Complex event processing | EPFL Graph Search

Event processing is a method of tracking and analyzing (processing) streams of information (data) about things that happen (events), and deriving a conclusion from them. Complex event processing (CEP) consists of a set of concepts and techniques developed in the early 1990s for processing real-time events and extracting information from event streams as they arrive. The goal of complex event processing is to identify meaningful events (such as opportunities or threats) in real-time situations and respond to them as quickly as possible. These events may be happening across the various layers of an organization as sales leads, orders or customer service calls. Or, they may be news items, text messages, social media posts, stock market feeds, traffic reports, weather reports, or other kinds of data. An event may also be defined as a "change of state," when a measurement exceeds a predefined threshold of time, temperature, or other value. Analysts have suggested that

Exploring policy change through agent-based simulation

Raphaël Klein

Policymaking is a complex process that has been studied using policy process theories almost exclusively. These theories have been built using a large number of qualitative cases. Such methods are useful for theory building but remain limited for theory exploration and policy advice. On a different front, socio-technical system (STS) simulations are often used to test the impact and effectiveness of policies. This is done by using policy scenarios. This manner of dealing with deep uncertainty disregards the dynamic aspect of the policy process and of the way STS interact with policies. The objective of this dissertation is to present an approach that can be used to systematically model and simulate the policy process. This dissertation demonstrates how such a model can be used in these two widely different applications to explore the policy process theories and to better account for deep uncertainty in STS simulations. In the first part of the dissertation, I present a common language. It considers four building blocks using concepts from prominent theories as requirements to any model of the policy process: time, the policy arena, actors and the environment, and the actor interactions. Additionally, in this part, I argue why agent-based modelling is best suited to simulate the policy process. Finally, I present the hybrid modelling approach that is used to incorporate the policy process model into already existing STS models. The second part focuses on the simplest implementation model. This model is the first use of the common language and is entitled simplest implementation as the common language is used to build the simplest model possible capable of emulating the policy process. It is created to demonstrate the potential of the common language and is tested with three different STS. A predation model is used to present the possibilities stemming from this simplest implementation model. It is then added to a model of the Swiss electricity market to demonstrate what benefits the endogenisation of the policy process can have on the study of a complex STS. As a third example, the policy process model is added to a system dynamics model of flood safety in The Netherlands to demonstrate its versatility. The third part of this dissertation consists of the advocacy coalition framework (ACF) implementation model. In this part, I present a model, also developed using the common language, that is aimed at emulating the ACF as closely as possible. The ACF being a complex framework, this implementation is done in steps of increasing complexity. The first step introduces policy learning to the model, the second introduces coalitions, the third and fourth steps introduce aspects of partial knowledge and imperfect information. This ACF implementation model is also demonstrated using first a predation model and second a model of the Swiss electricity market. In conclusion, this dissertation shows that it is possible to model the policy process. However it was shown that the policy process theories could benefit from additional and more focused studies so that behaviours and mechanisms can be better understood. This work can be the first step to such studies. It was also shown that introducing the policy process in a STS simulation can be useful to better understand said system. This approach allows for an easy integration to already existing models. Finally, this work can be extended by looking at other theories or different applications.

EPFL2021

Les mathématiques financières robustes, le maritime et les FFA

Jérôme Reboulleau

Powerful mathematical tools have been developed for trading in stocks and bonds, but other markets that are equally important for the globalized world have to some extent been neglected. We decided to study the shipping market as an new area of development in mathematical finance. The market in shipping derivatives (FFA and FOSVA) has only been developed after 2000 and now exhibits impressive growth. Financial actors have entered the field, but it is still largely undiscovered by institutional investors. The first part of the work was to identify the characteristics of the market in shipping, i.e. the segmentation and the volatility. Because the shipping business is old-fashioned, even the leading actors on the world stage (ship owners and banks) are using macro-economic models to forecast the rates. If the macro-economic models are logical and make sense, they fail to predict. For example, the factor port congestion has been much cited during the last few years, but it is clearly very difficult to control and is simply an indicator of traffic. From our own experience it appears that most ship owners are in fact market driven and rather bad at anticipating trends. Due to their ability to capture large moves, we chose to consider Lévy processes for the underlying price process. Compared with the macro-economic approach, the main advantage is the uniform and systematic structure this imposed on the models. We get in each case a favorable result for our technology and a gain in forecasting accuracy of around 10% depending on the maturity. The global distribution is more effectively modelled and the tails of the distribution are particularly well represented. This model can be used to forecast the market but also to evaluate the risk, for example, by computing the VaR. An important limitation is the non-robustness in the estimation of the Lévy processes. The use of robust estimators reinforces the information obtained from the observed data. Because maximum likelihood estimation is not easy to compute with complex processes, we only consider some very general robust score functions to manage the technical problems. Two new class of robust estimators are suggested. These are based on the work of F. Hampel ([29]) and P. Huber ([30]) using influence functions. The main idea is to bound the maximum likelihood score function. By doing this a bias is created in the parameters estimation, which can be corrected by using a modification of the following type and as proposed by F. Hampel. The procedure for finding a robust estimating equation is thus decomposed into two consecutive steps : Subtract the bias correction and then Bound the score function. In the case of complex Lévy processes, the bias correction is difficult to compute and generally unknown. We have developed a pragmatic solution by inverting the Hampel's procedure. Bound the score function and then Correct for the bias. The price is a loss of the theoretical properties of our estimators, besides the procedure converges to maximum likelihood estimate. A second solution to for achieving robust estimation is presented. It considers the limiting case when the upper and lower bounds tend to zero and leads to B-robust estimators. Because of the complexity of the Lévy distributions, this leads to identification problems.

EPFL2008

Complex event recognition through wearable sensors

Jean-Eudes Marie Ranvier

Complex events are instrumental in understanding advanced behaviours and properties of a system. They can represent more meaningful events as compared to simple events. In this thesis we propose to use wearable sensor signals to detect complex events. These signals are pertaining to the user's state and therefore allow us to understand advanced characteristics about her. We propose a hierarchical approach to detect simple events from the wearable sensors data and then build complex events on top of them. In order to address privacy concerns that rise from the use of sensitive signals, we propose to perform all the computation on device. While this ensures the privacy of the data, it poses the problem of having limited computational resources. This problem is tackled by introducing energy efficient approaches based on incremental algorithms. A second challenge is the multiple levels of noise in the process. A first level of noise concerns the raw signals that are inherently imprecise (e.g. inaccuracy in GPS readings). A second level of noise, that we call semantic noise, is present among the simple events detected. Some of these simple events can disturb the detection of complex events effectively acting as noise. We apply the hierarchical approach in two different contexts defining the two different parts of our thesis. In the first part, we present a mobile system that builds a representation of the user's life. This system is based on the episodic memory model, which is responsible for the storage and recollection of past experiences. Following the hierarchical approach, the system processes raw signals to detect simple events such as places where the user stayed a certain amount of time to perform an activity, therefore building sequences of detected activities. These activities are in turn processed to detect complex events that we call routines and that represent recurrent patterns in the life of the user. In the second part of this thesis, we focus on the detection of glycemic events for diabetes type-1 patients in a non-invasive manner. Diabetics are not able to properly regulate their glucose, leading to periods of high and low blood sugar. We leverage signals (Electrocardiogram (ECG), accelerometer, breathing rate) from a sport belt to infer such glycemic events. We propose a physiological model based on the variations of the ECG when the patient has low blood sugar, and an energy-based model that computes the current glucose level of the user based on her glucose intake, insulin intake and glucose consumption via physical activity. For both contexts, we evaluate our systems in term of accuracy by assessing wether the detected routines are meaningful, and wether the glycemic events are correctly detected, and in term of mobile performance, which confirms the fitness of our approaches for mobile computation.

EPFL2017