Exploiting Big Data for Experiment Reporting: The Hi-Drive Collaborative Research Project Case
As timely information about a project’s state is key for management, we developed a data toolchain to support the monitoring of a project’s progress. By extending the Measurify framework, which is dedicated to efficiently building measurement-rich applications on MongoDB, we were able to make the pr...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-09-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/18/7866 |
| _version_ | 1797576984272830464 |
|---|---|
| author | Alessio Capello Matteo Fresta Francesco Bellotti Hamed Haghighi Johannes Hiller Sajjad Mozaffari Riccardo Berta |
| author_facet | Alessio Capello Matteo Fresta Francesco Bellotti Hamed Haghighi Johannes Hiller Sajjad Mozaffari Riccardo Berta |
| author_sort | Alessio Capello |
| collection | DOAJ |
| description | As timely information about a project’s state is key for management, we developed a data toolchain to support the monitoring of a project’s progress. By extending the Measurify framework, which is dedicated to efficiently building measurement-rich applications on MongoDB, we were able to make the process of setting up the reporting tool just a matter of editing a couple of .json configuration files that specify the names and data format of the project’s progress/performance indicators. Since the quantity of data to be provided at each reporting period is potentially overwhelming, some level of automation in the extraction of the indicator values is essential. To this end, it is important to make sure that most, if not all, of the quantities to be reported can be automatically extracted from the experiment data files actually used in the project. The originating use case for the toolchain is a collaborative research project on driving automation. As data representing the project’s state, 330+ numerical indicators were identified. According to the project’s pre-test experience, the tool is effective in supporting the preparation of periodic progress reports that extensively exploit the actual project data (i.e., obtained from the sensors—real or virtual—deployed for the project). While the presented use case concerns the automotive industry, we have taken care that the design choices (particularly, the definition of the resources exposed by the Application Programming Interfaces, APIs) abstract the requirements, with an aim to guarantee effectiveness in virtually any application context. |
| first_indexed | 2024-03-10T22:02:27Z |
| format | Article |
| id | doaj.art-8484e4cbdb3a4d7daa864ac304366ebd |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T22:02:27Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-8484e4cbdb3a4d7daa864ac304366ebd2023-11-19T12:55:19ZengMDPI AGSensors1424-82202023-09-012318786610.3390/s23187866Exploiting Big Data for Experiment Reporting: The Hi-Drive Collaborative Research Project CaseAlessio Capello0Matteo Fresta1Francesco Bellotti2Hamed Haghighi3Johannes Hiller4Sajjad Mozaffari5Riccardo Berta6Department of Electrical, Electronic and Telecommunication Engineering (DITEN), University of Genoa, Via Opera Pia 11A, 16145 Genoa, ItalyDepartment of Electrical, Electronic and Telecommunication Engineering (DITEN), University of Genoa, Via Opera Pia 11A, 16145 Genoa, ItalyDepartment of Electrical, Electronic and Telecommunication Engineering (DITEN), University of Genoa, Via Opera Pia 11A, 16145 Genoa, ItalyWMG, University of Warwick, Coventry CV4 7AL, UKInstitute for Automotive Engineering (IKA), RWTH Aachen University, Steinbachstr. 7, 52074 Aachen, GermanyWMG, University of Warwick, Coventry CV4 7AL, UKDepartment of Electrical, Electronic and Telecommunication Engineering (DITEN), University of Genoa, Via Opera Pia 11A, 16145 Genoa, ItalyAs timely information about a project’s state is key for management, we developed a data toolchain to support the monitoring of a project’s progress. By extending the Measurify framework, which is dedicated to efficiently building measurement-rich applications on MongoDB, we were able to make the process of setting up the reporting tool just a matter of editing a couple of .json configuration files that specify the names and data format of the project’s progress/performance indicators. Since the quantity of data to be provided at each reporting period is potentially overwhelming, some level of automation in the extraction of the indicator values is essential. To this end, it is important to make sure that most, if not all, of the quantities to be reported can be automatically extracted from the experiment data files actually used in the project. The originating use case for the toolchain is a collaborative research project on driving automation. As data representing the project’s state, 330+ numerical indicators were identified. According to the project’s pre-test experience, the tool is effective in supporting the preparation of periodic progress reports that extensively exploit the actual project data (i.e., obtained from the sensors—real or virtual—deployed for the project). While the presented use case concerns the automotive industry, we have taken care that the design choices (particularly, the definition of the resources exposed by the Application Programming Interfaces, APIs) abstract the requirements, with an aim to guarantee effectiveness in virtually any application context.https://www.mdpi.com/1424-8220/23/18/7866big data architectureproject monitoring and reportingnon-relational DBRESTful APIsfield operational testsautomated driving |
| spellingShingle | Alessio Capello Matteo Fresta Francesco Bellotti Hamed Haghighi Johannes Hiller Sajjad Mozaffari Riccardo Berta Exploiting Big Data for Experiment Reporting: The Hi-Drive Collaborative Research Project Case Sensors big data architecture project monitoring and reporting non-relational DB RESTful APIs field operational tests automated driving |
| title | Exploiting Big Data for Experiment Reporting: The Hi-Drive Collaborative Research Project Case |
| title_full | Exploiting Big Data for Experiment Reporting: The Hi-Drive Collaborative Research Project Case |
| title_fullStr | Exploiting Big Data for Experiment Reporting: The Hi-Drive Collaborative Research Project Case |
| title_full_unstemmed | Exploiting Big Data for Experiment Reporting: The Hi-Drive Collaborative Research Project Case |
| title_short | Exploiting Big Data for Experiment Reporting: The Hi-Drive Collaborative Research Project Case |
| title_sort | exploiting big data for experiment reporting the hi drive collaborative research project case |
| topic | big data architecture project monitoring and reporting non-relational DB RESTful APIs field operational tests automated driving |
| url | https://www.mdpi.com/1424-8220/23/18/7866 |
| work_keys_str_mv | AT alessiocapello exploitingbigdataforexperimentreportingthehidrivecollaborativeresearchprojectcase AT matteofresta exploitingbigdataforexperimentreportingthehidrivecollaborativeresearchprojectcase AT francescobellotti exploitingbigdataforexperimentreportingthehidrivecollaborativeresearchprojectcase AT hamedhaghighi exploitingbigdataforexperimentreportingthehidrivecollaborativeresearchprojectcase AT johanneshiller exploitingbigdataforexperimentreportingthehidrivecollaborativeresearchprojectcase AT sajjadmozaffari exploitingbigdataforexperimentreportingthehidrivecollaborativeresearchprojectcase AT riccardoberta exploitingbigdataforexperimentreportingthehidrivecollaborativeresearchprojectcase |