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Tutorials

The role of the tutorials is to provide a platform for a more intensive scientific exchange amongst researchers interested in a particular topic and as a meeting point for the community. Tutorials complement the depth-oriented technical sessions by providing participants with broad overviews of emerging fields. A tutorial can be scheduled for 1.5 or 3 hours.

TUTORIALS LIST

How to Mine Enterprise Ontologies  (IC3K)
Lecturer(s): Linda Terlouw and Jan Dietz

Hudup - A Framework of E-commercial Recommendation Algorithms  (IC3K)
Lecturer(s): Loc Nguyen

Traceability and Structuring Knowledge from Cooperative Activity  (IC3K)
Lecturer(s): Nada Matta



How to Mine Enterprise Ontologies


Lecturers

Linda Terlouw
ICRIS Consultancy, Antwerp Management School, Avans University of Applied Sciences, Nyenrode Business University
Netherlands
 
Brief Bio
Dr. ir. Linda Terlouw holds both an MSc in Computer Science and an Msc in Business Information from the University of Twente. Her PhD research focused on modularization of organizations and IT systems using Enterprise Ontology and Service-Oriented Architecture. At the moment she is mainly working on data science (e.g. forecasting), data visualization and process mining (www.processminingfactory.com). Before she started her own company, Icris, she worked for IBM and Ordina (a large Dutch consulting firm). Clients she has been working for include several Dutch water suppliers, a large municipality, several factories, and the Ministry of Defense. She is lector (professor) at the Avans University of Applied Sciences and teaches at Nyenrode Business University and Antwerp Management School.
Jan Dietz
Delft University of Technology
Netherlands
 
Brief Bio
Jan L.G. Dietz is emeritus full professor in Information Systems Design at Delft University of Technology, full professor in Enterprise Engineering at Delft University of Technology, and director of Sapio (www.sapio.nl). He holds a Master degree in Electrical Engineering and a Doctoral degree in Computer Science. He has published over 200 scientific and professional articles and books. His current research interests are in the emerging discipline of Enterprise Engineering, of which Enterprise Architecture, Enterprise Ontology, and Enterprise Governance are the major pillars. Before his academic career, he has practiced application software engineering for ten years in industry. Jan Dietz is the spiritual father of DEMO (Design & Engineering Methodology for Organizations), and honorary chairman of the Enterprise Engineering Institute (www.ee-institute.com). For the development of Enterprise Engineering, he chairs the international research network CIAO! (www.ciaonetwork.org). He also acts as editor-in-chief of a book series on Enterprise Engineering, published by Springer. For more information, visit http://en.wikipedia.org/wiki/Jan_Dietz
Abstract

Enterprise ontologies enjoy a growing interest among various kinds of professionals: information systems engineers, enterprise architects, organisation designers, etc. Having a common and stable understanding of the essential parts of an enterprise’s operations, abstracted from the ever-changing way in which they are realised and implemented, is growingly considered to be a key asset for managing and governing enterprises. The term “enterprise ontology” is the modern name for such an understanding. Enterprise ontologies also constitute the basis for designing enterprise information systems (including data bases), knowledge management systems, and business processes.
Recognising the importance of having an appropriate ontological model of the enterprise (or of a part of it) is one thing, being able to construct it is a quite different matter. Often, constructing is considered similar to designing, but that is incorrect. The ontology of a running enterprise is just there, it is present in the operations. It only has to be uncovered or, in modern terminology, mined. To achieve this, it is crucial to have an appropriate meta model. Most current meta models, commonly called universal or root or base ontologies, appear to originate from academic communities with a greater interest in their formal beauty and their ultimate and universal truth, than in their truthful reflection of how the people in the enterprise’s organisation experience their daily work. Thus, being human-centric seems to be an indispensable quality for enterprise ontologies, in addition to other qualities.
In this tutorial you will acquire knowledge about two things. The first one is an ontological meta model that has proven, in over twenty years of practical application, to be appropriate and effective in uncovering the ontological model of an enterprise. It is the notion of enterprise ontology as contained in the DEMO methodology. An ontological model produced by applying DEMO is not only abstracted from implementation, notably from ICT, but also from realisation, i.e. from all informational and documental issues, thus from information systems, data bases, document management systems, etc. The resulting ontological model of the enterprise is called its essential model. The theoretical basis of DEMO is constituted by the core theories of the emerging discipline of Enterprise Engineering, in particular the PSI theory (Performance in Social Interaction), which is truly human-centric. It is rooted in the theory of communicative action by Jürgen Habermas, and it focuses on the competence, authority and responsibility of the employees.
The second thing that you will learn is a method and tool to actually mine the ingredients for validating and/or establishing the essential model of an enterprise. Often interactions between people are registered in information systems, like ERP or CRM systems. We can use these data to find deviations from the desired way of working. The essential model of the organisation, combined with analysed data concerning identified deviations, provides crucial insight for business process redesign. In addition, we can find bottlenecks in the process by analysing waiting times, throughput times, etc.













Secretariat Contacts
e-mail: ic3k.secretariat@insticc.org

Hudup - A Framework of E-commercial Recommendation Algorithms


Lecturer

Loc Nguyen
Sunflower Soft Company, Vietnam
Vietnam
 
Brief Bio
Loc Nguyen is Director of Sunflower Soft Company, Vietnam from 2011. He holds Postdoctoral degree in Computer Science, certified by Institute for Systems and Technologies of Information, Control and Communication (INSTICC) in 2015. Currently, he is interested in poet, computer science, statistics, and mathematics. He serves as reviewer and editor in a wide range of international journals and conferences from 2014. He is volunteer of Statistics Without Borders - American Statistics Association from 2015. He is granted as Mathematician by London Mathematical Society for Postdoctoral research in Mathematics and awarded as Professor by International Journal of Applied Mathematics and Machine Learning from 2016. He has published 38 papers in journals, books and conference proceedings, including 6 ISI papers from 2009. He is author of 2 scientific books from 2015. He is author and creator of 5 scientific and technology products from 2004. Moreover, he is Vietnamese-language poet who composed 1 verse story and 5 collections of 280 poems from 1993. He also has 2 music albums in which many poems are chanted by famous artists from 2007.
Abstract

Recommendation algorithm is very important to e-commercial websites when it can provide favorite products to online customers, which results out an increase in sale revenue. I propose the infrastructure for e-commercial recommendation solutions. It is a middleware framework of e-commercial recommendation software, which supports scientists and software developers to build up their own recommendation algorithms with low cost, high achievement and fast speed. The description of proposed framework is published in American Journal of Computer Science and Information Engineering – American Association for Science and Technology (AASCIT). The framework is also accepted in European Project Space – Institute for Systems and Technologies of Information, Control and Communication (INSTICC). Here I would like to make a tutorial which helps scientists to comprehend and use the framework for their own researches. The trial product is available at http://www.hudup.net.


Keywords

Recommendation Algorithm, Recommendation Server, Middleware Framework.

Target Audience

Audiences should be researchers who are interested in recommendation field. Software developers are welcome in warm.

Detailed Outline

The product provides infrastructure for e-commercial recommendation solutions, named Hudup. This is a middleware framework of e-commercial recommendation software, which supports scientists and software developers to build up their own recommendation solutions. The term “recommendation solution” refers to computer algorithm that introduces online customer a list of items such as books, products, services, news papers, and fashion clothes on e-commercial websites with expectation that customer will like these recommended items. The goal of recommendation algorithm is to gain high sale revenue.
You need to develop a recommendation solution for online-sale website. You, a scientist, invent a new algorithm after researching many years. Your solution is excellent and very useful and so you are very excited but:
- You cope with complicated computations when analyzing big data and there are a variety of heterogeneous models in recommendation study.
- It is impossible for you to evaluate your algorithm according to standard metrics.
- There is no simulation environment or simulator for you to test feasibility of your algorithm.

The innovative product Hudup supports you to solve perfectly three difficulties above and so following are your achievements: 1) Realizing your solution is very fast and easy. 2) Evaluating your solution according to standard metrics by the best way. 3) Determining feasibility of your algorithm in real-time applications. Hudup aims to help you, a scientist or software developer, to solve three core problems above. Hudup proposes three solution stages for developing a recommendation algorithm.
- Base stage builds up algorithm model and data model to help you to create new software with lowest cost.
- Evaluation stage builds up evaluation metrics and algorithm evaluator to help you to assess your own algorithm.
- Simulation stage builds up recommendation server (simulator), which helps you to test feasibility of your algorithm.

Therefore, the tutorial aims to help scientists to comprehend Hudup first and apply it into their researches second. The tutorial includes 3 sections:
1. The first section “General Description” introduces features of Hudup such as its purposes, general architectures, and similar products.
2. The second section “Core Classes and Interfaces” focuses on describing internal programming classes and interfaces of Hudup according to viewpoint of software engineering. This section is very important for software developers who are willing to use and improve Hudup.
3. The third section “Tutorial on Proposed Framework” is the guidance which helps scientists how to implement, evaluate, and deploy their algorithm based on Hudup according to three aforementioned stages (base stage, evaluation stage, simulation stage).

Secretariat Contacts
e-mail: ic3k.secretariat@insticc.org

Traceability and Structuring Knowledge from Cooperative Activity


Lecturer

Nada Matta
University of Technology of Troyes
France
 
Brief Bio
Nada Matta is Professor at the University of Technology of Troyes. Studies techniques in knowledge engineering and management and specially to handle cooperative activities. Currently director of Human, Environment and Technology of Information and Communication Department. Occupied several responsibilities: Director of Scientific Group of Safety and Security for 5 years, director of Human and ICT Department for one year, Director of Information System Department for 2 years. organized several workshops and did Tutorials in Knowledge management jointly to IJCAI, ECAI, CTS, and conferences. Did the PhD in knowledge engineering and Artificial Intelligence at University of Paul Sabatier in collaboration with ARTEMIS. Worked for four years at INRIA in projects with Dassault-Aviation and Airbus Industry.
Abstract

The aim of this tutorial is to present techniques that help to capture and manage knowledge from cooperative activities. After presenting collaboration principles, approaches of traceability, multi-level and multi-views structuring from several sources: daily works, discussions, interactions, etc. are shown.












Secretariat Contacts
e-mail: ic3k.secretariat@insticc.org

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