Funnelback and Me: Celebrating 30 Years of Funnelback Technology 1991-2021
Bibliographic record
Abstract
In 1991, under the auspices of the ANU-Fujitsu CAP project, the author started a research project in Information Retrieval whose aim was to support fast and effective search over enormous collections of electronic documents. Later in the 1990s the project moved to the ANU-CSIRO Advanced Computational Systems (ACSys) Cooperative Research Centre and eventually worked to create a commercial product — a search engine for the websites and document repositories held by organisations, known as P@NOPTIC. The first P@NOPTIC installation provided search of hundreds of web sites at the ANU, and it delivered obvious benefits. From the associated research, the author gained a PhD by Published Work, and became a research scientist in CSIRO Mathematical and Information Sciences. After ACSys ended in 2000, CSIRO took on commercialisation, licensing P@NOPTIC to universities, companies and government agencies. After a slow start, the business grew too large to remain within CSIRO and Funnelback Pty Ltd was spun off. \n \nIn 2009, Funnelback was acquired by another Australian company, Squiz Pty Ltd. At the time of writing (2021) Funnelback technologies were still being sold and supported by Squiz. P@NOPTIC/Funnelback earned tens of millions of dollars in revenue, created a peak of around 50 hi-tech jobs, and improved the quality of search in hundreds of organisations in Australia, the UK, the US, and Europe. \n \nThe book attempts to describe in easily understandable form the quarter century of research behind Funnelback — questions addressed, discoveries made, breakthroughs achieved, and challenges faced. It also chronicles the commercialisation journey, with its many ups and downs, and discusses possible reasons why Funnelback never became as successful as Google. Academics contemplating the commercialisation of their research may be interested in the Funnelback journey and in the lessons learned.
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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.019 | 0.020 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.044 | 0.017 |
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".