Computational Thinking - Revision history

Admin: /* High-level Description of Project */

2021-01-16T17:01:57Z

High-level Description of Project

2021-01-15T20:37:36Z

2021-01-15T20:34:57Z

2021-01-15T20:06:52Z

2021-01-15T20:05:57Z

2021-01-15T19:38:46Z

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← Older revision		Revision as of 17:01, 16 January 2021
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	==High-level Description of Project==		==High-level Description of Project==

−	(For additional detailed information and background on this project, see [[Chronology: Data-driven Sports Image Indexing Research]] and sections of my [[2018-19 Academic Year Research Report]])	+	(For additional detailed information and background on this project, see [[Chronology: Data-driven Sports Image Indexing Research]] and sections of my [[2018-19 Academic Year Research Report]])<br>
		+	<br>
		+	How can we index individual images that document football game action at scale? There are so many teams, so many photographers, so many photographs, all problems compounded by two material states of images (born digital and film photographs). Content-based computational methods are available to analyze images; however, these approaches alone are subject to [https://en.wikipedia.org/wiki/Semantic_gap#Image_analysis semantic gap] problems.

← Older revision		Revision as of 20:37, 15 January 2021
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	Both themes can be operationalized using an extract metadata/transform semantically/load (ETL) workflow framework to further characterize the CT constructs selected from the University of Maryland iSchool model for archival science.		Both themes can be operationalized using an extract metadata/transform semantically/load (ETL) workflow framework to further characterize the CT constructs selected from the University of Maryland iSchool model for archival science.

−	=High-level Description of Project=	+	==High-level Description of Project==
		+
		+	(For additional detailed information and background on this project, see [[Chronology: Data-driven Sports Image Indexing Research]] and sections of my [[2018-19 Academic Year Research Report]])

← Older revision		Revision as of 20:34, 15 January 2021
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	Both themes can be operationalized using an extract metadata/transform semantically/load (ETL) workflow framework to further characterize the CT constructs selected from the University of Maryland iSchool model for archival science.		Both themes can be operationalized using an extract metadata/transform semantically/load (ETL) workflow framework to further characterize the CT constructs selected from the University of Maryland iSchool model for archival science.
		+
		+	=High-level Description of Project=

@@ Line 6: / Line 6: @@
 The CT constructs below were adapted from [[Computational Practices Adapted to Archival Science]] work at the University of Maryland iSchool and are aimed at delineating CT processes needed to understand the Link Data Research Group's work on project 1 above.
-We have chosen to distill our research and teaching efforts for using linked data to organize resource collections into the following three themes:
+We have chosen to distill our research and teaching efforts for using linked data to organize resource collections into the following two themes:
 <blockquote>
-# Data-driven semantic indexing
+# Data-driven semantic indexing over RDF graphs
 # Our data is metadata
 </blockquote>
 Both themes can be operationalized using an extract metadata/transform semantically/load (ETL) workflow framework to further characterize the CT constructs selected from the University of Maryland iSchool model for archival science.

@@ Line 6: / Line 6: @@
 The CT constructs below were adapted from [[Computational Practices Adapted to Archival Science]] work at the University of Maryland iSchool and are aimed at delineating CT processes needed to understand the Link Data Research Group's work on project 1 above.
-We have chosen to globalize our examination of linked data use for organizing resource collections for research and teaching purposes using the following three themes:
+We have chosen to distill our research and teaching efforts for using linked data to organize resource collections into the following three themes:
 <blockquote>
 # Data-driven semantic indexing

← Older revision		Revision as of 19:38, 15 January 2021
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	</blockquote>		</blockquote>

−	Both themes can be operationalized using an extract/[semantically~~] transform~~/load (ETL) workflow framework to further characterize the CT constructs selected from the University of Maryland iSchool model for archival science.	+	Both themes can be operationalized using an extract metadata/transform semantically/load (ETL) workflow framework to further characterize the CT constructs selected from the University of Maryland iSchool model for archival science.

@@ Line 10: / Line 10: @@
 # Data-driven semantic indexing
 # Semantic indexing over RDF graphs
-# iii</blockquote>
+# Our data is metadata
 Both themes can be operationalized using an extract/[semantically] transform/load (ETL) workflow framework to further characterize the CT constructs selected from the University of Maryland iSchool model for archival science.

@@ Line 6: / Line 6: @@
 The CT constructs below were adapted from [[Computational Practices Adapted to Archival Science]] work at the University of Maryland iSchool and are aimed at delineating CT processes needed to understand the Link Data Research Group's work on project 1 above.
-We have chosen to globalize our examination of linked data use for organizing resource collections using the following theme:
+We have chosen to globalize our examination of linked data use for organizing resource collections for research and teaching purposes using the following theme:
-<blockquote><i>Investigation semantic indexing methods over RDF graphs</i></blockquote>
+<blockquote><i>Investigate semantic indexing methods over RDF graphs</i></blockquote>
 Also, with:
 <blockquote><i>Data-driven semantic indexing</i></blockquote>
 in both c