Managed Forgetting to Support Information Management and Knowledge Work

Jilek, Christian; Runge, Yannick; Niederée, Claudia; Maus, Heiko; Tempel, Tobias; Dengel, Andreas; Frings, Christian

doi:10.1007/s13218-018-00568-9

Cited by 9 publications

(7 citation statements)

References 34 publications

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“…all files of a person's or department's computer(s) instead of a selection stored in an organizational memory. In [8] we presented more challenges, especially also addressing forgetful information systems in general. One of the them is gaining and keeping user's trust in such a system, which we also mentioned in this paper when presenting our experience with the misused MB slider (see Section 3).…”

Section: Discussionmentioning

confidence: 99%

“…This is addressed in a future paper. The MB slider failure is not the only problem we experienced: In [8], we name more problems and challenges also addressing forgetful information systems in general. Several issues regarding MB can be traced back to two major problems: Looking at these elements from low to high MB, the context's "golden thread" could be as follows: The owner of the context and a colleague, Peter Stainer, planned to meet in Mannheim and then travel on to Rome.…”

Section: Memory Buoyancymentioning

confidence: 99%

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Advanced Memory Buoyancy for Forgetful Information Systems

Jilek,

Chwalek,

Schwarz

et al. 2018

Preprint

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Knowledge workers face an ever increasing flood of information in their daily lives. To counter this and provide better support for information management and knowledge work in general, we have been investigating solutions inspired by human forgetting since 2013. These solutions are based on Semantic Desktop (SD) and Managed Forgetting (MF) technology. A key concept of the latter is the so-called Memory Buoyancy (MB), which is intended to represent an information item's current value for the user and allows to employ forgetting mechanisms. The SD thus continuously performs information value assessment updating MB and triggering respective MF measures. We extended an SDbased organizational memory system, which we have been using in daily work for over seven years now, with MF mechanisms directly embedding them in daily activities, too, and enabling us to test and optimize them in real-world scenarios. In this paper, we first present our initial version of MB and discuss success and failure stories we have been experiencing with it during three years of practical usage. We learned from cognitive psychology that our previous research on context can be beneficial for MF. Thus, we created an advanced MB version especially taking user context, and in particular context switches, into account. These enhancements as well as a first prototypical implementation are presented, too.

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Section: Discussionmentioning

confidence: 99%

Section: Memory Buoyancymentioning

confidence: 99%

Advanced Memory Buoyancy for Forgetful Information Systems

Jilek,

Chwalek,

Schwarz

et al. 2018

Preprint

Self Cite

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“…Seasonality and non-stationarity in the data is one explanation for this. This strategy is also called managed forgetting [33].…”

Section: A Amount Of Training Datamentioning

confidence: 99%

Online Machine Learning for 1-Day-Ahead Prediction of Indoor Photovoltaic Energy

et al. 2023

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We explore the potential for predicting indoor photovoltaic energy on a forecasting horizon of up to 24 hours. The objective is to enable energy management approaches that exploit harvesting opportunities more strategically, for which they require more accurate energy intake predictions. Our study is based on a data set covering over 3 years, for which we simulate online machine learning algorithms with different amounts of training data and input features. Our results show that relatively simple machine learning methods can outperform a persistent predictor considerably, and we observed a reduction of errors of up to 56%. When devices obtain a significant amount of sunlight, adding the weather forecast improves the prediction accuracy. We discuss prediction features, the amount of training data and analyze the sources of errors to understand the potential of indoor photovoltaic energy harvesting predictions.INDEX TERMS energy harvesting, photovoltaic systems, energy predictions, machine learning.

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“…To support the user, cSpaces provides an assistant sidebar (as depicted in Fig. 1 on the right-hand side), which shows relevant information retrieved from repositories or belonging to the current context (e.g., a selected e-mail in an office program) [20]. This integration allows cSpaces to derive, build and formalize contexts that the DTO can use for authentic representation of process tasks supporting users' daily work.…”

Section: Conceptualizing the Digital Twin Of An Organizationmentioning

confidence: 99%

Digital Twins of an Organization for Enterprise Modeling

Riss

Maus

Javaid

et al. 2020

Lecture Notes in Business Information Processing

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Today's dynamic business environment requires enterprises to be agile in order to remain competitive. Competition also impacts enterprise modeling (EM) aiming at the systematic development of the enterprise's business processes, organizational structures, and information systems. Although EM is a mature discipline, organizations still do not exploit the full potential of EM. We suggest, the concept of a Digital Twin of an Organization (DTO) provides a means of digitalization to introduce more agility. A DTO draws upon graph-based, machine readable knowledge representation of enterprise models. In order to run through various scenarios in real time, the DTO approach makes use of Context Spaces that provide the required information semantically structured, which improves the comprehensibility and applicability of the used models. The DTO combines EM with operational reality and, thus, increases the agility of the enterprise.

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Managed Forgetting to Support Information Management and Knowledge Work

Cited by 9 publications

References 34 publications

Advanced Memory Buoyancy for Forgetful Information Systems

Advanced Memory Buoyancy for Forgetful Information Systems

Online Machine Learning for 1-Day-Ahead Prediction of Indoor Photovoltaic Energy

Digital Twins of an Organization for Enterprise Modeling

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