The future of urban models in the Big Data and AI era: a bibliometric analysis (2000-2019)

This article questions the effects on urban research dynamics of the Big Data and AI turn in urban management. To identify these effects, we use two complementary materials: bibliometric data and interviews. We consider two areas in urban research: one, covering the academic research dealing with transportation systems and the other, with water systems. First, we measure the evolution of AI and Big Data keywords in these two areas. Second, we measure the evolution of the share of publications published in computer science journals about urban traffic and water quality. To guide these bibliometric analyses, we rely on the content of interviews conducted with academics and higher education officials in Paris and Edinburgh at the beginning of 2018

Survey over Existing Query and Transformation Languages

A widely acknowledged obstacle for realizing the vision of the Semantic Web is the inability of many current Semantic Web approaches to cope with data available in such diverging representation formalisms as XML, RDF, or Topic Maps. A common query language is the first step to allow transparent access to data in any of these formats. To further the understanding of the requirements and approaches proposed for query languages in the conventional as well as the Semantic Web, this report surveys a large number of query languages for accessing XML, RDF, or Topic Maps. This is the first systematic survey to consider query languages from all these areas. From the detailed survey of these query languages, a common classification scheme is derived that is useful for understanding and differentiating languages within and among all three areas

Combining Static and Dynamic Analysis for Bug Detection and Program Understanding

This work proposes new combinations of static and dynamic analysis for bug detection and program understanding. There are 3 related but largely independent directions: a) In the area of dynamic invariant inference, we improve the consistency of dynamically discovered invariants by taking into account second-order constraints that encode knowledge aboutinvariants; the second-order constraints are either supplied by the programmer or vetted by the programmer (among candidate constraints suggested automatically); b) In the area of testing dataflow (esp. map-reduce) programs, our tool, SEDGE, achieves higher testing coverage by leveraging existinginput data and generalizing them using a symbolic reasoning engine (a powerful SMT solver); c) In the area of bug detection, we identify and present the concept of residual investigation: a dynamic analysis that serves as theruntime agent of a static analysis. Residual investigation identifies with higher certainty whether an error reported by the static analysis is likely true