4 research outputs found

    Ontology-Based Consistent Specification of Sensor Data Acquisition Plans in Cross-Domain IoT Platforms

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    Nowadays there is an high number of IoT applications that seldom can interact with each other because developed within different Vertical IoT Platforms that adopt different standards. Several efforts are devoted to the construction of cross-layered frameworks that facilitate the interoperability among cross-domain IoT platforms for the development of horizontal applications. Even if their realization poses different challenges across all layers of the network stack, in this paper we focus on the interoperability issues that arise at the data management layer. Specifically, starting from a flexible multi-granular Spatio-Temporal-Thematic data model according to which events generated by different kinds of sensors can be represented, we propose a Semantic Virtualization approach according to which the sensors belonging to different IoT platforms and the schema of the produced event streams are described in a Domain Ontology, obtained through the extension of the well-known Semantic Sensor Network ontology. Then, these sensors can be exploited for the creation of Data Acquisition Plans by means of which the streams of events can be filtered, merged, and aggregated in a meaningful way. A notion of consistency is introduced to bind the output streams of the services contained in the Data Acquisition Plan with the Domain Ontology in order to provide a semantic description of its final output. When these plans meet the consistency constraints, it means that the data they handle are well described at the Ontological level and thus the data acquisition process over passed the interoperability barriers occurring in the original sources. The facilities of the StreamLoader prototype are finally presented for supporting the user in the Semantic Virtualization process and for the construction of meaningful Data Acquisition Plans

    Visual Query System to Help Users Refine Queries from High-Dimensional Data: A Case Study

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    Temporal queries are normally issued for cohort selection from the high-dimensional dataset in many contexts, such as medical related research areas. The idea was inspired by the difficulties when interacting with the i2b2 system, an NIH-funded National Center for Biomedical Computing based at Partners HealthCare System, which seldom provides informative feedbacks and interactive exploration about the clinical events of each query or the expecting follow-up cohort. Considering the complexity and time-consuming nature of complicated temporal queries, it would be frustrating when iterative query refining is needed. The paper presents a newly designed web-based visual query system to facilitate refining the initial temporal query to select a satisfactory cohort for a given research. A detailed interface design associated with the query time frame and the implementation of the visual query algorithm that enables advanced arbitrary temporal query logic is included. In addition, a case study with 3 participants in medical related research areas was conducted that shows the system was overall useful to help the users to gain an idea about their follow-up queries.Master of Science in Information Scienc
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