scholarly journals Are Assessment Practices Well Aligned Over Time? A Big Data Exploration

2020 ◽  
pp. 147-164
Author(s):  
Jekaterina Rogaten ◽  
Doug Clow ◽  
Chris Edwards ◽  
Mark Gaved ◽  
Bart Rienties
Keyword(s):  
Big Data ◽  
Data Exploration ◽  
Assessment Practices ◽  
Over Time

ExpanDrogram: Dynamic Visualization of Big Data Segmentation over Time

2021 ◽  
Vol 13 (2) ◽  
pp. 1-27
Author(s):  
A. Khalemsky ◽  
R. Gelbard
Keyword(s):  
Big Data ◽  
Visual Presentation ◽  
Trend Detection ◽  
Dynamic Visualization ◽  
Segmentation Process ◽  
Key Points ◽  
Wide Range ◽  
History Of ◽  
The Individual ◽  
Over Time

In dynamic and big data environments the visualization of a segmentation process over time often does not enable the user to simultaneously track entire pieces. The key points are sometimes incomparable, and the user is limited to a static visual presentation of a certain point. The proposed visualization concept, called ExpanDrogram, is designed to support dynamic classifiers that run in a big data environment subject to changes in data characteristics. It offers a wide range of features that seek to maximize the customization of a segmentation problem. The main goal of the ExpanDrogram visualization is to improve comprehensiveness by combining both the individual and segment levels, illustrating the dynamics of the segmentation process over time, providing “version control” that enables the user to observe the history of changes, and more. The method is illustrated using different datasets, with which we demonstrate multiple segmentation parameters, as well as multiple display layers, to highlight points such as new trend detection, outlier detection, tracking changes in original segments, and zoom in/out for more/less detail. The datasets vary in size from a small one to one of more than 12 million records.

scholarly journals Coping with demand volatility in retail pharmacies with the aid of big data exploration

2018 ◽  
Vol 98 ◽  
pp. 343-354 ◽  
Author(s):  
Christos I. Papanagnou ◽  
Omeiza Matthews-Amune
Keyword(s):  
Big Data ◽  
Data Exploration ◽  
Demand Volatility

A Fitting Approach to Construct and Measurement Alignment

2017 ◽  
Vol 21 (3) ◽  
pp. 592-632 ◽  
Author(s):  
Margaret M. Luciano ◽  
John E. Mathieu ◽  
Semin Park ◽  
Scott I. Tannenbaum
Keyword(s):  
Big Data ◽  
Data Streams ◽  
Iterative Process ◽  
Emerging Technologies ◽  
Measurement Techniques ◽  
Great Promise ◽  
Big Data Technologies ◽  
Nearly Continuous ◽  
Dynamic Phenomena ◽  
Over Time

Many phenomena of interest to management and psychology scholars are dynamic and change over time. One of the primary impediments to the examination of dynamic phenomena has been challenges associated with collecting data at a sufficient frequency and duration to accurately model such changes. Emerging technologies that produce nearly continuous streams of big data offer great promise to address those challenges; however, they introduce new methodological challenges and construct validity concerns. We seek to integrate the emerging big data technologies into the existing repertoire of measurement techniques and advance an iterative process to enhance their measurement fit. First, we provide an overview of dynamic constructs and temporal frameworks, highlighting their measurement implications. Second, we discuss different data streams and feature emerging technologies that leverage big data as a means to index dynamic constructs. Third, we integrate the previous sections and advance an iterative approach to achieving measurement fit, highlighting factors that make some measurement choices more suitable and viable than others. In so doing, we hope to accelerate the advancement of dynamic theories and methods.

scholarly journals Practical approaches to big data privacy over time

2018 ◽  
Vol 8 (1) ◽  
pp. 29-51 ◽  
Author(s):  
Micah Altman ◽  
Alexandra Wood ◽  
David R O’Brien ◽  
Urs Gasser
Keyword(s):  
Big Data ◽  
Data Privacy ◽  
Big Data Privacy ◽  
Over Time

scholarly journals The Multi-method Comprehensive Review: Synthesis and Analysis when Scholarship is International, Interdisciplinary, and Immense

2021 ◽  
Author(s):  
Allison Schnable ◽  
Anthony J. DeMattee ◽  
Rachel Sullivan Robinson ◽  
Jennifer N. Brass ◽  
Wesley Longhofer
Keyword(s):  
Big Data ◽  
International Development ◽  
Comprehensive Review ◽  
Main Drawback ◽  
New Strategy ◽  
Over Time

AbstractThis article presents a new strategy for reviewing large, multidisciplinary academic literatures: a multi-method comprehensive review (MCR). We present this approach and demonstrate its use by the NGO Knowledge Collective, which aims to aggregate knowledge on NGOs in international development. We explain the process by which scholars can identify, analyze, and synthesize a population of hundreds or thousands of articles. MCRs facilitate cross-disciplinary synthesis, systematically identify gaps in a literature, and can create data for further scholarly use. The main drawback is the significant resources needed to manage the volume of text to review, although such obstacles may be mitigated through advances in “big data” methodologies over time.

Big Data Analytics

2018 ◽  
pp. 124-138
Author(s):  
Sreenu G. ◽  
M.A. Saleem Durai
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Real Life ◽  
Big Data Analytics ◽  
Speed Up ◽  
Processing Techniques ◽  
And Storage ◽  
Big Data Computing ◽  
New Algorithms ◽  
Over Time

Advances in recent hardware technology have permitted to document transactions and other pieces of information of everyday life at an express pace. In addition of speed up and storage capacity, real-life perceptions tend to transform over time. However, there are so much prospective and highly functional values unseen in the vast volume of data. For this kind of applications conventional data mining is not suitable, so they should be tuned and changed or designed with new algorithms. Big data computing is inflowing to the category of most hopeful technologies that shows the way to new ways of thinking and decision making. This epoch of big data helps users to take benefit out of all available data to gain more precise systematic results or determine latent information, and then make best possible decisions. Depiction from a broad set of workloads, the author establishes a set of classifying measures based on the storage architecture, processing types, processing techniques and the tools and technologies used.

Big Data, Data Science, and Career Pathways

2020 ◽  
pp. 239-254
Author(s):  
David W. Dorsey
Keyword(s):  
Big Data ◽  
Data Science ◽  
Career Pathways ◽  
Unstructured Data ◽  
Future Application ◽  
The Internet ◽  
The Future ◽  
Skill Requirements ◽  
Enormous Number ◽  
Over Time

With the rise of the internet and the related explosion in the amount of data that are available, the field of data science has expanded rapidly, and analytic techniques designed for use in “big data” contexts have become popular. These include techniques for analyzing both structured and unstructured data. This chapter explores the application of these techniques to the development and evaluation of career pathways. For example, data scientists can analyze online job listings and resumes to examine changes in skill requirements and careers over time and to examine job progressions across an enormous number of people. Similarly, analysts can evaluate whether information on career pathways accurately captures realistic job progressions. Within organizations, the increasing amount of data make it possible to pinpoint the specific skills, behaviors, and attributes that maximize performance in specific roles. The chapter concludes with ideas for the future application of big data to career pathways.

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