For interactive data exploration, approximate query processing (AQP) is a useful approach that provides a timely response by trading query accuracy. To reduce query latency, existing AQP methods use samples or models rather than the underlying data to answer queries. However, it is difficult to achieve satisfactory results in terms of query accuracy and query latency simultaneously with these methods. For the sample-based methods, this is because the more accurate the query results are, the more samples are needed and the more time cost is required for processing. The model-based methods have lower query latency, but they cannot return the approximate results with high accuracy because the existing models cannot capture the complex data distribution accurately. In this paper, we propose a fast and accurate AQP method FAAQP . In FAAQP, we propose a novel unsupervised model bitmap-augmented sum-product network (BSPN) that combines the advantages of the sum-product network with bitmaps to capture the characteristics of data distribution more accurately. Then, we propose a budget-aware BSPN construction method that builds BSPN models with the maximum query accuracy for the given storage budget. Furthermore, to reduce the query latency of FAAQP, we propose a bitmap merging strategy that makes a trade-off between query accuracy and query latency. Experimental results on real-world and synthetic datasets show that FAAQP outperforms the state-of-the-art AQP methods and achieves 1.3×-9.0× improvements in query accuracy with a low query latency.

In the interactive data exploration, approximate query processing (AQP) can be used to quickly return query results at the cost of accuracy. For online AQP, the sampler can be treated as an operator in the query plan. During the query optimization for AQP, heuristic rules are usually used to guide the sampler push-down. However, due to the complexity and changes of data distribution, the heuristic rule-based optimization methods cannot meet the users’ query accuracy requirements. In this article, we propose a learning-based query optimization method for online AQP. We first introduce the weak equivalence concept and propose a series of push-down rules to guide the sampler push-down during the query optimization. Then, to enable more queries to meet the users’ query accuracy requirements, we propose a deep learning model to further optimize the query plan. By using this model during each push-down process of the sampler, we try to avoid the negative effect of inappropriate sampler push-down on query accuracy, especially when there is an inconsistency between the underlying and intermediate data distribution. Extensive experiments show that the method proposed in this paper can outperform the state-of-the-art online sampling-based AQP method by 1.2×−7.9× in query accuracy.

For interactive data exploration, approximate query processing (AQP) is a useful approach that usually uses samples to provide a timely response for queries by trading query accuracy. Existing AQP systems often materialize samples in the memory for reuse to speed up query processing. How to tune the samples according to the workload is one of the key problems in AQP. However, since the data exploration workload is so complex that it cannot be accurately predicted, existing sample tuning approaches cannot adapt to the changing workload very well. To address this problem, this paper proposes a deep reinforcement learning-based sample tuner,RL-STuner. When tuning samples,RL-STunerconsiders the workload changes from a global perspective and uses a Deep Q-learning Network (DQN) model to select an optimal sample set that has the maximum utility for the current workload. In addition, this paper proposes a set of optimization mechanisms to reduce the sample tuning cost. Experimental results on both real-world and synthetic datasets show thatRL-STuneroutperforms the existing sample tuning approaches and achieves 1.6×-5.2× improvements on query accuracy with a low tuning cost.

Web-based online interactive visual analytics enjoys popularity in recent years. Traditionally, visualizations are produced directly from querying the underlying data. However, for a very large dataset, this way is so time-consuming that it cannot meet the low-latency requirements of interactive visual analytics. In this paper, we propose a learning-based visualization approach called BlinkViz, which uses a learned model to produce approximate visualizations by leveraging mixed sum-product networks to learn the distribution of the original data. In such a way, it makes visualization faster and more scalable by decoupling visualization and data. In addition, to improve the accuracy of approximate visualizations, we propose an enhanced model by incorporating a neural network with residual structures, which can refine prediction results, especially for visual requests with low selectivity. Extensive experiments show that BlinkViz is extremely fast even on a large dataset with hundreds of millions of data records (over 30GB), responding in sub-seconds (from 2ms to less than 500ms for different requests) while keeping a low error rate. Furthermore, our approach remains scalable on latency and memory footprint size regardless of data size.

Abstract The size of textual data continues to grow along with the need for timely and cost-effective analysis, while the growth of computation power cannot keep up with the growth of data. The delays when processing huge textual data can negatively impact user activity and insight. This calls for a paradigm shift from blocking fashion to progressive processing. In this paper, we propose a sample-based progressive processing model that focuses on term frequency calculation on text. The model is based on an incremental execution engine and will calculate a series of approximate results for a single query in a progressive way to provide a smooth trade-off between accuracy and latency. As a part, we proposed a new variant of the bootstrap technique to quantify result error progressively. We implemented this method in our system called Parrot on top of Apache Spark and used real-world data to test its performance. Experiments demonstrate that our method is 2.4×–19.7× faster to get a result within 1% error while the confidence interval always covers the accurate results very well.

Agile analytics can help organizations to gain and sustain a competitive advantage by making timely decisions. Approximate query processing (AQP) is one of the useful approaches in agile analytics, which facilitates fast queries on big data by leveraging a pre-computed sample. One problem such a sample faces is that when new data is being imported, re-sampling is most likely needed to keep the sample fresh and AQP results accurate enough. Re-sampling from scratch for every batch of new data, called the full re-sampling method and adopted by many existing AQP works, is obviously a very costly process, and a much quicker incremental sampling process, such as reservoir sampling, may be used to cover the newly arrived data. However, incremental update methods suffer from the fact that the sample size cannot be increased, which is a problem when the underlying data distribution dramatically changes and the sample needs to be enlarged to maintain the AQP accuracy. This paper proposes an adaptive sample update (ASU) approach that avoids re-sampling from scratch as much as possible by monitoring the data distribution, and uses instead an incremental update method before a re-sampling becomes necessary. The paper also proposes an enhanced approach (T-ASU), which tries to enlarge the sample size without re-sampling from scratch when a bit of query inaccuracy is tolerable to further reduce the sample update cost. These two approaches are integrated into a state-of-the-art AQP engine for an extensive experimental study. Experimental results on both real-world and synthetic datasets show that the two approaches are faster than the full re-sampling method while achieving almost the same AQP accuracy when the underlying data distribution continuously changes.