T
Thu-Le Pham
Researcher at National University of Ireland, Galway
Publications - 11
Citations - 273
Thu-Le Pham is an academic researcher from National University of Ireland, Galway. The author has contributed to research in topics: Data stream mining & Semantic Web. The author has an hindex of 5, co-authored 11 publications receiving 217 citations. Previous affiliations of Thu-Le Pham include National University of Ireland & Information Technology University.
Papers
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Journal ArticleDOI
CityPulse: Large Scale Data Analytics Framework for Smart Cities
Dan Puiu,Payam Barnaghi,Ralf Tönjes,Daniel Kumper,Muhammad Intizar Ali,Alessandra Mileo,Josiane Xavier Parreira,Marten Fischer,Sefki Kolozali,Nazli Farajidavar,Feng Gao,Thorben Iggena,Thu-Le Pham,Cosmin-Septimiu Nechifor,Daniel Puschmann,João Paulo Fernandes +15 more
TL;DR: The CityPulse framework supports smart city service creation by means of a distributed system for semantic discovery, data analytics, and interpretation of large-scale (near-)real-time Internet of Things data and social media data streams to break away from silo applications and enable cross-domain data integration.
Journal ArticleDOI
Real-time data analytics and event detection for IoT-enabled communication systems
Muhammad Intizar Ali,Naomi Ono,Mahedi Kaysar,Zia Ush Shamszaman,Thu-Le Pham,Feng Gao,Keith Griffin,Alessandra Mileo +7 more
TL;DR: This paper presents a semantic infrastructure for gathering, integrating and reasoning upon heterogeneous, distributed and continuously changing data streams by means of semantic technologies and rule-based inference, and provides flexible and efficient processing techniques that can transform low-level data into high-level abstractions and actionable knowledge.
Book ChapterDOI
Web Stream Reasoning in Practice: On the Expressivity vs. Scalability Tradeoff
TL;DR: The need for heuristics to design adaptive solutions for Web Stream Reasoning is motivated and, following an empirical approach, some key concepts and ideas that can guide the design of heuristic for adaptive optimization of Web Streamreasoning are highlighted.
Proceedings ArticleDOI
Towards Scalable Non-Monotonic Stream Reasoning via Input Dependency Analysis
TL;DR: An input dependency graph is introduced to represent the relationships between input events based on the structure of a given logical rule set based on disjunctive logic Datalog with negation under the stable model semantics, by analyzing input dependency.
Journal ArticleDOI
Enhancing the scalability of expressive stream reasoning via input-driven parallelization
TL;DR: This work designs an algorithm to analyze input dependency so as to enable parallel execution and combine the results of a rule layer based on a fragment of Answer Set Programming (ASP), and provides a proof of correctness for the approach.