Integrated Intelligence: Separating the Wheat from the Chaff in Sensor Data

Integrated Intelligence: Separating the Wheat from the Chaff in Sensor Data

Marcos M. Campos (Oracle Data Mining Technologies, USA) and Boriana L. Milenova (Oracle Data Mining Technologies, USA)
DOI: 10.4018/978-1-60566-328-9.ch001
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Warehousing and analytics of sensor network data is an area growing in relevance as more and more sensor data are collected and made available for analysis. Applications that involve processing of streaming sensor data require efficient storage, analysis, and monitoring of data streams. Traditionally, in these applications, RDBMSs have been confined to the storage stage. While contemporary RDBMSs were not designed to handle stream-like data, the tight integration of sophisticated analytic capabilities into the core database engine offers a powerful infrastructure that can more broadly support sensor network applications. Other useful components found in RDBMs include: extraction, transformation and load (ETL), centralized data warehousing, and automated alert capabilities. The combination of these components addresses significant challenges in sensor data applications such as data transformations, feature extraction, mining model build and deployment, distributed model scoring, and alerting/messaging infrastructure. This chapter discusses the usage of existing RDBMS functionality in the context of sensor network applications.
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Streaming sensor data is set apart from more traditional types of data by two important characteristics: its massive volume and its dynamic, distributed, and heterogeneous nature. Most applications that address domains with streaming data, strive to accumulate the data to some extent (often placing emphasis on recent data) and archive it in a, possibly off-site, data warehouse. Access to archived data can be prohibitively expensive and therefore can hinder analytical efforts. In addition to achieving some level of storage and retroactive analysis of continuous and unbounded data, applications processing sensor data are often required to perform online monitoring of the data stream and must be capable of real-time pattern detection and decision making.

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