Process Mining as a Strategy of Inquiry: Understanding Design Interventions and the Development of Business Processes

Process (re-)design and improvement are important aspectsof the Business Process Management (BPM) life-cycle. Yet, there is lit-tle empirical evidence on how design interventions materialize in actualprocess execution, leading to repeated failure of such initiatives. In thisdissertation I use the emerging affordances of process mining algorithmsto address this important limitation. In particular, I devise a methodthat combines process mining and grounded theory to study processualphenomena. Consequently, this method is applied to investigate changein business processes. This thesis contributes to the body of knowledgein BPM and bordering disciplines by demonstrating how process min-ing can be used as a method to study processual phenomena. Furtherthis research sheds light on the impact of design interventions on actualprocess execution and vica versa

What's unusual in online disease outbreak news?

Background: Accurate and timely detection of public health events of international concern is necessary to help support risk assessment and response and save lives. Novel event-based methods that use the World Wide Web as a signal source offer potential to extend health surveillance into areas where traditional indicator networks are lacking. In this paper we address the issue of systematically evaluating online health news to support automatic alerting using daily disease-country counts text mined from real world data using BioCaster. For 18 data sets produced by BioCaster, we compare 5 aberration detection algorithms (EARS C2, C3, W2, F-statistic and EWMA) for performance against expert moderated ProMED-mail postings. Results: We report sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), mean alerts/100 days and F1, at 95% confidence interval (CI) for 287 ProMED-mail postings on 18 outbreaks across 14 countries over a 366 day period. Results indicate that W2 had the best F1 with a slight benefit for day of week effect over C2. In drill down analysis we indicate issues arising from the granular choice of country-level modeling, sudden drops in reporting due to day of week effects and reporting bias. Automatic alerting has been implemented in BioCaster available from http://born.nii.ac.jp. Conclusions: Online health news alerts have the potential to enhance manual analytical methods by increasing throughput, timeliness and detection rates. Systematic evaluation of health news aberrations is necessary to push forward our understanding of the complex relationship between news report volumes and case numbers and to select the best performing features and algorithms

Intelligent Management and Efficient Operation of Big Data

This chapter details how Big Data can be used and implemented in networking and computing infrastructures. Specifically, it addresses three main aspects: the timely extraction of relevant knowledge from heterogeneous, and very often unstructured large data sources, the enhancement on the performance of processing and networking (cloud) infrastructures that are the most important foundational pillars of Big Data applications or services, and novel ways to efficiently manage network infrastructures with high-level composed policies for supporting the transmission of large amounts of data with distinct requisites (video vs. non-video). A case study involving an intelligent management solution to route data traffic with diverse requirements in a wide area Internet Exchange Point is presented, discussed in the context of Big Data, and evaluated.Comment: In book Handbook of Research on Trends and Future Directions in Big Data and Web Intelligence, IGI Global, 201

Machine learning paradigms for modeling spatial and temporal information in multimedia data mining

Multimedia data mining and knowledge discovery is a fast emerging interdisciplinary applied research area. There is tremendous potential for effective use of multimedia data mining (MDM) through intelligent analysis. Diverse application areas are increasingly relying on multimedia under-standing systems. Advances in multimedia understanding are related directly to advances in signal processing, computer vision, machine learning, pattern recognition, multimedia databases, and smart sensors. The main mission of this special issue is to identify state-of-the-art machine learning paradigms that are particularly powerful and effective for modeling and combining temporal and spatial media cues such as audio, visual, and face information and for accomplishing tasks of multimedia data mining and knowledge discovery. These models should be able to bridge the gap between low-level audiovisual features which require signal processing and high-level semantics. A number of papers have been submitted to the special issue in the areas of imaging, artificial intelligence; and pattern recognition and five contributions have been selected covering state-of-the-art algorithms and advanced related topics. The first contribution by D. Xiang et al. “Evaluation of data quality and drought monitoring capability of FY-3A MERSI data” describes some basic parameters and major technical indicators of the FY-3A, and evaluates data quality and drought monitoring capability of the Medium-Resolution Imager (MERSI) onboard the FY-3A. The second contribution by A. Belatreche et al. “Computing with biologically inspired neural oscillators: application to color image segmentation” investigates the computing capabilities and potential applications of neural oscillators, a biologically inspired neural model, to gray scale and color image segmentation, an important task in image understanding and object recognition. The major contribution of this paper is the ability to use neural oscillators as a learning scheme for solving real world engineering problems. The third paper by A. Dargazany et al. entitled “Multibandwidth Kernel-based object tracking” explores new methods for object tracking using the mean shift (MS). A bandwidth-handling MS technique is deployed in which the tracker reach the global mode of the density function not requiring a specific staring point. It has been proven via experiments that the Gradual Multibandwidth Mean Shift tracking algorithm can converge faster than the conventional kernel-based object tracking (known as the mean shift). The fourth contribution by S. Alzu’bi et al. entitled “3D medical volume segmentation using hybrid multi-resolution statistical approaches” studies new 3D volume segmentation using multiresolution statistical approaches based on discrete wavelet transform and hidden Markov models. This system commonly reduced the percentage error achieved using the traditional 2D segmentation techniques by several percent. Furthermore, a contribution by G. Cabanes et al. entitled “Unsupervised topographic learning for spatiotemporal data mining” proposes a new unsupervised algorithm, suitable for the analysis of noisy spatiotemporal Radio Frequency Identification (RFID) data. The new unsupervised algorithm depicted in this article is an efficient data mining tool for behavioral studies based on RFID technology. It has the ability to discover and compare stable patterns in a RFID signal, and is appropriate for continuous learning. Finally, we would like to thank all those who helped to make this special issue possible, especially the authors and the reviewers of the articles. Our thanks go to the Hindawi staff and personnel, the journal Manager in bringing about the issue and giving us the opportunity to edit this special issue

FDI another day: Russian reliance on European investment. Bruegel Policy Contribution Issue n˚3 | February 2020

Most foreign direct investment into Russia originates in the European Union: European investors own between 55 percent and 75 percent of Russian FDI stock. This points to a Russian dependence on European investment, making the EU paramount for Russian medium-term growth. Even if we consider ‘phantom’ FDI that transits through Europe, the EU remains the primary investor in Russia. Most phantom FDI into Russia is believed to originate from Russia itself and thus is by construction not foreign

Making Good on LSTMs' Unfulfilled Promise

LSTMs promise much to financial time-series analysis, temporal and cross-sectional inference, but we find that they do not deliver in a real-world financial management task. We examine an alternative called Continual Learning (CL), a memory-augmented approach, which can provide transparent explanations, i.e. which memory did what and when. This work has implications for many financial applications including credit, time-varying fairness in decision making and more. We make three important new observations. Firstly, as well as being more explainable, time-series CL approaches outperform LSTMs as well as a simple sliding window learner using feed-forward neural networks (FFNN). Secondly, we show that CL based on a sliding window learner (FFNN) is more effective than CL based on a sequential learner (LSTM). Thirdly, we examine how real-world, time-series noise impacts several similarity approaches used in CL memory addressing. We provide these insights using an approach called Continual Learning Augmentation (CLA) tested on a complex real-world problem, emerging market equities investment decision making. CLA provides a test-bed as it can be based on different types of time-series learners, allowing testing of LSTM and FFNN learners side by side. CLA is also used to test several distance approaches used in a memory recall-gate: Euclidean distance (ED), dynamic time warping (DTW), auto-encoders (AE) and a novel hybrid approach, warp-AE. We find that ED under-performs DTW and AE but warp-AE shows the best overall performance in a real-world financial task

China and India: Openness, Trade and Effects on Economic Growth

The paper has two objectives. Firstly, we wish to evaluate whether a greater economic integration has effects, and of what type, on migration flows from Central and Eastern Europe (New Member States of the EU, NMS) towards the fifteen countries of the European Union (EU-15). Secondly, we wish to understand what effect the migration flows from the NMS have on the labour market of the receiving countries in the EU-15. The most suitable theoretical context that seems to summarise European labour market characteristics is that of the insider/outsider model by Layard, Nickell and Jackman (Layard et al., 1991). We have modified the above mentioned model by introducing two innovations. Firstly, we constructed three measures that act as a proxy for economic integration: the Intra Regional Trade Index (IRTI), Global Trade Index (GTI) and Financial Market Integration (FMI). Then we placed the three indicators into the insider/outsider model to arrive at a modified version of Layard, Nickell and Jackman (Layard et al., 1991). The second innovative contribution was the introduction of an equation modelling migration flows. The creation of this equation is inspired by the neo-classical approach to migration theory (Harris-Todaro, 1970). The theoretical model, based on rational expectations, has been solved to find the equilibrium solution and the impact multipliers. We then carried out an empirical analysis, which involved estimating a Structural Vector Autoregression Model (SVAR). The aim of this estimation was to evaluate, on the one hand, the effect that greater European integration (a positive shock to the integration indicators) has on migration flows, and, on the other, to measure the type of effect that migration flows could have on the labour market of the EU-15 countries, considered as a single entity. The results of our empirical evidence show that economic integration does generate significant effects on migration flows from the enlargement countries towards the EU-15 countries. It also emerges that migration flows do generate an effect on the European labour market.China and India, economic growth, trade opening, trade specialisation, trade and growth