CONDA-PM -- A Systematic Review and Framework for Concept Drift Analysis in Process Mining

Business processes evolve over time to adapt to changing business environments. This requires continuous monitoring of business processes to gain insights into whether they conform to the intended design or deviate from it. The situation when a business process changes while being analysed is denoted as Concept Drift. Its analysis is concerned with studying how a business process changes, in terms of detecting and localising changes and studying the effects of the latter. Concept drift analysis is crucial to enable early detection and management of changes, that is, whether to promote a change to become part of an improved process, or to reject the change and make decisions to mitigate its effects. Despite its importance, there exists no comprehensive framework for analysing concept drift types, affected process perspectives, and granularity levels of a business process. This article proposes the CONcept Drift Analysis in Process Mining (CONDA-PM) framework describing phases and requirements of a concept drift analysis approach. CONDA-PM was derived from a Systematic Literature Review (SLR) of current approaches analysing concept drift. We apply the CONDA-PM framework on current approaches to concept drift analysis and evaluate their maturity. Applying CONDA-PM framework highlights areas where research is needed to complement existing efforts.Comment: 45 pages, 11 tables, 13 figure

Conformance Checking-based Concept Drift Detection in Process Mining

One of the main challenges of process mining is to obtain models that represent a process as simply and accurately as possible. Both characteristics can be greatly influenced by changes in the control flow of the process throughout its life cycle. In this thesis we propose the use of conformance metrics to monitor such changes in a way that allows the division of the log into sub-logs representing different versions of the process over time. The validity of the hypothesis has been formally demonstrated, showing that all kinds of changes in the process flow can be captured using these approaches, including sudden, gradual drifts on both clean and noisy environments, where differentiating between anomalous executions and real changes can be tricky

An Event-based Analysis Framework for Open Source Software Development Projects

The increasing popularity and success of Open Source Software (OSS) development projects has drawn significant attention of academics and open source participants over the last two decades. As one of the key areas in OSS research, assessing and predicting OSS performance is of great value to both OSS communities and organizations who are interested in investing in OSS projects. Most existing research, however, has considered OSS project performance as the outcome of static cross-sectional factors such as number of developers, project activity level, and license choice. While variance studies can identify some predictors of project outcomes, they tend to neglect the actual process of development. Without a closer examination of how events occur, an understanding of OSS projects is incomplete. This dissertation aims to combine both process and variance strategy, to investigate how OSS projects change over time through their development processes; and to explore how these changes affect project performance. I design, instantiate, and evaluate a framework and an artifact, EventMiner, to analyze OSS projects’ evolution through development activities. This framework integrates concepts from various theories such as distributed cognition (DCog) and complexity theory, applying data mining techniques such as decision trees, motif analysis, and hidden Markov modeling to automatically analyze and interpret the trace data of 103 OSS projects from an open source repository. The results support the construction of process theories on OSS development. The study contributes to literature in DCog, design routines, OSS development, and OSS performance. The resulting framework allows OSS researchers who are interested in OSS development processes to share and reuse data and data analysis processes in an open-source manner

Using contextual information to understand searching and browsing behavior

There is great imbalance in the richness of information on the web and the succinctness and poverty of search requests of web users, making their queries only a partial description of the underlying complex information needs. Finding ways to better leverage contextual information and make search context-aware holds the promise to dramatically improve the search experience of users. We conducted a series of studies to discover, model and utilize contextual information in order to understand and improve users' searching and browsing behavior on the web. Our results capture important aspects of context under the realistic conditions of different online search services, aiming to ensure that our scientific insights and solutions transfer to the operational settings of real world applications

Structural Performance Evaluation of Bridges: Characterizing and Integrating Thermal Response

Bridge monitoring studies indicate that the quasi-static response of a bridge, while dependent on various input forces, is affected predominantly by variations in temperature. In many structures, the quasi-static response can even be approximated as equal to its thermal response. Consequently, interpretation of measurements from quasi-static monitoring requires accounting for the thermal response in measurements. Developing solutions to this challenge, which is critical to relate measurements to decision-making and thereby realize the full potential of SHM for bridge management, is the main focus of this research. This research proposes a data-driven approach referred to as temperature-based measurement interpretation (TB-MI) approach for structural performance evaluation of bridges based on continuous bridge monitoring. The approach characterizes and predicts thermal response of structures by exploiting the relationship between temperature distributions across a bridge and measured bridge response. The TB-MI approach has two components - (i) a regression-based thermal response prediction (RBTRP) methodology and (ii) an anomaly detection methodology. The RBTRP methodology generates models to predict real-time structural response from distributed temperature measurements. The anomaly detection methodology analyses prediction error signals, which are the differences between predicted and real-time response to detect the onset of anomaly events. In order to generate realistic data-sets for evaluating the proposed TB-MI approach, this research has built a small-scale truss structure in the laboratory as a test-bed. The truss is subject to accelerated diurnal temperature cycles using a system of heating lamps. Various damage scenarios are also simulated on this structure. This research further investigates if the underlying concept of using distributed temperature measurements to predict thermal response can be implemented using physics-based models. The case study of Cleddau Bridge is considered. This research also extends the general concept of predicting bridge response from knowledge of input loads to predict structural response due to traffic loads. Starting from the TB-MI approach, it creates an integrated approach for analyzing measured response due to both thermal and vehicular loads. The proposed approaches are evaluated on measurement time-histories from a number of case studies including numerical models, laboratory-scale truss and full-scale bridges. Results illustrate that the approaches accurately predicts thermal response, and that anomaly events are detectable using signal processing techniques such as signal subtraction method and cointegration. The study demonstrates that the proposed TB-MI approach is applicable for interpreting measurements from full-scale bridges, and can be integrated within a measurement interpretation platform for continuous bridge monitoring

Observing pulsars and fast transients with LOFAR

Low frequency radio waves, while challenging to observe,are a rich source of information about pulsars. The LOw Frequency ARray (LOFAR) is a new radio interferometer operating in the lowest 4 octaves of the ionospheric “radio window”: 10–240 MHz, that will greatly facilitate observing pulsars at low radio frequencies. Through the huge collecting area, long baselines, and flexible digital hardware, it is expected that LOFAR will revolutionize radio astronomy at the lowest frequencies visible from Earth.LOFAR is a next-generation radio telescope and a pathfinder to the Square Kilometre Array (SKA), in that it incorporates advanced multi-beaming techniques between thousands of individual elements. We discuss the motivation for low-frequency pulsar observations in general and the potential of LOFAR in addressing these science goals.We present LOFAR as it is designed to perform high-time-resolution observations of pulsars and other fast transients, and outline the various relevant observing modes and data reduction pipelines that are already or will soon be implemented to facilitate these observations. A number of results obtained from commissioning observations are presented to demonstrate the exciting potential of the telescope. This paper outlines the case for low frequency pulsar observations and is also intended to serve as a reference for upcoming pulsar/fast transient science papers with LOFAR

Frequency and triggering mechanisms of submarine mass movements and their geohazard implications

Submarine mass movements are one of the most important processes for moving sediment across our planet. They represent the dominant process for moving sediment in many parts of the world’s oceans, freshwater lakes and reservoirs. These flows also represent a significant geohazard. They can generate damaging tsunami and have the potential to damage strategically important seafloor infrastructure. It is therefore important to understand the frequency and triggering mechanisms of these events. This thesis aims to further our understanding using a variety of different data types (artificial data, deposits found in cores, seismic stratigraphy and submarine cable breaks) across different spatial scales. First, artificial data is used to analyse the impacts of large age uncertainties on identifying a triggering mechanism for large (>1 km3) landslides in a global database. It is shown that the size of age uncertainties, the small number of landslides within the database and the combination of multiple different settings into one dataset will likely result in landslides appearing to occur randomly. As a result it is suggested that it is prudent to focus on well-dated landslides from one setting with similar triggers rather than having a poorly calibrated understanding of landslide ages in multiple settings which may prevent a trigger being identified. Second, a global database of subsea fibre optic cable breaks is used to investigate the triggering of submarine mass movements by earthquakes and tropical cyclones. Globally earthquakes between Mw 3 and Mw 9.2 are shown to trigger mass movements. However, in contrast to previous assertions it is shown that there is not a specific earthquake magnitude that will systematically trigger mass movements capable of breaking a cable. The susceptibility of slopes to fail as a consequence of large and small earthquakes is dependent on the average seismicity of the region and the volume of sediment supplied annually to the continental shelf. The frequency of damaging tropical cyclone triggered submarine mass movements is lower than earthquake triggered mass movements. Analysis of the cable break database reveals three mechanisms by which mass movements are triggered. First, tropical cyclones trigger flows directly, synchronous to their passage due to dynamic loading of the seabed. Second, flows are triggered indirectly, as a consequence of peak flood discharges delivering large volumes of sediment to the continental shelf. Third, flows are triggered indirectly following a delay as a consequence of the large volumes of rapidly deposited sediment that occurs after the passage of a tropical cyclone. No clear global relationship between future climate change and flow frequency is shown, however, changes to cyclone activity in specific regions appears likely to increase damaging flow frequency. Third, using a new piston core dataset, the timing and frequency of glacigenic debris-flows on the Bear Island Trough-Mouth Fan is investigated. The timing of glacigenic debris-flows over the last 140,000 years is shown to be controlled by the presence of an ice stream close to the shelf edge. Moreover, it is shown that the frequency and volumes of these flows is controlled by the overall dynamics of the Barents Sea Ice Sheet which vary significantly over the 140,000 year time period. Last, a review of the relationship between ice sheets and submarine mass movements around the Nordic Seas over the Quaternary is presented using published seismic and sediment core datasets. From these data sources, the growth and decay histories of the Greenland, Barents Sea and Scandinavian Ice Sheets are tracked relative to the different types of submarine mass movements identified on their margins. The type and frequency of submarine mass movement is shown to be highly variable as a consequence of variable ice sheet extent, rates of sediment transport and meltwater export of sediment. These records have allowed the identification of first order controls on sediment delivery to continental margins at ice sheet scales. It has also enabled updated conceptual models of trough-mouth fan processes, glaciated margin development and submarine landslide occurrence to be developed