Abstract
The Internet of things has produced several heterogeneous devices and data models for sensors/actuators, physical and virtual. Corresponding data must be aggregated and their models have to be put in relationships with the general knowledge to make them immediately usable by visual analytics tools, APIs, and other devices. In this paper, models and tools for data ingestion and regularization are presented to simplify and enable the automated visual representation of corresponding data. The addressed problems are related to the (i) regularization of the high heterogeneity of data that are available in the IoT devices (physical or virtual) and KPIs (key performance indicators), thus allowing such data in elements of hypercubes to be reported, and (ii) the possibility of providing final users with an index on views and data structures that can be directly exploited by graphical widgets of visual analytics tools, according to different operators. The solution analyzes the loaded data to extract and generate the IoT device model, as well as to create the instances of the device and generate eventual time series. The whole process allows data for visual analytics and dashboarding to be prepared in a few clicks. The proposed IoT device model is compliant with FIWARE NGSI and is supported by a formal definition of data characterization in terms of value type, value unit, and data type. The resulting data model has been enforced into the Snap4City dashboard wizard and tool, which is a GDPR-compliant multitenant architecture. The solution has been developed and validated by considering six different pilots in Europe for collecting big data to monitor and reason people flows and tourism with the aim of improving quality of service; it has been developed in the context of the HERIT-DATA Interreg project and on top of Snap4City infrastructure and tools. The model turned out to be capable of meeting all the requirements of HERIT-DATA, while some of the visual representation tools still need to be updated and furtherly developed to add a few features.
Highlights
Emerging smart cities bring forth the exploitation of big data analysis, including data collection, data ingestion, data processing, and data analytics, to produce hints about city conditions, evolution, and how to improve the quality of services and reduce costs.Smart cities are complex socio-technical systems composed of people, stakeholders, organizations that may present competing objectives [1], infrastructure to provide connectivity, and process components [2]
Device model, IoT devices, and data into the Snap4City infrastructure [15]; Ingest data modeled as hypercubes including a set of variables of different kinds and corresponding values which may typically change over space and time; Connect data ingested with other data entities already available in the storage and with the general model according to a dictionary of value types, value units, and data types
The proliferation data models been of stressed bywith the
Summary
Emerging smart cities bring forth the exploitation of big data analysis, including data collection, data ingestion, data processing, and data analytics, to produce hints about city conditions, evolution, and how to improve the quality of services and reduce costs. The major challenge is the time needed to ingest new data models that may arise from several different devices and sources in the context of the IoT for smart cities and Industry 4.0 Such models may provide different data structures and meanings, and, as a result, distinct data dictionaries for single entities, etc. Spain) are involved in collecting complex big datasets as IoT devices and KPIs to exploit them for monitoring and reasoning people flows and tourism in general in order to improve service quality With this aim, many heterogeneous data coming from several operators have been ingested and aggregated to provide representative dashboards that city operators.
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