A Review of Organizational Structures of Personal Information Management
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Personal information management (PIM) covers a large area of research fragmented into separate sub-areas such as file management, web bookmark organization, and email management.Consequently, it is hard to obtain a unified view of the various approaches to PIM developed in these different sub-areas.In this article, we synthesize and classify existing research on PIM based on the approach used to organize information items.We classify the organizational structures into five categories: hierarchical, flat, linear, spatial, and network.We discuss the strengths and weaknesses of each structure along with examples showing how to deal with the weaknesses.Finally, we provide design recommendations and a framework for researchers to experiment with various ideas for developing novel PIM tools.Personal information management (PIM) refers to users' activities in acquiring, organizing, retrieving, and processing information in their personal information spaces (Teevan et al., 2006).As part of their daily activities, users create new documents, receive and send email messages, manage appointments and to-do lists, and retrieve information from personal collections and other resources.With the declining prices of mass storage devices, users can store a lot of information items in their collections, eventually exceeding their capacity to manage the items effectively.As a result, they often have difficulties in organizing their collections, in finding needed information, and in using information to achieve their objectives (Bellotti et al., 2005;Malone, 1983;Ravasio et al., 2004).Such difficulties decrease productivity, as users have to spend a lot of time managing information items instead of processing and using the information to accomplish their tasks.Since PIM is integral to the everyday lives of many people, improvement in the design of PIM tools will have significant impact on human-computer interaction.Personal information in this context does not necessarily refer to information about users, such as their names, addresses, marital status, and occupations.Instead, it refers to information owned or managed by individual users, for example, spreadsheets, email messages, contact lists, calendar entries, to-do lists, and web bookmarks.Personal information, however, is not limited to digital items only, but also includes tangible items such as books and magazines.In this article, we will refer to such personal information as information items or documents interchangeably.PIM research usually focuses on a specific subject, such as email management, web bookmark management, or file management.Since the research is fragmented, it is hard to see the underlying principles of the existing approaches to PIM.In response to this problem, we provide a unified view of approaches to PIM based on their organizational
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it