Le Tuan Anh

Le Tuan Anh
Technische Universität Berlin | TUB · Open Distributed Systems

Doctor of Philosophy

About

14
Publications
3,216
Reads
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70
Citations
Additional affiliations
January 2019 - present
Technische Universität Berlin
Position
  • Research Assistant
February 2011 - March 2020
Digital Enterprise Research Institute (DERI)
Position
  • Internship
Education
March 2012 - August 2014
Digital Enterprise Research Institute (DERI)
Field of study
  • Computer Science

Publications

Publications (14)
Conference Paper
Full-text available
Semantic interoperability for the Internet of Things(IoT) is being enabled by standards and technologies from the Semantic Web. As recent research suggests a move towards decentralised IoT architectures, our focus is on how to enable scalable and robust RDF engines that can be embedded throughout the architecture, in particular at edge nodes. RDF p...
Chapter
Full-text available
RDF stores provide a simple abstraction for publishing and querying data, that is becoming a norm in data sharing practice. They also empower the decentralised architecture of data publishing for the Web or IoT-driven systems. Such architecture shares a lot in common with blockchain infrastructure and technologies. Therefore, there are emerging int...
Chapter
Full-text available
The wide adoption of increasingly cheap and computationally powerful single-board computers, has triggered the emergence of new paradigms for collaborative data processing among IoT devices. Motivated by the billions of ARM chips having been shipped as IoT gateways so far, our paper proposes a novel continuous federation approach that uses RDF Stre...
Article
Full-text available
Semantic interoperability for the Internet of Things (IoT) is enabled by standards and technologies from the Semantic Web. As recent research suggests a move towards decentralised IoT architectures, we have investigated the scalability and robustness of RDF (Resource Description Framework)engines that can be embedded throughout the architecture, in...
Poster
Full-text available
Computer Vision (CV) has recently achieved significant improvements , thanks to the evolution of deep learning. Along with advanced architectures and optimisations of deep neural networks, CV data for (cross-datasets) training, validating, and testing contributes greatly to the performance of CV models. Many CV datasets have been created for differ...
Preprint
Full-text available
We present CQELS 2.0, the second version of Continuous Query Evaluation over Linked Streams. CQELS 2.0 is a platform-agnostic federated execution framework towards semantic stream fusion. In this version, we introduce a novel neural-symbolic stream reasoning component that enables specifying deep neural network (DNN) based data fusion pipelines via...
Preprint
Full-text available
Stream processing and reasoning is getting considerable attention in various application domains such as IoT, Industry IoT and Smart Cities. In parallel, reasoning and knowledge-based features have attracted research into many areas of robotics, such as robotic mapping, perception and interaction. To this end, the Semantic Stream Reasoning (SSR) fr...
Preprint
Full-text available
It is commonly acknowledged that the availability of the huge amount of (training) data is one of the most important factors for many recent advances in Artificial Intelligence (AI). However, datasets are often designed for specific tasks in narrow AI sub areas and there is no unified way to manage and access them. This not only creates unnecessary...
Conference Paper
Full-text available
Video streams are becoming ubiquitous in smart cities and traffic monitoring. Recent advances in computer vision with deep neural networks enable querying a rich set of visual features from these video streams. However, it is challenging to deploy these queries on edge devices due to the resource intensive nature of the computing operations of this...
Conference Paper
Full-text available
With the growing adoption of IoT and sensor technologies, an enormous amount of data is being produced at a very rapid pace and in different application domains. This sensor data consists mostly of live data streams containing sensor observations, generated in a distributed fashion by multiple heterogeneous infrastructures with minimal or no intero...
Conference Paper
Full-text available
This paper presents a solution to the Grand Challenge using CQELS (Continuous Query Evaluation over Linked Stream), a general execution framework to build RDF Stream Processing engines to answer continuous analytical queries. It provides an efficient execution architecture whereby incremental computing algorithms can be implemented to boost the per...
Conference Paper
Full-text available
Mobile devices are becoming a central data integration hub for personal information. Thus, an up-to-date, comprehensive and con-solidated view of this information across heterogeneous personal informa-tion spaces is required. Linked Data offers various solutions for integrat-ing personal information, but none of them comprehensively addresses the s...
Conference Paper
Full-text available
The deployment and provisioning of intelligent systems and utility-based services will greatly benefit from a cloud-based intelligent middleware framework, which could be deployed over multiple infrastructure providers (such as smart cities, hospitals, campus and private enterprises, offices, etc.) in order to deliver on-demand access to smart serv...

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