Right-sizing resources for query execution is important for cost-efficient performance, but estimating how performance is affected by resource allocations, upfront, before query execution is difficult. We demonstrate AutoExecutor , a predictive system that uses machine learning models to predict query run times as a function of the number of allocated executors, that limits the maximum allowed parallelism, for Spark SQL queries running on Azure Synapse.

Ruiping Li, Xiang Cheng. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

In this paper, we present an encoder-decoder model for distant supervised relation extraction. Given an entity pair and its sentence bag as input, in the encoder component, we employ the convolutional neural network to extract the features of the sentences in the sentence bag and merge them into a bag representation. In the decoder component, we utilize the long short-term memory network to model relation dependencies and predict the target relations in a sequential manner. In particular, to enable the sequential prediction of relations, we introduce a measure to quantify the amounts of information the relations take in their sentence bag, and use such information to determine the order of the relations of a sentence bag during model training. Moreover, we incorporate the attention mechanism into our model to dynamically adjust the bag representation to reduce the impact of sentences whose corresponding relations have been predicted. Extensive experiments on a popular dataset show that our model achieves significant improvement over state-of-the-art methods.

In this paper, a localized horizontal spatial prediction (HSP) based algorithm was proposed for Intra coding of high definition (HD) inputs. In the algorithm, a block of size 32×16 is divided into two 16×16 MBs, consisting of the pixels from the even-numbered columns and the odd-numbered columns (termed the even MB and the odd MB) respectively. The even MB is encoded using conventional Intra coding techniques and then its reconstruction is used for the prediction of the odd MB. Experimental results show that up to 0.79 dB and on average 0.39 dB gain can be achieved for HD sequences with the proposed framework at lower complexity than H.264 Intra coding.