Welcome to Sigmod 2019 - The 2019 ACM SIGMOD International Conference on the Management of Data!
Bibliographic record
Abstract
on the Management of Data!This year, the conference is held in the city center of Amsterdam, capital of The Netherlands.Amsterdam is an internationally oriented city, home to people with origins from all over the world.This used to be already the case even back in the 16th and 17th century, when Amsterdam was the world's biggest trading and financial center; establishing the world's first stock exchange in 1602.SIGMOD/PODS 2019 is held in the original Amsterdam Stock and Commodities Exchange, constructed by Dutch architect Berlage between 1896 and 1903, which now serves as the wellequipped Amsterdam Conference Center.This architect and his apprentices (the school of Berlage) left an important mark on the city, being responsible for a major expansion of the city in the early 20th century.The sculptures and drawings in the Exchange building refer to the people behind the commodities traded in the various rooms ("Effecten" -stock; "Graan" -grains), e.g., depicting farmers in the grain exchange room; as a reminder that trading affects society.Amsterdam is a city that offers many cultural activities, including the world-famous classical Concertgebouw Orchestra, as well as many museums (Amsterdam Museum, Rijksmuseum, Rembrandthuis, Anne Frank Huis).In a slight break with SIGMOD tradition, the SIGMOD opening reception will be held one day later, on Tuesday night, when the SIGMOD/PODS attendees will have exclusive access to the Van Gogh museum.The Wednesday conference dinner is organized across the water in Amsterdam North, in Noorderlicht Cafe in a festival-like environment.This used to be harbour area and was less-populated and industrial, but in the recent decade has become a hotspot for nightlife activities.Amsterdam is also increasingly a hub for data science companies and services, with multiple universities and CWI in the vicinity; which all participate in the organization of SIGMOD/PODS 2019.On Thursday night, after the SIGMOD program finishes, there will be a meetup of Amsterdam Data Science, where the local data science community will be able to mingle with our data management research community.had received a weak accept (or higher) by at least one reviewer.Papers were discussed extensively online; 10 were accepted based on the first round of reviews, while 311 were rejected.The authors of the remaining 97 papers were asked to revise their papers to address reviewers' criticisms; 78 revisions were ultimately accepted for a total of 88 papers which are presented in the research track.Finally, 12 papers were shepherded after acceptance to guarantee that the camera-ready version addresses all of the reviewers' comments.
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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.021 | 0.019 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.441 | 0.421 |
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".