Community-Based Prediction of Activity Decay in a Social Network

Virtuaalsete sotsiaalvõrgustike haldajate seisukohalt on oluline tuvastada kasutajaid, kes kaotavad suure tõenäosusega lähitulevikus huvi nende teenuse vastu. Selliste kasutajate ennustamine lubab suunata neile kampaaniaid, hoidmaks või suurendamaks aktiivsust võrgustikus. Nimetatud probleemi lahendatakse sageli masinõppemeetodite abil, tehes ennustusi üksikisiku tasandil. Olemasolevad lahendused ei kasuta aga maksimaalselt ära kasutajate omavahelisi suhteid. Selles kontekstis tutvustame uut lähenemist, ennustamaks aktiivsuse langust kogukondade ehk omavahel tihedalt seotud kasutajate gruppide tasandil. Antud töös kasutame kahte meetodit kogukondade leidmiseks ning võrdleme tulemusi üksikkasutajate ja juhuslike kasutajate gruppidega. Analüüs näitab, et teenusest loobuda plaanivaid kasutajaid on lihtsam leida kogukondade kui üksikisiku tasandil. Tulemused näitavad, et ennustuste kvaliteet sõltub ka kasutatud kogukondade leidmise algoritmist. Meetod, mis leiab kogukonnad lokaalsel tasandil, lähtudes iga kasutaja otsesest suhtlusringkonnast, võimaldab paremaid ennustusi kui võrgustikule tervikuna orienteeritud meetod. Lisaks eelmainitule võimaldab kogukonnapõhine analüüs arvesse võtta täiendavaid tunnuseid, saamaks täpsemaid ennustusi. Saadud tulemused on aluseks uute kogukonnapõhiste meetodite väljatöötamisele, analüüsimaks kasutajate aktiivsust sotsiaalvõrgustikes ning tõstmaks turunduskampaaniate efektiivsust.An important problem for facilitators of online social networks is to identify the users who are likely to decrease their level of activity in the near future. Such predictions are the basis for targeted campaigns aimed at sustaining or increasing the overall user engagement in the network. A common approach to this problem is to apply machine learning methods to make predictions at the level of individual users. The existing approaches, however, do not consider the social connections of the individuals to their full extent, leaving room for improvement. In this context, we propose a new approach to the problem of activity decay prediction based on the idea of identifying groups of tightly inter-linked users (namely communities) where the level of social activity is likely to decay. We investigate two community detection methods and compare the resulting predictive accuracy against several baselines. We show that more individuals who are likely to decay can be reached by targeting communities instead of single users. Moreover, a bottom-up community detection method produces higher accuracy in this context than a top-down modularity-based approach. Additionally, a richer set of features related to user engagement can be used for prediction purposes, leading to more accurate predictions. The results pave the way for designing community-based approaches to analyze user engagement in social networks as well as associated community-based targeting methods

Äriprotsessi tulemuste ennustav ja korralduslik seire

Viimastel aastatel on erinevates valdkondades tegutsevad ettevõtted üles näidanud kasvavat huvi masinõppel põhinevate rakenduste kasutusele võtmiseks. Muuhulgas otsitakse võimalusi oma äriprotsesside efektiivsuse tõstmiseks, kasutades ennustusmudeleid protsesside jooksvaks seireks. Sellised ennustava protsessiseire meetodid võtavad sisendiks sündmuslogi, mis koosneb hulgast lõpetatud äriprotsessi juhtumite sündmusjadadest, ning kasutavad masinõppe algoritme ennustusmudelite treenimiseks. Saadud mudelid teevad ennustusi lõpetamata (antud ajahetkel aktiivsete) protsessijuhtumite jaoks, võttes sisendiks sündmuste jada, mis selle hetkeni on toimunud ning ennustades kas järgmist sündmust antud juhtumis, juhtumi lõppemiseni jäänud aega või instantsi lõpptulemust. Lõpptulemusele orienteeritud ennustava protsessiseire meetodid keskenduvad ennustamisele, kas protsessijuhtum lõppeb soovitud või ebasoovitava lõpptulemusega. Süsteemi kasutaja saab ennustuste alusel otsustada, kas sekkuda antud protsessijuhtumisse või mitte, eesmärgiga ära hoida ebasoovitavat lõpptulemust või leevendada selle negatiivseid tagajärgi. Erinevalt puhtalt ennustavatest süsteemidest annavad korralduslikud protsessiseire meetodid kasutajale ka soovitusi, kas ja kuidas antud juhtumisse sekkuda, eesmärgiga optimeerida mingit kindlat kasulikkusfunktsiooni. Käesolev doktoritöö uurib, kuidas treenida, hinnata ja kasutada ennustusmudeleid äriprotsesside lõpptulemuste ennustava ja korraldusliku seire raames. Doktoritöö pakub välja taksonoomia olemasolevate meetodite klassifitseerimiseks ja võrdleb neid katseliselt. Lisaks pakub töö välja raamistiku tekstiliste andmete kasutamiseks antud ennustusmudelites. Samuti pakume välja ennustuste ajalise stabiilsuse mõiste ning koostame raamistiku korralduslikuks protsessiseireks, mis annab kasutajatele soovitusi, kas protsessi sekkuda või mitte. Katsed näitavad, et väljapakutud lahendused täiendavad olemasolevaid meetodeid ning aitavad kaasa ennustava protsessiseire süsteemide rakendamisele reaalsetes süsteemides.Recent years have witnessed a growing adoption of machine learning techniques for business improvement across various fields. Among other emerging applications, organizations are exploiting opportunities to improve the performance of their business processes by using predictive models for runtime monitoring. Such predictive process monitoring techniques take an event log (a set of completed business process execution traces) as input and use machine learning techniques to train predictive models. At runtime, these techniques predict either the next event, the remaining time, or the final outcome of an ongoing case, given its incomplete execution trace consisting of the events performed up to the present moment in the given case. In particular, a family of techniques called outcome-oriented predictive process monitoring focuses on predicting whether a case will end with a desired or an undesired outcome. The user of the system can use the predictions to decide whether or not to intervene, with the purpose of preventing an undesired outcome or mitigating its negative effects. Prescriptive process monitoring systems go beyond purely predictive ones, by not only generating predictions but also advising the user if and how to intervene in a running case in order to optimize a given utility function. This thesis addresses the question of how to train, evaluate, and use predictive models for predictive and prescriptive monitoring of business process outcomes. The thesis proposes a taxonomy and performs a comparative experimental evaluation of existing techniques in the field. Moreover, we propose a framework for incorporating textual data to predictive monitoring systems. We introduce the notion of temporal stability to evaluate these systems and propose a prescriptive process monitoring framework for advising users if and how to act upon the predictions. The results suggest that the proposed solutions complement the existing techniques and can be useful for practitioners in implementing predictive process monitoring systems in real life

Clustering-Based Predictive Process Monitoring

Business process enactment is generally supported by information systems that record data about process executions, which can be extracted as event logs. Predictive process monitoring is concerned with exploiting such event logs to predict how running (uncompleted) cases will unfold up to their completion. In this paper, we propose a predictive process monitoring framework for estimating the probability that a given predicate will be fulfilled upon completion of a running case. The predicate can be, for example, a temporal logic constraint or a time constraint, or any predicate that can be evaluated over a completed trace. The framework takes into account both the sequence of events observed in the current trace, as well as data attributes associated to these events. The prediction problem is approached in two phases. First, prefixes of previous traces are clustered according to control flow information. Secondly, a classifier is built for each cluster using event data to discriminate between fulfillments and violations. At runtime, a prediction is made on a running case by mapping it to a cluster and applying the corresponding classifier. The framework has been implemented in the ProM toolset and validated on a log pertaining to the treatment of cancer patients in a large hospital

Fire now, fire later: alarm-based systems for prescriptive process monitoring

Predictive process monitoring is a family of techniques to analyze events produced during the execution of a business process in order to predict the future state or the final outcome of running process instances. Existing techniques in this field are able to predict, at each step of a process instance, the likelihood that it will lead to an undesired outcome. These techniques, however, focus on generating predictions and do not prescribe when and how process workers should intervene to decrease the cost of undesired outcomes. This paper proposes a framework for prescriptive process monitoring, which extends predictive monitoring with the ability to generate alarms that trigger interventions to prevent an undesired outcome or mitigate its effect. The framework incorporates a parameterized cost model to assess the cost–benefit trade-off of generating alarms. We show how to optimize the generation of alarms given an event log of past process executions and a set of cost model parameters. The proposed approaches are empirically evaluated using a range of real-life event logs. The experimental results show that the net cost of undesired outcomes can be minimized by changing the threshold for generating alarms, as the process instance progresses. Moreover, introducing delays for triggering alarms, instead of triggering them as soon as the probability of an undesired outcome exceeds a threshold, leads to lower net costs.Estonian Research Competency Council http://dx.doi.org/10.13039/501100005189H2020 European Research Council http://dx.doi.org/10.13039/100010663Peer Reviewe

Predictive Business Process Monitoring with Structured and Unstructured Data

Predictive business process monitoring is concerned with continuously analyzing the events produced by the execution of a business process in order to predict as early as possible the outcome of each ongoing case thereof. Previous work has approached the problem of predictive process monitoring when the observed events carry structured data payloads consisting of attribute-value pairs. In practice, structured data often comes in conjunction with unstructured (textual) data such as emails or comments. This paper presents a predictive process monitoring framework that combines text mining with sequence classification techniques so as to handle both structured and unstructured event payloads. The framework has been evaluated with respect to accuracy, prediction earliness and efficiency on two real-life datasets