Details
| Original language | English |
|---|---|
| Title of host publication | Springer Series on Cultural Computing |
| Place of Publication | Cham |
| Pages | 101-129 |
| Number of pages | 29 |
| ISBN (electronic) | 978-3-319-73465-1 |
| Publication status | Published - 2018 |
Publication series
| Name | Springer Series on Cultural Computing |
|---|---|
| ISSN (Print) | 2195-9056 |
| ISSN (electronic) | 2195-9064 |
Abstract
In the cognitive processes of humans, forgetting is a very effective way for focusing on the important things, while unstressing things, which are (currently) less important. The translation of forgetting into the digital world is, thus, a promising approach for better dealing with the increasing problem of information overload. Information overload is not only caused by the mere volume of information, it is also triggered by the fact that all information is seemingly on the same level of importance. In the ideal case, a perfect dynamic assessment of importance could restrict the information space strictly to the information currently needed, thus dramatically reducing information overload. The role of a digital memory including digital forgetting is to support, not to replace or to hinder human memory. Therefore, a useful approach for “managed forgetting”—a controlled form of digital forgetting—in a digital memory has to be carefully designed, such that it complements human memory. A core ingredient of managed forgetting is the assessment of the importance of information items. Furthermore, forgetting actions are required that go beyond the binary decision between keep and delete. For the short-term perspective, managed forgetting replaces the binary decision on importance by a gradually changing value: Information sinks away from the user with a decreasing value, which we call “Memory Buoyancy”. The transition from short-term value to long-term importance brings a variety of new challenges. When we look into the “Preservation Value” of an information item, we have to estimate the future importance of a resource. This challenging task is further complicated by the facts that (a) preservation looks into very long time frames (e.g., decades rather than months) and (b) the importance of information items may change over time. The Preservation Value provides the basis for making preservation decisions, e.g., how much to invest for ensuring that a media item such as a photo will survive the next years or decades. We, furthermore, investigate into methods for information value assessment in support of managed forgetting. For this purpose, we analyze existing methods for information value assessment and discuss their usefulness in the context of computing the Preservation Value. We also outline methods for Preservation Value computation for different exemplary settings. This will also point to the more in-depth discussion of computing Preservation Value in the semantic desktop and photo preservation as it is discussed in later chapters of the book. Finally, we discuss managed forgetting beyond assessing the importance of information items. We study a portfolio of forgetting methods, i.e., methods that can be used to implement managed forgetting on top of the values for information importance. This includes methods such as information hiding, forgetful search, summarization and aggregation as well as deletion.
ASJC Scopus subject areas
- Computer Science(all)
- Computer Science Applications
- Computer Science(all)
- Human-Computer Interaction
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
Springer Series on Cultural Computing. Cham, 2018. p. 101-129 (Springer Series on Cultural Computing).
Research output: Chapter in book/report/conference proceeding › Contribution to book/anthology › Research › peer review
}
TY - CHAP
T1 - Preservation value and managed forgetting
AU - Niederée, Claudia
AU - Kanhabua, Nattiya
AU - Tran, Tuan
AU - Naini, Kaweh Djafari
N1 - Publisher Copyright: © Springer International Publishing AG 2018. Copyright: Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2018
Y1 - 2018
N2 - In the cognitive processes of humans, forgetting is a very effective way for focusing on the important things, while unstressing things, which are (currently) less important. The translation of forgetting into the digital world is, thus, a promising approach for better dealing with the increasing problem of information overload. Information overload is not only caused by the mere volume of information, it is also triggered by the fact that all information is seemingly on the same level of importance. In the ideal case, a perfect dynamic assessment of importance could restrict the information space strictly to the information currently needed, thus dramatically reducing information overload. The role of a digital memory including digital forgetting is to support, not to replace or to hinder human memory. Therefore, a useful approach for “managed forgetting”—a controlled form of digital forgetting—in a digital memory has to be carefully designed, such that it complements human memory. A core ingredient of managed forgetting is the assessment of the importance of information items. Furthermore, forgetting actions are required that go beyond the binary decision between keep and delete. For the short-term perspective, managed forgetting replaces the binary decision on importance by a gradually changing value: Information sinks away from the user with a decreasing value, which we call “Memory Buoyancy”. The transition from short-term value to long-term importance brings a variety of new challenges. When we look into the “Preservation Value” of an information item, we have to estimate the future importance of a resource. This challenging task is further complicated by the facts that (a) preservation looks into very long time frames (e.g., decades rather than months) and (b) the importance of information items may change over time. The Preservation Value provides the basis for making preservation decisions, e.g., how much to invest for ensuring that a media item such as a photo will survive the next years or decades. We, furthermore, investigate into methods for information value assessment in support of managed forgetting. For this purpose, we analyze existing methods for information value assessment and discuss their usefulness in the context of computing the Preservation Value. We also outline methods for Preservation Value computation for different exemplary settings. This will also point to the more in-depth discussion of computing Preservation Value in the semantic desktop and photo preservation as it is discussed in later chapters of the book. Finally, we discuss managed forgetting beyond assessing the importance of information items. We study a portfolio of forgetting methods, i.e., methods that can be used to implement managed forgetting on top of the values for information importance. This includes methods such as information hiding, forgetful search, summarization and aggregation as well as deletion.
AB - In the cognitive processes of humans, forgetting is a very effective way for focusing on the important things, while unstressing things, which are (currently) less important. The translation of forgetting into the digital world is, thus, a promising approach for better dealing with the increasing problem of information overload. Information overload is not only caused by the mere volume of information, it is also triggered by the fact that all information is seemingly on the same level of importance. In the ideal case, a perfect dynamic assessment of importance could restrict the information space strictly to the information currently needed, thus dramatically reducing information overload. The role of a digital memory including digital forgetting is to support, not to replace or to hinder human memory. Therefore, a useful approach for “managed forgetting”—a controlled form of digital forgetting—in a digital memory has to be carefully designed, such that it complements human memory. A core ingredient of managed forgetting is the assessment of the importance of information items. Furthermore, forgetting actions are required that go beyond the binary decision between keep and delete. For the short-term perspective, managed forgetting replaces the binary decision on importance by a gradually changing value: Information sinks away from the user with a decreasing value, which we call “Memory Buoyancy”. The transition from short-term value to long-term importance brings a variety of new challenges. When we look into the “Preservation Value” of an information item, we have to estimate the future importance of a resource. This challenging task is further complicated by the facts that (a) preservation looks into very long time frames (e.g., decades rather than months) and (b) the importance of information items may change over time. The Preservation Value provides the basis for making preservation decisions, e.g., how much to invest for ensuring that a media item such as a photo will survive the next years or decades. We, furthermore, investigate into methods for information value assessment in support of managed forgetting. For this purpose, we analyze existing methods for information value assessment and discuss their usefulness in the context of computing the Preservation Value. We also outline methods for Preservation Value computation for different exemplary settings. This will also point to the more in-depth discussion of computing Preservation Value in the semantic desktop and photo preservation as it is discussed in later chapters of the book. Finally, we discuss managed forgetting beyond assessing the importance of information items. We study a portfolio of forgetting methods, i.e., methods that can be used to implement managed forgetting on top of the values for information importance. This includes methods such as information hiding, forgetful search, summarization and aggregation as well as deletion.
UR - http://www.scopus.com/inward/record.url?scp=85062752456&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-73465-1_4
DO - 10.1007/978-3-319-73465-1_4
M3 - Contribution to book/anthology
AN - SCOPUS:85062752456
SN - 978-3-319-73464-4
T3 - Springer Series on Cultural Computing
SP - 101
EP - 129
BT - Springer Series on Cultural Computing
CY - Cham
ER -