Towards Design Principles for Data-Driven Decision Making: An Action Design Research Project in the Maritime Industry

Data-driven decision making (DDD) refers to organizational decision-making practices that emphasize the use of data and statistical analysis instead of relying on human judgment only. Various empirical studies provide evidence for the value of DDD, both on individual decision maker level and the organizational level. Yet, the path from data to value is not always an easy one and various organizational and psychological factors mediate and moderate the translation of data-driven insights into better decisions and, subsequently, effective business actions. The current body of academic literature on DDD lacks prescriptive knowledge on how to successfully employ DDD in complex organizational settings. Against this background, this paper reports on an action design research study aimed at designing and implementing IT artifacts for DDD at one of the largest ship engine manufacturers in the world. Our main contribution is a set of design principles highlighting, besides decision quality, the importance of model comprehensibility, domain knowledge, and actionability of results

Supporting software processes analysis and decision-making using provenance data

Data provenance can be defined as the description of the origins of a piece of data and the process by which it arrived in a database. Provenance has been successfully used in health sciences, chemical industries, and scientific computing, considering that these areas require a comprehensive traceability mechanism. Moreover, companies have been increasing the amount of data they collect from their systems and processes, considering the dropping cost of memory and storage technologies in the last years. Thus, this thesis investigates if the use of provenance models and techniques can support software processes execution analysis and data-driven decision-making, considering the increasing availability of process data provided by companies. A provenance model for software processes was developed and evaluated by experts in process and provenance area, in addition to an approach for capturing, storing, inferencing of implicit information, and visualization to software process provenance data. In addition, a case study using data from industry’s processes was conducted to evaluate the approach, with a discussion about several specific analysis and data-driven decision-making possibilities.Proveniência de dados é definida como a descrição da origem de um dado e o processo pelo qual este passou até chegar ao seu estado atual. Proveniência de dados tem sido usada com sucesso em domínios como ciências da saúde, indústrias químicas e computação científica, considerando que essas áreas exigem um mecanismo abrangente de rastreabilidade. Por outro lado, as empresas vêm aumentando a quantidade de dados que coletam de seus sistemas e processos, considerando a diminuição no custo das tecnologias de memória e armazenamento nos últimos anos. Assim, esta tese investiga se o uso de modelos e técnicas de proveniência é capaz de apoiar a análise da execução de processos de software e a tomada de decisões baseada em dados, considerando a disponibilização cada vez maior de dados relativos a processos pelas empresas. Um modelo de proveniência para processos de software foi desenvolvido e avaliado por especialistas em processos e proveniência, além de uma abordagem e ferramental de apoio para captura, armazenamento, inferência de novas informações e posterior análise e visualização dos dados de proveniência de processos. Um estudo de caso utilizando dados de processos da indústria foi conduzido para avaliação da abordagem e discussão de possibilidades distintas para análise e tomada de decisão orientada por estes dados

An accurate method to correct atmospheric phase delay for InSAR with the ERA5 global atmospheric model

Differential SAR Interferometry (DInSAR) has proven its unprecedented ability and merits of monitoring ground deformation on a large scale with centimeter to millimeter accuracy. However, atmospheric artifacts due to spatial and temporal variations of the atmospheric state often affect the reliability and accuracy of its results. The commonly-known Atmospheric Phase Screen (APS) appears in the interferograms as ghost fringes not related to either topography or deformation. Atmospheric artifact mitigation remains one of the biggest challenges to be addressed within the DInSAR community. State-of-the-art research works have revealed that atmospheric artifacts can be partially compensated with empirical models, point-wise GPS zenith path delay, and numerical weather prediction models. In this study, we implement an accurate and realistic computing strategy using atmospheric reanalysis ERA5 data to estimate atmospheric artifacts. With this approach, the Line-of-Sight (LOS) path along the satellite trajectory and the monitored points is considered, rather than estimating it from the zenith path delay. Compared with the zenith delay-based method, the key advantage is that it can avoid errors caused by any anisotropic atmospheric phenomena. The accurate method is validated with Sentinel-1 data in three different test sites: Tenerife island (Spain), Almería (Spain), and Crete island (Greece). The effectiveness and performance of the method to remove APS from interferograms is evaluated in the three test sites showing a great improvement with respect to the zenith-based approach.Peer ReviewedPostprint (published version