M
Muhammad Intizar Ali
Researcher at Dublin City University
Publications - 104
Citations - 2371
Muhammad Intizar Ali is an academic researcher from Dublin City University. The author has contributed to research in topics: Analytics & Semantic Web. The author has an hindex of 19, co-authored 99 publications receiving 1566 citations. Previous affiliations of Muhammad Intizar Ali include National University of Ireland, Galway & Vienna University of Technology.
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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.
Proceedings ArticleDOI
Agri-IoT: A semantic framework for Internet of Things-enabled smart farming applications
TL;DR: Agri-IoT is proposed, a semantic framework for IoT-based smart farming applications, which supports reasoning over various heterogeneous sensor data streams in real-time, and can integrate multiple cross-domain data streams, providing a complete semantic processing pipeline.
Journal ArticleDOI
Big data and stream processing platforms for Industry 4.0 requirements mapping for a predictive maintenance use case
TL;DR: This paper uses a systematic methodology to review the strengths and weaknesses of existing open-source technologies for big data and stream processing to establish their usage for Industry 4.0 use cases, and proposes some optimal combinations ofopen-source big data technologies for selected use cases.
Book ChapterDOI
CityBench: A Configurable Benchmark to Evaluate RSP Engines Using Smart City Datasets
TL;DR: Performance, correctness and technical soundness of few existing RSP engines have been evaluated in controlled settings using existing benchmarks like LSBench and SRBench, but these benchmarks focus merely on features of the RSP query languages and engines and do not consider dynamic application requirements and data-dependent properties.
Journal ArticleDOI
On Using the Intelligent Edge for IoT Analytics
TL;DR: A flexible architecture for Internet of Things data analytics using the concept of fog computing can be used to effectively design robust IoT applications that require a tradeoff between cloud- and edge-based computing depending on dynamic application requirements.