Towards a New Approach to Empower Periodic Pattern Mining for Massive Data using Map-Reduce
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
Recent applications like social networks and IoT are the main source of the massive amount of data generated every day. Time series data is a major form where data is sequenced and indexed by timestamps. Multiple data mining techniques are applied to discover the behavior of time series datasets, periodic pattern mining is one of them. Many sequential pattern mining algorithms were presented, some of them built suffix trees and performed early pruning while other algorithms used pattern-growth techniques such as projection. A few algorithms performed Apriori-based techniques where lattice trees were built and traversed. However, most algorithms suffer from time and space issues when mining large scale time series sequences. In our paper, we present a solution that utilizes advanced and sophisticated distributed systems such as MapReduce framework. It splits the original sequence and distributes its segments across thousands of nodes in the MapReduce infrastructure. We use different training datasets to evaluate both traditional pattern mining algorithms and our MapReduce solution. After analyzing our solution in terms of time complexity, efficiency and accuracy, we clarify the advantages of processing data segments using periodic pattern mining along with MapReduce framework.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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