Abstract
Archives have well-established description standards, namely the ISAD(G) and ISAAR(CPF) with a hierarchical structure adapted to the nature of archival assets. However, as archives connect to a growing diversity of data, they aim to make their representations more apt to the so-called linked data cloud. The corresponding move from hierarchical, ISAD-conforming descriptions to graph counterparts requires state-of-the-art technologies, data models and vocabularies. Our approach addresses this problem from two perspectives. The first concerns the data model and description vocabularies, as we adopt and build upon the CIDOC-CRM standard. The second is the choice of technologies to support a knowledge graph, including a graph database and an Object Graph Mapping library. The case study is the Portuguese National Archives, Torre do Tombo, and the overall goal is to build a CIDOC-CRM-compliant system for document description and retrieval, to be used by professionals and the public. The early stages described here include the design of the core data model for archival records represented as the ArchOnto ontology and its embodiment in the ArchGraph knowledge graph. The goal of a semantic archival information system will be pursued in the migration of existing records to the richer representation and the development of applications supported on the graph. © Springer Nature Switzerland AG 2019.

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Abstract
ProcessPAIR is a novel method and tool for automating the performance analysis in software development. Based on performance models structured by process experts and calibrated from the performance data of many developers, it automatically identifies and ranks potential performance problems and root causes of individual developers. However, the current calibration method is not fully automatic, because, in the case of performance indicators that affect other indicators in a conflicting way, the process expert has to manually calibrate the optimal value in a way that balances those impacts. In this paper we propose a novel method to automate this step, taking advantage of training data sets. We demonstrate the feasibility of the method with an example related with the Code Review Rate indicator, with conflicting impacts on Productivity and Quality.

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Abstract