With the proliferation of Large Language Models (LLMs) in Business Intelligence (BI), existing solutions face critical challenges in industrial deployments: functionality deficiencies from legacy systems failing to meet evolving LLM-era user demands, interaction limitations from single-round SQL generation paradigms inadequate for multi-round clarification, and cost for domain adaptation arising from cross-domain methods migration. We present SiriusBI, a practical LLM-powered BI system addressing the challenges of industrial deployments through three key innovations: (a) An end-to-end architecture integrating multi-module coordination to overcome functionality gaps in legacy systems; (b) A multi-round dialogue with querying mechanism, consisting of semantic completion, knowledge-guided clarification, and proactive querying processes, to resolve interaction constraints in SQL generation; (c) A data-conditioned SQL generation method selection strategy that supports both an efficient one-step Fine-Tuning approach and a two-step method leveraging Semantic Intermediate Representation for low-cost cross-domain applications. Experiments on both real-world datasets and public benchmarks demonstrate the effectiveness of SiriusBI. User studies further confirm that SiriusBI enhances both productivity and user experience. As an independent service on Tencent's data platform, SiriusBI is deployed across finance, advertising, and cloud sectors, serving dozens of enterprise clients. It achieves over 93% accuracy in SQL generation and reduces data analysts' query time from minutes to seconds in real-world applications.

How to effectively diagnose and mitigate database performance anomalies remains a significant concern for modern database systems. Manually identifying the root causes of the anomalies is a labor-intensive process and significantly relies on professional experience. Meanwhile, existing work on automatic database diagnosis mainly focuses on detecting anomalous performance metrics or system log. These solutions lack the power to pinpoint detailed issues such as bad queries or problematic operators, which are indispensable for most database troubleshooting processes. In this paper, we propose OpDiag, a diagnosis framework that attributes database performance anomalies to query operators. In this framework, we first construct models offline to represent the relationship between query operators, performance metrics, and anomalies. These models can capture query plan features and support ad-hoc queries and schemas. Then, through feature attribution on these models during online diagnosis, OpDiag can effectively identify critical anomalous metrics and further trace back to suspicious queries and operators. This can provide concrete guidance for subsequent steps in anomaly mitigation. We applied OpDiag to both synthetic benchmark and real industry cases from ZTE Corporation. Empirical studies prove that OpDiag can accurately localize anomalous queries and operators, thus reducing human efforts in diagnosing and mitigating database performance anomalies.

This paper researches on the power consumption of AIGC applications based on LLM with different parameter scales across different hardware platforms. Artificial Intelligence Generated Content (AIGC) represents a leading-edge application of AI technology, primarily driven by large language models (LLMs) and their associated technologies. The deployment of LLM typically relies on critical facilities with three layers, i.e., the hardware, model, and application layers. This empirical study aims to identify key factors in power consumption when a large model is serving in the inference stage, which will hint the insights for improving the energy efficiency of computational infrastructures. In the context of the "dual carbon" goals, i.e., carbon peaking and carbon neutrality, this study aims to find an effective way to reduce the energy cost of AIGC applications, thereby supporting sustainable AI development in industry.

High-quality labels are expensive to obtain for many machine learning tasks, such as medical image classification tasks. Therefore, probabilistic (weak) labels produced by weak supervision tools are used to seed a process in which influential samples with weak labels are identified and cleaned by several human annotators to improve the model performance. To lower the overall cost and computational overhead of this process, we propose a solution called CHEF (CHEap and Fast label cleaning), which consists of the following three components. First, to reduce the cost of human annotators, we use INFL, which prioritizes the most influential training samples for cleaning and provides cleaned labels to save the cost of one human annotator. Second, to accelerate the sample selector phase and the model constructor phase, we use Increm-INFL to incrementally produce influential samples, and DeltaGrad-L to incrementally update the model. Third, we redesign the typical label cleaning pipeline so that human annotators iteratively clean smaller batch of samples rather than one big batch of samples. This yields better overall model performance and enables possible early termination when the expected model performance has been achieved. Extensive experiments show that our approach gives good model prediction performance while achieving significant speed-ups.

Non-volatile memory (NVM) is an emerging technology, which has the persistence characteristics of large capacity storage devices, while providing the low access latency and byte-addressablity of traditional DRAM memory. In this paper, we provide extensive performance evaluations on a recently released NVM device, Intel Optane DC Persistent Memory (PMem), under different configurations with several micro-benchmark tools. Further, we evaluate OLTP and OLAP database workloads with Microsoft SQL Server 2019 when using PMem as buffer pool or persistent storage. From the lessons learned we share some recommendations for future DBMS design with PMem, e.g. simple hardware or software changes are not enough for the best use of PMem in DBMSs.

The ubiquitous use of machine learning algorithms brings new challenges to traditional database problems such as incremental view update. Much effort is being put in better understanding and debugging machine learning models, as well as in identifying and repairing errors in training datasets. Our focus is on how to assist these activities when they have to retrain the machine learning model after removing problematic training samples in cleaning or selecting different subsets of training data for interpretability. This paper presents an efficient provenance-based approach, PrIU, and its optimized version, PrIU-opt, for incrementally updating model parameters without sacrificing prediction accuracy. We prove the correctness and convergence of the incrementally updated model parameters, and validate it experimentally. Experimental results show that up to two orders of magnitude speed-ups can be achieved by PrIU-opt compared to simply retraining the model from scratch, yet obtaining highly similar models.

As research products expand to include structured datasets, the challenge arises of how to automatically generate citations to the results of arbitrary queries against such datasets. Previous work explored this problem in the context of conjunctive queries and views using a Rewriting-Based Model (RBM). However, an increasing number of scientific queries are aggregate, e.g. statistical summaries of the underlying data, for which the RBM cannot be easily extended. In this paper, we show how a Provenance-Based Model (PBM) can be leveraged to 1) generate citations to conjunctive as well as aggregate queries and views; 2) associate citations with individual result tuples to enable arbitrary subsets of the result set to be cited ( fine-grained citations ); and 3) be optimized to return citations in acceptable time. Our implementation of PBM in ProvCite shows that it not only handles a larger class of queries and views than RBM, but can outperform it when restricted to conjunctive views in some cases.

An increasing amount of information is being published in structured databases and retrieved using queries, raising the question of how query results should be cited. Since there are a large number of possible queries over a database, one strategy is to specify citations to a small set of frequent queries - citation views - and use these to construct citations to other "general" queries. We present three approaches to implementing citation views and describe alternative policies for the joint, alternate and aggregated use of citation views. Extensive experiments using both synthetic and realistic citation views and queries show the trade-offs between the approaches in terms of the time to generate citations, as well as the size of the resulting citation. They also show that the choice of policy has a huge effect both on performance and size, leading to useful guidelines for what policies to use and how to specify citation views.

An increasing amount of information is being collected in structured, evolving, curated databases, driving the question of how information extracted from such datasets via queries should be cited. While several databases say how data should be cited for web-page views of the database, they leave it to users to manually construct the citations. Furthermore, they do not say how data extracted by queries other than web-page views -- general queries -- should be cited. This demo shows how citations can be specified for a small set of views of the database, and used to automatically generate citations for general queries against the database.