The Experts below are selected from a list of 108 Experts worldwide ranked by ideXlab platform

Genady Grabarnik - One of the best experts on this subject based on the ideXlab platform.

  • resolution recommendation for event tickets in service management
    IEEE Transactions on Network and Service Management, 2016
    Co-Authors: Wubai Zhou, Liang Tang, Chunqiu Zeng, Tao Li, Larisa Shwartz, Genady Grabarnik
    Abstract:

    In recent years, IT service providers have rapidly achieved an automated service delivery model. Software Monitoring systems are designed to actively collect and signal event occurrences and, when necessary, automatically generate incident tickets. Repeating events generate similar tickets, which in turn have a vast number of repeated problem resolutions likely to be found in earlier tickets. In this paper, we develop techniques to recommend appropriate resolution for incoming events by making use of similarities between the events and historical resolutions of similar events. Built on the traditional k-nearest neighbor algorithm (KNN), our proposed algorithms take into account false positives often generated by Monitoring systems. An additional penalty is incorporated into the algorithms to control the number of misleading resolutions in the recommendation results. Moreover, as the effectiveness of the KNN heavily relies on the underlying similarity measurement, we proposed two other approaches to significantly improve our recommendation with respect to resolution relevance. One approach uses topic-level features to incorporate resolution information into the similarity measurement; the other uses metric learning to learn a more effective similarity measure. Extensive empirical evaluations on three ticket data sets demonstrate the effectiveness and efficiency of our proposed methods.

  • resolution recommendation for event tickets in service management
    Integrated Network Management, 2015
    Co-Authors: Wubai Zhou, Liang Tang, Tao Li, Larisa Shwartz, Genady Grabarnik
    Abstract:

    In recent years, IT Service Providers have been rapidly transforming to an automated service delivery model. This is due to advances in technology and driven by the unrelenting market pressure to reduce cost and maintain quality. Tremendous progress has been made to date towards attainment of truly automated service delivery; that is, the ability to deliver the same service automatically using the same process with the same quality. However, automating Incident and Problem Management continuous to be a difficult problem, particularly due to the growing complexity of IT environments. Software Monitoring systems are designed to actively collect and signal event occurrances and, when necessary, automatically generate incident tickets. Repeating events generate similar tickets, which in turn have a vast number of repeated problem resolutions likely to be found in earlier tickets. In this paper we find an appropriate resolution by making use of similarities between the events and previous resolutions of similar events. Traditional KNN (K Nearest Neighbor) algorithm has been used to recommend resolutions for incoming tickets. However, the effectiveness of recommendation heavily relies on the underlying similarity measure in KNN. In this paper, we significantly improve the similarity measure used in KNN by utilizing both the event and resolution information in historical tickets via a topic-level feature extraction using the LDA (Latent Dirichlet Allocation) model. In addition, when resolution categories are available, we propose to learn a more effective similarity measure using metric learning. Extensive empirical evaluations on three ticket data sets demonstrate the effectiveness and efficiency of our proposed methods.

Wubai Zhou - One of the best experts on this subject based on the ideXlab platform.

  • resolution recommendation for event tickets in service management
    IEEE Transactions on Network and Service Management, 2016
    Co-Authors: Wubai Zhou, Liang Tang, Chunqiu Zeng, Tao Li, Larisa Shwartz, Genady Grabarnik
    Abstract:

    In recent years, IT service providers have rapidly achieved an automated service delivery model. Software Monitoring systems are designed to actively collect and signal event occurrences and, when necessary, automatically generate incident tickets. Repeating events generate similar tickets, which in turn have a vast number of repeated problem resolutions likely to be found in earlier tickets. In this paper, we develop techniques to recommend appropriate resolution for incoming events by making use of similarities between the events and historical resolutions of similar events. Built on the traditional k-nearest neighbor algorithm (KNN), our proposed algorithms take into account false positives often generated by Monitoring systems. An additional penalty is incorporated into the algorithms to control the number of misleading resolutions in the recommendation results. Moreover, as the effectiveness of the KNN heavily relies on the underlying similarity measurement, we proposed two other approaches to significantly improve our recommendation with respect to resolution relevance. One approach uses topic-level features to incorporate resolution information into the similarity measurement; the other uses metric learning to learn a more effective similarity measure. Extensive empirical evaluations on three ticket data sets demonstrate the effectiveness and efficiency of our proposed methods.

  • resolution recommendation for event tickets in service management
    Integrated Network Management, 2015
    Co-Authors: Wubai Zhou, Liang Tang, Tao Li, Larisa Shwartz, Genady Grabarnik
    Abstract:

    In recent years, IT Service Providers have been rapidly transforming to an automated service delivery model. This is due to advances in technology and driven by the unrelenting market pressure to reduce cost and maintain quality. Tremendous progress has been made to date towards attainment of truly automated service delivery; that is, the ability to deliver the same service automatically using the same process with the same quality. However, automating Incident and Problem Management continuous to be a difficult problem, particularly due to the growing complexity of IT environments. Software Monitoring systems are designed to actively collect and signal event occurrances and, when necessary, automatically generate incident tickets. Repeating events generate similar tickets, which in turn have a vast number of repeated problem resolutions likely to be found in earlier tickets. In this paper we find an appropriate resolution by making use of similarities between the events and previous resolutions of similar events. Traditional KNN (K Nearest Neighbor) algorithm has been used to recommend resolutions for incoming tickets. However, the effectiveness of recommendation heavily relies on the underlying similarity measure in KNN. In this paper, we significantly improve the similarity measure used in KNN by utilizing both the event and resolution information in historical tickets via a topic-level feature extraction using the LDA (Latent Dirichlet Allocation) model. In addition, when resolution categories are available, we propose to learn a more effective similarity measure using metric learning. Extensive empirical evaluations on three ticket data sets demonstrate the effectiveness and efficiency of our proposed methods.

Larisa Shwartz - One of the best experts on this subject based on the ideXlab platform.

  • resolution recommendation for event tickets in service management
    IEEE Transactions on Network and Service Management, 2016
    Co-Authors: Wubai Zhou, Liang Tang, Chunqiu Zeng, Tao Li, Larisa Shwartz, Genady Grabarnik
    Abstract:

    In recent years, IT service providers have rapidly achieved an automated service delivery model. Software Monitoring systems are designed to actively collect and signal event occurrences and, when necessary, automatically generate incident tickets. Repeating events generate similar tickets, which in turn have a vast number of repeated problem resolutions likely to be found in earlier tickets. In this paper, we develop techniques to recommend appropriate resolution for incoming events by making use of similarities between the events and historical resolutions of similar events. Built on the traditional k-nearest neighbor algorithm (KNN), our proposed algorithms take into account false positives often generated by Monitoring systems. An additional penalty is incorporated into the algorithms to control the number of misleading resolutions in the recommendation results. Moreover, as the effectiveness of the KNN heavily relies on the underlying similarity measurement, we proposed two other approaches to significantly improve our recommendation with respect to resolution relevance. One approach uses topic-level features to incorporate resolution information into the similarity measurement; the other uses metric learning to learn a more effective similarity measure. Extensive empirical evaluations on three ticket data sets demonstrate the effectiveness and efficiency of our proposed methods.

  • resolution recommendation for event tickets in service management
    Integrated Network Management, 2015
    Co-Authors: Wubai Zhou, Liang Tang, Tao Li, Larisa Shwartz, Genady Grabarnik
    Abstract:

    In recent years, IT Service Providers have been rapidly transforming to an automated service delivery model. This is due to advances in technology and driven by the unrelenting market pressure to reduce cost and maintain quality. Tremendous progress has been made to date towards attainment of truly automated service delivery; that is, the ability to deliver the same service automatically using the same process with the same quality. However, automating Incident and Problem Management continuous to be a difficult problem, particularly due to the growing complexity of IT environments. Software Monitoring systems are designed to actively collect and signal event occurrances and, when necessary, automatically generate incident tickets. Repeating events generate similar tickets, which in turn have a vast number of repeated problem resolutions likely to be found in earlier tickets. In this paper we find an appropriate resolution by making use of similarities between the events and previous resolutions of similar events. Traditional KNN (K Nearest Neighbor) algorithm has been used to recommend resolutions for incoming tickets. However, the effectiveness of recommendation heavily relies on the underlying similarity measure in KNN. In this paper, we significantly improve the similarity measure used in KNN by utilizing both the event and resolution information in historical tickets via a topic-level feature extraction using the LDA (Latent Dirichlet Allocation) model. In addition, when resolution categories are available, we propose to learn a more effective similarity measure using metric learning. Extensive empirical evaluations on three ticket data sets demonstrate the effectiveness and efficiency of our proposed methods.

Liang Tang - One of the best experts on this subject based on the ideXlab platform.

  • resolution recommendation for event tickets in service management
    IEEE Transactions on Network and Service Management, 2016
    Co-Authors: Wubai Zhou, Liang Tang, Chunqiu Zeng, Tao Li, Larisa Shwartz, Genady Grabarnik
    Abstract:

    In recent years, IT service providers have rapidly achieved an automated service delivery model. Software Monitoring systems are designed to actively collect and signal event occurrences and, when necessary, automatically generate incident tickets. Repeating events generate similar tickets, which in turn have a vast number of repeated problem resolutions likely to be found in earlier tickets. In this paper, we develop techniques to recommend appropriate resolution for incoming events by making use of similarities between the events and historical resolutions of similar events. Built on the traditional k-nearest neighbor algorithm (KNN), our proposed algorithms take into account false positives often generated by Monitoring systems. An additional penalty is incorporated into the algorithms to control the number of misleading resolutions in the recommendation results. Moreover, as the effectiveness of the KNN heavily relies on the underlying similarity measurement, we proposed two other approaches to significantly improve our recommendation with respect to resolution relevance. One approach uses topic-level features to incorporate resolution information into the similarity measurement; the other uses metric learning to learn a more effective similarity measure. Extensive empirical evaluations on three ticket data sets demonstrate the effectiveness and efficiency of our proposed methods.

  • resolution recommendation for event tickets in service management
    Integrated Network Management, 2015
    Co-Authors: Wubai Zhou, Liang Tang, Tao Li, Larisa Shwartz, Genady Grabarnik
    Abstract:

    In recent years, IT Service Providers have been rapidly transforming to an automated service delivery model. This is due to advances in technology and driven by the unrelenting market pressure to reduce cost and maintain quality. Tremendous progress has been made to date towards attainment of truly automated service delivery; that is, the ability to deliver the same service automatically using the same process with the same quality. However, automating Incident and Problem Management continuous to be a difficult problem, particularly due to the growing complexity of IT environments. Software Monitoring systems are designed to actively collect and signal event occurrances and, when necessary, automatically generate incident tickets. Repeating events generate similar tickets, which in turn have a vast number of repeated problem resolutions likely to be found in earlier tickets. In this paper we find an appropriate resolution by making use of similarities between the events and previous resolutions of similar events. Traditional KNN (K Nearest Neighbor) algorithm has been used to recommend resolutions for incoming tickets. However, the effectiveness of recommendation heavily relies on the underlying similarity measure in KNN. In this paper, we significantly improve the similarity measure used in KNN by utilizing both the event and resolution information in historical tickets via a topic-level feature extraction using the LDA (Latent Dirichlet Allocation) model. In addition, when resolution categories are available, we propose to learn a more effective similarity measure using metric learning. Extensive empirical evaluations on three ticket data sets demonstrate the effectiveness and efficiency of our proposed methods.

Tao Li - One of the best experts on this subject based on the ideXlab platform.

  • resolution recommendation for event tickets in service management
    IEEE Transactions on Network and Service Management, 2016
    Co-Authors: Wubai Zhou, Liang Tang, Chunqiu Zeng, Tao Li, Larisa Shwartz, Genady Grabarnik
    Abstract:

    In recent years, IT service providers have rapidly achieved an automated service delivery model. Software Monitoring systems are designed to actively collect and signal event occurrences and, when necessary, automatically generate incident tickets. Repeating events generate similar tickets, which in turn have a vast number of repeated problem resolutions likely to be found in earlier tickets. In this paper, we develop techniques to recommend appropriate resolution for incoming events by making use of similarities between the events and historical resolutions of similar events. Built on the traditional k-nearest neighbor algorithm (KNN), our proposed algorithms take into account false positives often generated by Monitoring systems. An additional penalty is incorporated into the algorithms to control the number of misleading resolutions in the recommendation results. Moreover, as the effectiveness of the KNN heavily relies on the underlying similarity measurement, we proposed two other approaches to significantly improve our recommendation with respect to resolution relevance. One approach uses topic-level features to incorporate resolution information into the similarity measurement; the other uses metric learning to learn a more effective similarity measure. Extensive empirical evaluations on three ticket data sets demonstrate the effectiveness and efficiency of our proposed methods.

  • resolution recommendation for event tickets in service management
    Integrated Network Management, 2015
    Co-Authors: Wubai Zhou, Liang Tang, Tao Li, Larisa Shwartz, Genady Grabarnik
    Abstract:

    In recent years, IT Service Providers have been rapidly transforming to an automated service delivery model. This is due to advances in technology and driven by the unrelenting market pressure to reduce cost and maintain quality. Tremendous progress has been made to date towards attainment of truly automated service delivery; that is, the ability to deliver the same service automatically using the same process with the same quality. However, automating Incident and Problem Management continuous to be a difficult problem, particularly due to the growing complexity of IT environments. Software Monitoring systems are designed to actively collect and signal event occurrances and, when necessary, automatically generate incident tickets. Repeating events generate similar tickets, which in turn have a vast number of repeated problem resolutions likely to be found in earlier tickets. In this paper we find an appropriate resolution by making use of similarities between the events and previous resolutions of similar events. Traditional KNN (K Nearest Neighbor) algorithm has been used to recommend resolutions for incoming tickets. However, the effectiveness of recommendation heavily relies on the underlying similarity measure in KNN. In this paper, we significantly improve the similarity measure used in KNN by utilizing both the event and resolution information in historical tickets via a topic-level feature extraction using the LDA (Latent Dirichlet Allocation) model. In addition, when resolution categories are available, we propose to learn a more effective similarity measure using metric learning. Extensive empirical evaluations on three ticket data sets demonstrate the effectiveness and efficiency of our proposed methods.