Primer on policy implications of cloud computing
Notice bibliographique
Résumé
This guide is one in a series of Operational Policy documents being developed by GeoConnections. This guide is intended to inform CGDI stakeholders about the nature and scope of cloud computing and the realities, challenges and good practices of related operational policies. Cloud computing provides flexible, location-independent access to computing resources that are quickly and seamlessly allocated or released in response to demand. Computing clouds provide computation, software, data access, and storage resources without requiring cloud users to know the details of the computing infrastructure. For geospatial data and software providers, cloud computing represents a potential new way of doing business, by providing lower cost or free options for clients to access products and services online. Rather than acquiring software for in-house implementation and downloading complete databases, clients can "rent" the software and access only the data they need through web services, on an as-required basis. The "cloud" is poised to become the accepted place for a broader range of relatively unsophisticated users of geospatial data to access and use this powerful technology. Moving to the cloud seems inevitable. A Cloud Computing Roadmap is an integral component of the Government of Canada's information technology shared services (ITSS) model (Danek, 2010). Shared Services Canada is responsible for the delivery of certain IT services on behalf of all government departments, including data centre management. At the provincial level, at least two governments are assessing cloud computing (CC). The Government of Ontario is exploring the potential of CC as a better way of using and delivering online services (Microsoft, 2011). And in its IM/IT strategy document, the Government of British Columbia identifies the leveraging of CC services as one of two key IT/IM hosting strategies for the province (Office of the Chief Information Officer, 2011). In an international example, the US federal government has introduced a "cloud-first" policy for new government computing solutions (Zients, 2010). American adoption estimates across all sectors peg growth in spending for managed cloud services at $14B by 2014 compared with $3B in Feb 2011. According to a Financial Times article [http://www.ft.com/cms/s/0/934dcf92-703d-11e0-bea7-00144feabdc0.html] in May 2011, the global value of the cloud sector could reach $150B by 2014. Other estimates differ, but all agree that cloud computing is becoming a significant business. A growing number of organizations already on the cloud further reflect this trend. This guide introduces key issues in geospatial operational policy, imperative to the success of any venture into cloud computing. Operational policies are the guidelines, directives and policies that an organization employs to address the life cycle of geospatial data (i.e., collection, management, dissemination and use). This guide will be of interest to anyone seeking a better understanding of cloud computing and areas of related operational policy, such as liability, privacy and confidentiality, security, licensing, copyright, archiving, regulations and standards.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,004 |
| Études des sciences et des technologies | 0,006 | 0,008 |
| Communication savante | 0,014 | 0,013 |
| Science ouverte | 0,003 | 0,004 |
| Intégrité de la recherche | 0,012 | 0,017 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,026 | 0,006 |
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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».