In reverse engineering of database queries, we aim to construct a query from a given set of answers and non-answers; it can then be used to explore the data further or as an explanation of the answers and non-answers. We investigate this query-by-example problem for queries formulated in positive fragments of linear temporal logic LTL over timestamped data, focusing on the design of suitable query languages and the combined and data complexity of deciding whether there exists a query in the given language that separates the given answers from non-answers. We consider both plain LTL queries and those mediated by LTL-ontologies.

QBE North America recently announced its investment and multi-year commercial use agreement with HyperScience (New York), a machine learning, enterprise-grade artificial intelligence (AI) solution, which the insurer intends to use drive operational efficiency and unlock new data and insights for underwriting, pricing and claims. The acquisition was the third in a series flowing from a $50 million commitment QBE announced in 2017 to invest in early-stage businesses working on technically-challenging and industry-changing ideas. The company earlier announced its investments in RiskGenius (Overland Park, Kan.), a machine learning platform for analyzing policy wordings, in Oct. 2017, and Cytora, a London-based company that uses open source data to help commercial insurers lower loss ratios, grow premiums and improve expense ratios, in Dec. 2017. David McMillan, QBE's Group COO has characterized the acquisitions as contributing to the company's objective of delivering "Brilliant Basics" in underwriting, pricing and claims. Insurance Innovation Reporter talked with Ted Stuckey, SVP, Managing Director of QBE Ventures, and Head of QBE's Global Innovation Lab, to talk about the acquisitions and how they fit into QBE's broader strategy.