Modern smartphones offer advanced sensing, connectivity, and processing capabilities for data acquisition, processing, and generation: but it can be difficult and costly to develop mobile research apps that leverage these features. Nevertheless, in life sciences and other scientific domains, there often exists a need to develop advanced mobile apps that go beyond simple questionnaires: ranging from sensor data collection and processing to self-management tools for chronic patients in healthcare. We present Punya, an open source, web-based platform based on MIT App Inventor that simplifies building Linked Data-enabled, advanced mobile apps that exploit smartphone capabilities. We posit that its integration with Linked Data facilitates the development of complex application and business rules, communication with heterogeneous online services, and interaction with the Internet of Things (IoT) data sources using the smartphone hardware. To that end, Punya includes an embedded semantic rule engine, integration with GraphQL and SPARQL to access remote graph data, and support for IoT devices using Bluetooth Low Energy and Linked Data Platform Constrained Application Protocol (LDP-CoAP). Moreover, Punya supports generating Linked Data descriptions of collected data. The platform includes built-in tutorials to quickly build apps using these different technologies. In this paper, we present a short discussion of the Punya platform, its current adoption that includes over 500 active users as well as the larger app-building MIT App Inventor community of which it is a part, and future development directions that would greatly benefit Semantic Web and Linked Data application developers as well as researchers who leverage Linked Open Data resources for their research.

The Punya Platform: Building Mobile Research Apps with Linked Data and Semantic Features

Loseto Giuseppe;
2021

Abstract

Modern smartphones offer advanced sensing, connectivity, and processing capabilities for data acquisition, processing, and generation: but it can be difficult and costly to develop mobile research apps that leverage these features. Nevertheless, in life sciences and other scientific domains, there often exists a need to develop advanced mobile apps that go beyond simple questionnaires: ranging from sensor data collection and processing to self-management tools for chronic patients in healthcare. We present Punya, an open source, web-based platform based on MIT App Inventor that simplifies building Linked Data-enabled, advanced mobile apps that exploit smartphone capabilities. We posit that its integration with Linked Data facilitates the development of complex application and business rules, communication with heterogeneous online services, and interaction with the Internet of Things (IoT) data sources using the smartphone hardware. To that end, Punya includes an embedded semantic rule engine, integration with GraphQL and SPARQL to access remote graph data, and support for IoT devices using Bluetooth Low Energy and Linked Data Platform Constrained Application Protocol (LDP-CoAP). Moreover, Punya supports generating Linked Data descriptions of collected data. The platform includes built-in tutorials to quickly build apps using these different technologies. In this paper, we present a short discussion of the Punya platform, its current adoption that includes over 500 active users as well as the larger app-building MIT App Inventor community of which it is a part, and future development directions that would greatly benefit Semantic Web and Linked Data application developers as well as researchers who leverage Linked Open Data resources for their research.
978-3-030-88361-4
Mobile app development, Research apps, Linked Data, Semantic rules, Internet of Things, GraphQL, Rapid prototyping
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12572/8296
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