The Baltic Dry Index (BDI) is a commonly utilized indicator of global shipping and trade activity. It influences stakeholders’ and ship-owners’ decisions respecting investments, chartering, operational plans, and export and import activities. Accurate prediction of the BDI is very challenging due to its volatility, non-stationarity, and complexity. To help stakeholders and ship-owners make sound short- and long-term maritime business decisions and avoid market risk, we performed short- and long-term predictions of BDI using an ensemble deep-learning approach. In this study, we propose to apply recurrent neural network models for BDI prediction. The state-of-the-art of sequential deep-learning models such as RNN, LSTM, and GRU are employed to predict one- and multi-step-ahead BDI values. In order to increase the accuracy, we assemble the models. In experiments, we compared our results with those of traditional methods such as ARIMA and MLP. The results showed that our proposed method outperforms ARIMA, MLP, RNN, LSTM, and GRU in both short- and long-term prediction of BDI.

The most common method is used and claimed excellent in statistical mapping is Jenk's Natural Breaks, while Head-tail Breaks is a new classification method, this method also is claimed to be the best in classifying data with a heavy-tailed distribution. Landslide is one of the disasters that often occur in Indonesia. In Ponorogo, East Java has a landslide susceptibility area that is not balanced. Most of the area was never had landslide and only a small area frequently had landslide per year, the landslide data occurrence has similarity with heavy-tailed distribution. Nevertheless the number of both fatalities and loss of material is not little. To reduce the losses, this study propose classification mapping for landslide susceptibility area using Natural Breaks and Head/tail Breaks, and compare the both of result classification method to get the best landslide hazard map. Classification consists of high susceptibility area, moderate susceptibility area, low susceptibility area and very low susceptibility area. Classification results using both Natural Breaks and Head/tail Breaks are validated by using Pearson Chi-Square test, and significant value of Natural Breaks (184.9166) is higher than Head/tail Breaks (105.5885). So Natural Breaks is considered better than the Head/tail Breaks in this case.

Forecasting the maritime economics index, including container volume and Baltic Panamax Index, is essential for long‐term planning and decision‐making in the shipping industry. However, studies on container volume prediction are not sufficient, and the bulk freight index has highly fluctuating characteristics, which pose a challenge in long‐term prediction. This study proposes a new hybrid framework for the long‐term prediction of the maritime economics index. The framework consists of time‐series decomposition to break down a time‐series into several components (trend, seasonality, and residual), a two‐stage attention mechanism that prioritizes important variables to increase long‐term prediction accuracy and a long short‐term memory network that predicts and combines all components to derive the final predictive outcome. Extensive experiments are conducted using the container volume data, bulk freight index data, and various external variables. The proposed framework achieved a better predictive performance than existing time‐series methods, including conventional machine learning and deep learning‐based models, in the long‐term prediction of container volume and the Baltic Panamax Index. Hence, the proposed method can help in decision‐making through accurate long‐term predictions of the maritime economics index.

Event logs are records of events that are generally used in process mining to determine the manner in which various processes are practically implemented. Previous studies on process mining attempted to combine the results based on different perspectives such as control flow, data, performance, and resources (organizational) to create a simulation model. This study focuses on the resource perspective. A prior study from the resource perspective focused on clustering the resources into organizational units. Implementing the results of the above study in a simulation model will yield inaccurate results because the resources are assumed to always be available if no task is performed. In a practical scenario, resources (particularly humans) tend to work based on shifts. Thus, we propose mining the shift work operation of resources from event logs to tackle this issue. We utilized a self-organizing map and k-means clustering to incorporate the shift work information from the event logs into the simulation model. Moreover, we introduce a distance function and weight-centroid updating rule in the clustering technique to realize our objective. We conducted extensive experiments with artificial data sets to assess the effectiveness of the proposed method. The simulation shows that introducing the shift work operation time of resources can yield more accurate results. Furthermore, the proposed distance function can capture the shift work operation of the resources more precisely compared with the general distance function.

Key resources are important, influential and powerful performers in a social network structure. Identifying them in the social network of a business process activity is beneficial and rewarding. One of the most effective centrality measures for identification of the key nodes in a social network is to rank resources based on a selection of criteria. PageRank is a representative example of such algorithms, which was first utilized in the Google search engine in 1998. However, the PageRank approach merely assumes a single link as a vote, which allows one originator to link or transfer his work to others more than once in a handover work scenario. We argue that this problem can lead to inaccurate influence based ranking in the context of business processes for resources in a social network. In this paper, we propose f-PageRank, a new approach specifically designed to identify the key resources in a social network generated from a business process activity. We evaluate our proposed method by comparing it with the existing approaches in process mining tools, such as degree centrality, betweenness centrality, BaryRanker, and HITS. The experimental results show that our approach can obtain a satisfying outcome.