Alarm-Based Prescriptive Process Monitoring

Predictive process monitoring is concerned with the analysis of events produced during the execution of a process in order to predict the future state of ongoing cases thereof. Existing techniques in this field are able to predict, at each step of a case, the likelihood that the case will end up in an undesired outcome. These techniques, however, do not take into account what process workers may do with the generated predictions in order to decrease the likelihood of undesired outcomes. This paper proposes a framework for prescriptive process monitoring, which extends predictive process monitoring approaches with the concepts of alarms, interventions, compensations, and mitigation effects. The framework incorporates a parameterized cost model to assess the cost-benefit tradeoffs of applying prescriptive process monitoring in a given setting. The paper also outlines an approach to optimize the generation of alarms given a dataset and a set of cost model parameters. The proposed approach is empirically evaluated using a range of real-life event logs

A hybrid approach to classification with shapelets

Shapelets are phase independent subseries that can be used to discriminate between time series. Shapelets have proved to be very effective primitives for time series classification. The two most prominent shapelet based classification algorithms are the shapelet transform (ST) and learned shapelets (LS). One significant difference between these approaches is that ST is data driven, whereas LS searches the entire shapelet space through stochastic gradient descent. The weakness of the former is that full enumeration of possible shapelets is very time consuming. The problem with the latter is that it is very dependent on the initialisation of the shapelets. We propose hybridising the two approaches through a pipeline that includes a time constrained data driven shapelet search which is then passed to a neural network architecture of learned shapelets for tuning. The tuned shapelets are extracted and formed into a transform, which is then classified with a rotation forest. We show that this hybrid approach is significantly better than either approach in isolation, and that the resulting classifier is not significantly worse than a full shapelet search

Application des recommandations dans la prise en charge du cancer de l’endomètre en pratique clinique. Étude rétrospective bretonne

National audienceObjective - To assess the use of French Cancer Institute recommendations for the diagnosis and treatment of endometrial cancer. Methods - Retrospective observational study involving 137 patients with endometrial cancer between 2011 and 2013. Results - Both MRI and pathological assessment with correct report as recommended were used for 66.4% of patients with endometrial cancer. For patients with correct preoperative assessment, 44.9% of patients were uncorrectly classified and upgraded on final pathological analysis of hysterectomy concerning lymph node involvement risk. These patients did not have confident surgical assessment, according this risk. Conclusion - To improve relevance of preoperative assessment in endometrial cancer, radiological and pathological expertise is required. However, even performed optimally, preoperative assessment does not allow correct risk stratification of lymph node involvement in endometrial cancer. This ineffective stratification leads to propose sentinel lymph node biopsy with hysterectomy in case of preoperative low risk endometrial cancer assessment

Sensor networks for ambient intelligence

Due to rapid advances in networking and sensing technology we are witnessing a growing interest in sensor networks, in which a variety of sensors are connected to each other and to computational devices capable of multimodal signal processing and data analysis. Such networks are seen to play an increasingly important role as key enablers in emerging pervasive computing technologies. In the rst part of this paper we give an overview of recent developments in the area of multimodal sensor networks, paying special attention to ambient intelligence applications. In the second part, we discuss how the time series generated by data streams emanating from the sensors can be mined for temporal patterns, indicating cross-sensor signal correlations. I

Searching for Temporal Patterns in AmI Sensor Data

Abstract. Anticipation is a key property of human-human communication, and it is highly desirable for ambient environments to have the means of anticipating events to create a feeling of responsiveness and intelligence in the user. In a home or work environment, a great number of low-cost sensors can be deployed to detect simple events: the passing of a person, the usage of an object, the opening of a door. The methods that try to discover re-usable and interpretable patterns in temporal event data have several shortcomings. Using a testbed that we have developed for this purpose, we first contrast current approaches to the problem. We then extend the best of these approaches, the T-Pattern algorithm, with Gaussian Mixture Models, to obtain a fast and robust algorithm to find patterns in temporal data. Our algorithm can be used to anticipate future events, as well as to detect unexpected events as they occur.

Localized Random Shapelets

International audienceShapelet models have attracted a lot of attention from researchers in the time series community, due in particular to its good classification performance. However, such models only inform about the presence / absence of local temporal patterns. Structural information about the localization of these patterns is ignored. In addition, end-to-end learning shapelet models tend to generate meaningless shapelets, leading to poorly interpretable models. In this paper, we aim at designing an interpretable shapelet model that takes into account the localization of the shapelets in the time series. Time series are transformed into feature vectors composed of both a distance and a localization information. Then, we design a hierarchical feature selection process using regular-ization. This process can be tuned to select, for each shapelet, either only its distance information or both distance and localization information. It is hence possible for every selected shapelet to analyze whether only the presence or the presence and the localization contributed to the decision process improving interpretability of the decision. Experiments show that this feature selection process has competitive performance compared to state-of-the-art shapelet-based classifiers, while providing better interpretability

Demand Forecasting in the Presence of Privileged Information

Predicting the amount of sales in the future is a fundamental problem in the replenishment process of retail companies. Models for forecasting the demand of an item typically rely on influential features and historical sales of the item. However, the values of some influential features (to which we refer as non-plannable features) are only known during model training (for the past), and not for the future at prediction time. Examples of such features include sales in other channels, such as other stores in chain supermarkets. Existing forecasting methods ignore such non-plannable features or wrongly assume that they are also known at prediction time. We identify non-plannable features as privileged information, i.e., information that is available at training time but not at prediction time, and design a neural network to leverage this source of data accordingly. We present a dual branch neural network architecture that incorporates non-plannable features at training time, with a first branch to embed the historical information, and a second branch, the privileged information (PI) branch, to predict demand based on privileged information. Next, we leverage a single branch network at prediction time, which applies a simulation component to mimic the behavior of the PI branch, whose inputs are not available at prediction time. We evaluate our approach on two real-world forecasting datasets, and find that it outperforms state-of-the-art competitors in terms of mean absolute error and symmetric mean absolute percentage error metrics. We further provide visualizations and conduct experiments to validate the contribution of different components in our proposed architecture

A prospective study of the frequency of severe pain and predictive factors in women undergoing first-trimester surgical abortion under local anaesthesia

International audienceObjective - To determine the frequency of severe pain among women and to identify the associated predictive factors during first-trimester surgical abortion under local anaesthesia (LA).Study design - A prospective cohort study from November 2013 to January 2014 at the Department of Gynecology and Obstetrics, Rennes, France. The study population was composed of one hundred and ninety-four patients who underwent an elective first-trimester surgical abortion under LA. In an anonymized questionnaire, the participants were asked to self-record their perceived pain level 30 min after the completion of the procedure using a 10 cm visual analogue scale (VAS). The main outcome measure was the frequency of severe pain among women, defined as VAS ≥ 7. Secondary outcome measure was the risk factor(s) for severe pain.Results - Severe pain (i.e. VAS ≥ 7) was experienced by 46% (95% CI: 39%-53%) of the population. Multivariate analysis confirmed that >10 weeks of gestation (OR: 2.530 [95% CI: 1.1-5.81], p = .0287) and having 0 or 1 child (OR: 5.206 [95% CI: 1.87-14.49], p = .0016) were significant independent factors of severe pain.Conclusion - Nearly half of the women experienced severe pain. More than 10 weeks of gestation and parity were predictive factors of severe pain. These findings should be useful in counselling women undergoing surgical abortion under LA.<br

Identifying seasonal patterns of phosphorus storm dynamics with dynamic time warping

International audiencePhosphorus (P) transfer during storm events represents a significant part of annual P loads in streams and contributes to eutrophication in downstream water bodies. To improve understanding of P storm dynamics, automated or semiautomated methods are needed to extract meaningful information from ever-growing water quality measurement data sets. In this paper, seasonal patterns of P storm dynamics are identified in two contrasting watersheds (arable and grassland) through Dynamic Time Warping (DTW) combined with k-means clustering. DTW was used to align discharge time series of different lengths and with differences in phase, which allowed robust application of a k-means clustering algorithm on rescaled P time series. In the arable watershed, the main storm pattern identified from autumn to winter displayed distinct export dynamics for particulate and dissolved P, which suggests independent transport mechanisms for both P forms. Conversely, the main storm pattern identified in spring displayed synchronized export of particulate and dissolved P. In the grassland watershed, the occurrence of synchronized export of dissolved and particulate P forms was not related to the season, but rather to the amplitude of storm events. Differences between the seasonal distributions of the patterns identified for the two watersheds were interpreted in terms of P sources and transport pathways. The DTW-based clustering algorithm used in this study proved useful for identifying common patterns in water quality time series and for isolating unusual events. It will open new possibilities for interpreting the high-frequency and multiparameter water quality time series that are currently acquired worldwide