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In large scale Internet of Things (IoT) systems, statistical analysis is a crucial technique for transforming data into knowledge and for obtaining overall information about the physical world. However, most existing statistical analysis methods for sensor sampling data are implemented outside the database kernel and focus on specialized analytics, making them unsuited for the IoT environment where...
Efficient retrieval of dynamically generated, spatial-temporal, and heterogeneous sampling data in the Internet of Things is a key challenge in recent years. However, current search engine techniques are not suitable for such kind of data. To solve this problem, we propose a Hybrid Search Engine Technique for the Internet of Things based on Spatial-Temporal, Value-based, and Keyword-based Conditions...
In recent years, the Internet of Things (IoT) has become a hot research issue and is changing the way how people live and work. IoT has a lot of benefits and meanwhile, it also brings about great challenges to the search engine community. In this paper, we analyze the challenges in the IoT search engine technology and propose a """"Hybrid Search Engine Framework for the Internet...
With the advances in sensor networks and in the Internet of Things, the data management problem for massive heterogeneous sensor data has become a hot research issue. It is obvious that a cloud data management framework for this purpose is greatly desirable. In this paper, we propose a Relational Data-Base and Key-Value store combined Cloud Data management ("RDB-KV CloudDB") framework for...
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