Real-time data analysis applications increasingly rely on complex streaming computations over time-series data. We propose StreamQL, a language that facilitates the high-level specification of complex analyses over streaming time series. StreamQL is designed as an algebra of stream transformations and provides a collection of combinators for composing them. It integrates three language-based approaches for data stream processing: relational queries, dataflow composition, and temporal formalisms. The relational constructs are useful for specifying simple transformations, aggregations, and the partitioning of data into key-based groups or windows. The dataflow abstractions enable the modular description of a computation as a pipeline of stages or, more generally, as a directed graph of independent tasks. Finally, temporal constructs can be used to specify complex temporal patterns and time-varying computations. These constructs can be composed freely to describe complex streaming computations. We provide a formal denotational semantics for StreamQL using a class of monotone functions over streams. We have implemented StreamQL as a lightweight Java library, which we use to experimentally evaluate our approach. The experiments show that the throughput of our implementation is competitive compared to state-of-the-art streaming engines such as RxJava and Reactor.

Regular pattern matching is used in numerous application domains, including text processing, bioinformatics, and network security. Patterns are typically expressed with an extended syntax of regular expressions. This syntax includes the computationally challenging construct of bounded repetition or counting, which describes the repetition of a pattern a fixed number of times. We develop a specialized in-memory hardware architecture that integrates counter and bit vector modules into a state-of-the-art in-memory NFA accelerator. The design is inspired by the theoretical model of nondeterministic counter automata (NCA). A key feature of our approach is that we statically analyze regular expressions to determine bounds on the amount of memory needed for the occurrences of bounded repetition. The results of this analysis are used by a regex-to-hardware compiler in order to make an appropriate selection of counter or bit vector modules. We evaluate our hardware implementation using a simulator based on circuit parameters collected by SPICE simulation in TSMC 28nm CMOS process. We find that the use of counter and bit vector modules outperforms unfolding

Regular expressions (regexes) are ubiquitous in modern software. There is a variety of implementation techniques for regex matching, which can be roughly categorized as (1) relying on backtracking search, or (2) being based on finite-state automata. The implementations that use backtracking are often chosen due to their ability to support advanced pattern-matching constructs. Unfortunately, they are known to suffer from severe performance problems. For some regular expressions, the running time for matching can be exponential in the size of the input text. In order to provide stronger guarantees of matching efficiency, automata-based regex matching is the preferred choice. However, even these regex engines may exhibit severe performance degradation for some patterns. The main reason for this is that regexes used in practice are not exclusively built from the classical regular constructs, i.e., concatenation, nondeterministic choice and Kleene's star. They involve additional constructs that provide succinctness and convenience of expression. The most common such construct is bounded repetition (also called counting), which describes the repetition of the pattern a fixed number of times. In this paper, we propose a new algorithm for the efficient matching of regular expressions that involve bounded repetition. Our algorithms are based on a new model of automata, which we call nondeterministic bit vector automata (NBVA). This model is chosen to be expressively equivalent to nondeterministic counter automata with bounded counters, a very natural model for expressing patterns with bounded repetition. We show that there is a class of regular expressions with bounded repetition that can be matched in time that is independent from the repetition bounds. Our algorithms are general enough to cover the vast majority of challenging bounded repetitions that arise in practice. We provide an implementation of our approach in a regex engine, which we call BVA-Scan. We compare BVA-Scan against state-of-the-art regex engines on several real datasets.