Review Comment:
Summary of the paper
The authors design an original framework called Mafalta (MAtchmarking Features for mAchine Learning Data Analysis) to run ML techniques on IoT data streams using the benefits of semantic web technologies for adding metadata instead of trivial classification labels. The road and traffic monitoring use case is provided to demonstrate the usefulness of the framework. This use case improves the functionality of navigation systems with real-time driver assistance. The weka machine learning tool for Java has been used to test the framework with a real dataset collected for experiments. The goal of the system is to detect type of roads (even, slightly uneven or uneven), type of traffic (low, medium, high) and driving style (aggressive, even pace). The dataset comprises altitude change, speed, longitudinal and vertical acceleration, engine load and engine coolant temperature, etc.)
An evaluation has been done to measure the processing time to load ontologies, for data mapping, etc. and evaluated on various devices (smartphone, raspberry, etc.) to demonstrate that the proposed approach with semantic web technologies is faster than just with machine learning.
Strengths of the paper:
• Original work since it covers three domains: semantic web, iot, machine learning
• background section provided since it covers 3 domains: semantic web, iot, machine learning
• Prototype implemented
• Code with ontologies and dataset available ion Github
o https://github.com/sisinflab-swot/mafalda
• Pervasive computing mentioned. Indeed in IoT, frequently previous similar research field is neglected.
• Well structured and well-written.
Weaknesses of the paper:
• The ontology is online but could be improved with tools for automatic visualization, documentation, ontology validation, etc. (see [9] [10])
• Does the ontology reuses or is aligned with IoT ontologies or transport ontologies? Not explained in the paper. When looking at the code it does seem to use the SSN ontology, etc.
o Check ontologies in transportation on ontology catalogues (e.g., LOV4IoT [13], OpenSensingCity [14], Ready4SmartCities [15], LOV [16]). We encourage the alignment of common concepts and properties when possible.
• Ontology labels and comments are missing something really important for automatic ontology matching and documentation for instance.
• the related work section really lacks of important references, see literature recommendations.
• The prototype code is available on the web but could be improved by following linked open data trends and linked open vocabularies trend
Additional comments - Section Introduction:
• “ontology-driven resource discovery”-> not clear enough
• “distributed knowledge-based systems [28]” -> this concept is not clearly explained in the paper referenced.
Additional comments - Section Motivation:
“Semantic Web of Things (SWoT) [28]”-> the authors are citing themselves but other important references should be included. For instance, SPITFIRE project [3] has been published earlier, see also Jara et al. [4], Gyrard et al. [12], Wu et al. [7], etc.
We encourage the authors to search on web browsers the most important references with this “Semantic Web of Things (SWoT)” keyphrase.
Additional comments - Background:
In section 3.3 it would be nice to have a conclusion to explain better the limitations and how the proposed work will cover some of the limitations.
What would be the difference with Complex Event Processing (CEP). It has been introduced in the background section some work mixing semantic web + CEP but more explanations are needed.
Additional comments - Section Case study:
“the system should detect the following classes” -> such events are relevant for other project, following linked data philosophy how such events could be shared (e.g., see reference)?
Additional comments - Section experiments:
Explain better what is the result with the ontology based machine learning approach compared to other approaches. highlights this sentence “this is a significant outcome because it suggests … multiple features” perhaps in bold, perhaps into a separated “conclusion” paragraph within this section.
The name given Mafalda could be introduced at the beginning of the paper. The first explanation about it is in the section 6 Experiments. + typo issue Analisys -> Analysis
The main benefit of using semantic web technologies is to get meaningful information from data, but the main drawbacks is that it requires more processing time. This paper demonstrates that not necessarily.
Literature recommendations:
• IoT + Semantic Web + Machine Learning:
o Moraru et al. [1] [2]
o Zhang et al. (pollution detection from vehicles and traffic pattern detection) [5]
o Henson et al., IntelligO [6]
o Wu et al. SWOTWCPS [7]
• Semantic Web + Data mining Survey [8]
[1] Master's thesis: Enrichment of sensor descriptions and measurements using semantic technologies [Moraru et al. June 2011]
[2] Using machine learning on sensor data [Moraru et al. 2010]
[3] SPITFIRE: Towards a Semantic Web of Things [Pfisterer et al. 2011]
[4] Semantic web of things: an analysis of the application semantics for the iot moving towards the iot convergence [Jara et al. 2014]
[5] Semantic framework of internet of things for smart cities: case studies [Zhang et al. 2016]
[6] PhD Thesis: A semantics-based approach to machine perception [Henson et al. 2013]
[7] Towards a Semantic Web of Things: A Hybrid Semantic Annotation, Extraction, and Reasoning Framework for Cyber-Physical System [Wu et al. 2017]
[8] Semantic Web in data mining and knowledge discovery: A comprehensive survey [Ristoski et al. 2016]
[9] http://perfectsemanticweb.appspot.com/?p=ontologyValidation
[10] Semantic Web Methodologies, Best Practices and Ontology Engineering Applied to Internet of Things [Gyrard et al. 2015]
[11] Sensor-based Linked Open Rules (S-LOR): An Automated Rule Discovery Approach for IoT Applications and its use in Smart Cities [Gyrard et al. 2017]
[12] Semantic Web of Things: http://sensormeasurement.appspot.com/
[13] LOV4IoT ontology catalogue: http://sensormeasurement.appspot.com/?p=ontologies
[14] OpenSensingCity ontology catalogue : http://ci.emse.fr/opensensingcity/ns/ontologies/
[15] Ready4SmartCity ontology catalogue : http://smartcity.linkeddata.es/
[16] LOV ontology catalogue: http://lov.okfn.org/dataset/lov/vocabs
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