High-Efficient Fuzzy Querying With HiveQL for Big Data Warehousing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Querying and reporting from large volumes of structured, semistructured, and unstructured data often requires some flexibility. This flexibility provided by fuzzy sets allows for categorization of the surrounding world in a flexible, human-mind-like manner. Apache Hive is a data warehousing framework working on top of the Hadoop platform for big data processing. Hive allows executing queries and aggregating and analyzing data stored in Hadoop distributed file system and other repositories. Hive responds to the current needs for efficient big data warehousing, which is impossible with traditional data warehouses due to their rigid nature. This article presents the FuzzyHive library that extends the Hive framework with fuzzy sets based techniques for querying, analyzing, and reporting on big data warehouses. We formalize the fuzzy techniques used while operating on Hive-based data warehouses (including fuzzy filtering on dimensional attributes, projection with fuzzy transformation, fuzzy grouping, and joining). We also show how we embedded these operations in Hive query language, which was not studied so far. Such extensions make big data warehousing more flexible and contribute to the portfolio of tools used by the community of people working with fuzzy sets and data analysis. The FuzzyHive library complements the spectrum of available solutions for fuzzy data processing and querying in large datasets. We investigate Hive fuzzy querying performance, effectiveness, and scalability for various data storage formats (text, Avro, and Parquet). Our experiments demonstrate that the proposed extensions introduce more elasticity and are also efficient for big data warehousing, which is the first such kind of solution for this environment.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it