Matches in ESWC 2020 for { ?s ?p 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.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."".