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.

Reduced-reference image quality assessment (RR-IQA) algorithm aims to automatically evaluate the image quality using only partial information about the reference image. In this paper, we propose a new RR-IQA metric by employing the entropy features of each frequency band in the DCT domain. It is well known that human eyes have different sensitivity to different bands, and distortions on each band result in individual quality degradations. Therefore, we suggest to separately compute the visual information degradations on different band for quality assessment. The degradations on each DCT band are firstly analyzed according to the entropy difference. And then, the quality score is obtained using the weighted sum of the entropy difference of each band from low frequency to high frequency. Experimental results on several public image databases show that the proposed method uses limited reference data (8 values) and performs highly consistent with human perception.

The human visual system is highly adaptive to extract structure information for scene perception, and structure character is widely used in perception-oriented image processing works. However, the existing structure descriptors mainly describe the luminance contrast of a local region, but cannot effectively represent the spatial correlation of structure. In this paper, we introduce a novel structure descriptor according to the orientation selectivity mechanism in the primary visual cortex. Research on cognitive neuroscience indicate that the arrangement of excitatory and inhibitory cortex cells arise orientation selectivity in a local receptive field, within which the primary visual cortex performs visual information extraction for scene understanding. Inspired by the orientation selectivity mechanism, we compute the correlations among pixels in a local region based on the similarities of their preferred orientation. By imitating the arrangement of the excitatory/inhibitory cells, the correlations between a central pixel and its local neighbors are binarized, and the spatial correlation is represented with a set of binary values, which is named the orientation selectivity-based pattern. Then, taking both the gradient magnitude and the orientation selectivity-based pattern into account, a rotation invariant structure descriptor is introduced. The proposed structure descriptor is applied in texture classification and reduced reference image quality assessment, as two different application domains to verify its generality and robustness. Experimental results demonstrate that the orientation selectivity-based structure descriptor is robust to disturbance, and can effectively represent the structure degradation caused by different types of distortion.