Aggregate queries are essential for summarizing data and obtaining condensed information. Explaining such queries—by identifying how specific predicates influence the result—provides deeper insights into the factors shaping query outcomes. However, existing statistical, interventional, and game theoretic explanation methods lack causal grounding, while causal methods require complete causal graphs, which are rarely available in large databases. To address this, we propose Causal Banzhaf Value (CBV): introducing causal awareness into Banzhaf values, our CBV method delivers explanations even in the absence of full causal graphs. Experiments on real world data demonstrate that CBV is computationally efficient, aligns with human intuition, and is consistent with causal explanations.
Large Language Models (LLMs) can enhance analytics systems with powerful data summarization, cleaning, and semantic transformation capabilities. However, deploying LLMs at scale – processing millions to billions of rows – remains prohibitively expensive in computation and memory. We present IOLM-DB, a novel system that makes LLM-enhanced database queries practical through query-specific model optimization. Instead of using general-purpose LLMs, IOLM-DB generates lightweight, specialized models tailored to each query's specific needs using representative data samples. IOLM-DB reduces model footprints by up to 76
Refinement is a critical step in supply-driven conceptual design of multidimensional cubes because it can hardly be automated. In fact, it includes steps such as the labeling of attributes as descriptive and the removal of uninteresting attributes, thus relying on the end-users' requirements on the one hand, and on the semantics of measures, dimensions, and attributes on the other. As a consequence, it is normally carried out manually by designers in close collaboration with end-users. The goal of this work is to check whether LLMs can act as facilitators for the refinement task, so as to let it be carried out entirely – or mostly – by end-users. The Dimensional Fact Model is the target formalism for our study; as a representative LLM, we use ChatGPT's model GPT-4o. To achieve our goal, we formulate three research questions aimed at (i) understanding the basic competences of ChatGPT in multidimensional modeling; (ii) understanding the basic competences of ChatGPT in refinement; and (iii) investigating if the latter can be improved via prompt engineering. The results of our experiments show that, indeed, a careful prompt engineering can significantly improve the accuracy of refinement, and that the residual errors can quickly be fixed via one additional prompt. However, we conclude that, at present, some involvement of designers in refinement is still necessary to ensure the validity of the refined schemata.
Within the dynamic world of Big Data, traditional systems typically operate in a passive mode, processing and responding to user queries by returning the requested data. However, this methodology falls short of meeting the evolving demands of users who not only wish to analyze data but also to receive proactive updates on topics of interest. To bridge this gap, Big Active Data (BAD) frameworks have been proposed to support extensive data subscriptions and analytics for millions of subscribers. As data volumes and the number of interested users continue to increase, the imperative to optimize BAD systems for enhanced scalability, performance, and efficiency becomes paramount. To this end, this paper introduces three main optimizations, namely: strategic aggregation, intelligent modifications to the query plan, and early result filtering, all aimed at reinforcing a BAD platform's capability to actively manage and efficiently process soaring rates of incoming data and distribute notifications to larger numbers of subscribers.
Due to the popularity of JSON, several systems have been developed that store data in collections of JSON documents. Each system has developed its own query language, sometimes in an ad-hoc manner. This makes difficult to formally define and analyze query optimization techniques. We propose an algebra tailored to JSON documents. First, we argue that JSON is different from nested relations and XML and therefore requires its own solution. Then, we propose an algebra on 3 levels: the first level defines operators to manipulate individual documents, providing an abstraction over different serializations. The second level provides operators over collections of JSON documents, while the third level defines also collection operators which are not primitive, but that enable direct and efficient implementation of data manipulation operations. We provide a number of properties of the algebraic operators which provide a solid basis for query optimization.
