Linear Memory Networks

Recurrent neural networks can learn complex transduction problems that require maintaining and actively exploiting a memory of their inputs. Such models traditionally consider memory and input-output functionalities indissolubly entangled. We introduce a novel recurrent architecture based on the conceptual separation between the functional input-output transformation and the memory mechanism, showing how they can be implemented through different neural components. By building on such conceptualization, we introduce the Linear Memory Network, a recurrent model comprising a feedforward neural network, realizing the non-linear functional transformation, and a linear autoencoder for sequences, implementing the memory component. The resulting architecture can be efficiently trained by building on closed-form solutions to linear optimization problems. Further, by exploiting equivalence results between feedforward and recurrent neural networks we devise a pretraining schema for the proposed architecture. Experiments on polyphonic music datasets show competitive results against gated recurrent networks and other state of the art models

Conformance Checking Based on Multi-Perspective Declarative Process Models

Process mining is a family of techniques that aim at analyzing business process execution data recorded in event logs. Conformance checking is a branch of this discipline embracing approaches for verifying whether the behavior of a process, as recorded in a log, is in line with some expected behaviors provided in the form of a process model. The majority of these approaches require the input process model to be procedural (e.g., a Petri net). However, in turbulent environments, characterized by high variability, the process behavior is less stable and predictable. In these environments, procedural process models are less suitable to describe a business process. Declarative specifications, working in an open world assumption, allow the modeler to express several possible execution paths as a compact set of constraints. Any process execution that does not contradict these constraints is allowed. One of the open challenges in the context of conformance checking with declarative models is the capability of supporting multi-perspective specifications. In this paper, we close this gap by providing a framework for conformance checking based on MP-Declare, a multi-perspective version of the declarative process modeling language Declare. The approach has been implemented in the process mining tool ProM and has been experimented in three real life case studies

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.Comment: Article accepted for publication in 2017 IEEE Symposium on Deep Learning (IEEE DL'17) @ SSC

A tree-based kernel for graphs with continuous attributes

The availability of graph data with node attributes that can be either discrete or real-valued is constantly increasing. While existing kernel methods are effective techniques for dealing with graphs having discrete node labels, their adaptation to non-discrete or continuous node attributes has been limited, mainly for computational issues. Recently, a few kernels especially tailored for this domain, and that trade predictive performance for computational efficiency, have been proposed. In this paper, we propose a graph kernel for complex and continuous nodes' attributes, whose features are tree structures extracted from specific graph visits. The kernel manages to keep the same complexity of state-of-the-art kernels while implicitly using a larger feature space. We further present an approximated variant of the kernel which reduces its complexity significantly. Experimental results obtained on six real-world datasets show that the kernel is the best performing one on most of them. Moreover, in most cases the approximated version reaches comparable performances to current state-of-the-art kernels in terms of classification accuracy while greatly shortening the running times.Comment: This work has been submitted to the IEEE Transactions on Neural Networks and Learning Systems for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

An Empirical Study on Budget-Aware Online Kernel Algorithms for Streams of Graphs

Kernel methods are considered an effective technique for on-line learning. Many approaches have been developed for compactly representing the dual solution of a kernel method when the problem imposes memory constraints. However, in literature no work is specifically tailored to streams of graphs. Motivated by the fact that the size of the feature space representation of many state-of-the-art graph kernels is relatively small and thus it is explicitly computable, we study whether executing kernel algorithms in the feature space can be more effective than the classical dual approach. We study three different algorithms and various strategies for managing the budget. Efficiency and efficacy of the proposed approaches are experimentally assessed on relatively large graph streams exhibiting concept drift. It turns out that, when strict memory budget constraints have to be enforced, working in feature space, given the current state of the art on graph kernels, is more than a viable alternative to dual approaches, both in terms of speed and classification performance.Comment: Author's version of the manuscript, to appear in Neurocomputing (ELSEVIER

Heuristics Miners for Streaming Event Data

More and more business activities are performed using information systems. These systems produce such huge amounts of event data that existing systems are unable to store and process them. Moreover, few processes are in steady-state and due to changing circumstances processes evolve and systems need to adapt continuously. Since conventional process discovery algorithms have been defined for batch processing, it is difficult to apply them in such evolving environments. Existing algorithms cannot cope with streaming event data and tend to generate unreliable and obsolete results. In this paper, we discuss the peculiarities of dealing with streaming event data in the context of process mining. Subsequently, we present a general framework for defining process mining algorithms in settings where it is impossible to store all events over an extended period or where processes evolve while being analyzed. We show how the Heuristics Miner, one of the most effective process discovery algorithms for practical applications, can be modified using this framework. Different stream-aware versions of the Heuristics Miner are defined and implemented in ProM. Moreover, experimental results on artificial and real logs are reported

“A Basket of Mysteries” A Cognitive Linguistic Analysis of Rita Dove’s reworking of Greek myths

This dissertation consists of a cognitive linguistic analysis of two works written by the American poet Rita Dove, the play "The Darker Face of the Earth" and the collection of sonnets "Mother Love". The aim of my work is to analyse, from a cognitive perspective, the creative process involved in the reception and interpretation of ancient Greek myths and their re-adaptation and reinvention in a specific African American context. The linguistic data are elicited from "The Darker Face of the Earth" and "Mother Love" and, in addition, from Dove’s interviews, in which the author is explicit about her use of myth and its relation with the modern and contemporary world. I collected linguistic data through a close reading and a linguistic analysis of Dove’s texts in combination with a linguistic analysis of Greek parallels. My work is situated within the theoretical framework of the cognitive approach to literary studies. Over the last thirty years, scholars in the humanities have integrated insights from the cognitive sciences into their research, demonstrating the productivity of such an approach. Cognitive literary studies is not a unified theory, instead it has come to be understood as a broad “field” with different methodologies, assumptions and committments. My own approach to literary discourse is based on cognitive linguistics. Specifically, I follow the development of Conceptual Metaphor Theory and Turner and Fauconnier’s theory of blending as applied to literature. Through Conceptual Blending Theory (CBT) and Conceptual Metaphor Theory (CMT), I have given an account of Dove’s works on different levels. In my work, I have concentrated on several areas, from larger to smaller scale levels, such as story-plots, episodes, formation of characters, highly lyrical and compressed metaphorical imagery. I have shown how CBT and CMT can contribute to an understanding of the production of a literary text, while, at the same time, developing a new approach to the study of classical reception. A cognitive linguistic approach to literary discourse, and specifically to Dove’s reception of the classics, is highly productive in unravelling the complexity of literary works that blend different histories and literary traditions, in explaining Dove’s process of mythologising history and historicising myth, and in providing a plausible explanation and interpretation of creative poetic metaphor and imagery