Exploratory Sequential Data Analysis: Foundations

1994 ◽  
Vol 9 (3) ◽  
pp. 251-317 ◽  
Author(s):  
Penelope Sanderson ◽  
Carolanne Fisher
Keyword(s):  
Data Analysis ◽  
Sequential Data ◽  
Exploratory Sequential Data Analysis

Macshapa: Software Support for Exploratory Sequential Data Analysis

1995 ◽  
Vol 39 (9) ◽  
pp. 511-515
Author(s):  
Penelope M. Sanderson
Keyword(s):  
Data Analysis ◽  
Observational Data ◽  
Sequential Analysis ◽  
Cognitive Engineering ◽  
Sequential Data ◽  
Software Support ◽  
Transition Analysis ◽  
Engineering Research ◽  
Lag Sequential Analysis ◽  
Exploratory Sequential Data Analysis

This paper outlines the need for better conceptual and methodological tools for performing observational data analysis in support of cognitive engineering research and practice and presents a tool, MacSHAPA, that has been designed to support such work. MacSHAPA is particularly suited for cognitive engineering studies of complex real-world decisionmaking. MacSHAPA lets users (1) enter or import data into a spreadsheet-like viewing medium, (2) annotate, manipulate, and visualize data in various ways, (3) carry out statistical analyses of various kinds, and (4) export data and results to other applications. MacSHAPA controls video devices, capturing timecode and inserting it into the database, and using timestamps in the database to locate events of interest on videotape. MacSHAPA's statistical routines include content and duration analysis, transition analysis (with some Markov statistics), lag sequential analysis, cycles reports, and some kinds of sequential pattern matching. The paper concludes with several examples of how MacSHAPA has been used to obtain useful results from observational data collected in laboratory and field settings.

On Learning to Play with Other Kids′ Toys: A Response to Sanderson and Benda

1998 ◽  
Vol 40 (4) ◽  
pp. 685-687
Author(s):  
Clint A. Bowers ◽  
Florian Jentsch ◽  
Eduardo Salas
Keyword(s):  
Data Analysis ◽  
Training Needs ◽  
Needs Analysis ◽  
Analysis Tool ◽  
Sequential Data ◽  
Exploratory Sequential Data Analysis

In their critique of our research, Sanderson and Benda (1998, this issue) suggest several concerns with our characterization and utilization of the Exploratory Sequential Data Analysis (ESDA) approach. In this response, we consider each of the concerns in the context of training needs analysis. We conclude that the ESDA framework appears to hold promise as a training needs analysis tool. However, further dialogue between the experts in the ESDA approach and those in training is required to realize this potential.

Exploratory sequential data analysis

interactions ◽  
1996 ◽  
Vol 3 (2) ◽  
pp. 25-34 ◽  
Author(s):  
Carolanne Fisher ◽  
Penelope Sanderson
Keyword(s):  
Data Analysis ◽  
Sequential Data ◽  
Exploratory Sequential Data Analysis

Exploratory sequential data analysis

1993 ◽  
Vol 25 (1) ◽  
pp. 34-40 ◽  
Author(s):  
Carolanne Fisher ◽  
Penelope Sanderson
Keyword(s):  
Data Analysis ◽  
Sequential Data ◽  
Exploratory Sequential Data Analysis

Exploring Sequential Data: Commentary on Bowers, Jentsch, Salas, and Braun (1998)

1998 ◽  
Vol 40 (4) ◽  
pp. 680-684 ◽  
Author(s):  
Penelope M. Sanderson ◽  
Peter J. Benda
Keyword(s):  
Data Analysis ◽  
Training Needs ◽  
Sequential Data ◽  
Fundamental Understanding ◽  
Exploratory Sequential Data Analysis ◽  
Issue Report

In an investigation intended to determine training needs of flight crews, Bowers et al. (1998, this issue) report two studies showing that the patterning of communication is a better discriminator of good and poor crews than is the content of communication. Bowers et al. characterize their studies as intended to generate hypotheses for training needs and draw connections with Exploratory Sequential Data Analysis (ESDA). Although applauding the intentions of Bowers et al., we point out some concerns with their characterization and implementation of ESDA. Our principal concern is that the Bowers et al. exploration of the data does not convincingly lead them back to a better fundamental understanding of the original phenomena they are investigating.

MacSHAPA and the enterprise of exploratory sequential data analysis (ESDA)

1994 ◽  
Vol 41 (5) ◽  
pp. 633-681 ◽  
Author(s):  
Penelope Sanderson ◽  
Jay Scott ◽  
Tom Johnston ◽  
John Mainzer ◽  
Larry Watanabe ◽  
...  
Keyword(s):  
Data Analysis ◽  
Sequential Data ◽  
Exploratory Sequential Data Analysis

Machine Learning Based Predictive Action on Categorical Non-Sequential Data

2020 ◽  
Vol 13 (5) ◽  
pp. 1020-1030
Author(s):  
Pradeep S. ◽  
Jagadish S. Kallimani
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Categorical Data ◽  
Numerical Data ◽  
Processing Technique ◽  
Machine Learning Algorithms ◽  
Sequential Data ◽  
Industry Standard ◽  
Robust Model ◽  
Future Work

Background: With the advent of data analysis and machine learning, there is a growing impetus of analyzing and generating models on historic data. The data comes in numerous forms and shapes with an abundance of challenges. The most sorted form of data for analysis is the numerical data. With the plethora of algorithms and tools it is quite manageable to deal with such data. Another form of data is of categorical nature, which is subdivided into, ordinal (order wise) and nominal (number wise). This data can be broadly classified as Sequential and Non-Sequential. Sequential data analysis is easier to preprocess using algorithms. Objective: The challenge of applying machine learning algorithms on categorical data of nonsequential nature is dealt in this paper. Methods: Upon implementing several data analysis algorithms on such data, we end up getting a biased result, which makes it impossible to generate a reliable predictive model. In this paper, we will address this problem by walking through a handful of techniques which during our research helped us in dealing with a large categorical data of non-sequential nature. In subsequent sections, we will discuss the possible implementable solutions and shortfalls of these techniques. Results: The methods are applied to sample datasets available in public domain and the results with respect to accuracy of classification are satisfactory. Conclusion: The best pre-processing technique we observed in our research is one hot encoding, which facilitates breaking down the categorical features into binary and feeding it into an Algorithm to predict the outcome. The example that we took is not abstract but it is a real – time production services dataset, which had many complex variations of categorical features. Our Future work includes creating a robust model on such data and deploying it into industry standard applications.

A Novel Distributed Duration-Aware LSTM for Large Scale Sequential Data Analysis

2019 ◽  
pp. 120-134
Author(s):  
Dejiao Niu ◽  
Yawen Liu ◽  
Tao Cai ◽  
Xia Zheng ◽  
Tianquan Liu ◽  
...  
Keyword(s):  
Data Analysis ◽  
Large Scale ◽  
Sequential Data
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