Collaborative methods for business process discovery

Information flows across the organization are complex and procedures employed to understand, share and control organizational knowledge and experiences should be properly supported by collaborative environments. Nevertheless, few collaborative methodologies had been proposed to describe and evolve business processes. Existing tools don't provide the right methods for business processes discovery, modelling, monitoring and improvement. In the future, business processes models should be the result of cross-team and cross-departmental collaboration, with involved business people sharing their personal knowledge and formalizing it. This discussion paper focuses on collaborative process discovery methods, tools and the importance to integrate local information into coherent and sound process definitions. Business Alignment Methodology is a methodology that provides guidance about how organizational practices and knowledge are gathered to contribute for business process improvement against current Business Process Modelling approaches

Extraction of Process Models from Business Process Descriptions

The purpose of my work is to design a method to transform a textual process description (in English) into a business process model. This is of practical relevance, since process models are often designed by business analysts starting from textual documentation. The method to be designed aims at automating the text-to-diagram conversion phase as much as possible. Natural languages are known to be highly complex and ambiguous. Accordingly, for this project we will approach the problem using a best-effort approach, meaning that the method is not intended to work always. Instead, the proposed approach will be able to detect certain sentence structures and extract actors, actions and objects/artifacts from them. Coordinating and subordinating conjunctions, as well as punctuation and other markers, will be used to identify sequencing, parallelism, conditional branching and repetition. The output of the method will be a block-structured process model. The method is being implemented in Java based on open-source Natural-Language Processing (NLP) libraries. Specifically, Part-of-Speech (POS) tagging is performed using the Stanford parser and according to the POS tags, corresponding process entities are identified using Tregex and Tsurgeon. The current implementation is already able to identify actors, actions/tasks and artifacts from sentences that abide to certain common structures. Additionally the implementation is able to correctly interpret passive voice construction, avoid articles, parenthesis and other complex structures for the purpose of extracting essential information about the process

On the automatic labeling of process models

Process models are essential tools for managing, understanding and changing business processes. Yet, from a user perspective they can quickly become too complex to deal with. Abstraction – aggregating detailed fragments into more coarse-grained ones – has proven to be a valuable technique to simplify the view on a process model. Various techniques that automate the decision of which model fragments to aggregate have been defined and validated by recent research, but their application is hampered by the lack of abilities to generate meaningful names for such aggregated parts. In this paper, we address this problem by investigating naming strategies for individual model fragments and process models as a whole. Our contribution is an automatic naming approach that builds on the linguistic analysis of process models from industry

Montana Business Quarterly, Summer 2014

This is an academic publication produced by the Bureau of Business and Economic Research (BBER) at the University of Montana’s College of Business. This is volume 52, number 2.https://scholarworks.umt.edu/mtbusinessquarterly/1218/thumbnail.jp

Extending evolutionary multi-objective optimization of business process designs

Διπλωματική εργασία--Πανεπιστήμιο Μακεδονίας, Θεσσαλονίκη, 2019.Optimizing a problem to produce a set of improved solutions is not a new concept. Many scientific areas have been benefited by the application of optimizations techniques and so have business processes. The competitive business environments have led organizations into examining and re-designing their core business processes, aiming for improving their performance and market responsiveness. The optimization and the continuous improvement of business processes within a company, can give the advantage to the company to be more competitive by reducing its costs, improving the delivery quality and efficiency, and enabling adaptation to changing environments. This thesis focuses on business process multi-objective optimization with evolutionary algorithms. There have already been optimization approaches with evolutionary algorithms for business process optimization problems that demonstrated rather satisfactory results. This thesis aims to improve and extent those approaches by providing a revised and refined version of an existing business process optimization framework by Vergidis (2008), that incorporates a pre-processing technique for enhancing the efficiency of the employed Evolutionary Multi-objective Optimization Algorithms (EMOAs), a new process composition algorithm that make the new framework capable of fulfilling more real-life constraints and handling more complex problems and many other features such as ease of use, more efficient I/O, better interactivity and easy maintenance. The proposed pre-processing technique was tested as a standalone procedure and demonstrated satisfactory results, managing to reduce drastically the problem dataset of all scenarios examined. The results of the whole optimization framework for the real-life scenarios examined, were very promising and indicated that the framework work as expected. It can automate the process composition and identify alternative business process designs with optimized attribute values

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

America’s Energy Security Options

As US gasoline prices approached $4 a gallon in spring 2011, energy security moved to the forefront of the American political debate. Politicians have been quick to offer silver bullet solutions to lower gas prices and make America more energy secure. Houser and Mohan analyze the various recent policy proposals, from expanded offshore drilling to new vehicle efficiency standards, and compare their effects on US oil imports, US oil demand, gasoline prices, and energy expenditures over the 2011–2035 period. They find that despite recent political rhetoric, when it comes to energy security there is no policy panacea. Current proposals vary widely in the time frame, magnitude, and nature of their impact. Rather than debate whether expanded domestic production, improved efficiency, or development of oil alternatives is the right course to take, the United States needs to start moving down all three roads simultaneously to significantly alter the country’s energy trajectory. An "all of the above" strategy is required, which combines increased domestic production (important in the near term) with long-term investments in energy-efficient vehicles and oil alternatives, whether electric, natural gas, or biofuels. A carbon tax, while still a long shot politically, would deliver further energy security gains and help reduce the US deficit in the process. But even if all proposals currently on the table are adopted, the US will remain dependent on the international oil market for decades to come. Therefore Washington needs a strategy for improving the stability and reliability of that market, something missing from the current policy debate.

Treating Bipolar Disorder and Schizophrenia with Biomedical Protocol

Bipolar Disorder and schizophrenia afflict approximately 2% of the human population, more than 12,400,000 people. When family members, co-workers, and care-givers are factored in, more than 10% of the population is directly impacted by these medical conditions. Finding a way to reduce the severity of the symptoms or cure these diseases would positively impact a large number of people. This paper defines and differentiates between these two similar-but-different maladies, and explores both the traditional courses of treatment as well as the most current research with potential to help combat these diseases – especially genetic mapping with the use of neuroimaging. Because genetic mapping has been successfully used to treat cancers, applying this technique to neurological disorders is the next logical step to take

Finding the Signal in the Noise: Information Governance, Analytics, and the Future of Legal Practice

In the watershed year of 2012, the world of law witnessed the first concrete discussion of how predictive analytics may be used to make legal practice more efficient. That the conversation about the use of predictive analytics has emerged out of the e-Discovery sector of the law is not all that surprising: in the last decade and with increasing force since 2006— with the passage of revised Federal Rules of Civil Procedure that expressly took into account the fact that lawyers must confront “electronically stored information” in all its varieties—there has been a growing recognition among courts and commentators that the practice of litigation is changing dramatically. What needs now to be recognized, however, is that the rapidly evolving tools and techniques that have been so helpful in providing efficient responses to document requests in complex litigation may be used in a variety of complementary ways to the discovery process itself