The Experts below are selected from a list of 53784 Experts worldwide ranked by ideXlab platform
Jiaguang Sun - One of the best experts on this subject based on the ideXlab platform.
-
IDEAL - Dynamic Symbolization of Streaming Time Series
Lecture Notes in Computer Science, 2004Co-Authors: Xiaoming Jin, Jianmin Wang, Jiaguang SunAbstract:Symbolization of time series is an important preprocessing subroutine for many data mining tasks. However, it is usually difficult, if not impossible, to apply the traditional static Symbolization approach on streaming time series, because of either the low efficiency of re-computing the typical sub-series, or the low capability of representing the up-to-date series characters. This paper presents a novel Symbolization method, in which the typical sub-series are dynamically adjusted to fit the up-to-date characters of streaming time series. It works in an incremental form without scanning the whole date set. Experiments on data set from stock market justify the superiority of the proposed method over the traditional ones.
-
Dynamic Symbolization of streaming time series
Lecture Notes in Computer Science, 2004Co-Authors: Xiaoming Jin, Jianmin Wang, Jiaguang SunAbstract:Symbolization of time series is an important preprocessing subroutine for many data mining tasks. However, it is usually difficult, if not impossible, to apply the traditional static Symbolization approach on streaming time series, because of either the low efficiency of re-computing the typical sub-series, or the low capability of representing the up-to-date series characters. This paper presents a novel Symbolization method, in which the typical sub-series are dynamically adjusted to fit the up-to-date characters of streaming time series. It works in an incremental form without scanning the whole date set. Experiments on data set from stock market justify the superiority of the proposed method over the traditional ones.
Xiaoming Jin - One of the best experts on this subject based on the ideXlab platform.
-
IDEAL - Dynamic Symbolization of Streaming Time Series
Lecture Notes in Computer Science, 2004Co-Authors: Xiaoming Jin, Jianmin Wang, Jiaguang SunAbstract:Symbolization of time series is an important preprocessing subroutine for many data mining tasks. However, it is usually difficult, if not impossible, to apply the traditional static Symbolization approach on streaming time series, because of either the low efficiency of re-computing the typical sub-series, or the low capability of representing the up-to-date series characters. This paper presents a novel Symbolization method, in which the typical sub-series are dynamically adjusted to fit the up-to-date characters of streaming time series. It works in an incremental form without scanning the whole date set. Experiments on data set from stock market justify the superiority of the proposed method over the traditional ones.
-
Dynamic Symbolization of streaming time series
Lecture Notes in Computer Science, 2004Co-Authors: Xiaoming Jin, Jianmin Wang, Jiaguang SunAbstract:Symbolization of time series is an important preprocessing subroutine for many data mining tasks. However, it is usually difficult, if not impossible, to apply the traditional static Symbolization approach on streaming time series, because of either the low efficiency of re-computing the typical sub-series, or the low capability of representing the up-to-date series characters. This paper presents a novel Symbolization method, in which the typical sub-series are dynamically adjusted to fit the up-to-date characters of streaming time series. It works in an incremental form without scanning the whole date set. Experiments on data set from stock market justify the superiority of the proposed method over the traditional ones.
Jianmin Wang - One of the best experts on this subject based on the ideXlab platform.
-
IDEAL - Dynamic Symbolization of Streaming Time Series
Lecture Notes in Computer Science, 2004Co-Authors: Xiaoming Jin, Jianmin Wang, Jiaguang SunAbstract:Symbolization of time series is an important preprocessing subroutine for many data mining tasks. However, it is usually difficult, if not impossible, to apply the traditional static Symbolization approach on streaming time series, because of either the low efficiency of re-computing the typical sub-series, or the low capability of representing the up-to-date series characters. This paper presents a novel Symbolization method, in which the typical sub-series are dynamically adjusted to fit the up-to-date characters of streaming time series. It works in an incremental form without scanning the whole date set. Experiments on data set from stock market justify the superiority of the proposed method over the traditional ones.
-
Dynamic Symbolization of streaming time series
Lecture Notes in Computer Science, 2004Co-Authors: Xiaoming Jin, Jianmin Wang, Jiaguang SunAbstract:Symbolization of time series is an important preprocessing subroutine for many data mining tasks. However, it is usually difficult, if not impossible, to apply the traditional static Symbolization approach on streaming time series, because of either the low efficiency of re-computing the typical sub-series, or the low capability of representing the up-to-date series characters. This paper presents a novel Symbolization method, in which the typical sub-series are dynamically adjusted to fit the up-to-date characters of streaming time series. It works in an incremental form without scanning the whole date set. Experiments on data set from stock market justify the superiority of the proposed method over the traditional ones.
Richard J Swartz - One of the best experts on this subject based on the ideXlab platform.
-
when moral identity Symbolization motivates prosocial behavior the role of recognition and moral identity internalization
Journal of Applied Psychology, 2013Co-Authors: Karen Page Winterich, Karl Aquino, Vikas Mittal, Richard J SwartzAbstract:This article examines the role of moral identity Symbolization in motivating prosocial behaviors. We propose a 3-way interaction of moral identity Symbolization, internalization, and recognition to predict prosocial behavior. When moral identity internalization is low, we hypothesize that high moral identity Symbolization motivates recognized prosocial behavior due to the opportunity to present one's moral characteristics to others. In contrast, when moral identity internalization is high, prosocial behavior is motivated irrespective of the level of Symbolization and recognition. Two studies provide support for this pattern examining volunteering of time. Our results provide a framework for predicting prosocial behavior by combining the 2 dimensions of moral identity with the situational factor of recognition. [ABSTRACT FROM AUTHOR]
-
when moral identity Symbolization motivates prosocial behavior the role of recognition and moral identity internalization
2013Co-Authors: Karen Page Winterich, Karl Aquino, Vikas Mittal, Richard J SwartzAbstract:This paper examines the role of moral identity Symbolization in motivating prosocial behaviors. We propose a three-way interaction of moral identity Symbolization, internalization, and recognition to predict prosocial behavior. When moral identity internalization is low, we hypothesize that high moral identity Symbolization motivates recognized prosocial behavior due to the opportunity to present one’s moral characteristics to others. In contrast, when moral identity internalization is high, prosocial behavior is motivated irrespective of the level of Symbolization and recognition. Two studies provide support for this pattern examining volunteering of time. Our results provide a framework for predicting prosocial behavior by combining the two dimensions of moral identity with the situational factor of recognition.
Karen Page Winterich - One of the best experts on this subject based on the ideXlab platform.
-
when moral identity Symbolization motivates prosocial behavior the role of recognition and moral identity internalization
Journal of Applied Psychology, 2013Co-Authors: Karen Page Winterich, Karl Aquino, Vikas Mittal, Richard J SwartzAbstract:This article examines the role of moral identity Symbolization in motivating prosocial behaviors. We propose a 3-way interaction of moral identity Symbolization, internalization, and recognition to predict prosocial behavior. When moral identity internalization is low, we hypothesize that high moral identity Symbolization motivates recognized prosocial behavior due to the opportunity to present one's moral characteristics to others. In contrast, when moral identity internalization is high, prosocial behavior is motivated irrespective of the level of Symbolization and recognition. Two studies provide support for this pattern examining volunteering of time. Our results provide a framework for predicting prosocial behavior by combining the 2 dimensions of moral identity with the situational factor of recognition. [ABSTRACT FROM AUTHOR]
-
when moral identity Symbolization motivates prosocial behavior the role of recognition and moral identity internalization
2013Co-Authors: Karen Page Winterich, Karl Aquino, Vikas Mittal, Richard J SwartzAbstract:This paper examines the role of moral identity Symbolization in motivating prosocial behaviors. We propose a three-way interaction of moral identity Symbolization, internalization, and recognition to predict prosocial behavior. When moral identity internalization is low, we hypothesize that high moral identity Symbolization motivates recognized prosocial behavior due to the opportunity to present one’s moral characteristics to others. In contrast, when moral identity internalization is high, prosocial behavior is motivated irrespective of the level of Symbolization and recognition. Two studies provide support for this pattern examining volunteering of time. Our results provide a framework for predicting prosocial behavior by combining the two dimensions of moral identity with the situational factor of recognition.
-
when does recognition increase charitable behavior toward a moral identity based model
Journal of Marketing, 2013Co-Authors: Karen Page Winterich, Vikas Mittal, Karl AquinoAbstract:Abstract Each year, people in the United States donate more than $200 billion to charitable causes. Despite the lack of understanding of whether and how recognition increases charitable behavior, charities often offer it to motivate donor action. This research focuses on how the effectiveness of recognition on charitable behavior is dependent on the joint influence of two distinct dimensions of moral identity: internalization and Symbolization. Three studies examining both monetary donations and volunteering behavior show that recognition increases charitable behavior among those characterized by high moral identity Symbolization and low moral identity internalization. Notably, those who show high levels of moral identity internalization are uninfluenced by recognition, regardless of their Symbolization. By understanding correlates of the two dimensions of moral identity among donors, nonprofits can strategically recognize potential donors to maximize donation and volunteering behavior.
