quantMD: Ontology-Based Management of Many-Dimensional Quantitative Data

基于本体的数据管理（Ontology Based Data Management，OBDM）是近十年来发展起来的一项技术，旨在促进对各种类型数据源的访问。一般来说，本体为感兴趣的领域提供了形式化的模型和词汇表。在OBDM中，本体的作用有三个方面：为了说明，在能源公司中，地质学家寻找他们的信息需求的答案的传统工作流程是要么在他们的数据库上执行覆盖部分需求的预定义查询，然后手动集成结果，这是繁重且容易出错的，或者要求IT部门构建自定义SQL查询，这可能需要几天甚至几周的时间。OBDM通过允许地质学家用自然语言术语制定他们的查询，然后通过OBDM工具在他们的数据库上运行这些查询，将寻找答案的时间缩短到几分钟。因此，通过将知识表示和数据库技术结合起来，OBDM有可能通过允许领域专家在没有数据库专业人员的帮助下高效地查询复杂和分布式数据来改造信息系统。该项目解决了实现这一潜力的主要瓶颈：到目前为止，OBDM主要是为了访问纯定性和一维数据而开发的，但是现在的数据大多是数值的、多维的、经常是时间的，用户信息需求通常涉及定量分析。因此，现有的OBDM工具根本不支持定量查询，例如“查找所有英国赞助的欧洲研究机构，其来自英国私营公司的三年期财务捐款总额超过1000万欧元”。此外，由于OBDM中所谓的开放世界假设，开发理论和实用工具来处理这样的查询是非常具有挑战性的。本项目的目的是开发一个新的OBDM框架查询和分析多维数值数据。为了应对这些挑战，我们汇集了来自数据库，知识表示和形式化方法，特别是时态和模态逻辑的技术，并进一步发展这些技术。我们将开发一个查询这些数据的理论框架，开发在实践中使用这个框架的工具，并与来自行业和公共部门的合作伙伴测试我们的工具。

Ontology-based data management (OBDM) is a technology that has been developed over the past decade with the aim of facilitating access to various types of data sources. In general, ontologies provide a formal model and vocabulary for a domain of interest. In OBDM, the role of ontologies is threefold: to integrate distributed and heterogeneous data sources, enrich incomplete data with background knowledge, and provide a user-friendly language for querying.To illustrate, in an energy company the traditional workflow for geologists to find answers to their information needs is to either execute pre-defined queries covering parts of the needs over their databases and then integrate the results manually, which is onerous and error-prone, or to ask the IT department to construct custom SQL queries, which may takes days or even weeks. OBDM reduces the time for finding answers to minutes by allowing the geologists to formulate their queries in natural-language terms and then run these queries via the OBDM tools over their databases. Thus, by bringing together knowledge representation and database technologies, OBDM has the potential to transform information systems by allowing domain experts to query complex and distributed data efficiently without the help of database professionals.This project addresses the main bottleneck in the way to realise this potential: so far, OBDM has been developed primarily for access to purely qualitative and one-dimensional data, but nowadays data is mostly numerical, many-dimensional, often temporal, and user information needs usually involve quantitative analysis. Thus, quantitative queries such as "find all UK-sponsored research institutions in Europe whose total triennial financial contributions from UK-based private companies exceeds euro 10M" are not supported at all by existing OBDM tools. Moreover, because of the so-called open world assumption made in OBDM, developing the theory and practical tools for dealing with such queries is extremely challenging.The aim of this project is to develop a novel OBDM framework for querying and analysing many-dimensional numerical data. To address the challenges, we bring together techniques from databases, knowledge representation, and formal methods, in particular temporal and modal logics, and develop these further. We will develop a theoretical framework for querying such data, develop tools for using this framework in practice, and test our tools with partners from industry and the public sector.