Process Mining Monitoring for Map Reduce Applications in the Cloud

Ciampolini

et al. 2019

Knowl Inf Syst

Self Cite

As recent years have seen the rise of a new discipline commonly addressed as process mining, focused on the management of business processes, two tasks have gained increasing attention in research: process discovery and compliance monitoring. In both these fields, the demand for event log benchmarks with predefined characteristics has determined the design of various methodologies and tools for synthetic log generation. However, artificially created as well as real-life logs often contain positive examples only (i.e., process instances deemed as compliant w.r.t. the model), while the presence of negative process instances (i.e., non-compliant traces) can be crucial to correctly evaluate the performance and robustness of a novel process discovery or conformance checking technique.In this work, we investigate positive and negative trace generation in case of both declarative and procedural model specifications and we present our abduction-based approach to log synthesis. The theoretical study is concretely applied in a software prototype for log generation, which takes as input a declarative or structured workflow model, and emits logs containing positive and negative traces. The approach provides both a highly expressive notation for the description of the business model, and the ability to generate logs with various customizable features. The final comparative study of other existing log generators reveals several advantages of the proposed approach and draws the direction of future improvements.

Section: Discussionmentioning

confidence: 99%

Generating synthetic positive and negative business process traces through abduction

Loreti¹,

Ciampolini

et al. 2019

Knowl Inf Syst

Self Cite

“…In order to simplify the temporal reasoning on incoming events, our approach applies the REC theory (Chesani et al, 2010a;Bragaglia et al, 2012a) alongside the stream processing technology of CEP. Being firstly conceived to apply the EC paradigm to the more dynamic field of event flow runtime monitoring, REC has been employed in a variety of application domains e.g, BPM (Montali, 2010;Chesani et al, 2016), clinical guidelines and care-flow protocols (Bottrighi et al, 2011;Bragaglia et al, 2014), service oriented architectures (Chesani et al, 2008) and multi-agent systems (Chesani et al, 2010b).…”

Section: Related Workmentioning

confidence: 99%

Complex reactive event processing for assisted living: The Habitat project case study

Loreti

Expert Systems with Applications

Mello

et al. 2019

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While the increasing average age of population is posing new challenges to societies and healthcare systems, the emergence of the Internet of Things research area is generating the hope for automated assisted environments, which could combine the advances in sensors networks with that of runtime monitoring systems, in order to create smart houses able to take care of their older inhabitants and delay the recourse to hospitals and nursing homes. However, although various assisted living systems have been proposed in the last decade, the goal of realizing an effective domestic support system for elderly is still far from reached.In this work, we present a project aiming to re-engineer a set of everyday life objects, equipping them with environmental and wearable sensors, thus

“…In [53,54], the authors provide a centralised solution for the monitoring of distributed MapReduce application based on a simple set of behavioural declarative properties. Contrarily, the aim of the work at hand is to exploit a distributed MapReduce architecture for the monitoring of business process compliance.…”

Section: Related Workmentioning

confidence: 99%

A distributed approach to compliance monitoring of business process event streams

Loreti¹,

Future Generation Computer Systems

Ciampolini

et al. 2018

Self Cite

In recent years, the significant advantages brought to business processes by process mining account for its evolution as a major concern in both industrial and academic research. In particular, increasing attention has been turned to compliance monitoring as a way to identify when a sequence of events deviates from the expected behaviour. As we are entering the IoT era, an increasing variety of smart objects can be introduced in business processes (e.g., tags to track products in a plant, smartphones and badge swiping to draw the activities of customers and employees in a shopping center, etc.). All these objects produce large volumes of log data in the form of streams, which need to be run-time analysed to extract further knowledge about the underlying business process and to identify unexpected, non-conforming events.Albeit rather straightforward on a small log file, compliance verification techniques may show poor performances when dealing with big data and streams, thus calling for scalable approaches.This work investigates the possibility of spreading the compliance monitoring task over a network of computing nodes, achieving the desired scalability. The monitor is realised through the existing SCIFF framework for compliance checking, which provides a high level logic-based language for expressing the properties to be monitored and nicely supports the partitioning