Complex Event Processing in Web Streams with Ontology-Based Abstraction Layers for Smart City Frameworks
Bibliographic record
Abstract
Smart cities embody the integration of information technology, urban infrastructure, and citizen engagement to optimize city functions and drive economic growth. A critical component in achieving this integration is the ability to process complex events in real-time, particularly from diverse web streams. This paper introduces a novel framework for Complex Event Processing (CEP) in web streams by employing ontology-based abstraction layers, aiming to enhance the interpretability and interoperability of heterogeneous data sources within smart city frameworks. The proposed approach uses a layered architecture where the bottom layer deals with raw data streams from various sources, including IoT devices, social media, and sensor networks. The middle layer employs an ontology-based model to abstract and enrich the raw data, providing a unified and semantic representation. This representation facilitates the identification and correlation of complex events, which are pertinent to city management and planning. The top layer involves the application of advanced data mining techniques to predict, detect, and respond to urban events in a timely and efficient manner. Through this methodology, the framework addresses the challenges of scalability, semantic heterogeneity, and real-time processing needs inherent in smart city applications. Empirical evaluations demonstrate the efficacy of the approach in various urban scenarios, showing promising improvements over existing methods. This work lays a foundational architecture for future research and development in smart city technologies, aiming to make urban areas more livable, sustainable, and efficient.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".