Breaking the Chains: On Declarative Analysis and Independence in the Big Data Era.
Bibliographic record
Abstract
Data management research, systems, and technologies have drastically improved the availability of data analysis capabilities, particularly for non-experts, due in part to low-entry barriers and reduced ownership costs (e.g., for data management infrastructures and applications). Major reasons for the widespread success of database systems and today’s multi-billion dollar data management market include data independence, separating physical representation and storage from the actual information, and declarative languages, separating the program specification from its intended execution environment. In contrast, today’s big data solutions do not offer data independence and declarative specification. As a result, big data technologies are mostly employed in newly-established companies with IT-savvy employees or in large well-established companies with big IT departments. We argue that current big data solutions will continue to fall short of widespread adoption, due to usability problems, despite the fact that in-situ data analytics technologies achieve a good degree of schema independence. In particular, we consider the lack of a declarative specification to be a major roadblock, contributing to the scarcity in available data scientists available and limiting the application of big data to the IT-savvy industries. In particular, data scientists currently have to spend a lot of time on tuning their data analysis programs for specific data characteristics and a specific execution environment. We believe that the research community needs to bring the powerful concepts of declarative specification to current data analysis systems, in order to achieve the broad big data technology adoption and effectively deliver the promise that novel big data technologies offer. In addition, we will present the vision of the Berlin Big Data Center (BBDC) with respect to combining machine learning and data management. A 4-page paper extended abstract of this presentation has appeared at PVLDB Volume 7. Volker Markl is a Full Professor and Chair of the Database Systems and Information Management (DIMA) group at the Technische Universitat Berlin (TU Berlin) as well as an adjunct status-only professor at the University of Toronto. Earlier in his career, Dr. Markl lead a research group at FORWISS, the Bavarian Research Center for Knowledge-based Systems in Munich, Germany, and was a Research Staff member & Project Leader at the IBM Almaden Research Center in San Jose, California, USA. Dr. Markl has published numerous research papers on indexing, query optimization, lightweight information integration, and scalable data processing. He holds 7 patents, has transferred technology into several commercial products, and advises several companies and startups. He has been
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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 | 0.066 | 0.125 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.004 | 0.032 |
| Scholarly communication | 0.015 | 0.053 |
| Open science | 0.007 | 0.020 |
| Research integrity | 0.005 | 0.016 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".