Mirko Polato

Data-aware remaining time prediction of business process instances

Accurate prediction of the completion time of a business process instance would constitute a valuable tool when managing processes under service level agreement constraints. Such prediction, however, is a very challenging task. A wide variety of factors could influence the trend of a process instance, and hence just using time statistics of historical cases cannot be sufficient to get accurate predictions. Here we propose a new approach where, in order to improve the prediction quality, both the control and the data flow perspectives are jointly used. To achieve this goal, our approach builds a process model which is augmented by time and data information in order to enable remaining time prediction. The remaining time prediction of a running case is calculated combining two factors: (a) the likelihood of all the following activities, given the data collected so far; and (b) the remaining time estimation given by a regression model built upon the data.

Time and activity sequence prediction of business process instances

The ability to know in advance the trend of running process instances, with respect to different features, such as the expected completion time, would allow business managers to timely counteract to undesired situations, in order to prevent losses. Therefore, the ability to accurately predict future features of running business process instances would be a very helpful aid when managing processes, especially under service level agreement constraints. However, making such accurate forecasts is not easy: many factors may influence the predicted features. Many approaches have been proposed to cope with this problem but, generally, they assume that the underlying process is stationary. However, in real cases this assumption is not always true. In this work we present new methods for predicting the remaining time of running cases. In particular we propose a method, assuming process stationarity, which achieves state-of-the-art performances and two other methods which are able to make predictions even with non-stationary processes. We also describe an approach able to predict the full sequence of activities that a running case is going to take. All these methods are extensively evaluated on different real case studies.

Exploiting sparsity to build efficient kernel based collaborative filtering for top-N item recommendation

Abstract The increasing availability of implicit feedback datasets has raised the interest in developing effective collaborative filtering techniques able to deal asymmetrically with unambiguous positive feedback and ambiguous negative feedback. In this paper, we propose a principled kernel-based collaborative filtering method for top-N item recommendation with implicit feedback. We present an efficient implementation using the linear kernel, and we show how to generalize it to kernels of the dot product family preserving the efficiency. We also investigate on the elements which influence the sparsity of a standard cosine kernel. This analysis shows that the sparsity of the kernel strongly depends on the properties of the dataset, in particular on the long tail distribution. We compare our method with state-of-the-art algorithms achieving good results both in terms of efficiency and effectiveness.

Boolean kernels for collaborative filtering in top-N item recommendation

Abstract In many personalized recommendation problems available data consists only of positive interactions (implicit feedback) between users and items. This problem is also known as One-Class Collaborative Filtering (OC-CF). Linear models usually achieve state-of-the-art performances on OC-CF problems and many efforts have been devoted to build more expressive and complex representations able to improve the recommendations. Recent analysis show that collaborative filtering (CF) datasets have peculiar characteristics such as high sparsity and a long tailed distribution of the ratings. In this paper we propose a boolean kernel, called Disjunctive kernel, which is less expressive than the linear one but it is able to alleviate the sparsity issue in CF contexts. The embedding of this kernel is composed by all the combinations of a certain arity d of the input variables, and these combined features are semantically interpreted as disjunctions of the input variables. Experiments on several CF datasets show the effectiveness and the efficiency of the proposed kernel.

LSTM networks for data-aware remaining time prediction of business process instances

Predicting the completion time of business process instances would be a very helpful aid when managing processes under service level agreement constraints. The ability to know in advance the trend of running process instances would allow business managers to react in time, in order to prevent delays or undesirable situations. However, making such accurate forecasts is not easy: many factors may influence the required time to complete a process instance. In this paper, we propose an approach based on deep Recurrent Neural Networks (specifically LSTMs) that is able to exploit arbitrary information associated to single events, in order to produce an as-accurate-as-possible prediction of the completion time of running instances. Experiments on real-world datasets confirm the quality of our proposal.

A novel Boolean kernels family for categorical data

Kernel based classifiers, such as SVM, are considered state-of-the-art algorithms and are widely used on many classification tasks. However, this kind of methods are hardly interpretable and for this reason they are often considered as black-box models. In this paper, we propose a new family of Boolean kernels for categorical data where features correspond to propositional formulas applied to the input variables. The idea is to create human-readable features to ease the extraction of interpretation rules directly from the embedding space. Experiments on artificial and benchmark datasets show the effectiveness of the proposed family of kernels with respect to established ones, such as RBF, in terms of classification accuracy.

A Game-Theoretic Framework for Interpretable Preference and Feature Learning

We are living in an era that we can call machine learning revolution. Started as a pure academic and research-oriented domain, we have seen widespread commercial adoption across diverse domains, such as retail, healthcare, finance, and many more. However, the usage of machine learning poses its own set of challenges when it comes to explain what is going on under the hood. The reason being models interpretability is very important for the business is to explain each and every decision being taken by the model. In order to take a step forward in this direction, we propose a principled algorithm inspired by both preference learning and game theory for classification. Particularly, the learning problem is posed as a two player zero-sum game which we show having theoretical guarantees about its convergence. Interestingly, feature selection can be straightforwardly plugged into such algorithm. As a consequence, the hypotheses space consists on a set of preference prototypes along with (possibly non-linear) features making the resulting models easy to interpret.

Learning Preferences for Large Scale Multi-label Problems.

Despite that the majority of machine learning approaches aim to solve binary classification problems, several real-world applications require specialized algorithms able to handle many different classes, as in the case of single-label multi-class and multi-label classification problems. The Label Ranking framework is a generalization of the above mentioned settings, which aims to map instances from the input space to a total order over the set of possible labels. However, generally these algorithms are more complex than binary ones, and their application on large-scale datasets could be untractable.

Efficient Similarity Based Methods For The Playlist Continuation Task

In this paper, the pipeline we used in the RecSys challenge 2018 is reported. We present content-based and collaborative filtering approaches for the definition of the similarity matrices for top-500 recommendation task. In particular, the task consisted in recommending songs to add to partial playlists. Different methods have been proposed depending on the number of available songs in a playlist. We show how an hybrid approach which exploits both content-based and collaborative filtering is effective in this task. Specifically, information derived by the playlist titles helped to tackle the cold-start issue.

Classification of Categorical Data in the Feature Space of Monotone DNFs

Nowadays, kernel based classifiers, such as SVM, are widely used on many different classification tasks. One of the drawbacks of these kind of approaches is their poor interpretability. In the past, some efforts have been devoted in designing kernels able to construct a more understandable feature space, e.g., boolean kernels, but only combinations of simple conjunctive clauses have been proposed.