Äriprotsesside ajaliste näitajate selgitatav ennustav jälgimine

Kaasaegsed ettevõtte infosüsteemid võimaldavad ettevõtetel koguda detailset informatsiooni äriprotsesside täitmiste kohta. Eelnev koos masinõppe meetoditega võimaldab kasutada andmejuhitavaid ja ennustatavaid lähenemisi äriprotsesside jõudluse jälgimiseks. Kasutades ennustuslike äriprotsesside jälgimise tehnikaid on võimalik jõudluse probleeme ennustada ning soovimatu tegurite mõju ennetavalt leevendada. Tüüpilised küsimused, millega tegeleb ennustuslik protsesside jälgimine on “millal antud äriprotsess lõppeb?” või “mis on kõige tõenäolisem järgmine sündmus antud äriprotsessi jaoks?”. Suurim osa olemasolevatest lahendustest eelistavad täpsust selgitatavusele. Praktikas, selgitatavus on ennustatavate tehnikate tähtis tunnus. Ennustused, kas protsessi täitmine ebaõnnestub või selle täitmisel võivad tekkida raskused, pole piisavad. On oluline kasutajatele seletada, kuidas on selline ennustuse tulemus saavutatud ning mida saab teha soovimatu tulemuse ennetamiseks. Töö pakub välja kaks meetodit ennustatavate mudelite konstrueerimiseks, mis võimaldavad jälgida äriprotsesse ning keskenduvad selgitatavusel. Seda saavutatakse ennustuse lahtivõtmisega elementaarosadeks. Näiteks, kui ennustatakse, et äriprotsessi lõpuni on jäänud aega 20 tundi, siis saame anda seletust, et see aeg on moodustatud kõikide seni käsitlemata tegevuste lõpetamiseks vajalikust ajast. Töös võrreldakse omavahel eelmainitud meetodeid, käsitledes äriprotsesse erinevatest valdkondadest. Hindamine toob esile erinevusi selgitatava ja täpsusele põhinevale lähenemiste vahel. Töö teaduslik panus on ennustuslikuks protsesside jälgimiseks vabavaralise tööriista arendamine. Süsteemi nimeks on Nirdizati ning see süsteem võimaldab treenida ennustuslike masinõppe mudeleid, kasutades nii töös kirjeldatud meetodeid kui ka kolmanda osapoole meetodeid. Hiljem saab treenitud mudeleid kasutada hetkel käivate äriprotsesside tulemuste ennustamiseks, mis saab aidata kasutajaid reaalajas.Modern enterprise systems collect detailed data about the execution of the business processes they support. The widespread availability of such data in companies, coupled with advances in machine learning, have led to the emergence of data-driven and predictive approaches to monitor the performance of business processes. By using such predictive process monitoring approaches, potential performance issues can be anticipated and proactively mitigated. Various approaches have been proposed to address typical predictive process monitoring questions, such as what is the most likely continuation of an ongoing process instance, or when it will finish. However, most existing approaches prioritize accuracy over explainability. Yet in practice, explainability is a critical property of predictive methods. It is not enough to accurately predict that a running process instance will end up in an undesired outcome. It is also important for users to understand why this prediction is made and what can be done to prevent this undesired outcome. This thesis proposes two methods to build predictive models to monitor business processes in an explainable manner. This is achieved by decomposing a prediction into its elementary components. For example, to explain that the remaining execution time of a process execution is predicted to be 20 hours, we decompose this prediction into the predicted execution time of each activity that has not yet been executed. We evaluate the proposed methods against each other and various state-of-the-art baselines using a range of business processes from multiple domains. The evaluation reaffirms a fundamental trade-off between explainability and accuracy of predictions. The research contributions of the thesis have been consolidated into an open-source tool for predictive business process monitoring, namely Nirdizati. It can be used to train predictive models using the methods described in this thesis, as well as third-party methods. These models are then used to make predictions for ongoing process instances; thus, the tool can also support users at runtime

Prediction of Product Adoption in Social Networks Using the Network Value of Users

Käesolevas töös uurime uue toote kasutuselevõtmist sotsiaalvõrgustikus, eesmärgiga tuvastada grupp kasutajaid kellele suunatud turunduskampaania oleks võimalikult suure efektiivsusega ning mille tagajärjel suureneks toote kasutajate arv. Alusmudelina kasutame olemasolevat meetodit hindamaks kasutajate individuaalset tõenäosust toote kasutuselevõtuks. Mudelit treenitakse ja hinnatakse ajaliselt eraldatud andmetel. Saadud mudeli täpsus on oluliselt parem kui kasutada juhuslikku arvamist. Mudeli analüüsil avastame, et eksisteerib tugev surve kaaslastelt toote kasutuselevõtuks. Me hindame kasutajate omavahelist mõju üksteisele analüüsides ajaliselt korreleeritud toote tarvituselevõtu omadusi. Me rakendame seda mudelis, mis tuvastab mõjukad kasutajad võrgustikus, kellel on võime veenda oma kaaslasi toodet kasutama. Töös tutvustame kasutaja kasulikkuse mõistet, mis ühendab kasutaja individuaalse tõenäosuse toote kasutuselevõtuks ja tema võimalikku mõju kaaslastele võrgustikus. Kasutades simuleeritud turunduskampaaniat andmetel, me näitame, et sihtides sama arvu kasutajaid, on kõrge kasulikkusega kasutajate sihtimise tulemusena tootel rohkem uusi kasutajaid kui kasutada ainult kasutaja individuaalse tõenäosuse või mõjupõhist mudelit, mis kinnitab meetodi suuremat praktilist väärtust.In this work we study the adoption of a product in a social network with the purpose of determining the set of users to target during a marketing campaign to maximize the campaign return. As a baseline, we use a model to estimate users' propensity to adopt the product. The model is trained and evaluated on temporally split data and shows a significant lift over random guessing. We also find the strong evidence of the peer pressure in our network. To utilize the network value of users, we infer interpersonal influence with the notion of temporally correlated adoptions. Then we design a model to determine influential network users, who, given that they adopt the product, will trigger subsequent adoptions among their friends. Finally, we introduce the concept of a users' utility that combines users' propensity to adopt the product with their potential influence on their friends. On a simulated marketing campaign we show that targeting a fixed number of high-utility users results in more adoptions, than targeting either highly influential users or users with high propensity to adopt, which confirms the practical value of our complementary approach

White-box prediction of process performance indicators via flow analysis

Predictive business process monitoring methods exploit historical process execution logs to provide predictions about running instances of a process, which enable process workers and managers to preempt performance issues or compliance violations. A number of approaches have been proposed to predict quantitative process performance indicators, such as remaining cycle time, cost, or probability of deadline violation. However, these approaches adopt a black-box approach, insofar as they predict a single scalar value without decomposing this prediction into more elementary components. In this paper, we propose a white-box approach to predict performance indicators of running process instances. The key idea is to first predict the performance indicator at the level of activities, and then to aggregate these predictions at the level of a process instance by means of flow analysis techniques. The paper specifically develops this idea in the context of predicting the remaining cycle time of ongoing process instances. The proposed approach has been evaluated on four real-life event logs and compared against several baselines

A General Framework for Predictive Business Process Monitoring

Abstract. As organizations gain awareness of the potential business value locked in their process execution event logs, "evidence-based" business process management (BPM) becomes a common tool for process analysts. In contrast to traditional process monitoring techniques which are typically performed using data from running process instances only, predictive evidence-based BPM methods tap also into historical data, to allow process workers to respond, in real-time, to specific process performance issues and compliance violations as they arise or even before they arise. In previous work, various approaches have been proposed to address typical predictive process monitoring problems, such as whether a running process instance will meet its performance targets, or when will an instance be finally finished. However, these approaches are rather ad-hoc and lack generality, as they tackle only particular, pre-defined aspects of predictive monitoring and often only work with specific characteristics of the dataset. The proposed research project aims at developing a general and robust framework for predictive process monitoring that will address a variety of process monitoring tasks such as predicting the outcome of individual activities or of the whole process instance, or predicting the completion path of an instance

Explainable predictive monitoring of temporal measures of business processes

This thesis explores data-driven, predictive approaches to monitor business process performance. These approaches allow process stakeholders to prevent or mitigate potential performance issues or compliance violations in real time, as early as possible. To help users understand the rationale for the predictions and build trust in them, the thesis proposes two techniques for explainable predictive process monitoring: one based on deep learning, the other driven by process models. This is achieved by decomposing a prediction into its elementary components. The techniques are compared against state-of-the-art baselines and a trade-off between accuracy and explainability of the predictions is evaluated

A general framework for predictive business process monitoring

As organizations gain awareness of the potential business value locked in their process execution event logs, evidence-based” business process management (BPM) becomes a common tool for process analysts. In contrast to traditional process monitoring techniques which are typically performed using data from running process instances only, predictive evidence-based BPM methods tap also into historical data, to allow process workers to respond, in real-time, to specific process performance issues and compliance violations as they arise or even before they arise. In previous work, various approaches have been proposed to address typical predictive process monitoring problems, such as whether a running process instance will meet its performance targets, or when will an instance be finally finished. However, these approaches are rather ad-hoc and lack generality, as they tackle only particular, pre-defined aspects of predictive monitoring and often only work with specific characteristics of the dataset. The proposed research project aims at developing a general and robust framework for predictive process monitoring that will address a variety of process monitoring tasks such as predicting the outcome of individual activities or of the whole process instance, or predicting the completion path of an instance

Complex symbolic sequence clustering and multiple classifiers for predictive process monitoring

This paper addresses the following predictive business process monitoring problem: Given the execution trace of an ongoing case,and given a set of traces of historical (completed) cases, predict the most likely outcome of the ongoing case. In this context, a trace refers to a sequence of events with corresponding payloads, where a payload consists of a set of attribute-value pairs. Meanwhile, an outcome refers to a label associated to completed cases, like, for example, a label indicating that a given case completed “on time” (with respect to a given desired duration) or “late”, or a label indicating that a given case led to a customer complaint or not. The paper tackles this problem via a two-phased approach. In the first phase, prefixes of historical cases are encoded using complex symbolic sequences and clustered. In the second phase, a classifier is built for each of the clusters. To predict the outcome of an ongoing case at runtime given its (uncompleted) trace, we select the closest cluster(s) to the trace in question and apply the respective classifier(s), taking into account the Euclidean distance of the trace from the center of the clusters. We consider two families of clustering algorithms – hierarchical clustering and k-medoids – and use random forests for classification. The approach was evaluated on four real-life datasets

Minimizing overprocessing waste in business processes via predictive activity ordering

Overprocessing waste occurs in a business process when effort is spent in a way that does not add value to the customer nor to the business. Previous studies have identied a recurrent overprocessing pattern in business processes with so-called "knockout checks", meaning activities that classify a case into "accepted" or "rejected", such that if the case is accepted it proceeds forward, while if rejected, it is cancelled and all work performed in the case is considered unnecessary. Thus, when a knockout check rejects a case, the effort spent in other (previous) checks becomes overprocessing waste. Traditional process redesign methods propose to order knockout checks according to their mean effort and rejection rate. This paper presents a more fine-grained approach where knockout checks are ordered at runtime based on predictive machine learning models. Experiments on two real-life processes show that this predictive approach outperforms traditional methods while incurring minimal runtime overhead