The Experts below are selected from a list of 2817 Experts worldwide ranked by ideXlab platform

Joonghwan Baek - One of the best experts on this subject based on the ideXlab platform.

  • a systems level approach to Perimeter Protection
    Workshop on Applications of Computer Vision, 2012
    Co-Authors: Dashan Gao, Ram Nevatia, Sung Chun Lee, Hale Kim, Phill Kyu Rhee, Joonghwan Baek
    Abstract:

    Effective Perimeter Protection mechanisms for industrial sites and critical infrastructure must contend with a large variety of potential threats as well as with the fact that normal site activity can be both complex and diverse. This paper documents the development of a system level approach capable of functioning under such challenging conditions. A multi-view tracking system is used to provide real-time site wide trajectories of all observed individuals. A Radar-based system is also used for tracking if and when camera coverage of various regions is not available. Track information is then analyzed with respect to articulated motion analysis, complex event analysis and normalcy analysis. In addition, object recognition is used to classify left behind objects using high resolution PTZ imagery. A real-time integrated version of this comprehensive approach to Perimeter Protection was deployed using a single standard off-the-shelf desktop computer.

  • WACV - A systems level approach to Perimeter Protection
    2012 IEEE Workshop on the Applications of Computer Vision (WACV), 2012
    Co-Authors: Dashan Gao, Ram Nevatia, Sung Chun Lee, Hale Kim, Phill Kyu Rhee, Joonghwan Baek
    Abstract:

    Effective Perimeter Protection mechanisms for industrial sites and critical infrastructure must contend with a large variety of potential threats as well as with the fact that normal site activity can be both complex and diverse. This paper documents the development of a system level approach capable of functioning under such challenging conditions. A multi-view tracking system is used to provide real-time site wide trajectories of all observed individuals. A Radar-based system is also used for tracking if and when camera coverage of various regions is not available. Track information is then analyzed with respect to articulated motion analysis, complex event analysis and normalcy analysis. In addition, object recognition is used to classify left behind objects using high resolution PTZ imagery. A real-time integrated version of this comprehensive approach to Perimeter Protection was deployed using a single standard off-the-shelf desktop computer.

  • avss 2011 demo session a systems level approach to Perimeter Protection
    Advanced Video and Signal Based Surveillance, 2011
    Co-Authors: Dashan Gao, Ram Nevatia, Sung Chun Lee, Hale Kim, Phill Kyu Rhee, Joonghwan Baek
    Abstract:

    Summary form only given. The rapid evolution of tools and software systems to design experiments, automatically monitor, collect and warehouse large amounts of data, from applications such as life sciences and industrial processes has resulted in a new paradigm shift. This change of paradigm is so fast that some of the practices for optimization and management of these processes that were valid only 5–10 years ago may no longer be fully acceptable or sufficient for today's business optimization and management. This has a direct influence on the best practices for knowledge discovery and management of the discovered knowledge in real-world data mining applications. Establishing and managing a real-world data mining project in any domain, in particular in today's life science industry, is not a trivial task. A few approaches have been proposed in the literature. However, initiation and successful management of such efforts may depend on where a given case study fits in the overall classification of data mining approaches. Today's knowledge discovery from data can be classified in several ways: (i) data mining on engineered systems (e.g. complex equipment) or systems designed by nature (e.g. life sciences), (ii) explanatory or predictive data mining, (iii) data mining from static data (e.g. data warehouse) or dynamic data (e.g. data streams), (iv) user operated or automated data mining. There could still be other ways to classify data mining applications. This talk provides an overview of the above listed knowledge discovery applications. We provide examples where we demonstrate how small or large amounts of data, when understood from a real-world data mining point of view and the required data is properly integrated, can result in novel knowledge discovery case studies. We explain motivations and challenges of establishing real-world dat

  • AVSS - AVSS 2011 demo session: A systems level approach to Perimeter Protection
    2011 8th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 2011
    Co-Authors: Dashan Gao, Ram Nevatia, Sung Chun Lee, Hale Kim, Phill Kyu Rhee, Joonghwan Baek
    Abstract:

    Summary form only given. The rapid evolution of tools and software systems to design experiments, automatically monitor, collect and warehouse large amounts of data, from applications such as life sciences and industrial processes has resulted in a new paradigm shift. This change of paradigm is so fast that some of the practices for optimization and management of these processes that were valid only 5–10 years ago may no longer be fully acceptable or sufficient for today's business optimization and management. This has a direct influence on the best practices for knowledge discovery and management of the discovered knowledge in real-world data mining applications. Establishing and managing a real-world data mining project in any domain, in particular in today's life science industry, is not a trivial task. A few approaches have been proposed in the literature. However, initiation and successful management of such efforts may depend on where a given case study fits in the overall classification of data mining approaches. Today's knowledge discovery from data can be classified in several ways: (i) data mining on engineered systems (e.g. complex equipment) or systems designed by nature (e.g. life sciences), (ii) explanatory or predictive data mining, (iii) data mining from static data (e.g. data warehouse) or dynamic data (e.g. data streams), (iv) user operated or automated data mining. There could still be other ways to classify data mining applications. This talk provides an overview of the above listed knowledge discovery applications. We provide examples where we demonstrate how small or large amounts of data, when understood from a real-world data mining point of view and the required data is properly integrated, can result in novel knowledge discovery case studies. We explain motivations and challenges of establishing real-world dat

Dashan Gao - One of the best experts on this subject based on the ideXlab platform.

  • a systems level approach to Perimeter Protection
    Workshop on Applications of Computer Vision, 2012
    Co-Authors: Dashan Gao, Ram Nevatia, Sung Chun Lee, Hale Kim, Phill Kyu Rhee, Joonghwan Baek
    Abstract:

    Effective Perimeter Protection mechanisms for industrial sites and critical infrastructure must contend with a large variety of potential threats as well as with the fact that normal site activity can be both complex and diverse. This paper documents the development of a system level approach capable of functioning under such challenging conditions. A multi-view tracking system is used to provide real-time site wide trajectories of all observed individuals. A Radar-based system is also used for tracking if and when camera coverage of various regions is not available. Track information is then analyzed with respect to articulated motion analysis, complex event analysis and normalcy analysis. In addition, object recognition is used to classify left behind objects using high resolution PTZ imagery. A real-time integrated version of this comprehensive approach to Perimeter Protection was deployed using a single standard off-the-shelf desktop computer.

  • WACV - A systems level approach to Perimeter Protection
    2012 IEEE Workshop on the Applications of Computer Vision (WACV), 2012
    Co-Authors: Dashan Gao, Ram Nevatia, Sung Chun Lee, Hale Kim, Phill Kyu Rhee, Joonghwan Baek
    Abstract:

    Effective Perimeter Protection mechanisms for industrial sites and critical infrastructure must contend with a large variety of potential threats as well as with the fact that normal site activity can be both complex and diverse. This paper documents the development of a system level approach capable of functioning under such challenging conditions. A multi-view tracking system is used to provide real-time site wide trajectories of all observed individuals. A Radar-based system is also used for tracking if and when camera coverage of various regions is not available. Track information is then analyzed with respect to articulated motion analysis, complex event analysis and normalcy analysis. In addition, object recognition is used to classify left behind objects using high resolution PTZ imagery. A real-time integrated version of this comprehensive approach to Perimeter Protection was deployed using a single standard off-the-shelf desktop computer.

  • avss 2011 demo session a systems level approach to Perimeter Protection
    Advanced Video and Signal Based Surveillance, 2011
    Co-Authors: Dashan Gao, Ram Nevatia, Sung Chun Lee, Hale Kim, Phill Kyu Rhee, Joonghwan Baek
    Abstract:

    Summary form only given. The rapid evolution of tools and software systems to design experiments, automatically monitor, collect and warehouse large amounts of data, from applications such as life sciences and industrial processes has resulted in a new paradigm shift. This change of paradigm is so fast that some of the practices for optimization and management of these processes that were valid only 5–10 years ago may no longer be fully acceptable or sufficient for today's business optimization and management. This has a direct influence on the best practices for knowledge discovery and management of the discovered knowledge in real-world data mining applications. Establishing and managing a real-world data mining project in any domain, in particular in today's life science industry, is not a trivial task. A few approaches have been proposed in the literature. However, initiation and successful management of such efforts may depend on where a given case study fits in the overall classification of data mining approaches. Today's knowledge discovery from data can be classified in several ways: (i) data mining on engineered systems (e.g. complex equipment) or systems designed by nature (e.g. life sciences), (ii) explanatory or predictive data mining, (iii) data mining from static data (e.g. data warehouse) or dynamic data (e.g. data streams), (iv) user operated or automated data mining. There could still be other ways to classify data mining applications. This talk provides an overview of the above listed knowledge discovery applications. We provide examples where we demonstrate how small or large amounts of data, when understood from a real-world data mining point of view and the required data is properly integrated, can result in novel knowledge discovery case studies. We explain motivations and challenges of establishing real-world dat

  • AVSS - AVSS 2011 demo session: A systems level approach to Perimeter Protection
    2011 8th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 2011
    Co-Authors: Dashan Gao, Ram Nevatia, Sung Chun Lee, Hale Kim, Phill Kyu Rhee, Joonghwan Baek
    Abstract:

    Summary form only given. The rapid evolution of tools and software systems to design experiments, automatically monitor, collect and warehouse large amounts of data, from applications such as life sciences and industrial processes has resulted in a new paradigm shift. This change of paradigm is so fast that some of the practices for optimization and management of these processes that were valid only 5–10 years ago may no longer be fully acceptable or sufficient for today's business optimization and management. This has a direct influence on the best practices for knowledge discovery and management of the discovered knowledge in real-world data mining applications. Establishing and managing a real-world data mining project in any domain, in particular in today's life science industry, is not a trivial task. A few approaches have been proposed in the literature. However, initiation and successful management of such efforts may depend on where a given case study fits in the overall classification of data mining approaches. Today's knowledge discovery from data can be classified in several ways: (i) data mining on engineered systems (e.g. complex equipment) or systems designed by nature (e.g. life sciences), (ii) explanatory or predictive data mining, (iii) data mining from static data (e.g. data warehouse) or dynamic data (e.g. data streams), (iv) user operated or automated data mining. There could still be other ways to classify data mining applications. This talk provides an overview of the above listed knowledge discovery applications. We provide examples where we demonstrate how small or large amounts of data, when understood from a real-world data mining point of view and the required data is properly integrated, can result in novel knowledge discovery case studies. We explain motivations and challenges of establishing real-world dat

Phill Kyu Rhee - One of the best experts on this subject based on the ideXlab platform.

  • a systems level approach to Perimeter Protection
    Workshop on Applications of Computer Vision, 2012
    Co-Authors: Dashan Gao, Ram Nevatia, Sung Chun Lee, Hale Kim, Phill Kyu Rhee, Joonghwan Baek
    Abstract:

    Effective Perimeter Protection mechanisms for industrial sites and critical infrastructure must contend with a large variety of potential threats as well as with the fact that normal site activity can be both complex and diverse. This paper documents the development of a system level approach capable of functioning under such challenging conditions. A multi-view tracking system is used to provide real-time site wide trajectories of all observed individuals. A Radar-based system is also used for tracking if and when camera coverage of various regions is not available. Track information is then analyzed with respect to articulated motion analysis, complex event analysis and normalcy analysis. In addition, object recognition is used to classify left behind objects using high resolution PTZ imagery. A real-time integrated version of this comprehensive approach to Perimeter Protection was deployed using a single standard off-the-shelf desktop computer.

  • WACV - A systems level approach to Perimeter Protection
    2012 IEEE Workshop on the Applications of Computer Vision (WACV), 2012
    Co-Authors: Dashan Gao, Ram Nevatia, Sung Chun Lee, Hale Kim, Phill Kyu Rhee, Joonghwan Baek
    Abstract:

    Effective Perimeter Protection mechanisms for industrial sites and critical infrastructure must contend with a large variety of potential threats as well as with the fact that normal site activity can be both complex and diverse. This paper documents the development of a system level approach capable of functioning under such challenging conditions. A multi-view tracking system is used to provide real-time site wide trajectories of all observed individuals. A Radar-based system is also used for tracking if and when camera coverage of various regions is not available. Track information is then analyzed with respect to articulated motion analysis, complex event analysis and normalcy analysis. In addition, object recognition is used to classify left behind objects using high resolution PTZ imagery. A real-time integrated version of this comprehensive approach to Perimeter Protection was deployed using a single standard off-the-shelf desktop computer.

  • avss 2011 demo session a systems level approach to Perimeter Protection
    Advanced Video and Signal Based Surveillance, 2011
    Co-Authors: Dashan Gao, Ram Nevatia, Sung Chun Lee, Hale Kim, Phill Kyu Rhee, Joonghwan Baek
    Abstract:

    Summary form only given. The rapid evolution of tools and software systems to design experiments, automatically monitor, collect and warehouse large amounts of data, from applications such as life sciences and industrial processes has resulted in a new paradigm shift. This change of paradigm is so fast that some of the practices for optimization and management of these processes that were valid only 5–10 years ago may no longer be fully acceptable or sufficient for today's business optimization and management. This has a direct influence on the best practices for knowledge discovery and management of the discovered knowledge in real-world data mining applications. Establishing and managing a real-world data mining project in any domain, in particular in today's life science industry, is not a trivial task. A few approaches have been proposed in the literature. However, initiation and successful management of such efforts may depend on where a given case study fits in the overall classification of data mining approaches. Today's knowledge discovery from data can be classified in several ways: (i) data mining on engineered systems (e.g. complex equipment) or systems designed by nature (e.g. life sciences), (ii) explanatory or predictive data mining, (iii) data mining from static data (e.g. data warehouse) or dynamic data (e.g. data streams), (iv) user operated or automated data mining. There could still be other ways to classify data mining applications. This talk provides an overview of the above listed knowledge discovery applications. We provide examples where we demonstrate how small or large amounts of data, when understood from a real-world data mining point of view and the required data is properly integrated, can result in novel knowledge discovery case studies. We explain motivations and challenges of establishing real-world dat

  • AVSS - AVSS 2011 demo session: A systems level approach to Perimeter Protection
    2011 8th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 2011
    Co-Authors: Dashan Gao, Ram Nevatia, Sung Chun Lee, Hale Kim, Phill Kyu Rhee, Joonghwan Baek
    Abstract:

    Summary form only given. The rapid evolution of tools and software systems to design experiments, automatically monitor, collect and warehouse large amounts of data, from applications such as life sciences and industrial processes has resulted in a new paradigm shift. This change of paradigm is so fast that some of the practices for optimization and management of these processes that were valid only 5–10 years ago may no longer be fully acceptable or sufficient for today's business optimization and management. This has a direct influence on the best practices for knowledge discovery and management of the discovered knowledge in real-world data mining applications. Establishing and managing a real-world data mining project in any domain, in particular in today's life science industry, is not a trivial task. A few approaches have been proposed in the literature. However, initiation and successful management of such efforts may depend on where a given case study fits in the overall classification of data mining approaches. Today's knowledge discovery from data can be classified in several ways: (i) data mining on engineered systems (e.g. complex equipment) or systems designed by nature (e.g. life sciences), (ii) explanatory or predictive data mining, (iii) data mining from static data (e.g. data warehouse) or dynamic data (e.g. data streams), (iv) user operated or automated data mining. There could still be other ways to classify data mining applications. This talk provides an overview of the above listed knowledge discovery applications. We provide examples where we demonstrate how small or large amounts of data, when understood from a real-world data mining point of view and the required data is properly integrated, can result in novel knowledge discovery case studies. We explain motivations and challenges of establishing real-world dat

Ram Nevatia - One of the best experts on this subject based on the ideXlab platform.

  • a systems level approach to Perimeter Protection
    Workshop on Applications of Computer Vision, 2012
    Co-Authors: Dashan Gao, Ram Nevatia, Sung Chun Lee, Hale Kim, Phill Kyu Rhee, Joonghwan Baek
    Abstract:

    Effective Perimeter Protection mechanisms for industrial sites and critical infrastructure must contend with a large variety of potential threats as well as with the fact that normal site activity can be both complex and diverse. This paper documents the development of a system level approach capable of functioning under such challenging conditions. A multi-view tracking system is used to provide real-time site wide trajectories of all observed individuals. A Radar-based system is also used for tracking if and when camera coverage of various regions is not available. Track information is then analyzed with respect to articulated motion analysis, complex event analysis and normalcy analysis. In addition, object recognition is used to classify left behind objects using high resolution PTZ imagery. A real-time integrated version of this comprehensive approach to Perimeter Protection was deployed using a single standard off-the-shelf desktop computer.

  • WACV - A systems level approach to Perimeter Protection
    2012 IEEE Workshop on the Applications of Computer Vision (WACV), 2012
    Co-Authors: Dashan Gao, Ram Nevatia, Sung Chun Lee, Hale Kim, Phill Kyu Rhee, Joonghwan Baek
    Abstract:

    Effective Perimeter Protection mechanisms for industrial sites and critical infrastructure must contend with a large variety of potential threats as well as with the fact that normal site activity can be both complex and diverse. This paper documents the development of a system level approach capable of functioning under such challenging conditions. A multi-view tracking system is used to provide real-time site wide trajectories of all observed individuals. A Radar-based system is also used for tracking if and when camera coverage of various regions is not available. Track information is then analyzed with respect to articulated motion analysis, complex event analysis and normalcy analysis. In addition, object recognition is used to classify left behind objects using high resolution PTZ imagery. A real-time integrated version of this comprehensive approach to Perimeter Protection was deployed using a single standard off-the-shelf desktop computer.

  • avss 2011 demo session a systems level approach to Perimeter Protection
    Advanced Video and Signal Based Surveillance, 2011
    Co-Authors: Dashan Gao, Ram Nevatia, Sung Chun Lee, Hale Kim, Phill Kyu Rhee, Joonghwan Baek
    Abstract:

    Summary form only given. The rapid evolution of tools and software systems to design experiments, automatically monitor, collect and warehouse large amounts of data, from applications such as life sciences and industrial processes has resulted in a new paradigm shift. This change of paradigm is so fast that some of the practices for optimization and management of these processes that were valid only 5–10 years ago may no longer be fully acceptable or sufficient for today's business optimization and management. This has a direct influence on the best practices for knowledge discovery and management of the discovered knowledge in real-world data mining applications. Establishing and managing a real-world data mining project in any domain, in particular in today's life science industry, is not a trivial task. A few approaches have been proposed in the literature. However, initiation and successful management of such efforts may depend on where a given case study fits in the overall classification of data mining approaches. Today's knowledge discovery from data can be classified in several ways: (i) data mining on engineered systems (e.g. complex equipment) or systems designed by nature (e.g. life sciences), (ii) explanatory or predictive data mining, (iii) data mining from static data (e.g. data warehouse) or dynamic data (e.g. data streams), (iv) user operated or automated data mining. There could still be other ways to classify data mining applications. This talk provides an overview of the above listed knowledge discovery applications. We provide examples where we demonstrate how small or large amounts of data, when understood from a real-world data mining point of view and the required data is properly integrated, can result in novel knowledge discovery case studies. We explain motivations and challenges of establishing real-world dat

  • AVSS - AVSS 2011 demo session: A systems level approach to Perimeter Protection
    2011 8th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 2011
    Co-Authors: Dashan Gao, Ram Nevatia, Sung Chun Lee, Hale Kim, Phill Kyu Rhee, Joonghwan Baek
    Abstract:

    Summary form only given. The rapid evolution of tools and software systems to design experiments, automatically monitor, collect and warehouse large amounts of data, from applications such as life sciences and industrial processes has resulted in a new paradigm shift. This change of paradigm is so fast that some of the practices for optimization and management of these processes that were valid only 5–10 years ago may no longer be fully acceptable or sufficient for today's business optimization and management. This has a direct influence on the best practices for knowledge discovery and management of the discovered knowledge in real-world data mining applications. Establishing and managing a real-world data mining project in any domain, in particular in today's life science industry, is not a trivial task. A few approaches have been proposed in the literature. However, initiation and successful management of such efforts may depend on where a given case study fits in the overall classification of data mining approaches. Today's knowledge discovery from data can be classified in several ways: (i) data mining on engineered systems (e.g. complex equipment) or systems designed by nature (e.g. life sciences), (ii) explanatory or predictive data mining, (iii) data mining from static data (e.g. data warehouse) or dynamic data (e.g. data streams), (iv) user operated or automated data mining. There could still be other ways to classify data mining applications. This talk provides an overview of the above listed knowledge discovery applications. We provide examples where we demonstrate how small or large amounts of data, when understood from a real-world data mining point of view and the required data is properly integrated, can result in novel knowledge discovery case studies. We explain motivations and challenges of establishing real-world dat

Sung Chun Lee - One of the best experts on this subject based on the ideXlab platform.

  • a systems level approach to Perimeter Protection
    Workshop on Applications of Computer Vision, 2012
    Co-Authors: Dashan Gao, Ram Nevatia, Sung Chun Lee, Hale Kim, Phill Kyu Rhee, Joonghwan Baek
    Abstract:

    Effective Perimeter Protection mechanisms for industrial sites and critical infrastructure must contend with a large variety of potential threats as well as with the fact that normal site activity can be both complex and diverse. This paper documents the development of a system level approach capable of functioning under such challenging conditions. A multi-view tracking system is used to provide real-time site wide trajectories of all observed individuals. A Radar-based system is also used for tracking if and when camera coverage of various regions is not available. Track information is then analyzed with respect to articulated motion analysis, complex event analysis and normalcy analysis. In addition, object recognition is used to classify left behind objects using high resolution PTZ imagery. A real-time integrated version of this comprehensive approach to Perimeter Protection was deployed using a single standard off-the-shelf desktop computer.

  • WACV - A systems level approach to Perimeter Protection
    2012 IEEE Workshop on the Applications of Computer Vision (WACV), 2012
    Co-Authors: Dashan Gao, Ram Nevatia, Sung Chun Lee, Hale Kim, Phill Kyu Rhee, Joonghwan Baek
    Abstract:

    Effective Perimeter Protection mechanisms for industrial sites and critical infrastructure must contend with a large variety of potential threats as well as with the fact that normal site activity can be both complex and diverse. This paper documents the development of a system level approach capable of functioning under such challenging conditions. A multi-view tracking system is used to provide real-time site wide trajectories of all observed individuals. A Radar-based system is also used for tracking if and when camera coverage of various regions is not available. Track information is then analyzed with respect to articulated motion analysis, complex event analysis and normalcy analysis. In addition, object recognition is used to classify left behind objects using high resolution PTZ imagery. A real-time integrated version of this comprehensive approach to Perimeter Protection was deployed using a single standard off-the-shelf desktop computer.

  • avss 2011 demo session a systems level approach to Perimeter Protection
    Advanced Video and Signal Based Surveillance, 2011
    Co-Authors: Dashan Gao, Ram Nevatia, Sung Chun Lee, Hale Kim, Phill Kyu Rhee, Joonghwan Baek
    Abstract:

    Summary form only given. The rapid evolution of tools and software systems to design experiments, automatically monitor, collect and warehouse large amounts of data, from applications such as life sciences and industrial processes has resulted in a new paradigm shift. This change of paradigm is so fast that some of the practices for optimization and management of these processes that were valid only 5–10 years ago may no longer be fully acceptable or sufficient for today's business optimization and management. This has a direct influence on the best practices for knowledge discovery and management of the discovered knowledge in real-world data mining applications. Establishing and managing a real-world data mining project in any domain, in particular in today's life science industry, is not a trivial task. A few approaches have been proposed in the literature. However, initiation and successful management of such efforts may depend on where a given case study fits in the overall classification of data mining approaches. Today's knowledge discovery from data can be classified in several ways: (i) data mining on engineered systems (e.g. complex equipment) or systems designed by nature (e.g. life sciences), (ii) explanatory or predictive data mining, (iii) data mining from static data (e.g. data warehouse) or dynamic data (e.g. data streams), (iv) user operated or automated data mining. There could still be other ways to classify data mining applications. This talk provides an overview of the above listed knowledge discovery applications. We provide examples where we demonstrate how small or large amounts of data, when understood from a real-world data mining point of view and the required data is properly integrated, can result in novel knowledge discovery case studies. We explain motivations and challenges of establishing real-world dat

  • AVSS - AVSS 2011 demo session: A systems level approach to Perimeter Protection
    2011 8th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 2011
    Co-Authors: Dashan Gao, Ram Nevatia, Sung Chun Lee, Hale Kim, Phill Kyu Rhee, Joonghwan Baek
    Abstract:

    Summary form only given. The rapid evolution of tools and software systems to design experiments, automatically monitor, collect and warehouse large amounts of data, from applications such as life sciences and industrial processes has resulted in a new paradigm shift. This change of paradigm is so fast that some of the practices for optimization and management of these processes that were valid only 5–10 years ago may no longer be fully acceptable or sufficient for today's business optimization and management. This has a direct influence on the best practices for knowledge discovery and management of the discovered knowledge in real-world data mining applications. Establishing and managing a real-world data mining project in any domain, in particular in today's life science industry, is not a trivial task. A few approaches have been proposed in the literature. However, initiation and successful management of such efforts may depend on where a given case study fits in the overall classification of data mining approaches. Today's knowledge discovery from data can be classified in several ways: (i) data mining on engineered systems (e.g. complex equipment) or systems designed by nature (e.g. life sciences), (ii) explanatory or predictive data mining, (iii) data mining from static data (e.g. data warehouse) or dynamic data (e.g. data streams), (iv) user operated or automated data mining. There could still be other ways to classify data mining applications. This talk provides an overview of the above listed knowledge discovery applications. We provide examples where we demonstrate how small or large amounts of data, when understood from a real-world data mining point of view and the required data is properly integrated, can result in novel knowledge discovery case studies. We explain motivations and challenges of establishing real-world dat