Developing a Real-Time Data Analytics Framework Using Hadoop
Bibliographic record
Abstract
Currently, the majority of existing workflows are based on meta-heuristics that produce good heuristics that are dynamic in nature, and map the workflow tasks to services on-the-fly, but unfortunately, they lack the ability of supporting analytical tasks considering data types and real-time processing. This paper aims to address this problem by developing a real-time data analytics framework capable of handling real-time processing of structured and unstructured data needed for performing different analytical tasks, ranging from data ingestion and processing to data exploration, and visualization. We propose architecture based on the Storm/YARN projects for data ingestion, processing exploration and visualization of streaming structured and unstructured data. We have implemented the proposed architecture using Apache Storm related APIs for both of a local mode and a distributed mode. We describe our experiments for the architecture prototype implementation and evaluate the functional requirements for each component and non-functional tests such as real time update performance and time taken for data flow among components. All components were able to handle their own functionalities properly. Also, we provide the main results for a non-functional test in order to discuss our system efficiency.
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 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 | 0.001 | 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 | 0.000 | 0.000 |
| Open science | 0.002 | 0.003 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".