Discovering Business Processes models expressed as DNF or CNF formulae of Declare constraints

In the field of Business Process Management, the Process Discovery task is one of the most important and researched topics. It aims to automatically learn process models starting from a given set of logged execution traces. The majority of the approaches employ procedural languages for describing the discovered models, but declarative languages have been proposed as well. In the latter category there is the Declare language, based on the notion of constraint, and equipped with a formal semantics on LTLf. Also, quite common in the field is to consider the log as a set of positive examples only, but some recent approaches pointed out that a binary classification task (with positive and negative examples) might provide better outcomes. In this paper, we discuss our preliminary work on the adaptation of some existing algorithms for Inductive Logic Programming, to the specific setting of Process Discovery: in particular, we adopt the Declare language with its formal semantics, and the perspective of a binary classification task (i.e., with positive and negative examples

Generating Synthetic Event Logs based on Multi- perspective Business Rules

Traditsiooniline äriprotsesside modelleerimine kasutab imperatiivset lähenemist, kus äri-protsesse kirjeldatakse üksteise järel sooritatavate tegevuste abil. On näidatud, et imper-atiivne lähenemine on sobivam lahendus stabiilsete ja ennustatavate protsesside puhul. Deklaratiivsed mudelid seevastu sobivad muutuvate protsesside kirjeldamiseks. Deklaratiivne mudel sisaldab endas reeglite hulka mida ei tohi eirata protsessi käitamisel. Viimastel aastatel on arendatud mitmeid uusi meetodeid deklaratiivsete protsessimudelite leidmiseks sündmuste logidest. Meetodite testimiseks on vajalik tööriistade olemasolu, mis genereerivad sünteetilisi sündmuste logisid, mille peal neid meetodeid katsetada. Enamus olemasolevaid tööriistu kasutavad imperatiivseid protsessimudelid logide genereerimiseks. Selline lähenemine ei ole sobiv deklaratiivsete protsessimudelite avastamise meetodite tes-timiseks. Sarnaselt on olemas vajadus tööriistade järgi, mis genereeriks sündmuste logisid kasutades mitmeperspektiivseid Declare mudeleid. Käesolevas töös esitleme tööriista mitmeperspektiivsete Declare mudelite genereerimiseks. See töörist tõlgib Declare piirangud lõpliku olekumasina esitusse,et neid kasutada deklaratiivsete mudelite simu-leerimiseks. Tööriist võimaldab kasutajatel genereerida logisid eeldefineeritud omadustega ( näiteks protsessi instantside arv ja protsessi pikkus), mis on kooskõlas Declare mudeli-tega.\n\rMärksõnad: Declare, deklaratiivne protsessimudel, protsessi simuleerimine, logide gene-reerimine, mitmeperspektiive, lineaarne taisarvuline planeerimineTraditional business modelling is imperative in the sense that activities are provided step by step, from start to end, leading towards full business process. It has been proved that the imperative paradigm is most suitable in the context of stable and predictable processes. Declarative models are more suitable for variable processes. A declarative model is made of a set of constrains that cannot be violated during the process execution. In recent years, many techniques have been developed to discover declarative process model from event logs. To test these techniques it is sometime necessary to have tools that generate synthetic logs on which the techniques can be applied. However, majority of the existing tools avail-able in this field use simulation of an imperative process model to generate synthetic event logs. These approaches are not suitable for the evaluation of process discovery techniques using declarative process models. Additionally, there is a need for tools to generate event logs based on the simulation of multi-perspective declarative models. To close this gap, we developed a tool for log generation based on multi- perspective Declare models. This mod-el simulator will base on the translation of Declare constraints into Finite State Automata for the simulation of declarative processes. The tool will allows users to generate logs with predefined characteristics (e.g., number and length of the process instances), which is compliant with a given Declare model.\n\rKeywords: Declare, Declarative Process Models, Process Simulation, Log Generation, Multi-perspective, Integer Linear Programmin

Process Discovery on Deviant Traces and Other Stranger Things

As the need to understand and formalise business processes into a model has grown over the last years, the process discovery research field has gained more and more importance, developing two different classes of approaches to model representation: procedural and declarative. Orthogonally to this classification, the vast majority of works envisage the discovery task as a one-class supervised learning process guided by the traces that are recorded into an input log. In this work instead, we focus on declarative processes and embrace the less-popular view of process discovery as a binary supervised learning task, where the input log reports both examples of the normal system execution, and traces representing a “stranger” behaviour according to the domain semantics. We therefore deepen how the valuable information brought by both these two sets can be extracted and formalised into a model that is “optimal” according to user-defined goals. Our approach, namely NegDis, is evaluated w.r.t. other relevant works in this field, and shows promising results regarding both the performance and the quality of the obtained solution

A Constraint-Based Approach for Managing Declarative Temporal Business Process Models

There is an increasing interest in aligning information systems in a process-oriented way. As an alternative of the traditional imperative models which tend to be too rigid, processes may be specified in a declarative (e.g., constraint-based) way. Nonetheless, in general, offering operational support (e.g., generating possible execution traces) to declarative business process models entails more complexity when compared to imperative modeling alternatives. Such support becomes even more complex in many real scenarios where the management of complex temporal relations between the process activities is crucial (i.e., the temporal perspective should be managed). Despite the needs for enabling process flexibility and dealing with temporal constraints, most existing tools are unable to manage both. In a previous work, we then proposed TConDec-R, which is a constraint-based process modeling language which allows for the specification of temporal constraints. However, TConDec-R revealed a number of limitations that are overcome with the present work. More specifically, this paper significantly extends and improves our previous work by (1) defining TConDec-R process models based on high-level elements from the constraint programming paradigm, (2) introducing a constraint-based tool with a client/server architecture for providing operational support to TConDec-R process models, and (3) performing an empirical evaluation of the approach

Aligning Data-Aware Declarative Process Models and Event Logs

Vastavusanalüüs on haru protsessikaevanduses, mis võimaldab analüütikutel saada aru, kas äriprotsesside sooritused järgivad mudeldatud käitumist. Protsesside mudelid võivad olla nii protseduurilised kui ka deklaratiivsed. Kui protseduurilised mudelid kirjeldavad ära täpsed võimalikud tegevused, siis deklaratiivsed mudelid kirjeldavad reeglid, mis peavad olema protsessi sooritusel olema järgitud. Äriprotsesside täitmiste hoiustamiseks kasutatakse sündmuste logisid. Vastavusanalüüsi meetodid kontrollivad erinevaid protsessi sooritusega seotud vaateid, milleks on juhtimisvoog, andmed ja ressursid. Meetodid, mis käsitlevad endas lisaks juhtimisvoole ka andmeid ning ressursse kutsutakse mitmevaatelisteks või andmeteadlikeks lähenemisteks. Mitmevaatelised meetodid annavad rohkem informatsiooni kõrvalekallete kohta võrreldes juhtimisvoogudel põhinevate meetoditega. Joondustel põhinevad vastavusanalüüsi meetodid on olnud edukad nii juhtimisvool põhinevate kui ka andmeteadlike lähenemiste puhul. On olemas mitmeid joondamisel põhinevaid andmeteadlikke lähenemisi protseduuriliste mudelite jaoks, kuid deklaratiivsete mudelite jaoks need puuduvad.Antud töös on kohandatud olemasolev meetod, mis võimaldab sooritada vastavusanalüüsi andmeteadlike protseduuriliste mudelite puhul, kasutades logide joondustel põhinevat meetodit, võimaldamaks kasutamist ka deklaratiivsetel mudelitel. Deklaratiivsetel mudelitel rakendatav meetod implementeeriti moodulina protsessikaeve keskkonna ProM jaoks ja hinnati implementatsiooni kasutades erinevaid sündmuste logisid.Märksõnad: Protsessikaevandus, Deklaratiivsed protsessimudelid, Andmeteadlik vastavusanalüüs, JoondamineConformance checking, a branch of process mining, allows analysts to determine whether the execution of a business process matches the modeled behavior. Process models can be procedural or declarative. Procedural models dictate the exact behavior that is allowed to execute a specific process whilst declarative models implicitly specify allowed behavior with the rules that must be followed during execution. The execution of a business process is represented by event logs. Conformance checking approaches check various perspectives of a process execution including control-flow, data and resources. Approaches that checks not only the control-flow perspective, but also data and resources are called multi-perspective or data-aware approaches. The approaches provide more deviation information than control-flow based techniques. Alignment based techniques of conformance checking have proved to be advantageous in both control-flow based and data-aware approaches. While there exist several data-aware approaches for procedural process models that are based on the principle of finding alignments, there is none so far for declarative process models. In this thesis, we adapt an existing technique for finding alignments of logs and data-aware procedural models to declarative models. We implemented our approach as a plugin of the process mining framework ProM and evaluated it using event logs with different characteristics.Keywords: Process Mining, Declarative Process Models, Data-aware Conformance checking, Alignmen

Discovering Declarative Process Models from Event Logs through Temporal Logic Query Checking

Käesolev magistritöö keskendub protsessile seatud piirangute avastamisele sündmuste logist, mida saab väljendada temporaalloogika abil. Piirangute avastamise meetodina kasutame temporaalloogika päringute kontrollimist sündmuste logi vastu. Temporaalloogika päring on modaalloogika avaldis, mis sisaldab muutujaid, mis võtavad oma väärtuse automaarpropositsioonide hulgast. Temporaalloogika päring käivitatakse vastu olekumasinat, mis on konstrueeritud sündmuste logi järgi. Päringu tulemuseks on kõik temporaalloogika avaldised, kus muutujad on asendatud kõikvõimalike automaarpropositsioonidega, mis muudavad avaldise tõeseks antud olekumasinas. See meetod ei vaja protsessi piirangute avastamiseks negatiivseid näiteid (protsessi juhtumid, mis ei tohi aset leida) sündmuste logis nagu osa avaldatuid meetodeid vajab. See meetod samuti laiendab võimalike avastatavate piirangute hulka võrreldes olemas olevate meetoditega.This thesis will focus on the discovery of temporal logic constraints from an event log. The constraints are the description of the behavior of a business process. We will use Temporal Logic Query Checking for this purpose. A temporal logic query is a type of modal logic expression containing one or more placeholders that are checked against a transition system. The transition system is built from an event log. The result lists all possible activities that can replace the placeholders to satisfy the constraints described by the query in the log. This approach does not require (as many other approaches in the literature) negative examples as (additional) input and it provides the possibility of discovering a wider range of constraints to describe the process with respect to the existing approaches

Data-aware Synthetic Log Generation for Declarative Process Models

Äriprotsesside juhtimises on protsessikaeve klass meetodeid, mida kasutatakse protsessi struktuuri õppimiseks täitmislogist. Selle struktuur on esindatud kui protsessi mudel: kas menetluslik või deklaratiivne. Näited deklaratiivsetest keeltest on Declare, DPIL ja DCR Graphs. Selleks, et testida ja parandada protsessi kaevandamise algoritme on vaja palju logisid erinevate parameetritega ja alati ei ole võimalik saada piisavalt reaalseid logisid. See on koht, kus tehislikud logid tulevad kasuks. On olemas meetodeid logi genereerimiseks DPIL-ist ja deklaratiivsetest mudelitest, kuid puuduvad vahendid logi genereerimiseks MPDeclare-ist, mis on multiperspektiivne versioon Declare-ist andmete toega. Käesolev magistritöö käsitleb MP-Declare mudelitest logide genereerimist kasutades kaht erinevat mudelite kontrollijat: Alloy ja NuSMV. Selleks, et parandada jõudlust, optimeerisime kirjanduses saadaval olevaid baaslähenemisi. Kõik käsitletud tehnikad implementeeritakse ja testitakse kasutades saadaval olevat sobivuse testimise tööriistu ja meie enda väljatöötatud teste. Meie generaatorite hindamiseks ja võrdluseks olemasolevate lahendustega mõõtsime me logide genereerimise aega ja seda, kuidas see muutub erinevate parameetrite ja mudelitega. Me töötasime välja erinevad mõõdupuud logide varieeruvuse arvutamiseks ja rakendasime neid uuritavatele generaatoritele.In Business Process Management, process mining is a class of techniques for learning process structure from an execution log. This structure is represented as a process model: either procedural or declarative. Examples of declarative languages are Declare, DPIL and DCR Graphs. In order to test and improve process mining algorithms a lot of logs with different parameters are required, and it is not always possible to get enough real logs. And this is where artificial logs are useful. There exist techniques for log generation from DPIL and declare-based models. But there are no tools for generating logs from MP-Declare – multiperspective version of Declare with data support. This thesis introduces an approach to log generation from MP-Declare models using two different model checkers: Alloy and NuSMV. In order to improve performance, we applied optimization to baseline approaches available in the literature. All of the discussed techniques are implemented and tested using existing conformance checking tools and our tests. To evaluate performance of our generators and compare them with existing ones, we measured time required for generating log and how it changes with different parameters and models. We also designed several metrics for computing log variability, and applied them to reviewed generators

Leveraging intelligence from network CDR data for interference aware energy consumption minimization

Cell densification is being perceived as the panacea for the imminent capacity crunch. However, high aggregated energy consumption and increased inter-cell interference (ICI) caused by densification, remain the two long-standing problems. We propose a novel network orchestration solution for simultaneously minimizing energy consumption and ICI in ultra-dense 5G networks. The proposed solution builds on a big data analysis of over 10 million CDRs from a real network that shows there exists strong spatio-temporal predictability in real network traffic patterns. Leveraging this we develop a novel scheme to pro-actively schedule radio resources and small cell sleep cycles yielding substantial energy savings and reduced ICI, without compromising the users QoS. This scheme is derived by formulating a joint Energy Consumption and ICI minimization problem and solving it through a combination of linear binary integer programming, and progressive analysis based heuristic algorithm. Evaluations using: 1) a HetNet deployment designed for Milan city where big data analytics are used on real CDRs data from the Telecom Italia network to model traffic patterns, 2) NS-3 based Monte-Carlo simulations with synthetic Poisson traffic show that, compared to full frequency reuse and always on approach, in best case, proposed scheme can reduce energy consumption in HetNets to 1/8th while providing same or better Qo

Conformance checking and diagnosis for declarative business process models in data-aware scenarios

A business process (BP) consists of a set of activities which are performed in coordination in an organizational and technical environment and which jointly realize a business goal. In such context, BP management (BPM) can be seen as supporting BPs using methods, techniques, and software in order to design, enact, control, and analyze operational processes involving humans, organizations, applications, and other sources of information. Since the accurate management of BPs is receiving increasing attention, conformance checking, i.e., verifying whether the observed behavior matches a modelled behavior, is becoming more and more critical. Moreover, declarative languages are more frequently used to provide an increased flexibility. However, whereas there exist solid conformance checking techniques for imperative models, little work has been conducted for declarative models. Furthermore, only control-flow perspective is usually considered although other perspectives (e.g., data) are crucial. In addition, most approaches exclusively check the conformance without providing any related diagnostics. To enhance the accurate management of flexible BPs, this work presents a constraint-based approach for conformance checking over declarative BP models (including both control-flow and data perspectives). In addition, two constraint-based proposals for providing related diagnosis are detailed. To demonstrate both the effectiveness and the efficiency of the proposed approaches, the analysis of different performance measures related to a wide diversified set of test models of varying complexity has been performed.Ministerio de Ciencia e Innovación TIN2009-1371