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Octavian Udrea
Researcher at IBM
Publications - 63
Citations - 3242
Octavian Udrea is an academic researcher from IBM. The author has contributed to research in topics: RDF & Automated planning and scheduling. The author has an hindex of 20, co-authored 63 publications receiving 3063 citations. Previous affiliations of Octavian Udrea include University of Maryland, College Park.
Papers
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Journal ArticleDOI
Machine Recognition of Human Activities: A Survey
TL;DR: A comprehensive survey of efforts in the past couple of decades to address the problems of representation, recognition, and learning of human activities from video and related applications is presented.
Proceedings ArticleDOI
Building an efficient RDF store over a relational database
Mihaela A. Bornea,Julian Dolby,Anastasios Kementsietsidis,Kavitha Srinivas,Patrick Dantressangle,Octavian Udrea,Bishwaranjan Bhattacharjee +6 more
TL;DR: This paper describes novel mechanisms to shred RDF into relational, and novel query translation techniques to maximize the advantages of this shredded representation, and shows that these mechanisms result in consistently good performance across multiple RDF benchmarks, even when compared with current state-of-the-art stores.
Proceedings ArticleDOI
Apples and oranges: a comparison of RDF benchmarks and real RDF datasets
TL;DR: This paper compares data generated with existing RDF benchmarks and data found in widely used real RDF datasets and shows that simple primitive data metrics are inadequate to flesh out the fundamental differences between real and benchmark data.
Proceedings Article
Plan recognition as planning revisited
TL;DR: This paper proposes to extend previous work to address observations over fluents, better address unreliable observations, and recognize plans in addition to goals, and approximate the posterior probabilities of generated plans by taking into account the combined costs that include penalties for missing or noisy observations.
Proceedings ArticleDOI
Leveraging data and structure in ontology integration
TL;DR: This paper presents a new algorithm (ILIADS) that tightly integrates both data matching and logical reasoning to achieve better matching of ontologies and compares against two systems - the ontology matching tool FCA-merge and the schema matching tool COMA++.