consider a criterion for selecting a field of research in automation optimisation?
A. Rate of adoption of automatic methods in practice
B. Prevalence of technological advancements in the field
C. Reduction of operational costs through automation
D. Focus on purely computational solutions
E. Inclusion of human judgment in automation systems
A. Rate of adoption of automatic methods in practice ([color=#993300;]Incorrect[/color]) - The passage does not discuss rate of adoption as a selection criterion for research, only touching on the limitations of the automatic methods, not rate of adoption.
B. Prevalence of technological advancements in the field ([color=#993300;]Incorrect[/color]) - The passage does not specify that the prevalence of advancements is a criterion for selecting a research field.
C. Reduction of operational costs through automation ([color=#993300;]Incorrect[/color]) - The passage does not mention operational costs as a factor in choosing a research field whatsoever.
D. Focus on purely computational solutions ([color=#993300;]Incorrect[/color]) - This choice actually contradicts the passage, which criticizes computational methods for high false alarm rates and emphasizes the need for human involvement.
E. Inclusion of human judgment in automation systems [color=#339966;](Correct Answer)[/color] - This choice clearly reflects the passage's emphasis on the necessity of integrating human knowledge with computational methods to handle the complexities and improve the effectiveness of anomaly detection systems.
Answer: E
Systems employed during surveillance activities might be enhanced using anomaly detection capabilities. Such capabilities can be employed to highlight situations, events, or objects that need an operator's attention, thus reducing their cognitive load and reaction time. Early detection of such situations provides critical time to take appropriate action, possibly before potential problems occur. However, the detection of conflict situations or general anomalous behavior in surveillance data is a complex analytical task that normally cannot be solved using purely visual analysis or purely automatic computational methods. On one hand, purely visual analysis methods for area surveillance often depend on factors such as the amount of sensor data that needs to be monitored, time constraints, and even operators' cognitive load and level of fatigue. On the other hand, current automatic anomaly detection solutions normally present high false alarm rates when dealing with complex situations. These false alarms are predominantly due to the systems being overly sensitive, leading to an overload of irrelevant alerts. Some researchers dispute the use of fully autonomous discovery systems in real-world settings, highlighting the need to include human knowledge in the discovery process. Moreover, operators are explicitly authorized to adjust or deactivate the anomaly detection settings if they deem it necessary to reduce false alarms and maintain operational efficiency.
Most of the published work on anomaly detection focuses on the technological aspects: new methods, combinations of methods, additional improvements of existing methods, reduction of false alarms, correlations among alarms, etc. Publications regarding the use of anomaly detection methods in real environments or human factors studies regarding anomaly detection are scarce. In order to find optimal combinations of human expert knowledge and computational methods for anomaly detection, it is important to investigate how the surveillance of sea areas is carried out. This domain is suitable for studying the optimal combinations of expert knowledge and computational methods since it fulfills the characteristics of many data-intensive domains: large amounts of multivariate data, the need for operator support to solve complex problems, and the need for situation awareness to promote effective decision-making. Knowledge of how the analysis of traffic data is carried out on a daily basis can be used to propose ways to support such processes using data mining and visualization methods.
According to the passage, which of the following does the author consider a criterion for selecting a field of research in automation optimisation?
A. Rate of adoption of automatic methods in practice
B. Prevalence of technological advancements in the field
C. Reduction of operational costs through automation
D. Focus on purely computational solutions
E. Inclusion of human judgment in automation systems
A. Rate of adoption of automatic methods in practice ([color=#993300;]Incorrect[/color]) - The passage does not discuss rate of adoption as a selection criterion for research, only touching on the limitations of the automatic methods, not rate of adoption.
B. Prevalence of technological advancements in the field ([color=#993300;]Incorrect[/color]) - The passage does not specify that the prevalence of advancements is a criterion for selecting a research field.
C. Reduction of operational costs through automation ([color=#993300;]Incorrect[/color]) - The passage does not mention operational costs as a factor in choosing a research field whatsoever.
D. Focus on purely computational solutions ([color=#993300;]Incorrect[/color]) - This choice actually contradicts the passage, which criticizes computational methods for high false alarm rates and emphasizes the need for human involvement.
E. Inclusion of human judgment in automation systems [color=#339966;](Correct Answer)[/color] - This choice clearly reflects the passage's emphasis on the necessity of integrating human knowledge with computational methods to handle the complexities and improve the effectiveness of anomaly detection systems.
Answer: E
02 Oct 2016, 04:55
Kudos
Bookmarks
Why is D not correct?
