A mobile agent for asynchronous administration of multiple DBMS servers
Bibliographic record
Abstract
Informix-Enterprise Command Center (IECC) is a graphical systems management tool to administer Informix database servers (DBMS) in a distributed network environment. It is designed to administer hundreds of DBMSs across multiple geographic regions from multiple remote clients. DBMS administrative tasks include such operations as start/stop DBMS, create new spaces for database tables, add/delete user privileges, query a database, backup/restore a database, etc., and can be repetitive in nature. The administrator may create a task, run the task on a specific remote server and wait for completion to see the results. However, when the tasks are time consuming and hundreds of remote servers have to be administered, synchronous monitoring for task completion is not practical. The administrators need the ability to schedule a task to run at a specific time on a set of remote servers in an unattended mode and to check the results at a later time. IECC is a client-server application where there is one stationary server administration agent per DBMS server and one or more clients connect to each of the stationary agents. The clients communicate with the stationary agents using CORBA. We first identify the limitations of CORBA in supporting unattended, schedulable tasks on several remote machines and then describe a mobile agent technology that overcomes those problems.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".