The predictive power of public Twitter sentiment for forecasting cryptocurrency prices

status: publishe

Behavioral constraint template-based sequence classification

In this paper we present the interesting Behavioral Constraint Miner (iBCM), a new approach towards classifying sequences. The prevalence of sequential data, i.e., a collection of ordered items such as text, website navigation patterns, traffic management, and so on, has incited a surge in research interest towards sequence classification. Existing approaches mainly focus on retrieving sequences of itemsets and checking their presence in labeled data streams to obtain a classifier. The proposed iBCM approach, rather than focusing on plain sequences, is template-based and draws its inspiration from behavioral patterns used for software verification. These patterns have a broad range of characteristics and go beyond the typical sequence mining representation, allowing for a more precise and concise way of capturing sequential information in a database. Furthermore, it is possible to also mine for negative information, i.e., sequences that do not occur. The technique is benchmarked against other state-of-the-art approaches and exhibits a strong potential towards sequence classification. Code related to this chapter is available at: http://feb.kuleuven.be/public/u0092789/status: publishe

Mixed-Paradigm Process Modeling with Intertwined State Spaces

Business process modeling often deals with the trade-off between comprehensibility and flexibility. Many languages have been proposed to support different paradigms to tackle these characteristics. Well-known procedural, token-based languages such as Petri nets, BPMN, EPC, etc. have been used and extended to incorporate more flexible use cases, however the declarative workflow paradigm, most notably represented by the Declare framework, is still widely accepted for modeling flexible processes. A real trade-off exists between the readable, rather inflexible procedural models, and the highly-expressive but cognitively demanding declarative models containing a lot of implicit behavior. This paper investigates in detail the scenarios in which combining both approaches is useful, it provides a scoring table for Declare constructs to capture their intricacies and similarities compared to procedural ones, and offers a step-wise approach to construct mixed-paradigm models. Such models are especially useful in the case of environments with different layers of flexibility and go beyond using atomic subprocesses modeled according to either paradigm. The paper combines Petri nets and Declare to express the findings

Predictive Process Model Monitoring using Recurrent Neural Networks

The field of predictive process monitoring focuses on modelling future characteristics of running business process instances, typically by either predicting the outcome of particular objectives (e.g. completion (time), cost), or next-in-sequence prediction (e.g. what is the next activity to execute). This paper introduces Processes-As-Movies (PAM), a technique that provides a middle ground between these predictive monitoring. It does so by capturing declarative process constraints between activities in various windows of a process execution trace, which represent a declarative process model at subsequent stages of execution. This high-dimensional representation of a process model allows the application of predictive modelling on how such constraints appear and vanish throughout a process' execution. Various recurrent neural network topologies tailored to high-dimensional input are used to model the process model evolution with windows as time steps, including encoder-decoder long short-term memory networks, and convolutional long short-term memory networks. Results show that these topologies are very effective in terms of accuracy and precision to predict a process model's future state, which allows process owners to simultaneously verify what linear temporal logic rules hold in a predicted process window (objective-based), and verify what future execution traces are allowed by all the constraints together (trace-based)

Signature-Based Community Detection for Time Series

Community detection for time series without prior knowledge poses an open challenge within complex networks theory. Traditional approaches begin by assessing time series correlations and maximizing modularity under diverse null models. These methods suffer from assuming temporal stationarity and are influenced by the granularity of observation intervals. In this study, we propose an approach based on the signature matrix, a concept from path theory for studying stochastic processes. By employing a signature-derived similarity measure, our method overcomes drawbacks of traditional correlation-based techniques. Through a series of numerical experiments, we demonstrate that our method consistently yields higher modularity compared to baseline models, when tested on the Standard and Poor's 500 dataset. Moreover, our approach showcases enhanced stability in modularity when the length of the underlying time series is manipulated. This research contributes to the field of community detection by introducing a signature-based similarity measure, offering an alternative to conventional correlation matrices

Hearing the voice of citizens in smart city design:The CitiVoice framework

In the last few years, smart cities have attracted considerable attention because they are considered a response to the complex challenges that modern cities face. However, smart cities often do not optimally reach their objectives if the citizens, the end-users, are not involved in their design. The aim of this paper is to provide a framework to structure and evaluate citizen participation in smart cities. By means of a literature review from different research areas, the relevant enablers of citizen participation are summarized and bundled in the proposed CitiVoice framework. Then, following the design science methodology, the content and the utility of CitiVoice are validated through the application to different smart cities and through in-depth interviews with key Belgian smart city stakeholders. CitiVoice is used as an evaluation tool for several Belgian smart cities allowing drawbacks and flaws in citizens’ participation to be discovered and analysed. It is also demonstrated how CitiVoice can act as a governance tool for the ongoing smart city design of Namur (Belgium) to help define the citizen participation strategy. Finally, it is used as a comparison and creativity tool to compare several cities and design new means of participation.status: publishe