Towards a new approach for enterprise integration : the semantic modeling approach
Bibliographic record
Abstract
Manufacturing today has become a matter of the effective and efficient application of information technology and knowledge engineering. Manufacturing firms’ success depends to a great extent on information technology, which emphasizes the integration of the information systems used by a manufacturing enterprise. This integration is also called enterprise application integration (here the term application means information systems or software systems). The methodology for enterprise application integration, in particular enterprise application integration automation, has been studied for at least a decade; however, no satisfactory solution has been found. Enterprise application integration is becoming even more difficult due to the explosive growth of various information systems as a result of ever increasing competition in the software market. This thesis aims to provide a novel solution to enterprise application integration. The semantic data model concept that evolved in database technology is revisited and applied to enterprise application integration. This has led to two novel ideas developed in this thesis. First, an ontology of an enterprise with five levels (following the data abstraction: generalization/specialization) is proposed and represented using unified modeling language. Second, both the ontology for the enterprise functions and the ontology for the enterprise applications are modeled to allow automatic processing of information back and forth between these two domains. The approach with these novel ideas is called the enterprise semantic model approach. The thesis presents a detailed description of the enterprise semantic model approach, including the fundamental rationale behind the enterprise semantic model, the ontology of enterprises with levels, and a systematic way towards the construction of a particular enterprise semantic model for a company. A case study is provided to illustrate how the approach works and to show the high potential of solving the existing problems within enterprise application integration.
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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.013 | 0.024 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".