Investigation of Mobile Network Traffic Using Hadoop and Mahout Machine Learning Methods
Bibliographic record
Abstract
Since the emergence of mobile networks, the number of mobile subscriptions has continued to increase year after year.To efficiently assign mobile network resources such as spectrum (which is rare and expensive), the network operator needs to process and analyze information and statistics about each base station and the traffic that passes through it.This thesis focuses on processing and analyzing two datasets provided by our industrial partner, Ericsson, Canada.A detailed approach that uses Apache Hadoop and the Mahout machine learning library to process and analyze the datasets is presented.The analysis provides insights to the network operator about the resource usage of network devices.This information is of great importance to network operators for efficient and effective management of resources and user experience.Furthermore, an investigation has been conducted that evaluates the impact of executing the Mahout clustering algorithms with various system and workload parameters on a Hadoop cluster.introduced by Fisher in 1936 [21].The dataset contains 3 types of iris plants and gives the measurements in centimeters of the flowers' four attributes which include sepal length, sepal width, petal length, and petal width.Each iris data sample has a 4 dimensional vector that represent these four attributes, and a label to denote its flower type.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".