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Matches in ESWC 2020 for { ?s ?p ?o. }

• Author.135.2 isHeldBy Catherine_Faron_Zucker.
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• Molka_Tounsi_Dhouib name "Molka Tounsi Dhouib".
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• Catherine_Faron_Zucker name "Catherine Faron Zucker".
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• Author.136.1 type RoleDuringEvent.
• Author.136.1 label "Philipp Zehnder, 1st Author for Paper 136".
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• Author.136.2 label "Patrick Wiener, 2nd Author for Paper 136".
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• Author.136.2 isHeldBy Patrick_Wiener.
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• Author.136.3 label "Tim Straub, 3rd Author for Paper 136".
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• Author.136.4 label "Dominik Riemer, 4th Author for Paper 136".
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• Patrick_Wiener name "Patrick Wiener".
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• Tim_Straub type Person.
• Tim_Straub name "Tim Straub".
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• Dominik_Riemer type Person.
• Dominik_Riemer name "Dominik Riemer".
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• Paper.136_Review.0_Reviewer type RoleDuringEvent.
• Paper.136_Review.0_Reviewer label "Anonymous Reviewer for Paper 136".
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• Paper.136_Review.0 issued "2001-01-29T20:37:00.000Z".
• Paper.136_Review.0 creator Paper.136_Review.0_Reviewer.
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• Paper.136_Review.0 reviews Paper.136.
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• Paper.136_Review.0 hasContent "This paper presents a system StreamPipes Connect which targets domain experts to facilitate the process of connecting new Industrial IOT time series data sources and harmonize data at the edge with an intuitive GUI. The system uses a master/worker paradigm where master manages and controls all distributed workers (containers) at the edge for pre-processing data. The proposed system is evaluated by a user study with respect to its usability and user experience, and its performance. Results show that the system provides good usability for non-technical users (domain experts) and good user experience. Overall, the paper is well structured and easy to read. Some questions and comments --------------------------- - Section 3: Which triple store does the system use and is there specific reasons or benefits of choosing that one? - Section 6.3: It would be better to make it clear that whether the usage is StreamPipes including the StreamPipes Connect proposed in this paper or not. If I understand correctly, the usage of the StreamPipes Connect alone is difficult to measure since it is distributed as part of the StreamPipes? - It would be great to discuss and share some of the challenges or lessons learned associated with the use of semantic technologies for the system. Minor things --------------------------- - Section 4.3 (P8): url for qudt ontology should be here instead of P11 - Section 5.3 (P11): Users able to refine - Section 6.2 (P14): all with a different lengths After rebuttal --------------------------- Thank you for the authors clarifying the comments. I think the work is a good fit for the in-use track for sharing the experience of building practical tools using semantic technologies."".
• Paper.136_Review.1_Reviewer type RoleDuringEvent.
• Paper.136_Review.1_Reviewer label "Anonymous Reviewer for Paper 136".
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• Paper.136_Review.1 issued "2001-01-27T08:27:00.000Z".
• Paper.136_Review.1 creator Paper.136_Review.1_Reviewer.
• Paper.136_Review.1 hasRating ReviewRating.1.
• Paper.136_Review.1 hasReviewerConfidence ReviewerConfidence.4.
• Paper.136_Review.1 reviews Paper.136.
• Paper.136_Review.1 issuedAt easychair.org.
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• Paper.136_Review.1 hasContent "The paper describes the "StreamPipes Connect" tool. The tool provides different connectors to easy the ingestion and harmonization of heterogeneous sensor sources. The tool is available within the Apache StreamPipes framework. The paper is well-written and easy to follow. The tool and its components are described clearly. Moreover, the tool is already being adopted. The paper also includes an evaluation of the tool, which indicates its usefulness, specially for less technical users. A number of semantic of IoT connectors have been suggested in the latest years, for instance within the GraphofThings middleware and the BIG IoT API. Authors could add those in the references. I believe the novelty on the current paper is not on the mapping itself but rather on the lightweight edge pre-processing. The set of current supported transformations is given in the paper. The set can be extended, but its not clear if end users can actually suggest new transformation rules. I would clarify that in the text. A small performance evaluation is presented but I feel that it could be extended. In particular, I would be interested in seeing how different transformation pipelines perform. The current results show only the transformation rules in isolation. -------- Authors' response I acknowledge the response provided by the authors and I think they clarify most of the comments. I think there is an interesting contribution in the paper, but its somewhat small giving the other existing systems."".
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• Paper.136_Review.2_Reviewer label "Anonymous Reviewer for Paper 136".
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• Paper.136_Review.2 issued "2001-02-03T08:14:00.000Z".
• Paper.136_Review.2 creator Paper.136_Review.2_Reviewer.
• Paper.136_Review.2 hasRating ReviewRating.1.
• Paper.136_Review.2 hasReviewerConfidence ReviewerConfidence.3.
• Paper.136_Review.2 reviews Paper.136.
• Paper.136_Review.2 issuedAt easychair.org.
• Paper.136_Review.2 issuedFor Conference.
• Paper.136_Review.2 releasedBy Conference.
• Paper.136_Review.2 hasContent "The paper introduces a tool, StreamPipes, designed to let users create data stream processing pipelines across different data sources. Semantic Technologies are used to power the adapters that connect to each data sources. It is an interesting piece of work and a sound paper but to better fit ESWC I would have like to see more information about the specifics challenges/motivations/value for using Semantic Web technologies here. In particular: * There is no indication of how identifiers can be consistently minted and re-used across sensors. If this is not an issue as no triples is created to connect across sensors, then where is the "Linked" of the linked data? * Following up on that, if the motivation for using JSON-LD and a triple store is the vocabulary (rather than linking resources), then I would argue the vocabulary presented in Listing 1.1 is very shallow semantic wise. It does not seem to be that JSON-LD brings much value there. * And lastly, this issue extends to the rules which are specified and apparently then processed outside of the semantic stack. Why not leverage OWL and the triple store to express and process this? * In terms of related work I would have expected to see a note on DataCube at least. And another on Prov-O eventually. A reference and comparision to https://dl.acm.org/doi/10.1145/1526709.1526788 would also make sense. Lastly, and more broadly, it seems the authors missed the work on semantic enriched IoT done in the context of the project "SpitFire" by Manfred Hauswirth and team. * Next to the interesting user study that validates the pipes system itself, the authors could also have considered adding an ablation study on the impact of taking the triple store and JSON-LD out of the picture. ------------------------ Update after rebuttal ------------------------ I would like to thank the authors for the responses to my question and those of the other reviewers. In the light of these (and trusting that some of those answers will make it through the final version of the paper), I'd like to raise my overall score for the paper and recommend its publication."".
• Author.138.1 type RoleDuringEvent.
• Author.138.1 label "Jan-Christoph Kalo, 1st Author for Paper 138".
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• Author.138.2 type RoleDuringEvent.
• Author.138.2 label "Stephan Mennicke, 2nd Author for Paper 138".
• Author.138.2 withRole PublishingRole.

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