IoT applications require a new type of database systems termed event stores for ingesting fast arriving event streams and efficiently supporting analytical ad-hoc queries over time. One of the most important operations in this regard is sequential pattern matching also known as Match\_Recognize, which matches user defined predicates to subsequences of events. While Match\_Recognize is well known in the field of event processing, it has only recently become part of the SQL standard. Despite of that, Match\_Recognize has received little attention in the database area so far. We present a novel approach to speed up an important class of Match\_Recognize queries on event stores by utilizing off-the-shelf secondary indexes on non-temporal attributes (e.g., B$^+$-trees, LSM-trees) and a cost model for selecting the most appropriate indexes. Our approach keeps temporal and sequential information in secondary indexes to prune large parts of the stream from further processing. However, simply using as many secondary indexes as available is not the right choice because the access cost for the index scans can exceed the processing time of the naï ve approach that scans the entire stream and replays it into an event processing system. In order to address this problem, we present a first cost model to estimate the total execution cost of a Match\_Recognize query for a set of available indexes. Based on this cost model, we devise an efficient index selection strategy that avoids a full enumeration of index configurations. Prototypical implementations of our approach are available in our open-source research prototype, a commercial database system, and Apache Flink. In experiments with synthetic and real-world data sets, all our index-based implementations clearly outperform the naï ve replay strategy that is currently offered in commercial database systems and Flink.

Due to the widespread availability of general purpose GPUs, an integration of their processing capabilities into an event stream pipeline presents an exciting opportunity riddled with challenging requirements: Even though the single instruction multiple data (SIMD) model is a natural fit to answer long running event queries on high volume streams, those queries are usually associated with latency requirements that make transferring data to GPUs unfeasible. Traditionally, this challenge is solved through software by scheduling some tasks to the GPU and some to the CPU. However, the assumptions about transfer do not hold for widely adopted integrated GPUs (iGPUs), which directly share memory with the CPU. We develop a prototypical event processing framework based on the Heterogeneous System Architecture (HSA) and show that a variety of new HSA features enable iGPUs to be an affordable accelerator for a wide variety of event processing queries.

Reactive security monitoring, self-driving cars, the Internet of Things (IoT), and many other novel applications require systems for both writing events arriving at very high and fluctuating rates to persistent storage as well as supporting analytical ad hoc queries. As standard database systems are not capable of delivering the required write performance, log-based systems, key-value stores, and other write-optimized data stores have emerged recently. However, the drawbacks of these systems are a fair query performance and the lack of suitable instant recovery mechanisms in case of system failures. In this article, we present ChronicleDB, a novel database system with a storage layout tailored for high write performance under fluctuating data rates and powerful indexing capabilities to support a variety of queries. In addition, ChronicleDB offers low-cost fault tolerance and instant recovery within milliseconds. Unlike previous work, ChronicleDB is designed either as a serverless library to be tightly integrated in an application or as a standalone database server. Our results of an experimental evaluation with real and synthetic data reveal that ChronicleDB clearly outperforms competing systems with respect to both write and query performance.