Developing LSTM neural networks that can accurately predict the future trajectory of ongoing cases and their remaining runtime is an active area of research in predictive process monitoring. In this work a novel complete remaining trace prediction (CRTP) LSTM is proposed. This model is trained to directly predict the complete remaining trace and runtime of cases in contrast to single event prediction as is considered in previously published research on this topic. This makes the CRTP-LSTM robust in terms of utilizing all available attributes of previously observed events for prediction, consequently it can be considered natively data aware. In an extensive experimental assessment the authors show that CRTP-LSTMs consistently outperform other considered approaches for both remaining trace and runtime prediction. Furthermore, the authors show that including all available information contained in previously observed events has a positive impact on the performance of the CRTP-LSTM model. This indicates that valuable information can be extracted from attributes of events in order to make more accurate trace and runtime predictions. This opens up interesting avenues for future research including the incorporation of inter-case features into a modeling setup when predicting the remaining trace and runtime of cases.

Predicting house prices is a challenging task that many researchers have attempted to address. As accurate house prices allow better informing parties in the real estate market, improving housing policies and real estate appraisal, a comprehensive overview of house price prediction strategies is valuable for both research and society. In this work, we present a systematic literature review in order to provide insights with regard to the data types and modeling approaches that have been utilized in the current body of research. As such, we identified 93 articles published between 1992 and 2021 presenting a particular technique for house price prediction. Subsequently, we scrutinized these works and scored them according to model and data novelty. A cluster analysis allowed mapping of the property valuation domain and identification of trends. Although conventional methods and traditional input data remain predominant, house price prediction research is slowly adopting more advanced techniques and innovative data sources. In addition, we identify opportunities to include more advanced input data types such as unstructured data and complex spatial data and to introduce deep learning and tailored methods, which could guide further research.

The General Data Protection Regulation (GDPR), adopted in 2018, profoundly impacts information processing organizations as they must comply with this regulation. In this research, we consider GDPR-compliance as a high-level goal in software development that should be addressed at the outset of software development, meaning during requirements engineering (RE). In this work, we hypothesize that natural language processing (NLP) can offer a viable means to automate this process. We conducted a systematic mapping study to explore the existing literature on the intersection of GDPR, NLP, and RE. As a result, we identified 448 relevant studies, of which the majority (420) were related to NLP and RE. Research on the intersection of GDPR and NLP yielded nine studies, while 20 studies were related to GDPR and RE. Even though only one study was identified on the convergence of GDPR, NLP, and RE, the mapping results indicate opportunities for bridging the gap between these fields. In particular, we identified possibilities for introducing NLP techniques to automate manual RE tasks in the crossing of GDPR and RE, in addition to possibilities of using NLP-based machine learning techniques to achieve GDPR-compliance in RE.