Expanding the Timeliness Dimension as a Means for Automatic Data Curation via Topological Data Analysis
<p>The chapter focuses on the timeliness dimension of information quality and considers situations in which the value of data can fluctuate over time. If timeliness is viewed in a rigid manner, blindly minimizing the time between an event's occurrence and its use, decision-making and analysis may ac...
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| Autores principales: | , |
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| Formato: | Capítulo de libro |
| Lenguaje: | inglés |
| Publicado: |
IntechOpen
2026
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| Acceso en línea: | https://www.intechopen.com/chapters/1237217 |
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| _version_ | 1879164887246045184 |
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| author | Tudoreanu, M. Eduard Sakthivel, Mahalakshmi |
| author_browse | Sakthivel, Mahalakshmi Tudoreanu, M. Eduard |
| author_facet | Tudoreanu, M. Eduard Sakthivel, Mahalakshmi |
| author_sort | Tudoreanu, M. Eduard |
| building | IntechOpen |
| collection | Acceso abierto |
| description | <p>The chapter focuses on the timeliness dimension of information quality and considers situations in which the value of data can fluctuate over time. If timeliness is viewed in a rigid manner, blindly minimizing the time between an event's occurrence and its use, decision-making and analysis may actually utilize information that is inappropriate, even if the data are otherwise accurate, complete, and free of duplicates. Many types of information, from economics to weather and traffic, follow a periodic pattern where knowledge from events in the more distant past may be more important than recent data. This occurs when the pattern transitions into a new cycle. This chapter proposes that timeliness is more than just time lag and presents a method for automatically quantifying how appropriate various historical portions of a dataset would be for machine learning and analysis in each present context. Our contribution is the development of an algorithm based on topological data analysis (TDA) to simplify and identify invariant properties of the dataset. The algorithm is evaluated and examined on a synthetic dataset to better understand how it performs with different types of cyclical patterns and how the values of parameters affect its behavior. Our experiments show that our approach outperforms time-agnostic, general-purpose TDA, and that our algorithm can be configured to exhibit stable behavior with respect to the type of cyclical pattern, while also providing good performance when assigning timeliness values to the dataset.</p> |
| doi_str_mv | 10.5772/intechopen.1013726 |
| first_indexed | 2026-10-15T21:41:28Z |
| format | Book Chapter |
| fullrecord | <oai_dc:dc xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><identifier>intech-1237217</identifier><datestamp>2026-01-19</datestamp>
<dc:title>Expanding the Timeliness Dimension as a Means for Automatic Data Curation via Topological Data Analysis</dc:title>
<dc:creator>M. Eduard Tudoreanu</dc:creator>
<dc:creator>Mahalakshmi Sakthivel</dc:creator>
<dc:subject>Physical Sciences, Engineering and Technology</dc:subject>
<dc:description><p>The chapter focuses on the timeliness dimension of information quality and considers situations in which the value of data can fluctuate over time. If timeliness is viewed in a rigid manner, blindly minimizing the time between an event's occurrence and its use, decision-making and analysis may actually utilize information that is inappropriate, even if the data are otherwise accurate, complete, and free of duplicates. Many types of information, from economics to weather and traffic, follow a periodic pattern where knowledge from events in the more distant past may be more important than recent data. This occurs when the pattern transitions into a new cycle. This chapter proposes that timeliness is more than just time lag and presents a method for automatically quantifying how appropriate various historical portions of a dataset would be for machine learning and analysis in each present context. Our contribution is the development of an algorithm based on topological data analysis (TDA) to simplify and identify invariant properties of the dataset. The algorithm is evaluated and examined on a synthetic dataset to better understand how it performs with different types of cyclical patterns and how the values of parameters affect its behavior. Our experiments show that our approach outperforms time-agnostic, general-purpose TDA, and that our algorithm can be configured to exhibit stable behavior with respect to the type of cyclical pattern, while also providing good performance when assigning timeliness values to the dataset.</p></dc:description>
<dc:publisher>IntechOpen</dc:publisher>
<dc:date>2026-01-19</dc:date>
<dc:type>Chapter, Part Of Book</dc:type>
<dc:identifier>https://www.intechopen.com/chapters/1237217</dc:identifier>
<dc:identifier>doi:10.5772/intechopen.1013726</dc:identifier>
<dc:language>en</dc:language>
<dc:relation>ISBN:978-1-83634-986-0</dc:relation>
<dc:rights>https://creativecommons.org/licenses/by/3.0/</dc:rights>
<dc:source>https://www.intechopen.com/books/1004801 ; Data Quality Matters - Best Practices for Integrity and Assurance</dc:source>
</oai_dc:dc> |
| id | intech-1237217 |
| institution | IntechOpen |
| isbn | 978-1-83634-986-0 |
| language | English |
| last_indexed | 2026-10-15T21:41:28Z |
| publishDate | 2026 |
| publishDateRange | 2026 |
| publishDateSort | 2026 |
| publisher | IntechOpen |
| publisherStr | IntechOpen |
| record_format | oai_dc |
| spelling | intech-12372172026-01-19 Expanding the Timeliness Dimension as a Means for Automatic Data Curation via Topological Data Analysis M. Eduard Tudoreanu Mahalakshmi Sakthivel Physical Sciences, Engineering and Technology <p>The chapter focuses on the timeliness dimension of information quality and considers situations in which the value of data can fluctuate over time. If timeliness is viewed in a rigid manner, blindly minimizing the time between an event's occurrence and its use, decision-making and analysis may actually utilize information that is inappropriate, even if the data are otherwise accurate, complete, and free of duplicates. Many types of information, from economics to weather and traffic, follow a periodic pattern where knowledge from events in the more distant past may be more important than recent data. This occurs when the pattern transitions into a new cycle. This chapter proposes that timeliness is more than just time lag and presents a method for automatically quantifying how appropriate various historical portions of a dataset would be for machine learning and analysis in each present context. Our contribution is the development of an algorithm based on topological data analysis (TDA) to simplify and identify invariant properties of the dataset. The algorithm is evaluated and examined on a synthetic dataset to better understand how it performs with different types of cyclical patterns and how the values of parameters affect its behavior. Our experiments show that our approach outperforms time-agnostic, general-purpose TDA, and that our algorithm can be configured to exhibit stable behavior with respect to the type of cyclical pattern, while also providing good performance when assigning timeliness values to the dataset.</p> IntechOpen 2026-01-19 Chapter, Part Of Book https://www.intechopen.com/chapters/1237217 doi:10.5772/intechopen.1013726 en ISBN:978-1-83634-986-0 https://creativecommons.org/licenses/by/3.0/ https://www.intechopen.com/books/1004801 ; Data Quality Matters - Best Practices for Integrity and Assurance |
| spellingShingle | Physical Sciences, Engineering and Technology Tudoreanu, M. Eduard Sakthivel, Mahalakshmi Expanding the Timeliness Dimension as a Means for Automatic Data Curation via Topological Data Analysis |
| title | Expanding the Timeliness Dimension as a Means for Automatic Data Curation via Topological Data Analysis |
| title_full | Expanding the Timeliness Dimension as a Means for Automatic Data Curation via Topological Data Analysis |
| title_fullStr | Expanding the Timeliness Dimension as a Means for Automatic Data Curation via Topological Data Analysis |
| title_full_unstemmed | Expanding the Timeliness Dimension as a Means for Automatic Data Curation via Topological Data Analysis |
| title_short | Expanding the Timeliness Dimension as a Means for Automatic Data Curation via Topological Data Analysis |
| title_sort | expanding the timeliness dimension as a means for automatic data curation via topological data analysis |
| topic | Physical Sciences, Engineering and Technology |
| url | https://www.intechopen.com/chapters/1237217 |
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