Log Skeletons: A Classification Approach to Process Discovery

To test the effectiveness of process discovery algorithms, a Process Discovery Contest (PDC) has been set up. This PDC uses a classification approach to measure this effectiveness: The better the discovered model can classify whether or not a new trace conforms to the event log, the better the discovery algorithm is supposed to be. Unfortunately, even the state-of-the-art fully-automated discovery algorithms score poorly on this classification. Even the best of these algorithms, the Inductive Miner, scored only 147 correct classified traces out of 200 traces on the PDC of 2017. This paper introduces the rule-based log skeleton model, which is closely related to the Declare constraint model, together with a way to classify traces using this model. This classification using log skeletons is shown to score better on the PDC of 2017 than state-of-the-art discovery algorithms: 194 out of 200. As a result, one can argue that the fully-automated algorithm to construct (or: discover) a log skeleton from an event log outperforms existing state-of-the-art fully-automated discovery algorithms.Comment: 16 pages with 9 figures, followed by an appendix of 14 pages with 17 figure

Inductive queries for a drug designing robot scientist

It is increasingly clear that machine learning algorithms need to be integrated in an iterative scientific discovery loop, in which data is queried repeatedly by means of inductive queries and where the computer provides guidance to the experiments that are being performed. In this chapter, we summarise several key challenges in achieving this integration of machine learning and data mining algorithms in methods for the discovery of Quantitative Structure Activity Relationships (QSARs). We introduce the concept of a robot scientist, in which all steps of the discovery process are automated; we discuss the representation of molecular data such that knowledge discovery tools can analyse it, and we discuss the adaptation of machine learning and data mining algorithms to guide QSAR experiments

Incremental Discovery of Process Maps

Protsessikaeve on meetodite kogu, analüüsimaks protsesside teostuse jooksul loodud sündmuste logisid, et saada teavet nende parandamiseks. Protsessikaeve meetodite kogu, mida nimetatakse automatiseeritud protsessi avastuseks, lubab analüütikutel leida informatsiooni äriprotsesside mudelite kohta sündmuste logidest. Automatiseeritud protsessi avastusmeetodeid kasutatakse tavaliselt ühenduseta keskkonnas, mis tähendab, et protsessi mudel avastatakse hetketõmmisena tervest sündmuste logist. Samas on olukordi, kus uued juhtumid tulevad peale sellise suure kiirusega, et ei ole mõtet salvestada tervet sündmuste logi ja pidevalt nullist taasavastada mudelit. Selliste olukordade jaoks oleks vaja võrgus olevaid protsessi avastusmeetmeid. Andes sisendiks protsessi teostuse käigus loodud sündmuste voo, võrgus oleva protsessi avastusmeetodi eesmärk on järjepidevalt uuendada protsessi mudelit, tehes seda piiratud hulga mäluga ja säilitades sama täpsust, mida suudavad meetodid ühenduseta keskkondades. Olemasolevad meetodid vajavad palju mälu, et saavutada tulemusi, mis oleks võrreldavad ühenduseta keskkonnas saadud tulemustega. Käesolev lõputöö pakub välja võrgus oleva protsessi avastusraamistiku, ühtlustades protsessi avastus probleemi vähemälu haldusega ja kasutades vähemälu asenduspoliitikaid lahendamaks antud probleemi. Loodud raamistik on kirjutatud kasutades .NET-i, integreeritud Minit protsessikaeve tööriistaga ja analüüsitud kasutades elulisi ärijuhte.Process mining is a body of methods to analyze event logs produced during the execution of business processes in order to extract insights for their improvement. A family of process mining methods, known as automated process discovery, allows analysts to extract business process models from event logs. Traditional automated process discovery methods are intended to be used in an offline setting, meaning that the process model is extracted from a snapshot of an event log stored in its entirety. In some scenarios however, events keep coming with a high arrival rate to the extent that it is impractical to store the entire event log and to continuously re-discover a process model from scratch. Such scenarios require online automated process discovery approaches. Given an event stream produced by the execution of a business process, the goal of an online automated process discovery method is to maintain a continuously updated model of the process with a bounded amount of memory while at the same time achieving similar accuracy as offline methods. Existing automated discovery approaches require relatively large amounts of memory to achieve levels of accuracy comparable to that of offline methods. This thesis proposes a online process discovery framework that addresses this limitation by mapping the problem of online process discovery to that of cache memory management, and applying well-known cache replacement policies to the problem of online process discovery. The proposed framework has been implemented in .NET, integrated with the Minit process mining tool and comparatively evaluated against an existing baseline, using real-life datasets

Automated Process Discovery: A Literature Review and a Comparative Evaluation with Domain Experts

Äriprotsesside kaeve meetodi võimaldavad analüütikul kasutada logisid saamaks teadmisi protsessi tegeliku toimise kohta. Neist meetodist üks enim uuritud on automaatne äriprotsesside avastamine. Sündmuste logi võetakse kui sisend automaatse äriprotsesside avastamise meetodi poolt ning väljundina toodetakse äriprotsessi mudel, mis kujutab logis talletatud sündmuste kontrollvoogu. Viimase kahe kümnendi jooksul on väljapakutud mitmeidki automaatseid äriprotsessi avastamise meetodeid balansseerides erinevalt toodetavate mudelite skaleeruvuse, täpsuse ning keerukuse vahel. Siiani on automaatsed äriprotsesside avastamise meetodid testitud ad-hoc kombel, kus erinevad autorid kasutavad erinevaid andmestike, seadistusi, hindamismeetrikuid ning alustõdesid, mis viib tihti võrdlematute tulemusteni ning mõnikord ka mittetaastoodetavate tulemusteni suletud andmestike kasutamise tõttu. Eelpool toodu mõistes sooritatakse antud magistritöö raames süstemaatiline kirjanduse ülevaade automaatsete äriprotsesside avastamise meetoditest ja ka süstemaatiline hindav võrdlus üle nelja kvaliteedimeetriku olemasolevate automaatsete äriprotsesside avastamise meetodite kohta koostöös domeeniekspertidega ning kasutades reaalset logi rahvusvahelisest tarkvara firmast. Kirjanduse ülevaate ning hindamise tulemused tõstavad esile puudujääke ning seni uurimata kompromisse mudelite loomiseks nelja kvaliteedimeetriku kontekstis. Antud magistritöö tulemused võimaldavad teaduritel parandada puudujäägid meetodites. Samuti vastatakse küsimusele automaatsete äriprotsesside avastamise meetodite kasutamise kohta väljaspool akadeemilist maailma.Process mining methods allow analysts to use logs of historical executions of business processes in order to gain knowledge about the actual performance of these processes.One of the most widely studied process mining operations is automated process discovery.An event log is taken as input by an automated process discovery method and produces a business process model as output that captures the control-flow relations between tasks that are described by the event log.Several automated process discovery methods have been proposed in the past two decades, striking different tradeoffs between scalability, accuracy and complexity of the resulting models.So far, automated process discovery methods have been evaluated in an ad hoc manner, with different authors employing different datasets, experimental setups, evaluation measures and baselines, often leading to incomparable conclusions and sometimes unreproducible results due to the use of non-publicly available datasets.In this setting, this thesis provides a systematic review of automated process discovery methods and a systematic comparative evaluation of existing implementations of these methods with domain experts by using a real-life event log extracted from a international software engineering company and four quality metrics.The review and evaluation results highlight gaps and unexplored tradeoffs in the field in the context of four business process model quality metrics.The results of this master thesis allows researchers to improve the lacks in the automated process discovery methods and also answers question about the usability of process discovery techniques in industry

Closing the loop: assisting archival appraisal and information retrieval in one sweep

In this article, we examine the similarities between the concept of appraisal, a process that takes place within the archives, and the concept of relevance judgement, a process fundamental to the evaluation of information retrieval systems. More specifically, we revisit selection criteria proposed as result of archival research, and work within the digital curation communities, and, compare them to relevance criteria as discussed within information retrieval's literature based discovery. We illustrate how closely these criteria relate to each other and discuss how understanding the relationships between the these disciplines could form a basis for proposing automated selection for archival processes and initiating multi-objective learning with respect to information retrieval

CLI Crawler

Many systems within the IT infrastructure have a Command Line Interface (CLI) for configuration changes. Some of these systems may expose a Configuration Management interface over a web service but this web service usually only exposes a fraction of the configuration possibilities in a CLI. Thus it would be of great help to investigate how a framework for automated CLI discovery can be developed, which is what this bachelor’s thesis is about. One objective of the bachelor’s thesis was to determine the best possible way to access the command structure of CLIs and to determine how a CLI discovery application can be developed. The other objective was to develop such a prototype. Such a CLI discovery application must support exporting the result of the discovery process into a YANG model (a hierarchical modeling language for NETCONF) in the future. A prototype, CLI Crawler, was developed. CLI Crawler was designed to be as automated as possible, however during the discovery process user interaction is required in order to help CLI Crawler get past certain obstacles. Such an obstacle could be when a CLI requires a certain input that only the user has knowledge of. At first CLI Crawler connects to a remote system with the use of Secure Shell (SSH) or Terminal Network (Telnet). Thereafter the discovery process is started which traverses all of the possible commands, modes and attributes in a certain CLI. During such a discovery process the command structure is both being printed in real-time in the GUI as a hierarchical tree structure and added to a database which will be used for exporting the command structure as YANG in the future. CLI Crawler shows that it is possible to develop a framework for automated CLI discovery. However more work and research has to be done before CLI Crawler will become a viable way of discovering and representing a CLI’s command structure. For instance more CLIs have to be integrated with CLI Crawler in order to make them compatible with the discovery process