A proposed framework for analytical processing of information in the dairy industry using multidimensional data models
Notice bibliographique
Résumé
In the dairy industry, datasets pertaining to the milk recording of cows can be extremely large and complex, especially in the Province of Quebec where management and feed information are also collected for on-farm advising. Any subsequent analysis of these data for strategic (or even tactical) decision making is often impeded by the transactional nature of the existing databases, whose main purpose is often to produce regular and routine reports. Since conventional database management systems mostly support simple and short queries and flat views of data, they are less than ideal for the analysis of large datasets, particularly those which contain data of varying dimensions. In recent years, the high value of multidimensional data has been recognized as an important resource in both the academic and business communities. The wider recognition of data warehousing and On-Line Analytical Processing (OLAP) applications has highlighted their importance. The dairy industry is an excellent example of an area where the analysis of its data, and the subsequent decision-making process, could significantly benefit from the implementation of data warehousing and OLAP techniques. While these technologies have already been used to good advantage for the analysis of business data, the unusual nature of dairy data poses certain challenges which are addressed in this study. These include selection of a data model which best suits the hierarchical nature of the data, selection of the highest and lowest hierarchy for data aggregation, and the definition of functions (pre-aggregation) to improve query performance. In order to investigate the use of an OLAP system for Quebec milk-recording data, a number of multidimensional data models were compared. The star, snowflake and fact-constellation schemes each displayed advantages and disadvantages for the particular data (and their structure) in this study. The star schema did not support many-to-many relationships between fact and dimension tables, and creating combination dimensions (e.g., herd_cow) with a key (such as herd_cow_testdate), resulted in an unmanageable record length in the dimension table, thus rendering the model impractical. Many-to-many relationships were captured by a snowflake schema, by normalizing herd, cow and test day dimensions. In order to achieve an exact aggregation of milk components on each test day and for each cow, a herd_cow bridge dimension was implemented within a snowflake model which had a composite key of herd and cow. The lowest granularity level was test day and the highest was herd, but data could also be rolled up to regions. Queries could subsequently be directly executed on a cube structure, since data were stored in a multidimensional online analytical processing (MOLAP) server. All of the pre-aggregation was typically based on the milk-production test date, but could also support analysis at the individual cow level. The cube structure supports "drill down", "roll up", and "slice and dice" operations as an aid to the data analyses. Data could also be exported to Excel pivot tables as a means of simple overview reporting. It is felt that the examination of these technologies, and their future implementation, may lead to increased value for the dairy industry as their large quantities of data are explored for better management and strategic decision making.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,011 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, 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 ».