A proposed framework for analytical processing of information in the dairy industry using multidimensional data models
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.011 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".