The Experts below are selected from a list of 927 Experts worldwide ranked by ideXlab platform

Sa-kwang Song - One of the best experts on this subject based on the ideXlab platform.

  • HCI (23) - Design of Marketing Scenario Planning Based on Business Big Data Analysis
    Lecture Notes in Computer Science, 2015
    Co-Authors: Seungkyun Hong, Choong Nyoung Seon, Sungho Shin, Young-min Kim, Sa-kwang Song
    Abstract:

    As the amount and the type of data for business decision making are rapidly increasing, the importance of big data Analytics is gradually critical for making effective business strategy. However, big data Analytics based decision making systems basically requires distributed parallel computing capability in order to make timely business strategy recommendation via processing huge amount unstructured as well as structured business data. We introduce a big data Analytics system for automatic marketing scenario planning based on big data platform software such as Hadoop and HBase. The Analytics methodology for scenario planning is based on Prescriptive Analytics which is the most advance methodology consisting of generation of business scenarios and their optimization, among the three Analytics of descriptive, predictive, and Prescriptive Analytics. Additionally, we developed a prototype of marketing scenario planning system and its graphical user interface, as well as the system architecture based on Hadoop eco-system based distributed parallel computing platform.

  • research capability enhancement system based on Prescriptive Analytics
    KIISE Transactions on Computing Practices, 2015
    Co-Authors: Jangwon Gim, Sa-kwang Song, Hanmin Jung, Doheon Jeong, Myunggwon Hwang
    Abstract:

    The explosive growth of data and the rapidly changing technical social evolution new analysis paradigm for predicting and reacting the future the past and present ig data. Prescriptive analysis has a fundamental difference because can support specific behaviors and results according to user's goals with defin researchers establish judgments and activities achiev the goals. However research methods not widely implemented and even the terminology, Prescriptive analysis, is still unfamiliar. This paper thus propose an infrastructure in the Prescriptive analysis field with key considerations for enhancing capability of researchers through a case study based on InSciTe Advisory developed with scientific big data. InSciTe Advisory system s developed in 2013, and offers a Prescriptive Analytics report which contains various As-Is analysis results and To-Be analysis results 5W1H methodology. InSciTe Advisory therefore shows possibility strategy aims to reach a target role model group. Through the availability and reliability of the measurement model the evaluation results obtained relative advantage of 118.8% compared to Elsevier SciVal.

  • Prescriptive Analytics for Planning Research-Performance Strategy
    Computer Science and its Applications, 2015
    Co-Authors: Min-hee Cho, Sa-kwang Song, Jens Weber, Hanmin Jung, Mikyoung Lee
    Abstract:

    Researchers establish their strategies by continually analyzing research performance and estimating future research directions to enhance their capability. Prescriptive Analytics can be a solution to an advanced-Analytics method that provides the optimal strategy from among the various actions and methods available for achieving an established goal. This paper introduces a journal recommendation model which provides prescribing a strategy to enhance research performance using the 5W1H methodology from the InSciTe system. This model suggests a journal-submission strategy for achieving the goal of strengthening the research capabilities of researchers in terms of their performance by considering their research subjects and capabilities.

  • Research Advising System Based on Prescriptive Analytics
    Lecture Notes in Electrical Engineering, 2014
    Co-Authors: Sa-kwang Song, Myunggwon Hwang, Doheon Jeong, Jangwon Gim, Jinhyung Kim, Hanming Jung
    Abstract:

    As the amount of data increases enormously, business Analytics such as descriptive, predictive, and Prescriptive Analytics is one of the most important topics for better decision making especially for CTO or CIO in corporate. Prescriptive Analytics shows fundamental difference with descriptive Analytics and predictive Analytics in that it requires high-value alternative actions or decisions to achieve a given goal. However, only a few studies have been introduced since it is a emerging technology. Thus, this study aims to trigger research on this technical area by implementing a Prescriptive Analytics system and by verifying it in the point of usability and usefulness. The system, InSciTe Advisory, is focused on improving research performance and is based on 5W1H questions to build actionable strategies to achieve a given goal. The comparison evaluation of the system with Elsevier SciVal showed a rate of 118.8% in usefulness and reliability.

  • system thinking crafting scenarios for Prescriptive Analytics
    Proceedings of the First International Workshop on Patent Mining and Its Applications (IPaMin 2014) co-located with Konvens 2014, 2014
    Co-Authors: Jens Weber, Min-hee Cho, Sa-kwang Song, Mikyoung Lee, Michaela Geierhos, Hanmin Jung
    Abstract:

    paper focuses on the first step in combining Prescriptive Analytics with scenario techniques in order to provide strategic development after the use of InSciTe, a data Prescriptive Analytics application. InSciTe supports the improvement of researchers 'individual performance by recommending new research directions. Standardized influential factors are presented as a foundation for automated scenario modelling such as the prototypical report generation function of InSciTe. Additionally, a use-case is shown which validates the potential of the standardized influential factors for raw scenario development.

Hanmin Jung - One of the best experts on this subject based on the ideXlab platform.

  • research capability enhancement system based on Prescriptive Analytics
    KIISE Transactions on Computing Practices, 2015
    Co-Authors: Jangwon Gim, Sa-kwang Song, Hanmin Jung, Doheon Jeong, Myunggwon Hwang
    Abstract:

    The explosive growth of data and the rapidly changing technical social evolution new analysis paradigm for predicting and reacting the future the past and present ig data. Prescriptive analysis has a fundamental difference because can support specific behaviors and results according to user's goals with defin researchers establish judgments and activities achiev the goals. However research methods not widely implemented and even the terminology, Prescriptive analysis, is still unfamiliar. This paper thus propose an infrastructure in the Prescriptive analysis field with key considerations for enhancing capability of researchers through a case study based on InSciTe Advisory developed with scientific big data. InSciTe Advisory system s developed in 2013, and offers a Prescriptive Analytics report which contains various As-Is analysis results and To-Be analysis results 5W1H methodology. InSciTe Advisory therefore shows possibility strategy aims to reach a target role model group. Through the availability and reliability of the measurement model the evaluation results obtained relative advantage of 118.8% compared to Elsevier SciVal.

  • Prescriptive Analytics for Planning Research-Performance Strategy
    Computer Science and its Applications, 2015
    Co-Authors: Min-hee Cho, Sa-kwang Song, Jens Weber, Hanmin Jung, Mikyoung Lee
    Abstract:

    Researchers establish their strategies by continually analyzing research performance and estimating future research directions to enhance their capability. Prescriptive Analytics can be a solution to an advanced-Analytics method that provides the optimal strategy from among the various actions and methods available for achieving an established goal. This paper introduces a journal recommendation model which provides prescribing a strategy to enhance research performance using the 5W1H methodology from the InSciTe system. This model suggests a journal-submission strategy for achieving the goal of strengthening the research capabilities of researchers in terms of their performance by considering their research subjects and capabilities.

  • InSciTe advisory: Prescriptive Analytics service for enhancing research performance
    2015 7th International Conference on Knowledge and Smart Technology (KST), 2015
    Co-Authors: Hanmin Jung
    Abstract:

    Explosively increased Big Data and very fast technical evolutions require an entirely new Analytics that is able to precisely analyze researchers' activities until now and to provide research directions from now on. Prescriptive Analytics shows fundamental difference with descriptive/predictive Analytics in that it should provide multiple strategies to achieve a given research direction. Complex event processing also shows a new way to read implicit intentions from many kinds of activities such as publishing article, travelling on business, and attending conference. Thus, this talk shows a case study by explaining requirements and factors for implementing a personalized research service with InSciTe Advisory, as a data-intensive intelligent service, for helping to find plausible research directions. This talk also covers data gathering, information extraction from entities to simple events, reasoning, and Hadoop ecosystem.

  • Prescriptive Analytics system for scholar research performance enhancement
    International Conference on Human-Computer Interaction, 2014
    Co-Authors: Mikyoung Lee, Min-hee Cho, Doheon Jeong, Jangwon Gim, Hanmin Jung
    Abstract:

    We introduce a Prescriptive Analytics system, InSciTe Advisory, to provide researchers with advice for their future research direction and strategy. It consists of two main parts: descriptive Analytics and Prescriptive Analytics. Descriptive Analytics provides results from research activity history as well as the research power index for the designated researcher. Prescriptive Analytics suggests a group of role model researchers to the researcher, as well as methods to adopt their best practices. The prescription for the researcher is provided according to 5W1H questions and their corresponding answers. All of the analytical results and their explanations related to the given researcher are automatically generated and saved to a report. This researcher-centric Prescriptive Analytics framework is expected to be a useful tool to understand the designated researcher from the perspective of Prescriptive and descriptive Analytics. We evaluated user satisfaction results for InSciTe Advisory and Elsvier Scival by five test users. The result of the evaluation demonstrated that user satisfaction of InSciTe Advisory is 126.5% higher than Scival.

  • system thinking crafting scenarios for Prescriptive Analytics
    Proceedings of the First International Workshop on Patent Mining and Its Applications (IPaMin 2014) co-located with Konvens 2014, 2014
    Co-Authors: Jens Weber, Min-hee Cho, Sa-kwang Song, Mikyoung Lee, Michaela Geierhos, Hanmin Jung
    Abstract:

    paper focuses on the first step in combining Prescriptive Analytics with scenario techniques in order to provide strategic development after the use of InSciTe, a data Prescriptive Analytics application. InSciTe supports the improvement of researchers 'individual performance by recommending new research directions. Standardized influential factors are presented as a foundation for automated scenario modelling such as the prototypical report generation function of InSciTe. Additionally, a use-case is shown which validates the potential of the standardized influential factors for raw scenario development.

Gregoris Mentzas - One of the best experts on this subject based on the ideXlab platform.

  • machine learning for predictive and Prescriptive Analytics of operational data in smart manufacturing
    Conference on Advanced Information Systems Engineering, 2020
    Co-Authors: Katerina Lepenioti, Alexandros Bousdekis, Gregoris Mentzas, Dimitris Apostolou, Minas Pertselakis, Andreas Louca, Fenareti Lampathaki, Stathis Anastasiou
    Abstract:

    Perceiving information and extracting insights from data is one of the major challenges in smart manufacturing. Real-time data Analytics face several challenges in real-life scenarios, while there is a huge treasure of legacy, enterprise and operational data remaining untouched. The current paper exploits the recent advancements of (deep) machine learning for performing predictive and Prescriptive Analytics on the basis of enterprise and operational data aiming at supporting the operator on the shopfloor. To do this, it implements algorithms, such as Recurrent Neural Networks for predictive Analytics, and Multi-Objective Reinforcement Learning for Prescriptive Analytics. The proposed approach is demonstrated in a predictive maintenance scenario in steel industry.

  • CAiSE Workshops - Machine Learning for Predictive and Prescriptive Analytics of Operational Data in Smart Manufacturing
    Lecture Notes in Business Information Processing, 2020
    Co-Authors: Katerina Lepenioti, Alexandros Bousdekis, Gregoris Mentzas, Dimitris Apostolou, Minas Pertselakis, Andreas Louca, Fenareti Lampathaki, Stathis Anastasiou
    Abstract:

    Perceiving information and extracting insights from data is one of the major challenges in smart manufacturing. Real-time data Analytics face several challenges in real-life scenarios, while there is a huge treasure of legacy, enterprise and operational data remaining untouched. The current paper exploits the recent advancements of (deep) machine learning for performing predictive and Prescriptive Analytics on the basis of enterprise and operational data aiming at supporting the operator on the shopfloor. To do this, it implements algorithms, such as Recurrent Neural Networks for predictive Analytics, and Multi-Objective Reinforcement Learning for Prescriptive Analytics. The proposed approach is demonstrated in a predictive maintenance scenario in steel industry.

  • Sensor-Driven Learning of Time-Dependent Parameters for Prescriptive Analytics
    IEEE Access, 2020
    Co-Authors: Alexandros Bousdekis, Dimitris Apostolou, Nikos Papageorgiou, Babis Magoutas, Gregoris Mentzas
    Abstract:

    Big data Analytics is rapidly emerging as a key Internet of Things (IoT) initiative aiming at providing meaningful insights and supporting optimal decision making under time constraints. In this direction, Prescriptive Analytics has just started to emerge. Prescriptive Analytics moves beyond descriptive and predictive Analytics aiming at providing adaptive, automated, constrained, time-dependent and optimal decisions. The use of time-dependent parameters in Prescriptive Analytics models provide a more reliable and realistic representation of the complex and dynamic environment and the associated decision making process; however, their estimation poses significant challenges due to the uncertainty derived from inaccurate user input, noisy data, and non-stationarity of real-world data streams. Since feedback and learning mechanisms for tracking the Prescriptive Analytics are crucial enablers for self-configuration and self-optimization, this paper proposes an approach for sensor-driven learning of time-dependent parameters for Prescriptive Analytics models deployed in streaming computational environments. The proposed approach was validated in an Industry 4.0 use case, while it was further evaluated through extensive simulation experiments. The proposed approach overcomes challenges related to uncertainty derived from user’s input, non-stationary data and sensor noise and provides estimates of time-dependent parameters that lead to more reliable prescriptions.

  • Prescriptive Analytics: Literature review and research challenges
    International Journal of Information Management, 2020
    Co-Authors: Katerina Lepenioti, Alexandros Bousdekis, Dimitris Apostolou, Gregoris Mentzas
    Abstract:

    Abstract Business Analytics aims to enable organizations to make quicker, better, and more intelligent decisions with the aim to create business value. To date, the major focus in the academic and industrial realms is on descriptive and predictive Analytics. Nevertheless, Prescriptive Analytics, which seeks to find the best course of action for the future, has been increasingly gathering the research interest. Prescriptive Analytics is often considered as the next step towards increasing data Analytics maturity and leading to optimized decision making ahead of time for business performance improvement. This paper investigates the existing literature pertaining to Prescriptive Analytics and prominent methods for its implementation, provides clarity on the research field of Prescriptive Analytics, synthesizes the literature review in order to identify the existing research challenges, and outlines directions for future research.

  • BIS (Workshops) - Prescriptive Analytics: A Survey of Approaches and Methods
    Business Information Systems Workshops, 2019
    Co-Authors: Katerina Lepenioti, Alexandros Bousdekis, Dimitris Apostolou, Gregoris Mentzas
    Abstract:

    Data Analytics has gathered a lot of attention during the last years. Although descriptive and predictive Analytics have become well-established areas, Prescriptive Analytics has just started to emerge in an increasing rate. In this paper, we present a literature review on Prescriptive Analytics, we frame the Prescriptive Analytics lifecycle and we identify the existing research challenges on this topic. To the best of our knowledge, this is the first literature review on Prescriptive Analytics. Until now, Prescriptive Analytics applications are usually developed in an ad-hoc way with limited capabilities of adaptation to the dynamic and complex nature of today’s enterprises. Moreover, there is a loose integration with predictive Analytics, something which does not enable the exploitation of the full potential of big data.

Dimitris Bertsimas - One of the best experts on this subject based on the ideXlab platform.

  • Prescriptive Analytics for reducing 30-day hospital readmissions after general surgery.
    PloS one, 2020
    Co-Authors: Dimitris Bertsimas, Ioannis Ch. Paschalidis, Taiyao Wang
    Abstract:

    Introduction New financial incentives, such as reduced Medicare reimbursements, have led hospitals to closely monitor their readmission rates and initiate efforts aimed at reducing them. In this context, many surgical departments participate in the American College of Surgeons National Surgical Quality Improvement Program (NSQIP), which collects detailed demographic, laboratory, clinical, procedure and perioperative occurrence data. The availability of such data enables the development of data science methods which predict readmissions and, as done in this paper, offer specific recommendations aimed at preventing readmissions. Materials and methods This study leverages NSQIP data for 722,101 surgeries to develop predictive and Prescriptive models, predicting readmissions and offering real-time, personalized treatment recommendations for surgical patients during their hospital stay, aimed at reducing the risk of a 30-day readmission. We applied a variety of classification methods to predict 30-day readmissions and developed two Prescriptive methods to recommend pre-operative blood transfusions to increase the patient’s hematocrit with the objective of preventing readmissions. The effect of these interventions was evaluated using several predictive models. Results Predictions of 30-day readmissions based on the entire collection of NSQIP variables achieve an out-of-sample accuracy of 87% (Area Under the Curve—AUC). Predictions based only on pre-operative variables have an accuracy of 74% AUC, out-of-sample. Personalized interventions, in the form of pre-operative blood transfusions identified by the Prescriptive methods, reduce readmissions by 12%, on average, for patients considered as candidates for pre-operative transfusion (pre-operative hematoctic

  • from predictive to Prescriptive Analytics
    Management Science, 2019
    Co-Authors: Dimitris Bertsimas, Nathan Kallus
    Abstract:

    We combine ideas from machine learning (ML) and operations research and management science (OR/MS) in developing a framework, along with specific methods, for using data to prescribe optimal decisi...

  • Prescriptive Analytics for human resource planning in the professional services industry
    European Journal of Operational Research, 2019
    Co-Authors: Lauren Berk, Dimitris Bertsimas, Alexander M. Weinstein, Julia Yan
    Abstract:

    Abstract In this paper, we examine human resource planning decisions made at firms that sell contract-based consulting projects. High levels of uncertainty in deals and revenue forecasts make it challenging for consulting firms to hire the right people to staff their projects. We present a human resource planning model using concepts from robust optimization to allow companies to dynamically make hiring decisions that maximize profit while remaining as flexible as possible, and demonstrate potential profit improvements through simulation on real data.

  • from predictive to Prescriptive Analytics
    arXiv: Machine Learning, 2014
    Co-Authors: Dimitris Bertsimas, Nathan Kallus
    Abstract:

    In this paper, we combine ideas from machine learning (ML) and operations research and management science (OR/MS) in developing a framework, along with specific methods, for using data to prescribe optimal decisions in OR/MS problems. In a departure from other work on data-driven optimization and reflecting our practical experience with the data available in applications of OR/MS, we consider data consisting, not only of observations of quantities with direct effect on costs/revenues, such as demand or returns, but predominantly of observations of associated auxiliary quantities. The main problem of interest is a conditional stochastic optimization problem, given imperfect observations, where the joint probability distributions that specify the problem are unknown. We demonstrate that our proposed solution methods, which are inspired by ML methods such as local regression, CART, and random forests, are generally applicable to a wide range of decision problems. We prove that they are tractable and asymptotically optimal even when data is not iid and may be censored. We extend this to the case where decision variables may directly affect uncertainty in unknown ways, such as pricing's effect on demand. As an analogue to R^2, we develop a metric P termed the coefficient of Prescriptiveness to measure the Prescriptive content of data and the efficacy of a policy from an operations perspective. To demonstrate the power of our approach in a real-world setting we study an inventory management problem faced by the distribution arm of an international media conglomerate, which ships an average of 1bil units per year. We leverage internal data and public online data harvested from IMDb, Rotten Tomatoes, and Google to prescribe operational decisions that outperform baseline measures. Specifically, the data we collect, leveraged by our methods, accounts for an 88\% improvement as measured by our P.

Alexandros Bousdekis - One of the best experts on this subject based on the ideXlab platform.

  • machine learning for predictive and Prescriptive Analytics of operational data in smart manufacturing
    Conference on Advanced Information Systems Engineering, 2020
    Co-Authors: Katerina Lepenioti, Alexandros Bousdekis, Gregoris Mentzas, Dimitris Apostolou, Minas Pertselakis, Andreas Louca, Fenareti Lampathaki, Stathis Anastasiou
    Abstract:

    Perceiving information and extracting insights from data is one of the major challenges in smart manufacturing. Real-time data Analytics face several challenges in real-life scenarios, while there is a huge treasure of legacy, enterprise and operational data remaining untouched. The current paper exploits the recent advancements of (deep) machine learning for performing predictive and Prescriptive Analytics on the basis of enterprise and operational data aiming at supporting the operator on the shopfloor. To do this, it implements algorithms, such as Recurrent Neural Networks for predictive Analytics, and Multi-Objective Reinforcement Learning for Prescriptive Analytics. The proposed approach is demonstrated in a predictive maintenance scenario in steel industry.

  • CAiSE Workshops - Machine Learning for Predictive and Prescriptive Analytics of Operational Data in Smart Manufacturing
    Lecture Notes in Business Information Processing, 2020
    Co-Authors: Katerina Lepenioti, Alexandros Bousdekis, Gregoris Mentzas, Dimitris Apostolou, Minas Pertselakis, Andreas Louca, Fenareti Lampathaki, Stathis Anastasiou
    Abstract:

    Perceiving information and extracting insights from data is one of the major challenges in smart manufacturing. Real-time data Analytics face several challenges in real-life scenarios, while there is a huge treasure of legacy, enterprise and operational data remaining untouched. The current paper exploits the recent advancements of (deep) machine learning for performing predictive and Prescriptive Analytics on the basis of enterprise and operational data aiming at supporting the operator on the shopfloor. To do this, it implements algorithms, such as Recurrent Neural Networks for predictive Analytics, and Multi-Objective Reinforcement Learning for Prescriptive Analytics. The proposed approach is demonstrated in a predictive maintenance scenario in steel industry.

  • Sensor-Driven Learning of Time-Dependent Parameters for Prescriptive Analytics
    IEEE Access, 2020
    Co-Authors: Alexandros Bousdekis, Dimitris Apostolou, Nikos Papageorgiou, Babis Magoutas, Gregoris Mentzas
    Abstract:

    Big data Analytics is rapidly emerging as a key Internet of Things (IoT) initiative aiming at providing meaningful insights and supporting optimal decision making under time constraints. In this direction, Prescriptive Analytics has just started to emerge. Prescriptive Analytics moves beyond descriptive and predictive Analytics aiming at providing adaptive, automated, constrained, time-dependent and optimal decisions. The use of time-dependent parameters in Prescriptive Analytics models provide a more reliable and realistic representation of the complex and dynamic environment and the associated decision making process; however, their estimation poses significant challenges due to the uncertainty derived from inaccurate user input, noisy data, and non-stationarity of real-world data streams. Since feedback and learning mechanisms for tracking the Prescriptive Analytics are crucial enablers for self-configuration and self-optimization, this paper proposes an approach for sensor-driven learning of time-dependent parameters for Prescriptive Analytics models deployed in streaming computational environments. The proposed approach was validated in an Industry 4.0 use case, while it was further evaluated through extensive simulation experiments. The proposed approach overcomes challenges related to uncertainty derived from user’s input, non-stationary data and sensor noise and provides estimates of time-dependent parameters that lead to more reliable prescriptions.

  • Prescriptive Analytics: Literature review and research challenges
    International Journal of Information Management, 2020
    Co-Authors: Katerina Lepenioti, Alexandros Bousdekis, Dimitris Apostolou, Gregoris Mentzas
    Abstract:

    Abstract Business Analytics aims to enable organizations to make quicker, better, and more intelligent decisions with the aim to create business value. To date, the major focus in the academic and industrial realms is on descriptive and predictive Analytics. Nevertheless, Prescriptive Analytics, which seeks to find the best course of action for the future, has been increasingly gathering the research interest. Prescriptive Analytics is often considered as the next step towards increasing data Analytics maturity and leading to optimized decision making ahead of time for business performance improvement. This paper investigates the existing literature pertaining to Prescriptive Analytics and prominent methods for its implementation, provides clarity on the research field of Prescriptive Analytics, synthesizes the literature review in order to identify the existing research challenges, and outlines directions for future research.

  • BIS (Workshops) - Prescriptive Analytics: A Survey of Approaches and Methods
    Business Information Systems Workshops, 2019
    Co-Authors: Katerina Lepenioti, Alexandros Bousdekis, Dimitris Apostolou, Gregoris Mentzas
    Abstract:

    Data Analytics has gathered a lot of attention during the last years. Although descriptive and predictive Analytics have become well-established areas, Prescriptive Analytics has just started to emerge in an increasing rate. In this paper, we present a literature review on Prescriptive Analytics, we frame the Prescriptive Analytics lifecycle and we identify the existing research challenges on this topic. To the best of our knowledge, this is the first literature review on Prescriptive Analytics. Until now, Prescriptive Analytics applications are usually developed in an ad-hoc way with limited capabilities of adaptation to the dynamic and complex nature of today’s enterprises. Moreover, there is a loose integration with predictive Analytics, something which does not enable the exploitation of the full potential of big data.