A Review of Organizational Structures of Personal Information Management
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Notice bibliographique
Résumé
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
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,006 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle