Meet Charles, big data query advisor

In scientific data management and business analytics, the most informative queries are a holy grail. Data collection becomes increasingly simpler, yet data exploration gets significantly harder. Exploratory querying is likely to return an empty or an overwhelming result set. On the other hand, data mining algorithms require extensive preparation, ample time and do not scale well. In this paper, we address this challenge at its core, i.e., how to query the query space associated with a given database. The space considered is formed by conjunctive predicates. To express them, we introduce the Segmentation Description Language (SDL). The user provides a query. Charles, our query advisory system, breaks its extent into meaningful segments and returns the subsequent SDL descriptions. This provides insight into the set described and offers the user directions for further exploration. We introduce a novel algorithm to generate SDL answers. We evaluate them using four orthogonal criteria: homogeneity, simplicity, breadth, and entropy. A prototype implementation has been constructed and the landscape of follow-up research is sketched

X-Device Query Processing by Bitwise Distribution

The diversity of hardware components within a single system calls for strategies for efficient cross-device data processing. For exam- ple, existing approaches to CPU/GPU co-processing distribute individual relational operators to the “most appropriate” device. While pleasantly simple, this strategy has a number of problems: it may leave the “inappropriate” devices idle while overloading the “appropriate” device and putting a high pressure on the PCI bus. To address these issues we distribute data among the devices by par- tially decomposing relations at the granularity of individual bits. Each of the resulting bit-partitions is stored and processed on one of the available devices. Using this strategy, we implemented a processor for spatial range queries that makes efficient use of all available devices. The performance gains achieved indicate that bitwise distribution makes a good cross-device processing strategy

Blaeu: Mapping and navigating large tables with cluster analysis

Blaeu is an interactive database exploration tool. Its aim is to guide casual users through large data tables, ultimately triggering insights and serendipity. To do so, it relies on a double cluster analysis mechanism. It clusters the data vertically: it detects themes, groups of mutually dependent columns that highlight one aspect of the data. Then it clusters the data horizontally. For each theme, it produces a data map, an interactive visualization of the clusters in the table. The data maps summarize the data. They provide a visual synopsis of the clusters, as well as facilities to inspect their content and annotate them. But they also let the users navigate further. Our explorers can change the active set of columns or drill down into the clusters to refine their selection. Our prototype is fully operational, ready to deliver insights from complex databases

Reward Gaming in Conditional Text Generation

To align conditional text generation model outputs with desired behaviors, there has been an increasing focus on training the model using reinforcement learning (RL) with reward functions learned from human annotations. Under this framework, we identify three common cases where high rewards are incorrectly assigned to undesirable patterns: noise-induced spurious correlation, naturally occurring spurious correlation, and covariate shift. We show that even though learned metrics achieve high performance on the distribution of the data used to train the reward function, the undesirable patterns may be amplified during RL training of the text generation model. While there has been discussion about reward gaming in the RL or safety community, in this discussion piece, we would like to highlight reward gaming in the natural language generation (NLG) community using concrete conditional text generation examples and discuss potential fixes and areas for future work

SQuId: Measuring Speech Naturalness in Many Languages

Much of text-to-speech research relies on human evaluation, which incurs heavy costs and slows down the development process. The problem is particularly acute in heavily multilingual applications, where recruiting and polling judges can take weeks. We introduce SQuId (Speech Quality Identification), a multilingual naturalness prediction model trained on over a million ratings and tested in 65 locales-the largest effort of this type to date. The main insight is that training one model on many locales consistently outperforms mono-locale baselines. We present our task, the model, and show that it outperforms a competitive baseline based on w2v-BERT and VoiceMOS by 50.0%. We then demonstrate the effectiveness of cross-locale transfer during fine-tuning and highlight its effect on zero-shot locales, i.e., locales for which there is no fine-tuning data. Through a series of analyses, we highlight the role of non-linguistic effects such as sound artifacts in cross-locale transfer. Finally, we present the effect of our design decision, e.g., model size, pre-training diversity, and language rebalancing with several ablation experiments.Comment: Accepted at ICASSP 2023, with additional material in the appendi