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

On relationships between the logic of law, legal positivism and semiotics of law

The issue of reciprocal relationships between the logic of law, positivistic theory of the logic of law, and legal semiotics is among the most important questions of the modern theoretical jurisprudence. This paper has not attempted to provide any comprehensive account of the modern jurisprudence (and legal logic). Instead, the emphasis has been laid on those aspects of positivist legal theories, logical studies of law and legal semiotics that allow tracing the common points or the differences between these paradigms of legal research. One of the theses of the present work is that, at the comparative methodological level, the limits of legal semiotics and its object of inquiry could only be defined in relation to legal posi tivism and logical studies of law. This paper also argues for a proper position for legal semiotics in between legal positivism and legal logic. The differences between legal positivism, legal logic and legal semiotics are best captured in the issue of referent

ROLE OF OXIDATIVE REACTIVE SPECIES AND ANTIOXIDANTS IN METABOLISM AND TRANSPORT OF THERAPEUTIC DRUGS

Oxidative stress (OS) is a frequent complication of various disease conditions such as Alzheimer’s and Parkinson’s disease, atherosclerosis, preeclampsia, rheumatoid arthritis, diabetes including gestational diabetes, etc. OS is defined as an imbalance between the production of reactive species and the ability of an organism to detoxify the reactive intermediates and repair the damage. As a result of OS, the excess of reactive species such as oxygen superoxide (O2-), hydroxyl radical (OH), peroxynitrite (ONOO−), 4-hydroxynonenal (4HNE), etc., have a tendency to react with nearby proteins/nucleic acids/lipids changing their functionality or inactivating them completely. The organism has many ways to protect itself from the harmful effects of oxidants. One strategy employs antioxidants introduced to the body with food. The purpose of this thesis was to investigate the effect of reactive species on the active transport mediated by ABC efflux transporters as well as exploring the possibility of using antioxidants not as interceptors of reactive species but rather as inhibitors of metabolic enzymes and transporters. The BCRP/ABCG2 efflux transporter was selected for the investigation of the effect of reactive anion, ONOO−, generated during OS and the product of OS, 4HNE, formed after a series of chain reactions involving ROS. Experiments conducted with Sf9 membrane vesicles overexpressing BCRP/ABCG2 revealed that both species are capable of inactivating this ABC transporter with IC50 being 31 ± 2.7 μM and 92 ± 1.4 μM for ONOO− and 4HNE, respectively. In presence of 4HNE, Vmax decreased 4-fold and Km remained unchanged, suggesting a noncompetitive inhibition mechanism. However, with addition of 4HNE, positive cooperativity was also observed. With ONOO−, the situation was different: both Vmax and Km changed consistent with mixed type inhibition. Overall, OS-mediated BCRP/ABCG2 inactivation occurred at biologically relevant concentrations of the reactive species. Antioxidants are substances that are known to reduce the amount of ROS/RNS accumulated during OS, but this research considered the use of antioxidants not only as interceptors of ROS/RNS but rather as inhibitors of metabolic enzymes. The effect of the dietary antioxidant, quercetin (Qc), on the metabolism of 2-methoxyestradiol (2Me-E2), a promising potential anticancer agent was investigated. Qc possesses five hydroxyl groups, several of which are targets for UDP-glucuronosyltransferases (UGTs). Thus, the simultaneous presence of Qc and 2Me-E2 could result in decreased glucuronidation of 2Me-E2. Using the LS180 intestinal human colon adenocarcinoma cell line, glucuronidation of 2Me-E2 resulted in formation of only one major glucuronide, 2-Methoxyestradiol-3-glucuronide (2Me-E2-3G). Qc effectively reduced its formation (IC50 = 7.8 ± 0.26 μM) to a minimum level. The decrease in the activity of UGTs increased the intracellular concentration of parent 2Me-E2. Additional increase in cellular concentration of 2Me-E2 was achieved when LS180 cells were pre-incubated with Qc prior the addition of 2Me-E2. Transwell experiments with MDCKII – BCRP cells revealed that BCRP/ABCG2 did not appear to transport 2Me-E2. All in all, the present study showed that OS has a negative impact on active transport mediated by ABC transporters. This, in turn, can affect drug disposition and protection of endogenous organs and tissues. Antioxidants are one of the mechanisms that can effectively reduce the negative impact caused by oxidative species. Nevertheless, this research revealed that they can also be an effective tool to reduce the excessive metabolism of therapeutic drugs. Thus, Qc was found to be a dietary antioxidant that could reduce metabolism of 2Me-E2 and increase it intracellular concentration