Advanced Memory Buoyancy for Forgetful Information Systems

Authors

  • Christian Jilek DFKI GmbH & TU Kaiserslautern
  • Jessica Chwalek Computer Science Department, TU Kaiserslautern, Kaiserslautern, Germany
  • Sven Schwarz Smart Data & Knowledge Services Department, DFKI GmbH, Kaiserslautern, Germany
  • Markus Schröder Smart Data & Knowledge Services Department, DFKI GmbH, Kaiserslautern, Germany & Computer Science Department, TU Kaiserslautern, Kaiserslautern, Germany
  • Heiko Maus Smart Data & Knowledge Services Department, DFKI GmbH, Kaiserslautern, Germany
  • Andreas Dengel Smart Data & Knowledge Services Department, DFKI GmbH, Kaiserslautern, Germany & Computer Science Department, TU Kaiserslautern, Kaiserslautern, Germany

DOI:

https://doi.org/10.30844/aistes.v4i1.11

Keywords:

Semantic desktop, (Intentional) Forgetting, Information value assessment, User context, Information management and knowledge work support

Abstract

Knowledge workers face an ever increasing flood of informa- tion 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 up- dating MB and triggering respective MF measures. We extended an SD- based 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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Published

2019-05-08

How to Cite

[1]
Jilek, C., Chwalek, J., Schwarz, S., Schröder, M., Maus, H. and Dengel, A. 2019. Advanced Memory Buoyancy for Forgetful Information Systems. AIS Transactions on Enterprise Systems. 4, 1 (May 2019). DOI:https://doi.org/10.30844/aistes.v4i1.11.