A Method of HBase Multi-Conditional Query for Ubiquitous Sensing Applications

Abstract

Big data gathered from real systems, such as public infrastructure, healthcare, smart homes, industries, and so on, by sensor networks contain enormous value, and need to be mined deeply, which depends on a data storing and retrieving service. HBase is playing an increasingly important part in the big data environment since it provides a flexible pattern for storing extremely large amounts of unstructured data. Despite the fast-speed reading by RowKey, HBase does not natively support multi-conditional query, which is a common demand and operation in relational databases, especially for data analysis of ubiquitous sensing applications. In this paper, we introduce a method to construct a linear index by employing a Hilbert space-filling curve. As a RowKey generating schema, the proposed method maps multiple index-columns into a one-dimensional encoded sequence, and then constructs a new RowKey. We also provide a R-tree-based optimization to reduce the computational cost of encoding query conditions. Without using a secondary index mode, experimental results indicate that the proposed method has better performance in multi-conditional queries.

An HBase-Based Optimization Model for Distributed Medical Data Storage and Retrieval

“…Hadoop is a distributed cluster deployed on multiple machines. A Hadoop cluster connects multiple servers through a network to provide external storage services as a whole [19,20]. Hadoop Distributed File System (HDFS) can store and read massive amounts of data in a distributed manner and provide high-throughput data access.…”