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

Developing a distributed electronic health-record store for India

The DIGHT project is addressing the problem of building a scalable and highly available information store for the Electronic Health Records (EHRs) of the over one billion citizens of India

An automated Model-based Testing Approach in Software Product Lines Using a Variability Language.

This paper presents the application of an automated testing approach for Software Product Lines (SPL) driven by its state-machine and variability models. Context: Model-based testing provides a technique for automatic generation of test cases using models. Introduction of a variability model in this technique can achieve testing automation in SPL. Method: We use UML and CVL (Common Variability Language) models as input, and JUnit test cases are derived from these models. This approach has been implemented using the UML2 Eclipse Modeling platform and the CVL-Tool. Validation: A model checking tool prototype has been developed and a case study has been performed. Conclusions: Preliminary experiments have proved that our approach can find structural errors in the SPL under test. In our future work we will introduce Object Constraint Language (OCL) constraints attached to the input UML mode

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FORTEST: Formal methods and testing

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