2,154 research outputs found
An Overview of the Use of Neural Networks for Data Mining Tasks
In the recent years the area of data mining has experienced a considerable demand for technologies that extract knowledge from large and complex data sources. There is a substantial commercial interest as well as research investigations in the area that aim to develop new and improved approaches for extracting information, relationships, and patterns from datasets. Artificial Neural Networks (NN) are popular biologically inspired intelligent methodologies, whose classification, prediction and pattern recognition capabilities have been utilised successfully in many areas, including science, engineering, medicine, business, banking, telecommunication, and many other fields. This paper highlights from a data mining perspective the implementation of NN, using supervised and unsupervised learning, for pattern recognition, classification, prediction and cluster analysis, and focuses the discussion on their usage in bioinformatics and financial data analysis tasks
A Web Tool for the Comparison of Predictive Process Monitoring Algorithms
Ennetava protsessi jĂ€lgimine analĂŒĂŒsib sĂŒndmuste logi, mille eesmĂ€rk on prognoosida kriitilisi Ă€rimÔÔdikuid aja, kulude ja protsessi tulemuste pĂ”hjal. Mitmed ennustamise tehnikad ja lĂ€henemisviisid on vĂ€lja töötatud nii akadeemilises kui ka tööstussektorites, et pakkuda kasutajatele arusaadavaid ennustusi. Selles magistritöös tutvustame veebipĂ”hist tööriista, et vĂ”rrelda prognoositavate algoritmiseirete protsesse. See pakub teadlastele vĂ”i selles valdkonnas olevatele lĂ”ppkasutajatele lihtsamaid viise valimaks kindlale logile sobivat prognoosimisviisi. See projekt kasutab jĂ€rjestavat sĂŒsteemi, mis suudab samal ajal luua erinevaid prognoosimudeleid. NĂ€itame erinevate prognoosimudelite tulemusi kasutades visuaalset vĂ”rdlust, mis vĂ”imaldab hinnata iga ennustatavat mudelit. Uued funktsioonid on seadistatud veebirakenduses, mis vĂ”imaldavad kasutajatel seadistada ja kĂ€ivitada jĂ€rjestava sĂŒsteemi ĂŒlesandeid ja seejĂ€rel nĂ€idata tulemusi. Rakendust on hinnatud pĂ€riselu logi pĂ”hjal, mis on seotud haiglas olevate sepsisehaigete patsientide raviprotseduuriga.Predictive Process Monitoring analyzes an event log aiming to predict critical business metrics as time, cost and process outcomes. Various techniques and approaches of predictions were developed in both academia and industry sectors in order to provide understandable predictions to the users. In this Masterâs Thesis, we introduce a web based tool for the comparison of predictive process monitoring algorithms which provides researchers or end users involved in this field an easier way for choosing the suitable prediction approach to a certain log. This project uses a queuing system which is able to build different predictive models at the same time. We show the results of different predictive models with a visual comparison that allows the evaluation of each predictive model. The new functionalities have been implemented in a web application, which allows users to configure and trigger the tasks of the queuing system and shows the results. The application has been evaluated on a real-life log pertaining to the treatment process of sepsis patients in a hospital
Genetic algorithms for hyperparameter optimization in predictive business process monitoring
Predictive business process monitoring exploits event logs to predict how ongoing (uncompleted) traces will unfold up to their completion. A predictive process monitoring framework collects a range of techniques that allow users to get accurate predictions about the achievement of a goal for a given ongoing trace. These techniques can be combined and their parameters configured in different framework instances. Unfortunately, a unique framework instance that is general enough to outperform others for every dataset, goal or type of prediction is elusive. Thus, the selection and configuration of a framework instance needs to be done for a given dataset. This paper presents a predictive process monitoring framework armed with a hyperparameter optimization method to select a suitable framework instance for a given dataset
LSTM Networks for Data-Aware Remaining Time Prediction of Business Process Instances
Predicting the completion time of business process instances would be a very
helpful aid when managing processes under service level agreement constraints.
The ability to know in advance the trend of running process instances would
allow business managers to react in time, in order to prevent delays or
undesirable situations. However, making such accurate forecasts is not easy:
many factors may influence the required time to complete a process instance. In
this paper, we propose an approach based on deep Recurrent Neural Networks
(specifically LSTMs) that is able to exploit arbitrary information associated
to single events, in order to produce an as-accurate-as-possible prediction of
the completion time of running instances. Experiments on real-world datasets
confirm the quality of our proposal.Comment: Article accepted for publication in 2017 IEEE Symposium on Deep
Learning (IEEE DL'17) @ SSC
Predictive Monitoring of Multi-level Processes
InfosĂŒsteemide laialdane kasutamine jĂ€rjest rohkemates valdkondades tekitab aina suuremaid salvestatavaid andmemahte. Organisatsioonide ja Ă€ride efektiivsuse kasvuga tekib suurem vajadus leida alternatiivseid viise konkurentsieelisteks. JĂ€rjest rohkem hakatakse antud infoajastul otsima Ă€rilist vÀÀrtust andmetest. Protsessikaeve meetodeid kasutades ĂŒritatakse justnimelt seda teha, kuid Ă€riprotsesside arenedes muutuvad keerukamaks ka andmed, mis neid protsesse kirjeldavad. Hetkel keskendutakse protsessikaeve uurimustes protsessidele, mida on vĂ”imalik vĂ€ljendada jĂ€rjestikkuste sĂŒndmuste jadana. KĂ€esolevas magistritöös esitatakse uudne lĂ€henemine Ă€riprotsesside ennustava seire rakendamiseks mitmetasandilistele Ă€riprotsessidele, mis sisaldavad paralleelseid alamprotsesse ning mida pole vĂ”imalik sĂŒndmuste jĂ€rjendina vĂ€ljendada. VĂ€ljapakutud meetodi suutlikkuse hindamiseks rakendatakse antud meetodit elulisel andmestikul telekommunikatsiooni tegevusalalt. Tulemusi vĂ”rreldakse lĂ€henemisega, mida kasutatakse ĂŒhetasandiliste Ă€riprotsesside ennustavaks seireks.The ever increasing use of Information Systems causes ever more information to be stored. As organizations and businesses become more efficient due to competition they need to gain competitive advantage over others. More and more companies and institutions have turned to Information Technology to find business value in a data-driven world. Modern Information Systems maintain records of process events, which correspond to real-life activities. As processes evolve and become more complex, so does the information that reflects them. In this thesis, we propose an approach to predictive monitoring of complex multi-level processes. In this context, a multi-level process consists of a high-level parent process which spawns multiple low-level subprocesses, which have their own life cycle and run independently of one another. The author proposes constructs called milestones, which include both parent- and subprocesses and are used for the predictive monitoring classification task. This approach has been validated on a real-life event log of the business-to-business change management process in place at Baltic's largest telecommunications company Telia Estonia
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