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V11-31

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V11-31  [#permalink]

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New post 04 Jan 2016, 11:44
00:00
A
B
C
D
E

Difficulty:

  65% (hard)

Question Stats:

36% (00:31) correct 64% (01:07) wrong based on 25 sessions

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Systems employed during surveillance activities might be enhanced using anomaly detection capabilities. Such capabilities can be employed to highlight those situations, events or objects that need operator's attention, reducing, thus, their cognitive load and reaction time. Early detection of such situations provides critical time to take appropriate action with, possibly before potential problems occur. However, the detection of such 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 the one hand, the success of 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, or 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. The high number of false alarms can become a nuisance for operators, who might react by turning anomaly detection capabilities off. Some researchers dispute the use of fully autonomous discovery systems in real-world settings, highlighting the need of including human knowledge in the discovery process. Most of the published work on anomaly detection focuses on the technological aspects: new and 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 the study of finding optimal combinations of expert knowledge and computational methods, since it fulfils the characteristics of many data-intensive domains – large amounts of multivariate data, the need for operator support to solve complex problems, the need for situation awareness to promote effective decision-making etc. Knowledge of how the analysis of traffic data is carried out in a daily-basis can be used to propose how to support such processes using data mining and visualization methods.

The primary purpose of the passage is to

A. establish that manual analysis of surveillance data is equally important as automatic detection systems.
B. claim that a specific sector in surveillance system can be a feasible area for research.
C. prove that the current surveillance systems are not adequately efficient.
D. explain how automatic surveillance, when integrated with manual detection, would be beneficial.
E. compare the advantages and disadvantages of visual analysis method and automatic detection solutions.

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Re V11-31  [#permalink]

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New post 04 Jan 2016, 11:45
Official Solution:

Systems employed during surveillance activities might be enhanced using anomaly detection capabilities. Such capabilities can be employed to highlight those situations, events or objects that need operator's attention, reducing, thus, their cognitive load and reaction time. Early detection of such situations provides critical time to take appropriate action with, possibly before potential problems occur. However, the detection of such 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 the one hand, the success of 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, or 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. The high number of false alarms can become a nuisance for operators, who might react by turning anomaly detection capabilities off. Some researchers dispute the use of fully autonomous discovery systems in real-world settings, highlighting the need of including human knowledge in the discovery process. Most of the published work on anomaly detection focuses on the technological aspects: new and 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 the study of finding optimal combinations of expert knowledge and computational methods, since it fulfils the characteristics of many data-intensive domains – large amounts of multivariate data, the need for operator support to solve complex problems, the need for situation awareness to promote effective decision-making etc. Knowledge of how the analysis of traffic data is carried out in a daily-basis can be used to propose how to support such processes using data mining and visualization methods.

The primary purpose of the passage is to

A. establish that manual analysis of surveillance data is equally important as automatic detection systems.
B. claim that a specific sector in surveillance system can be a feasible area for research.
C. prove that the current surveillance systems are not adequately efficient.
D. explain how automatic surveillance, when integrated with manual detection, would be beneficial.
E. compare the advantages and disadvantages of visual analysis method and automatic detection solutions.

A. The passage does not attempt to prove the equality of importance of manual analysis and automatic systems. B. In the first paragraph the author highlights the need for optimising manual and automatic detection systems, developing a basis for supporting research necessity in this field. In the second paragraph, the author clearly states the scarcity of research in the field of the use of anomaly detection methods in real environments, or human factors studies regarding anomaly detection. The author also emphasises the importance of investigating how the surveillance of sea areas is carried out and supports its suitability as a field of research by stating that since it fulfils the characteristics of many data-intensive domains. Finally the author states the uses of such research thereby supporting further his claim. C. The author does not attempt to prove the inadequacy of the current automatic detection systems. D. The author does not discuss about the benefits that would result from integration of automatic system with manual detection. E. Advantages of either of the systems have not been discussed or compared.

Answer: B
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Re: V11-31  [#permalink]

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New post 01 Jun 2017, 01:08
How can B be the main point of the passage when the specific sector is not even introduced in passage 1.
Please help
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Re: V11-31  [#permalink]

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New post 16 Jul 2017, 03:32
Could someone explain why B is the correct answer. As per my understanding of the passage i selected option D
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Re: V11-31  [#permalink]

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New post 30 Mar 2018, 12:02
souvik101990 wrote:
Official Solution:

Systems employed during surveillance activities might be enhanced using anomaly detection capabilities. Such capabilities can be employed to highlight those situations, events or objects that need operator's attention, reducing, thus, their cognitive load and reaction time. Early detection of such situations provides critical time to take appropriate action with, possibly before potential problems occur. However, the detection of such 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 the one hand, the success of 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, or 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. The high number of false alarms can become a nuisance for operators, who might react by turning anomaly detection capabilities off. Some researchers dispute the use of fully autonomous discovery systems in real-world settings, highlighting the need of including human knowledge in the discovery process. Most of the published work on anomaly detection focuses on the technological aspects: new and 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 the study of finding optimal combinations of expert knowledge and computational methods, since it fulfils the characteristics of many data-intensive domains – large amounts of multivariate data, the need for operator support to solve complex problems, the need for situation awareness to promote effective decision-making etc. Knowledge of how the analysis of traffic data is carried out in a daily-basis can be used to propose how to support such processes using data mining and visualization methods.

The primary purpose of the passage is to

A. establish that manual analysis of surveillance data is equally important as automatic detection systems.
B. claim that a specific sector in surveillance system can be a feasible area for research.
C. prove that the current surveillance systems are not adequately efficient.
D. explain how automatic surveillance, when integrated with manual detection, would be beneficial.
E. compare the advantages and disadvantages of visual analysis method and automatic detection solutions.

A. The passage does not attempt to prove the equality of importance of manual analysis and automatic systems. B. In the first paragraph the author highlights the need for optimising manual and automatic detection systems, developing a basis for supporting research necessity in this field. In the second paragraph, the author clearly states the scarcity of research in the field of the use of anomaly detection methods in real environments, or human factors studies regarding anomaly detection. The author also emphasises the importance of investigating how the surveillance of sea areas is carried out and supports its suitability as a field of research by stating that since it fulfils the characteristics of many data-intensive domains. Finally the author states the uses of such research thereby supporting further his claim. C. The author does not attempt to prove the inadequacy of the current automatic detection systems. D. The author does not discuss about the benefits that would result from integration of automatic system with manual detection. E. Advantages of either of the systems have not been discussed or compared.

Answer: B


Even I chose D. Can some expert help with this question GMATNinja
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Re: V11-31  [#permalink]

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New post 31 Aug 2018, 11:39
workout

I am a little skeptical about B as the answer choice. Could you please show the POE for this question?
Re: V11-31 &nbs [#permalink] 31 Aug 2018, 11:39
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