LAUSR

Many applications of complex event processing (CEP) in Cloud can tolerate analytical errors to some extent, and it provides us an opportunity to optimize real-time analytics using methods of approximate query processing over big data streams. In this article, we present a novel rules-based sampling technique, which supports to construct sketch over one-pass and high-speed asynchronous data streams and provides accurate answers for different types of analytical queries. Moreover, we propose two methods of distributed sketching implementation, i.e., D-AQP$_b$b and D-AQP$_i$i, to make our approach to be compatible with batch processing and interactive processing architectures respectively, and be appropriate for stream processing systems in Cloud. Experimental results with real-world and synthetic datasets indicate that our approach can obtain more accurate estimates and improve two times of system throughput when compared with state-of-the-art Hadoop-based approximate engine BlinkDB. When compared with current batch processing systems Spark and stream processing system Spark-Streaming, our methods of D-AQP$_b$b and D-AQP$_i$i can achieve 2 and 4 orders of magnitude improvement on query response time respectively.