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| en:iot-open:iotdatavsdb [2018/05/12 11:22] – created salvatdi | en:iot-open:iotdatavsdb [2018/05/12 12:01] (current) – removed salvatdi | ||
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| - | === IoT data management versus traditional database management systems === | ||
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| - | Based on the IoT data lifecycle discussed earlier, we divide an IoT data management system into i) an online (i.e. real-time) front-end that interacts directly with the interconnected IoT objects and sensors, and ii) an offline back-end that handles the mass storage and in-depth analysis of the IoT data. The data management frontend is communication-intensive, | ||
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| - | This envisioned data management architecture differs considerably from the existing database management systems (DBMSs), which are mainly storage-centric. In traditional databases, the bulk of data is collected from predefined and finite sources, and stored in scalar form according to strict normalisation rules in relations. Queries are used to retrieve specific “summary” views of the system or update specific items in the database. New data is inserted into the database when needed, also via insertion queries. Query operations are usually local, with execution costs bound to processing and intermediate storage. Transaction management mechanisms guarantee the ACID properties in order to enforce overall data integrity. Even if the database is distributed over multiple sites, query processing and distributed transaction management are enforced. The execution of distributed queries is based on the transparency principle, which dictates that the database is still viewed logically as one centralised unit, and the ACID properties are guaranteed via the two-phase commit protocol. | ||
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| - | In the IoT systems, the picture is dramatically different, with a massive and ever-growing number of data sources that include sensors, RFIDs, embedded systems, and mobile devices. Contrary to the occasional updates and queries submitted to traditional DBMSs, data is streaming constantly from a multitude of edge devices to the IoT data stores, and queries are more frequent and with more versatile needs. Hierarchical data reporting and aggregation may be required for scalability guarantees as well as to enable more prompt processing functionality. The strict relational database schema and the relational normalisation practice may be relaxed in favour of more unstructured and flexible forms that adapt to the diverse data types and sophisticated queries. Although distributed DBMSs optimise queries based on communication considerations, | ||
