An Eye into the Future: Leveraging A-Priori Knowledge in Predictive Business Process Monitoring

Ikka leidub juhtumeid, kus lisaks andmetele minevikust, eksisteerib täiendavaid teadmisi (apriori teadmised) selle kohta, kuidas protsessid teostuvad tulevikus. Neid teadmisi saab kasutada selleks, et parandada tuleviku ennustusi juhtumitele, mille kohta ei ole olevikus informatsiooni. Käesolevas töös tutvustame kahte meetodit - nad põhinevad rekurrentsetel tehisnärvivõrkudel, mis kasutavad pika lühiajalise mäluga (PLM) rakke. Need meetodid kasutavad informatsiooni protsessiteostusjuhtumite struktuuri kohta ja a priori teadmisi protsessi võimalike tulemite kohta, et ennustada järgmisi juhtumeid protsessi teostuse ahelas. Testides neid meetodeid kuue elulise sündmuste logiga näitavad meetodite täpsuse paranemist võrreldes tavaliste PLM-il põhinevate meetoditega.Predictive business process monitoring aims at leveraging past process execution data to predict how ongoing (uncompleted) process executions will unfold up to their completion. Nevertheless, cases exist in which, together with pastexecution data, some additional knowledge (a-prioriknowledge) about how a process execution will develop in the future is available. This knowledge about the future can be leveraged forimproving the quality of the predictions of events that are currently unknown. In this thesis, we present two techniques - based on Recurrent Neural Networks with Long Short-Term Memory (LSTM) cells - able to leverage knowledge about the structure of the process execution traces as well as a-priori knowledge about how they will unfold in the future for predicting the sequence of future activities of ongoing process executions. The results obtained by applying these techniques on six real-life logs show an improvement in terms of accuracy over a plain LSTM-based baseline

Peculiarities of the preparation and distribution of the workforce in the late 1970 -the first half of the 1980s. (on the materials of the Astrakhan region)

В статье дается характеристика особенностей подготовки и распределения рабочих кадров в Астраханской области в конце ХХ в. Указаны сильные и слабые стороны системы профессионально технического образования. Приведены цифровые данные.The article is devoted to the peculiarities of the preparation and distribution of the workforce in the Astrakhan region in the late twentieth century, the strengths and weaknesses of the system of professional technical education. Digital data is provided

Surface Plasmon Enhanced Photoluminescence of Rhodamine 6G on Au Nanoparticles 2D Array: Temperature Effects

Influence of temperature on the photoluminescence of rhodamine 6G deposited on 2D array of the gold nanoparticles was studied in the temperature range 78–278 K. The factor of surface plasmonic enhancement of rhodamine luminescence was found to decrease monotonically with increasing temperature. Electron-phonon scattering and thermal expansion of the gold nanoparticles were considered as two competing physical mechanisms of the temperature dependence of plasmonic enhancement factor. The calculations showed the significant prevalence of the electron-phonon scattering that causes the temperature induced decrease of plasmonic enhancement of rhodamine 6G luminescence observed

Діалог у новелі абсурду

The paper deals with the specifi c features of dialogs in contemporary Ukrainian short stories. The author stresses the non-communicative nature of absurdist poetics.У статті розглянуто особливості діалогів у сучасній українській новелістиці. Акцент зроблено на некомунікативності як складникові поетики абсурду

Comprehensive process drift analysis with the visual drift detection tool

Recent research has introduced ideas from concept drift into process mining to enable the analysis of changes in business processes over time. This stream of research, however, has not yet addressed the challenges of drift categorization, drilling-down, and quantification. In this tool demonstration paper, we present a novel software tool to analyze process drifts, called Visual Drift Detection (VDD), which fulfills these requirements. The tool is of benefit to the researchers and practitioners in the business intelligence and process analytics area, and can constitute a valuable aid to those who are involved in business process redesign endeavors