Analytics on structured data is a mature field with many successful methods.However, most real world data exists in unstructured form, such as images and conversations.We investigate the potential of Large Language Models (LLMs) to enable unstructured data analytics.In particular, we propose a new Universal Query Engine (UQE) that directly interrogates and draws insights from unstructured data collections.This engine accepts queries in a Universal Query Language (UQL), a dialect of SQL that provides full natural language flexibility in specifying conditions and operators.The new engine leverages the ability of LLMs to conduct analysis of unstructured data, while also allowing us to exploit advances in sampling and optimization techniques to achieve efficient and accurate query execution.In addition, we borrow techniques from classical compiler theory to better orchestrate the workflow between sampling methods and foundation model calls.We demonstrate the efficiency of UQE on data analytics across different modalities, including images, dialogs and reviews, across a range of useful query types, including conditional aggregation, semantic retrieval and abstraction aggregation.

Analytics on structured data is a mature field with many successful methods.However, most real world data exists in unstructured form, such as images and conversations.We investigate the potential of Large Language Models (LLMs) to enable unstructured data analytics.In particular, we propose a new Universal Query Engine (UQE) that directly interrogates and draws insights from unstructured data collections.This engine accepts queries in a Universal Query Language (UQL), a dialect of SQL that provides full natural language flexibility in specifying conditions and operators.The new engine leverages the ability of LLMs to conduct analysis of unstructured data, while also allowing us to exploit advances in sampling and optimization techniques to achieve efficient and accurate query execution.In addition, we borrow techniques from classical compiler theory to better orchestrate the workflow between sampling methods and foundation model calls.We demonstrate the efficiency of UQE on data analytics across different modalities, including images, dialogs and reviews, across a range of useful query types, including conditional aggregation, semantic retrieval and abstraction aggregation.

We study the automated derivation of safety requirements in a self-driving vehicle use case, leveraging LLMs in combination with agent-based retrieval-augmented generation. Conventional approaches that utilise pre-trained LLMs to assist in safety analyses typically lack domain-specific knowledge. Existing RAG approaches address this issue, yet their performance deteriorates when handling complex queries and it becomes increasingly harder to retrieve the most relevant information. This is particularly relevant for safety-relevant applications. In this paper, we propose the use of agent-based RAG to derive safety requirements and show that the retrieved information is more relevant to the queries. We implement an agent-based approach on a document pool of automotive standards and the Apollo case study, as a representative example of an automated driving perception system. Our solution is tested on a data set of safety requirement questions and answers, extracted from the Apollo data. Evaluating a set of selected RAG metrics, we present and discuss advantages of a agent-based approach compared to default RAG methods.

Serverless query processing has become increasingly popular due to its advantages, including automated hardware and software management, high elasticity, and pay-as-you-go pricing. For users who are not system experts, serverless query processing greatly reduces the cost of owning a data analytic system. However, it is still a significant challenge for non-expert users to transform their complex and evolving data analytic needs into proper SQL queries and select a serverless query engine that delivers satisfactory performance and price for each type of query. This paper presents PixelsDB, an open-source data analytic system that allows users who lack system or SQL expertise to explore data efficiently. It allows users to generate and debug SQL queries using a natural language interface powered by fine-tuned language models. The queries are then executed by a serverless query engine that offers varying prices for different service levels on query urgency. The service levels are natively supported by dedicated architecture design and heterogeneous resource scheduling that can apply cost-efficient resources to process non-urgent queries. We envision that the combination of a serverless paradigm, a natural-language-aided interface, and flexible service levels and prices will substantially improve the user experience in data analysis.

The Polkadot ecosystem is a disruptive and highly complex multi-chain architecture that poses challenges in terms of data analysis and communicability. Currently, there is a lack of standardized and holistic approaches to retrieve and analyze data across parachains and applications, making it difficult for general users and developers to access ecosystem data consistently. This paper proposes a conceptual framework that includes a domain ontology called POnto (a Polkadot Ontology) to address these challenges. POnto provides a structured representation of the ecosystem's concepts and relationships, enabling a formal understanding of the platform. The proposed knowledge-oriented approach enhances integration and communicability, enabling a wider range of users to participate in the ecosystem and facilitating the development of AI-based applications. The paper presents a case study methodology to validate the proposed framework, which includes expert feedback and insights from the Polkadot community. The POnto ontology and the roadmap for a query engine based on a Controlled Natural Language using the ontology, provide valuable contributions to the growth and adoption of the Polkadot ecosystem in heterogeneous socio-technical environments.

This paper introduces DIAMETRICS: a novel framework for end-to-end benchmarking and performance monitoring of query engines. DIAMETRICS consists of a number of components supporting tasks such as automated workload summarization, data anonymization, benchmark execution, monitoring, regression identification, and alerting. The architecture of DIAMETRICS is highly modular and supports multiple systems by abstracting their implementation details and relying on common canonical formats and pluggable software drivers. The end result is a powerful unified framework that is capable of supporting every aspect of benchmarking production systems and workloads. DIAMETRICS has been developed in Google and is being used to benchmark various internal query engines. In this paper, we give an overview of DIAMETRICS and discuss its design and implementation. Furthermore, we provide details about its deployment and example use cases. Given the variety of supported systems and use cases within Google, we argue that its core concepts can be used more widely to enable comparative end-to-end benchmarking in other industrial environments. The data management landscape has drastically changed over the last few years. The majority of database systems are no longer manually tuned and optimized for a specific application by well-versed administrators; instead, they are designed to support a variety of applications. To support all of these applications, a multitude of data models, storage formats, and query engines have transformed the data management landscape from standalone, specialized deployments to entire ecosystems.

Here we describe the SHARE system, a web service based framework for distributed querying and reasoning on the semantic web. The main innovations of SHARE are: (1) the extension of a SPARQL query engine to perform on-demand data retrieval from web services, and (2) the extension of an OWL reasoner to test property restrictions by means of web service invocations. In addition to enabling queries across distributed datasets, the system allows for a target dataset that is significantly larger than is possible under current, centralized approaches. Although the architecture is equally applicable to all types of data, the SHARE system targets bioinformatics, due to the large number of interoperable web services that are already available in this area. SHARE is built entirely on semantic web standards, and is the successor of the BioMOBY project.