Bibliographic record
Abstract
In the era of big data, companies are collecting and analyzing massive amount of data to help making business decisions. The focus of Business intelligence has been moved from reporting and performance monitoring to ad-hoc analysis, data exploration and knowledge self-discovery, where the user's train of thought is important. Business intelligence system must provide real time multidimensional analytic ability over big data volumes in order to meet the demands. Relational OLAP technologies provide better support for user-driven analysis over big volumes of dynamic data. In-memory OLAP technologies enable real time analytics experience. Their combination is the new trend to provide real time multidimensional analytics over big data volumes. However, Relational OLAP and in-memory OLAP have their own shortcomings and challenges. Relational OLAP could cause non-optimal relational database access. It often has intensive I/O and CPU demands. The biggest challenge of In-memory OLAP is combinatorial explosion. Transferring huge amount of data into multi-dimensional cache (cube) not only very time consuming but also takes considerable amount of resources. Increasing hardware resources, employing distributed in-memory data store, or re-designing MDX engine used by ROLAP to adopt hard to implement algorisms, e.g. parallel computation, are typically ways to overcome the above challenges. Instead of those costly approaches, this paper discusses several techniques that provide a way to improve performance and scalability without piling up hardware resources or going through major re-architecture. These techniques have been implemented in IBM Cognos Business Analytics (BA) solution and have been bringing success to customers since then.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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 source (direct Gemma or distilled Codex), 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".