Towards Scalable Exploration of Diagnoses in an Ontology Stream

Authors: Freddy Lecue

AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental The experiments have shown scalable diagnoses exploration in the context of real and live data from Dublin City. ... We report experiments in the context of real and live data from Dublin City.
Researcher Affiliation Industry Freddy L ecu e IBM Dublin Research Center Damastown Industrial Estate, Dublin, Ireland {(firstname.lastname)@ie.ibm.com}
Pseudocode Yes Algorithm 1: High Level Diagnoses V iewer T , On 1 , A .
Open Source Code No The paper does not provide any explicit statements about releasing source code or links to a code repository for the described methodology.
Open Datasets No Dynamic knowledge: XML stream data from Dublin Bus has been enriched with EL++ representations. GPS location, congestion status of 1000 buses (updated every 20 second) were axiomatized in a streaming ABox e.g., (10-11) through 12 RDF triples. All anomalies, described following Example 3, are captured from the Bus stream. ... City events e.g., concert were captured through events search engines Eventful, Event Brite where an average of 187 events. The paper describes custom-collected data but does not provide specific access information (link, DOI, or citation to a public repository).
Dataset Splits No The paper discusses 'stream windows' and 'snapshots' for data collection and processing, but it does not specify explicit training, validation, or test dataset splits in terms of percentages or sample counts.
Hardware Specification Yes Experiments were run on a server of 4 Intel(R) Xeon(R) X5650, 2.67GHz cores, 6GB RAM.
Software Dependencies No CEL (Baader, Lutz, and Suntisrivaraporn 2006) is used to check satisfiability, subsumption, while MAMAStng (Noia, Sciascio, and Donini 2007) constructs abduction of diagnoses. Software tools are mentioned with citations, but no specific version numbers are provided for CEL or MAMAStng.
Experiment Setup No The paper describes the data context and evaluation methods (user iterations, compactness factors) but does not provide specific experimental setup details such as hyperparameters, optimizer settings, or other training configurations.