kNN join over data streams is an important operation for location-aware systems, which correlates events from different sources based on their occurrence locations. Combining the complexity of kNN join and the dynamicity of data streams, kNN join in streaming environments is a computationally intensive operator, and its performance can be greatly improved by utilizing the computational capabilities of modern non-uniform memory access (NUMA) computing platforms. However, the conventional approaches to kNN join for prestored datasets do not work efficiently with the kind of highly dynamic data found in streaming environments.

Indexing sliding window content to enhance the performance of streaming queries can be greatly improved by utilizing the computational capabilities of a multicore processor. Conventional indexing data structures optimized for frequent search queries on a prestored dataset do not meet the demands of indexing highly dynamic data as in streaming environments. In this paper, we introduce an index data structure, called the partitioned in-memory merge tree, to address the challenges that arise when indexing highly dynamic data, which are common in streaming settings. Utilizing the specific pattern of streaming data and the distribution of queries, we propose a low-cost and effective concurrency control mechanism to meet the demands of high-rate update queries. To complement the index, we design an algorithm to realize a parallel index-based stream join that exploits the computational power of multicore processors. Our experiments using an octa-core processor show that our parallel stream join achieves up to 5.5 times higher throughput than a single-threaded approach.

An in-memory indexing tree is a critical component of many databases. Modern many-core processors, such as GPUs, are offering tremendous amounts of computing power making them an attractive choice for accelerating indexing. However, the memory available to the accelerating co-processor is rather limited and expensive in comparison to the memory available to the CPU. This drawback is a barrier to exploit the computing power of co-processors for arbitrarily large index trees. In this paper, we propose a novel design for a B+-tree based on the heterogeneous computing platform and the hybrid memory architecture found in GPUs. We propose a hybrid CPU-GPU B+-tree, "HB+-tree," which targets high search throughput use cases. Unique to our design is the joint and simultaneous use of computing and memory resources of CPU-GPU systems. Our experiments show that our HB+-tree can perform up to 240 million index queries per second, which is 2.4X higher than our CPU-optimized solution.