Report on Apache Big Data North America 2016 and Spark Summit 2016
Bibliographic record
Abstract
Review Apache Big Data North AmericaApache Big Data North America is one of the largest conferences related to open source projects on big data processing and is supported by the Apache Software Foundation.The conference features interesting presentations given by users and developers on various big data processing systems using Apache open source software (OSS) products such as Hadoop [1], Spark [2], Kafka [3], and Cassandra [4].This is a key conference for OSS developers and typically has higher numbers of developers than other events.Lively discussions were held at the conference that continued even during the coffee breaks. Conference summaryApache Big Data North America 2016 [5] was held in Vancouver, Canada from May 9 to 12.Many people attended the conference from hardware vendors to content providers, including representatives from Intel Corporation, Netflix, Inc., eBay Inc., Yahoo Japan Corporation, and Recruit Holdings Co., Ltd.These companies are active users of OSS products. Business use casesThe notable keywords appearing in the titles of the presentations at the conference were Spark, Hadoop, Kafka, and Cassandra.Of the total presentations, 55 of them, or more than 40%, were related to these Report on Apache Big Data North America
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 | 0.016 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.011 |
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".