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Opportunities For Students

PhD Position on Business process discovery and alignment @FBK and Univ. of Bozen-Bolzano

One four-year PhD grant on Business process discovery and alignment is offered by the faculty of Computer Science of the Free University of Bozen-Bolzano in Italy jointly with the Process & Data Intelligence research unit at Fondazione Bruno Kessler, Trento, Italy, where most of the research activities will be conducted. The grant amounts to 68,000 €; for research visits abroad the grant can increase up to 50%. Substantial extra funding is available for participation to international conferences, schools, workshops, research visits.
 
The deadline for applications is 3 July 2017.
For more info, the call, and applications look at: 
The language of the PhD program is English. 
 
Topic description: Process discovery techniques return process models that are either formal (precisely describing the possible behaviors) or informal (merely a “picture” not allowing for any form of formal reasoning). Formal models are able to classify traces (i.e., sequences of events) as fitting or non-fitting. Most process mining approaches described in the literature produce such models. This is in stark contrast with the over 25 available commercial process mining tools that only discover informal process models that remain deliberately vague on the precise set of possible traces. There are two main reasons why vendors resort to such models: scalability and simplicity. Within this PhD program, the candidate will have the chance to combine the best of both worlds: discovering hybrid process models that have formal and informal elements. Achieving this task requires dealing with several fascinating challenges as, for example, (i) the capability to work with formal and informal representations; (ii) the capability to envision new alignment strategies between execution traces and hybrid models; (iii) the capability to implement scalable solutions.
The work will put together theoretical and methodological aspects, including for example the problem conceptualization and representation, as well as implementation and optimization ones, aimed at the development of process mining and alignment tools.
 
About us: The Free University of Bozen-Bolzano and Fondazione Bruno Kessler are located 50 KM apart in one of the most fascinating European regions, the Dolomites. The young Free University of Bozen-Bolzano has already established itself as an important research institution, both in Italy and abroad. According to the Times Higher Education World University Rankings 2017, the university is the tenth world’s best small university, is the fifth best among all the Italian Universities with computer science departments, and it is the second best young Italian University. Fondazione Bruno Kessler is an internationally well-known research center, whose information technology department ranks first among the Engineering and Information Science research centers in Italy. 
The Process & Data Intelligence research unit (http://pdi.fbk.eu) is a young and internationally well known research group focused on the interplay between formal (and especially logic-based) representations of knowledge and data driven representations of knowledge, with particular emphasis on Process-aware systems and semantic web based systems. The PhD will be conducted under the joint supervision of Dr. Chiara Ghidini and Prof. Wil van der Aalst, in his capacity as visiting scholar at Fondazione Bruno Kessler. 
 
To get in contact with the PDI Research unit and discuss about the opportunities of this call contact Chiara Ghidini at ghidini@fbk.eu.
 
To know more on the topic have a look at: 
Wil M. P. van der Aalst, Riccardo De Masellis, Chiara Di Francescomarino, Chiara Ghidini:
Learning Hybrid Process Models From Events: Process Discovery Without Faking Confidence. 
In Proc. of the 15th International Conference on Business Process Management (BPM2017). 
To appear. Long version accessible at https://arxiv.org/abs/1703.06125.    

 

Master/Bachelor Stages and Thesis

We are currently offering several opportunities for undergrad and master thesis as well as stages. The topics are listed and briefly described below. The precise scope and workload of each assignment will be discussed with the students in order to match the effort required for the different types of  work (thesis vs stage, master vs undergrad, and so on).

An overview of topics and challenges addressed in previous internships may be found here.

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TITLE: Multilingual knowledge matching

TOPIC DESCRIPTION:
The purpose of this stage, that can be estended also for a thesis, is to investigate on using multilingual technologies for matching ontologies and processes.
The tasks assigned to the candidate will be (i) the extension of the current version of the system with feature for refining the current approach; (ii) the development a set of API in order to expose the platform as matching service; and, (iii) the refactor of the existing source code.

PREREQUISITES:

  • Java programming;
  • Ontologies;
  • Basic knowledge of existing linguistic resources like WordNet.

COMPETENCIES TO BE ACQUIRED:

  • Improve the development capabilities with the Java language
  • Knowledge of Semantic Web technologies

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TITLE: Javascript in the deep [Stage only]

TOPIC DESCRIPTION:
The student will work on the source code of the Signavio-core javascript library [1]: a library specifically created for modeling business processing directly on web pages. Recent updates of web browsers and of the policies of cross-domain interactions led to new versions of the most known javascript frameworks in order to make them compliant to such policies. Unfortunately, the Signavio-core library is not maintained since 2012 and some maintenance is required. During this stage the student will be responsible of: - fixing the library in order to make it compliant with recent web standards and policies; - add some functionalities useful for improving the effectiveness of the process modeling task; - analyze the structure of the library and produce documentation easing the integration of new functionalities.

PREREQUISITES:

  • Knowledge of the Javascript language.
  • Knowledge about how the Canvas object works.

COMPETENCIES TO BE ACQUIRED:

  • Improve javascript competencies.
  • Capacity of analyzing complex source code.
  • Produce effective documentation for supporting further development activities.


[1] https://github.com/dearshor/signavio-core-components

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TITLE: Refactor of the MoKi Tool source code

TOPIC DESCRIPTION:
MoKi [1] is a process and knowledge modeling tool, currently maintained by the PDI research unit, as extension of the MediaWiki library [2].
The current integration between MoKi and Mediawiki is quite strong and this dependency limits, sometimes, the possibility of extending or optimizing MoKi functionalities.
The long term goal is to detach many MoKi components from Mediawiki in order to improve the overall efficiency of the tool.
During this stage, that can be extended also for a thesis, the student will be responsible of:

  • analyze the MoKi source code and draw the interaction between all components;
  • understand the points of interaction between MoKi and Mediawiki in order to develop a first abstraction layer;
  • design a proposal for a new architecture;
  • update MoKi components by adapting them to the new architecture;
  • refactor and clean the MoKi code in order to optimize it.

PREREQUISITES:

  • Knowledge of the object-oriented paradigm.
  • Knowledge of the PHP language.
  • Knowledge of the Javascript language.

COMPETENCIES TO BE ACQUIRED:

  • Improve PHP and Javascript competencies.
  • Increase the capability of analyzing complex source code architectures.
  • Improve object-oriented development competences.
  • Improve software design capabilities.


[1] https://moki.fbk.eu/website/index.php
[2] https://www.mediawiki.org/wiki/MediaWiki

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TITLE: Deep knowledge extraction from text

Ontology Learning is the task to extract formal knowledge from natural language text. State of the art methods typically use hand-crafted rules over the result of standard Natural Language Processing (NLP) toolkits. Such rules are hard to build and maintain and unable to capture large part of the natural language variability. We building an entirely machine-learning system based on Deep Learning techniques which is able to learn such rules, to cover a larger variability over natural language and to be extended in a cheaper way. The first step of this journey is to build a significative dataset and some benchmark for this task and to validate the approach over them. You will surf the web searching for suitable data to be collected, cleaned up and annotated. Then, you will help developing neural network based models to be trained and evaluated over such data.

PREREQUISITES:

  • Being a B.Sc. or M.Sc. student in computer science

  • Working knowledge of Java/Scala (Apache Spark or similar is a plus) and Python (TensorFlow or Theano is a huge plus)

  • Basic knowledge of Neural Networks

  • Basic knowledge of typical NLP tasks

COMPETENCIES TO BE ACQUIRED:

  • Improve your programming skills in Java/Scala and/or Python using bleeding-edge technologies for data analytics (Apache Spark and TensorFlow) and NLP toolkits (like the Stanford CoreNLP toolkit and PIKES)

  • Improve your knowledge being in the sweet spot among Deep Learning, Natural Language Processing and Knowledge Extraction/Representation

  • Improve your confidence with tools and methodologies which are required in high profile developers and engineers.

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TITLE: Incremental Predictive Monitoring of Business Processes [STAGE+THESIS]

TOPIC DESCRIPTION: 
Predictive monitoring of business processes aims at providing predictions on the execution of business processes by learning from the past.
Traditional approaches to predictive process monitoring are based on a training phase, in which training data are used to learn, and a running phase in which the future of current ongoing traces is predicted.
However, as soon as the future of the current trace becomes present, more up-to-date training data is made available.
Purpose of the thesis is investigating incremental machine learning algorithms in order to be able to incrementally update the predictive model and provide more accurate and up-to-date predictions.

PREREQUISITES:

  • Programming skills (preferably Java).

COMPETENCIES TO BE ACQUIRED: 

  • Knowledge and analysis of ML online and incremental algorithms.
  • Knowledge on business process predictive monitoring.
  • Knowledge on log representation and standards (XES).

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TITLE: Data and time-aware Synthetic Log Generator [THESIS]

TOPIC DESCRIPTION: 
In the field of process mining, benchmark execution logs are of the utmost importance for testing, validating and comparing process mining tools and techniques.
Unfortunately, real-life benchmarks are often difficult to find, thus hampering the assessment and validation of the research in process mining.
A possibility to overcome such a big issue is synthetically generating execution logs starting from a business process model, which can also be enriched with data and temporal constraints.
Purpose of the thesis is realizing a tool for the generation of synthetic logs starting from an enriched business process model (e.g., enriched with data or time constraints).

PREREQUISITES:

  • Programming skills (preferably Java)

COMPETENCIES TO BE ACQUIRED

  • Knowledge on business process modelling languages (BPMN).
  • Knowledge on log representation and standards (XES).
  • Knowledge and analysis of business process replay algorithms
  • UI programming skills.       

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TITLE: Declarative Hierarchical Process Editor [STAGE+THESIS]

TOPIC DESCRIPTION: 
While the notion of hierarchy has been widely investigated in the literature for procedural process models in terms of languages for describing and applications of the process/subprocess relationship, it is still a relatively young field for declarative process models.  
No tools or instruments supporting the modeling of hierarchical process models are currently available.
Purpose of this thesis is implementing a tool which could support the modeling of hierarchical declarative process models and investigate its advantages in terms of understandability and reuse.

PREREQUISITES:

  • Programming skills (preferably Java)

COMPETENCIES TO BE ACQUIRED:

  • Knowledge on process modelling declarative languages (Declare).
  • Knowledge on Linear Temporal Logics (LTL).
  • UI programming skills.

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TITLE: Deviance Mining [THESIS]

TOPIC DESCRIPTION: 
Deviance mining aims at identifying business process cases that deviate from the standard behavior (e.g., process executions that do not respect a constraint, process executions that are slower than expected, etc.) and at explaining the reasons why those cases present a deviance based on the control flow and the data flow characteristics of the process cases.
Purpose of the work is identifying and explain deviant behaviors using declarative rules to describe control flow and the data flow characteristics of the process cases.

PREREQUISITES:

  • Programming skills (preferably Java)

COMPETENCIES TO BE ACQUIRED:

  • Knowledge on business process modelling languages (Petri Net, BPMN).
  • Knowledge on deviance mining.
  • Knowledge on declarative process modelling languages (Declare).
  • Knowledge and analysis of ML algorithms.
  • UI programming skills.

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TITLE: Change Propagation between Business Rules and Process Models

TOPIC DESCRIPTION: 
In real world situations it often happens that (procedural) business process models have to be compliant to some constraints (e.g., laws, policies, standards).
In these situations, whenever a change occurs in the rules, the process model has to be changed accordingly (i.e., the change has to be propagated to the process model), in order to guarantee that the compliance is preserved.
It can happen, however, that business process models evolve over the time and the rules become obsolete.
In this scenario, a change in the process model has to be propagated to the rules.
Purpose of this work is investigating how to propagate changes from the rules to the process models or vice versa so as to preserve compliance. 

PREREQUISITES:

  • Programming skills

COMPETENCIES TO BE ACQUIRED:

  • Knowledge on business process modelling languages (Petri Net, BPMN).
  • Knowledge on declarative process modelling languages (Declare).
  • Knowledge on process    

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TITLE: Monitoring of Business Processes with Fuzzy Logic

TOPIC DESCRIPTION: 
In different research fields a research issue has been to establish if the external, observed behavior of an entity is conformant to some rules/specifications/expectations.
Most of the available systems, however, provide only simple yes/no answers to the conformance issue.
Some works introduce the idea of a gradual conformance, expressed in fuzzy terms.
The conformance degree of a process execution is represented through a fuzzy score.
In this thesis, we provide an approach to monitor process executions ad give at runtime diagnostics based on fuzzy conformance.
The approach will be implemented in the process mining tool ProM and experimented in real life case studies.

PREREQUISITES:

  • Programming skills (preferably in Java)

COMPETENCIES TO BE ACQUIRED:

  • Knowledge on business process modelling languages (Petri Net).
  • Knowledge and analysis of conformance checking algorithms.
  • ProM tool.
  • UI programming skills.