LSTM Networks for Data-Aware Remaining Time Prediction of Business Process Instances

Predicting the completion time of business process instances would be a very helpful aid when managing processes under service level agreement constraints. The ability to know in advance the trend of running process instances would allow business managers to react in time, in order to prevent delays or undesirable situations. However, making such accurate forecasts is not easy: many factors may influence the required time to complete a process instance. In this paper, we propose an approach based on deep Recurrent Neural Networks (specifically LSTMs) that is able to exploit arbitrary information associated to single events, in order to produce an as-accurate-as-possible prediction of the completion time of running instances. Experiments on real-world datasets confirm the quality of our proposal.Comment: Article accepted for publication in 2017 IEEE Symposium on Deep Learning (IEEE DL'17) @ SSC

An intelligent alarm management system for large-scale telecommunication companies

This paper introduces an intelligent system that performs alarm correlation and root cause analysis. The system is designed to operate in large- scale heterogeneous networks from telecommunications operators. The pro- posed architecture includes a rules management module that is based in data mining (to generate the rules) and reinforcement learning (to improve rule se- lection) algorithms. In this work, we focus on the design and development of the rule generation part and test it using a large real-world dataset containing alarms from a Portuguese telecommunications company. The correlation engine achieved promising results, measured by a compression rate of 70% and as- sessed in real-time by experienced network administrator staff

Business Process Management and Process Mining within a Real Business Environment: An Empirical Analysis of Event Logs Data in a Consulting Project

Il presente elaborato esplora l’attitudine delle organizzazioni nei confronti dei processi di business che le sostengono: dalla semi-assenza di struttura, all’organizzazione funzionale, fino all’avvento del Business Process Reengineering e del Business Process Management, nato come superamento dei limiti e delle problematiche del modello precedente. All’interno del ciclo di vita del BPM, trova spazio la metodologia del process mining, che permette un livello di analisi dei processi a partire dagli event data log, ossia dai dati di registrazione degli eventi, che fanno riferimento a tutte quelle attività supportate da un sistema informativo aziendale. Il process mining può essere visto come naturale ponte che collega le discipline del management basate sui processi (ma non data-driven) e i nuovi sviluppi della business intelligence, capaci di gestire e manipolare l’enorme mole di dati a disposizione delle aziende (ma che non sono process-driven). Nella tesi, i requisiti e le tecnologie che abilitano l’utilizzo della disciplina sono descritti, cosi come le tre tecniche che questa abilita: process discovery, conformance checking e process enhancement. Il process mining è stato utilizzato come strumento principale in un progetto di consulenza da HSPI S.p.A. per conto di un importante cliente italiano, fornitore di piattaforme e di soluzioni IT. Il progetto a cui ho preso parte, descritto all’interno dell’elaborato, ha come scopo quello di sostenere l’organizzazione nel suo piano di improvement delle prestazioni interne e ha permesso di verificare l’applicabilità e i limiti delle tecniche di process mining. Infine, nell’appendice finale, è presente un paper da me realizzato, che raccoglie tutte le applicazioni della disciplina in un contesto di business reale, traendo dati e informazioni da working papers, casi aziendali e da canali diretti. Per la sua validità e completezza, questo documento è stata pubblicato nel sito dell'IEEE Task Force on Process Mining

ONTOLOGY-BASED INFORMATION EXTRACTION FOR ANALYZING IT SERVICES

Service Level Agreements (SLA) for multi-service Information Technology (IT) outsourcing contracts contain vast amounts of textual information. The SLAs provide details about a specific service, Key Performance Indicators (KPI) to measure its performance; as well as process elements, such as activities, events, and resources that are integral in achieving performance goals. However, KPIs and the process elements may be interrelated. The knowledge of such interrelationships is often tacitly present in the SLAs. The aim of our research is to extract this hidden information from IT service contracts and analyze them to empower customers of IT services to make better performance management and incentive decisions. We apply an Ontology- Based Information Extraction (OBIE) approach in developing a prototype decision support framework, named SLA-Miner. The results, obtained from analyzing a set of Industry SLAs, demonstrate the utility of SLA-Miner in identifying KPI interrelationships, deficiencies, and impacts of various process elements on individual KPIs

BUSINESS INTELLIGENCE FOR BUSINESS PROCESSES: THE CASE OF IT INCIDENT MANAGEMENT

IT service desks have become an integral part of intra-enterprise ecosystems, keeping IT hardware and software services within the company running. Business Intelligence methods have an enormous potential to support IT helpdesk employees by making implicit knowledge explicit, accelerating business processes throughout the entire company, and retaining the knowledge of experienced employees upon retirement. In this paper, we investigate these benefits by showing how analytics can automate the assignment of helpdesk tasks, enable early warning mechanisms for accumulated incidents, and enhance knowledge sharing among helpdesk users. For this purpose, we use a combination of topic modeling and predictive analytics, which is applied to an extensive dataset of support tickets from a global automotive supplier. Our approach identifies relevant topics and assigns these to helpdesk tickets, thereby decoding implicit knowledge into formal rules and business processes