Bibliographic record
Abstract
In recent years, the emergence of the Internet has resulted in a proliferation of data. This in turn has given rise to increasing demands of organizations to access accurate information swiftly and efficiently. Thus, the scope of functions for databases has expanded more than ever and the complexity of database systems has grown accordingly. Consequently, the burden on database administrators (DBAs) has increased significantly. The objective of this research is to address and propose a solution to overcome this problem of overburdened and expensive DBAs. This chapter focuses on relational database management systems in particular and proposes a novel and innovative multiagent system (MAS) that would autonomously and rationally administer and maintain databases. The proposed multi-agent system tool, ADAM (a MAS for autonomous database administration and maintenance), is in the form of a self-administering wrapper around database systems and it addresses, and offers a solution to, the problem of overburdened and expensive DBAs with the objective of making databases a cost-effective option for small/medium-sized organizations. An implementation of the agent-based system to proactively or reactively identify and resolve a small subset of DBA tasks is discussed and the GAIA methodology is used to outline the detailed analysis and design of the same. Role models describing the responsibilities, permissions, activities, and protocols of the candidate agents, and interaction models representing the links between the roles are explained. The coordinated intelligent rational agent model is used to describe the agent architecture and a brief description of the functionalities, responsibilities, and components of each agent type in the ADAM multiagent system is presented. Finally, a prototype system implementation using JADE 2.5 and Oracle 8.1.7 is presented as evidence of the feasibility of the proposed agent-based solution for the autonomous administration and maintenance of relational databases.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.332 | 0.190 |
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".