Abstract
In recent years, many data-intensive and location based applications have emerged that need to process stream data in applications such as network monitoring, telecommunications data management, and sensor networks. Unlike regular queries, a continuous query exists for certain period of time and need to be continuously processed during this time. The algorithms used for data processing for the traditional database systems are not suited to tackle complex and various continuous queries over dynamic streaming data. The indexing for finite queries is preferred to indexing on infinite data to avoid expensive operations of index maintenance. Previous related work focused on moving queries on static objects or static queries on moving object. But now-a-days queries as well as objects are dynamic. So, hybrid indexing for queries significantly reduces the space costs and scales well with the increasing data. To deal with the speed of unbounded data, it is necessary to use data parallelism in query processing. The data parallelism in query processing offers better performance, availability and scalability.
Highlights
In recent years, new data-intensive applications, such as network monitoring, traffic monitoring, sensor networks, telecommunications data management, and others, involve data streams
The CPU focuses on the task of creating and maintaining the index on queries while Graphical processing Unit (GPU) focuses on processing data stream using G-HKDB tree in parallel
For efficient continuous query processing, it will be better to use the index for queries which is finite rather than to build the index for data which is infinite
Summary
New data-intensive applications, such as network monitoring, traffic monitoring, sensor networks, telecommunications data management, and others, involve data streams. To filter the data which is rapidly coming at the system as time-varying, unbounded sequences of data objects, the user uses continuous queries as against one-time submitted queries. These queries are processed over streams continuously for a period of time and provide new results incrementally on the arrival of new data tuples. Re-execution of queries is a costly operation, and processing of continuous queries is considered an open issue in data stream systems using various indexing approaches. As the data is coming continuous and in unbounded manner, so it becomes a big challenge to filter massive data stream efficiently using query index.
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