PELP: Pioneer Event Log Prediction Using Sequence-to-Sequence Neural Networks

Process mining, a data-driven approach for analyzing, visualizing, and improving business processes using event logs, has emerged as a powerful technique in the field of business process management. Process forecasting is a sub-field of process mining that studies how to predict future processes and process models. In this paper, we introduce and motivate the problem of event log prediction and present our approach to solving the event log prediction problem, in particular, using the sequence-to-sequence deep learning approach. We evaluate and analyze the prediction outcomes on a variety of synthetic logs and seven real-life logs and show that our approach can generate perfect predictions on synthetic logs and that deep learning techniques have the potential to be applied in real-world event log prediction tasks. We further provide practical recommendations for event log predictions grounded in the outcomes of the conducted experiments.

NECA: Network-Embedded Deep Representation Learning for Categorical Data

We propose NECA, a deep representation learning method for categorical data. Built upon the foundations of network embedding and deep unsupervised representation learning, NECA deeply embeds the intrinsic relationship among attribute values and explicitly expresses data objects with numeric vector representations. Designed specifically for categorical data, NECA can support important downstream data mining tasks, such as clustering. Extensive experimental analysis demonstrated the effectiveness of NECA.

Seed Stocking Via Multi-Task Learning

Sellers of crop seeds need to plan for the variety and quantity of seeds to stock at least a year in advance. There are a large number of seed varieties of one crop, and each can perform best under different growing conditions. Given the unpredictability of weather, farmers need to make decisions that balance high yield and low risk. A seed vendor needs to be able to anticipate the needs of farmers and have them ready. In this study, we propose an analytical framework for estimating seed demand with three major steps. First, we will estimate the yield and risk of each variety as if they were planted at each location. Since past experiments performed with different seed varieties are highly unbalanced across varieties, and the combination of growing conditions is sparse, we employ multi-task learning to borrow information from similar varieties. Second, we will determine the best mix of seeds for each location by seeking a tradeoff between yield and risk. Third, we will aggregate such mix and pick the top five varieties to re-balance the yield and risk for each growing location. We find that multi-task learning provides a viable solution for yield prediction, and our overall analytical framework has resulted in a good performance.

Co-occurrence Background Model with Superpixels for Robust Background Initialization

Background initialization is an important step in many high-level applications of video processing,ranging from video surveillance to video inpainting.However,this process is often affected by practical challenges such as illumination changes,background motion,camera jitter and intermittent movement,etc.In this paper,we develop a co-occurrence background model with superpixel segmentation for robust background initialization. We first introduce a novel co-occurrence background modeling method called as Co-occurrence Pixel-Block Pairs(CPB)to generate a reliable initial background model,and the superpixel segmentation is utilized to further acquire the spatial texture Information of foreground and background.Then,the initial background can be determined by combining the foreground extraction results with the superpixel segmentation information.Experimental results obtained from the dataset of the challenging benchmark(SBMnet)validate it's performance under various challenges.