This document provides information about the IC3K 2015 conference proceedings. It lists the invited speakers, workshop chairs, special session chairs, organizing committees, program committee, and auxiliary reviewers for the conference. It also includes brief contents and forewords about the conference volumes. The conference was sponsored by INSTICC and technically co-sponsored by IEEE CS and was composed of three co-located conferences on knowledge discovery, knowledge engineering, and knowledge management.

2. IC3K 2015
Proceedings of the
7th International Joint Conference on
Knowledge Discovery, Knowledge Engineering and
Knowledge Management
Volume 3: KMIS
Lisbon - Portugal
November 12 - 14, 2015
Sponsored by
INSTICC - Institute for Systems and Technologies of Information, Control and Communication
Technically Co-sponsored by
IEEE CS - TCBIS - IEEE Technical Committee on Business Informatics and Systems
In Cooperation with
ACM SIGMIS - ACM Special Interest Group on Management Information Systems
ACM SIGAI - ACM Special Interest Group on Artificial Intelligence
AIXIA - Associazione Italiana per l’Intelligenza Artificiale
AAAI - Association for the Advancement of Artificial Intelligence
ERCIM - The European Research Consortium for Informatics and Mathematics

3. Copyright c 2015 by SCITEPRESS – Science and Technology Publications, Lda.
All rights reserved
Edited by Ana Fred, Jan Dietz, David Aveiro, Kecheng Liu and Joaquim Filipe
Printed in Portugal
ISBN: 978-989-758-158-8
Depósito Legal: 400034/15
http://www.kmis.ic3k.org
kmis.secretariat@insticc.org

4. BRIEF CONTENTS
INVITED SPEAKERS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IV
WORKSHOP CHAIRS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IV
SPECIAL SESSIONS CHAIRS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IV
ORGANIZING COMMITTEES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . V
PROGRAM COMMITTEE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . VI
AUXILIARY REVIEWERS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . VII
WORKSHOP PROGRAM COMMITTEE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . VII
SPECIAL SESSIONS PROGRAM COMMITTEE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . VIII
SELECTED PAPERS BOOK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . VIII
FOREWORD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IX
CONTENTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . XIII
III

5. INVITED SPEAKERS
Jan Vanthienen
KU Leuven
Belgium
João César das Neves
Universidade Católica Portuguesa (UCP)
Portugal
Giancarlo Guizzardi
Federal University of Espirito Santo, Brazil and Laboratory for Applied Ontology (LOA), Institute for
Cognitive Science and Technology, Italian National Research Council (CNR)
Italy
Ralf Bogusch
Airbus Defence and Space
Germany
WORKSHOP CHAIRS
1ST INTERNATIONAL WORKSHOP ON THE DESIGN, DEVELOPMENT AND USE OF KNOWLEDGE IT
ARTIFACTS IN PROFESSIONAL COMMUNITIES AND AGGREGATIONS
Federico Cabitza, Università degli Studi Milano-Bicocca, Italy
Angela Locoro, Università degli Studi Milano-Bicocca, Italy
Aurelio Ravarini, Università Carlo Cattaneo, Italy
SPECIAL SESSIONS CHAIRS
SPECIAL SESSION ON RESEARCH AND DEVELOPMENT ON BUSINESS PROCESS MANAGEMENT
Nuno Pina Gonçalves, Superior School of Technology, Polithecnical Institute of Setúbal, Portugal
SPECIAL SESSION ON INFORMATION SHARING ENVIRONMENTS TO FOSTER CROSS-SECTORIAL
AND CROSS-BORDER COLLABORATION BETWEEN PUBLIC AUTHORITIES
Rauno Pirinen, Laurea University of Applied Sciences, Finland
Fernando Sérgio Bryton Dias Marques, Directorate General for Maritime Policy, Portugal
IV

6. ORGANIZING COMMITTEES
CONFERENCE CHAIR
Joaquim Filipe, Polytechnic Institute of Setúbal / INSTICC, Portugal
PROGRAM CHAIR
Kecheng Liu, University of Reading, United Kingdom
SECRETARIAT
Susana Rodrigues, INSTICC, Portugal
CD-ROM PRODUCTION
Pedro Varela, INSTICC, Portugal
GRAPHICS PRODUCTION AND WEBDESIGNER
André Lista, INSTICC, Portugal
Mara Silva, INSTICC, Portugal
WEBMASTER
Susana Rodrigues, INSTICC, Portugal
V

7. PROGRAM COMMITTEE
Marie-Helene Abel, HEUDIASYC CNRS UMR,
University of Compiègne, France
Shamsuddin Ahmed, University of Malaya,
Malaysia
Miriam C. Bergue Alves, Institute of Aeronautics
and Space, Brazil
Rangachari Anand, IBM T. J. Watson Research
Center, United States
Chimay J. Anumba, Pennsylvania State
University, United States
Carlos Alberto Malcher Bastos, Universidade
Federal Fluminense, Brazil
Sonia Bergamaschi, DIEF - University of Modena
and Reggio Emilia, Italy
Silvana Castano, Università degli Studi di Milano,
Italy
Marcello Castellano, Politecnico di Bari, Italy
Xiaoyu Chen, School of Mathematics and Systems
Science, China
Dickson K. W. Chiu, Dickson Computer Systems,
Hong Kong
Byron Choi, Hong Kong Baptist University,
Hong Kong
Dominique Decouchant, LIG de Grenoble, France
& UAM Cuajimalpa, Mexico
Ian Douglas, Florida State University,
United States
Alan Eardley, Staffordshire University,
United Kingdom
Joao Carlos Amaro Ferreira, ISEL, Portugal
Joan-Francesc Fondevila-Gascón, CECABLE
(Centre d’Estudis sobre el Cable), UAO and UOC,
Spain
Anna Goy, University of Torino, Italy
Francesco Guerra, University of Modena and
Reggio Emilia, Italy
Renata Guizzardi, Federal University of Espirito
Santo (UFES), Brazil
Jennifer Harding, Loughborough University,
United Kingdom
Mounira Harzallah, LINA, France
Anca Daniela Ionita, University Politehnica of
Bucharest, Romania
Nikos Karacapilidis, University of Patras & CTI,
Greece
Radoslaw Katarzyniak, Wroclaw University of
Technology, Poland
Helmut Krcmar, Technische Universität München,
Germany
Elise Lavoué, Université Jean Moulin Lyon 3,
France
Kecheng Liu, University of Reading,
United Kingdom
Heide Lukosch, Delft University of Technology,
Netherlands
Xiaoyue Ma, Univesrity of Xidian, China
Federica Mandreoli, University of Modena and
Reggio Emilia Italy, Italy
Nada Matta, University of Technology of Troyes,
France
Christine Michel, INSA-Lyon, Laboratoire LIRIS,
France
Michele Missikoff, ISTC-CNR, Italy
Owen Molloy, National University of Ireland,
Galway, Ireland
Jean-Henry Morin, University of Geneva,
Switzerland
Minh Nhut, Institute for Infocomm Research (I2R),
A*STAR, Singapore, Singapore
Augusta Maria Paci, National Research Council of
Italy, Italy
Wilma Penzo, University of Bologna, Italy
José de Jesus Pérez-Alcázar, University of São
Paulo (USP), Brazil
Milly Perry, The Open University, Israel
Erwin Pesch, University Siegen, Germany
Filipe Portela, Centro Algoritmi, Universidade do
Minho, Portugal
Arkalgud Ramaprasad, University of Illinois at
Chicago, United States
Edie Rasmussen, University of British Columbia,
Canada
Marina Ribaudo, Università di Genova, Italy
VI

8. Colette Rolland, Université De Paris1 Panthèon
Sorbonne, France
Masaki Samejima, Osaka University, Japan
Marilde Terezinha Prado Santos, Federal
University of São Carlos - UFSCar, Brazil
Conrad Shayo, California State University,
United States
Paolo Spagnoletti, LUISS Guido Carli University,
Italy
Malgorzata Sterna, Poznan University of
Technology, Poland
Deborah Swain, North Carolina Central University,
United States
Esaú Villatoro Tello, Universidad Autonoma
Metropolitana (UAM), Mexico
Bhavani Thuraisingham, University of Texas at
Dallas, United States
Shu-Mei Tseng, I-SHOU University, Taiwan
Martin Wessner, Darmstadt University of Apllied
Sciences, Germany
Uffe K. Wiil, University of Southern Denmark,
Denmark
Leandro Krug Wives, Universidade Federal do Rio
Grande do Sul, Brazil
Jie Yang, Shanghai Jiao Tong University, China
AUXILIARY REVIEWERS
Fabio benedetti, Unimore, Italy Diego Magro, University of Torino, Italy
WORKSHOP PROGRAM COMMITTEE
1ST INTERNATIONAL WORKSHOP ON THE DESIGN, DEVELOPMENT AND USE OF KNOWLEDGE IT
ARTIFACTS IN PROFESSIONAL COMMUNITIES AND AGGREGATIONS
Jorgen Bansler, University of Copenhagen,
Denmark
Merja Bauters, Metropolia UAS, Finland
Peter Bednar, University of Portsmouth,
United Kingdom
Federico Cabitza, Università degli Studi di
Milano-Bicocca, Italy
Andrea Carugati, Aarhus University School of
Business and Social Sciences, Denmark
Claudia d’Amato, Università di Bari, Italy
U. Yeliz Eseryel, University of Groningen,
Netherlands
Daniela Fogli, Università degli Studi di Brescia,
Italy
Anna De Liddo, The Open University,
United Kingdom
Angela Locoro, Università degli Studi
Milano-Bicocca, Italy
Stefania Marrara, Università deli Studi di Milano
Bicocca, Italy
Andrea Maurino, University of Milano Bicocca,
Italy
Giorgio De Michelis, University of Milano -
Bicocca, Italy
Katia Passerini, NJIT, United States
Antonio Piccinno, University of Bari, Italy
Enrico Maria Piras, Fondazione Bruno Kessler -
Trento, Italy
Aurelio Ravarini, Università Carlo Cattaneo, Italy
Carla Simone, Università degli studi di
Milano-Bicocca, Italy
Emanuele Strada, Liuc, Italy
VII

9. Monica Chiarini Tremblay, Florida International
University, United States
Marco Viviani, Università di Milano Bicocca, Italy
Giuseppe Vizzari, University of Milano-Bicocca,
Italy
Massimo Zancanaro, Fondazione Bruno Kessler,
Italy
SPECIAL SESSIONS PROGRAM COMMITTEE
SPECIAL SESSION ON RESEARCH AND DEVELOPMENT ON BUSINESS PROCESS MANAGEMENT
Nuno Pina Gonçalves, Superior School of
Technology, Polithecnical Institute of Setúbal,
Portugal
Jose Antonio Sena Pereira, Superior School of
Technology, Polytechnical Institute of Setúbal,
Portugal
SPECIAL SESSION ON INFORMATION SHARING ENVIRONMENTS TO FOSTER CROSS-SECTORIAL
AND CROSS-BORDER COLLABORATION BETWEEN PUBLIC AUTHORITIES
Sotirios Kanellopoulos, National Center for
Scientific Research DEMOKRITOS, Greece
Fernando Sérgio Bryton Dias Marques,
Directorate General for Maritime Policy, Portugal
Jyri Rajamäki, Laurea University of Applied
Sciences, Finland
SELECTED PAPERS BOOK
A number of selected papers presented at KMIS 2015 will be published by Springer-Verlag in a CCIS Series
book. This selection will be done by the Conference Chair and Program Chair, among the papers actually
presented at the conference, based on a rigorous review by the KMIS 2015 Program Committee members.
VIII

10. FOREWORD
This volume contains the proceedings of the Seventh International Joint Conference on Knowledge Dis-
covery, Knowledge Engineering and Knowledge Management (IC3K 2015) which was sponsored by the
Institute for Systems and Technologies of Information, Control and Communication (INSTICC) and held in
Lisbon, Portugal.
IC3K was organized in cooperation with the AAAI - Association for the Advancement of Artificial Intelli-
gence, ACM SIGMIS - ACM Special Interest Group on Management Information Systems, ACM SIGAI -
ACM Special Interest Group on Artificial Intelligence, Associazione Italiana per l’Intelligenza Artificiale,
APPIA - Portuguese Association for Artificial Intelligence and ERCIM - European Research Consortium
for Informatics and Mathematics and technically co-sponsored by IEEE CS - TCBIS - IEEE Technical
Committee on Business Informatics and Systems.
The main objective of IC3K is to provide a point of contact for scientists, engineers and practitioners inter-
ested on the areas of Knowledge Discovery, Knowledge Engineering and Knowledge Management. IC3K
is composed of three co-located complementary conferences, each specialized in one of the aforementioned
main knowledge areas. Namely: International Conference on Knowledge Discovery and Information Re-
trieval (KDIR); International Conference on Knowledge Engineering and Ontology Development (KEOD);
International Conference on Knowledge Management and Information Sharing (KMIS).
The International Conference on Knowledge Discovery and Information Retrieval (KDIR) aims to provide a
major forum for the scientific and technical advancement of knowledge discovery and information retrieval.
Knowledge Discovery is an interdisciplinary area focusing upon methodologies for identifying valid, novel,
potentially useful and meaningful patterns from data, often based on underlying large data sets. A major
aspect of Knowledge Discovery is data mining, i.e. applying data analysis and discovery algorithms that
produce a particular enumeration of patterns (or models) over the data. Knowledge Discovery also includes
the evaluation of patterns and identification of which add to knowledge. This has proven to be a promising
approach for enhancing the intelligence of software systems and services. The ongoing rapid growth of
online data due to the Internet and the widespread use of large databases have created an important need for
knowledge discovery methodologies. The challenge of extracting knowledge from data draws upon research
in a large number of disciplines including statistics, databases, pattern recognition, machine learning, data
visualization, optimization, and high-performance computing, to deliver advanced business intelligence and
web discovery solutions.
Information retrieval (IR) is concerned with gathering relevant information from unstructured and seman-
tically fuzzy data in texts and other media, searching for information within documents and for metadata
about documents, as well as searching relational databases and the Web. Automation of information retrieval
enables the reduction of what has been called "information overload".
Information retrieval can be combined with knowledge discovery to create software tools that empower
users of decision support systems to better understand and use the knowledge underlying large data sets.
The purpose of the International Conference on Knowledge Engineering and Ontology Development
(KEOD) is to provide a point of contact for scientists, engineers and practitioners interested in the scientific
and technical advancement of methodologies and technologies for Knowledge Engineering and Ontology
Development both theoretically and in a broad range of application fields.
Knowledge Engineering (KE) refers to all technical, scientific and social aspects involved in building, main-
taining and using knowledge-based systems. KE is a multidisciplinary field, bringing in concepts and meth-
ods from several computer science domains such as artificial intelligence, databases, expert systems, deci-
sion support systems and geographic information systems. From the software development point of view,
KE uses principles that are strongly related to software engineering. KE is also related to mathematical
logic, as well as strongly involved in cognitive science and socio-cognitive engineering where the knowl-
edge is produced by humans and is structured according to our understanding of how human reasoning and
IX

11. logic works. Currently, KE is gradually more related to the construction of shared conceptual frameworks,
often designated as ontologies.
Ontology Development (OD) aims at building reusable semantic structures that can be informal vocabular-
ies, catalogs, glossaries as well as more complex finite formal structures specifying types of entities and
types of relationships relevant within a certain domain. Ontologies have been gaining interest and accep-
tance in computational audiences. For example, formal ontologies are increasingly used as one of the main
sources of software development and methodologies for this end can be adapted to include ontology devel-
opment. A wide range of applications is emerging, especially given the current web emphasis, including
library science, ontology-enhanced search, e-commerce and business process design.
The goal of the International Conference on Knowledge Management and Information Sharing (KMIS) is
to provide a major meeting point for researchers and practitioners interested in the study and application
of all perspectives of Knowledge Management and Information Sharing. Knowledge Management (KM)
is a discipline concerned with the analysis and technical support of practices used in an organization to
identify, create, represent, distribute and enable the adoption and leveraging of good practices embedded
in collaborative settings and, in particular, in organizational processes. Effective knowledge management
is an increasingly important source of competitive advantage, and a key to the success of contemporary
organizations, bolstering the collective expertise of its employees and partners.
There are several perspectives on KM, but all share the same core components, namely: People, Processes
and Technology. Some take a techno-centric focus, in order to enhance knowledge integration and creation;
some take an organizational focus, in order to optimize organization design and workflows; some take an
ecological focus, where the important aspects are related to people interaction, knowledge and environmen-
tal factors as a complex adaptive system similar to a natural ecosystem.
Information Sharing (IS) is a term used for a long time in the information technology (IT) lexicon, related
to data exchange, communication protocols and technological infrastructures. Although standardization is
indeed an essential element for sharing information, IS effectiveness requires going beyond the syntactic
nature of IT and delve into the human functions involved in the semantic, pragmatic and social levels of or-
ganizational semiotics. The two areas are intertwined as information sharing is the foundation for knowledge
management. KMIS aims at becoming a major meeting point for researchers and practitioners interested in
the study and application of all perspectives of Knowledge Management and Information Sharing.
The joint conference, IC3K received 314 paper submissions from 53 countries in all continents, of which
17% were accepted as full papers. The high quality of the papers received imposed difficult choices in the
review process. To evaluate each submission, a double blind paper review was performed by the Program
Committee, whose members are highly qualified independent researchers in the three IC3K Conferences
topic areas.
Moreover, the conference also featured a number of keynote lectures delivered by internationally well known
experts, namely Jan Vanthienen (KU Leuven, Belgium), João César das Neves (Universidade Católica Por-
tuguesa (UCP), Portugal), Giancarlo Guizzardi (Federal University of Espirito Santo, Brazil and Laboratory
for Applied Ontology (LOA), Institute for Cognitive Science and Technology, Italian National Research
Council (CNR), Italy) and Ralf Bogusch (Airbus Defence and Space, Germany), thus contributing to in-
crease the overall quality of the conferences and to provide a deeper understanding of the conferences
interest fields.
Workshops provide interactive fora that allow for a more in-depth discussion of particular areas within
the scope of the conference. We would like to thank the workshop chairs for their collaboration in pro-
viding these added-value satellite events of IC3K 2015 namely: 6th International Workshop on Software
Knowledge – SKY (chaired by Iaakov Exman, Juan Llorens, Anabel Fraga and Juan Miguel Gómez) and
1st International Workshop on the design, development and use of Knowledge IT Artifacts in professional
communities and aggregations – KITA (chaired by Federico Cabitza, Angela Locoro and Aurelio Ravarini).
IC3K was also complemented with the Special Session on Text Mining - SSTM (chaired by Ana Fred),
X

12. the Special Session on Information Filtering and Retrieval – DART (chaired by Cristian Lai, Alessandro
Giuliani and Giovanni Semeraro), the Special Session on Enterprise Ontology – SSEO (chaired by David
Aveiro), the Special Session on Research and Development on Business Process Management – RDBPM
(chaired by Nuno Pina Gonçalves) and the Special Session on Information Sharing Environments to Foster
Cross-Sectorial and Cross-Border Collaboration between Public Authorities - ISE (chaired by Rauno Pirinen
and Fernando Sérgio Bryton Dias Marques).
To recognize the best submissions and the best student contributions, awards based on the best combined
marks of paper reviewing, as assessed by the Program Committee, and the quality of the presentation, as
assessed by session chairs at the conference venue, were conferred at the closing session of the conference.
All presented papers will be submitted for indexation by Thomson Reuters Conference Proceedings Citation
Index (ISI), INSPEC, DBLP, EI (Elsevier Engineering Village Index) and Scopus, as well as being made
available at the SCITEPRESS Digital Library. Additionally, a short list of presented papers will be selected
to be expanded into a forthcoming book of IC3K Selected Papers to be published by Springer Verlag.
Building an interesting and successful program for the conference required the dedicated effort of many
people. We would like to express our thanks, first of all, to all authors including those whose papers were
not included in the program. We would also like to express our gratitude to all members of the Program
Committee and auxiliary reviewers, who helped us with their expertise and valuable time. Furthermore, we
thank the invited speakers for their invaluable contribution and for taking the time to synthesize and prepare
their talks.
Moreover, we thank the workshop and special session chairs whose contribution to the diversity of the
program was decisive. Finally, we gratefully acknowledge the professional support of the INSTICC team
for all organizational processes.
Ana Fred
Instituto de Telecomunicações / IST, Portugal
Jan Dietz
Delft University of Technology, Netherlands
David Aveiro
University of Madeira / Madeira-ITI, Portugal
Kecheng Liu
University of Reading, United Kingdom
Joaquim Filipe
Polytechnic Institute of Setúbal / INSTICC, Portugal
XI

14. CONTENTS
INVITED SPEAKERS
KEYNOTE SPEAKERS
On Smart Data, Decisions and Processes
Jan Vanthienen
5
Business Ethics as Personal Ethics
João César das Neves
7
Formal Ontology, Patterns and Anti-Patterns for Next-Generation Conceptual Modeling
Giancarlo Guizzardi
9
Ontology-based Systems Engineering - The Smart Way of Realizing Complex Systems
Ralf Bogusch
11
PAPERS
FULL PAPERS
repAIrC: A Tool for Ensuring Data Consistency - By Means of Active Integrity Constraints
Luís Cruz-Filipe, Michael Franz, Artavazd Hakhverdyan, Marta Ludovico, Isabel Nunes and
Peter Schneider-Kamp
17
A Practical Guide to Developing a Knowledge Management Culture (KMC) in a Non-Profit
Organization (NPO)
Tomasz Kampioni and Felicia Ciolfitto
27
Gaussian Process for Regression in Business Intelligence: A Fraud Detection Application
Bruno H. A. Pilon, Juan J. Murillo-Fuentes, João Paulo C. L. da Costa, Rafael T. de Sousa Júnior and
Antonio M. R. Serrano
39
Recommending Access Policies in Cross-domain Internet
Nuno Bettencourt, Nuno Silva and João Barroso
50
The Effect of Personality on Knowledge Creation Processes - Toward KC Optimization in Teams based
on Human Attributes
Jader Zelaya
62
Individual and Contextual Antecedents of Knowledge Acquisition Capability in Joint ICT Project
Teams in Malaysia
Adedapo Oluwaseyi Ojo and Murali Raman
70
Designing the Content of a Social e-Learning Dashboard - The Study is based on Novel Key
Performance Indicators
Paolo Avogadro, Silvia Calegari and Matteo Dominoni
79
XIII

15. SHORT PAPERS
The Epistemology of Resilient Organizations - Implications for Business Continuity Management
Eva Gatarik, Viktor Kulhavy and Rainer Born
93
Towards a Model to Reduce the Risk of Projects Guided by the Knowledge Management Process –
Application on FERTIAL
Brahami Menaouer, Nada Matta and Khalissa Semaoune
98
Concept, Information System, and Process: Exploring the Relationships Between Records and
Organizational Memory Towards an Integration
Qianqian Yang
106
Managing Knowledge in Enterprises - Evidences from China
Maria Obeso and Maria Jesus Luengo-Valderrey
111
How to Pick up the Needed Information about What Is Going Around Us: Information Awareness in
Crisis Management
Amina Saoutal, Nada Matta and Jean Pierre Cahier
119
Do Australian Universities Encourage Tacit Knowledge Transfer?
Ritesh Chugh
128
A Generic Interface Specification for Standardized Retrieval and Statistical Evaluation of Spatial and
Temporal Data
Jens Kohlmorgen
136
A Technique to Limit Packet Length Covert Channels
Anna Epishkina and Konstantin Kogos
144
Factors Affecting Knowledge Management & Knowledge Use - A Case Study
Leila Shahmoradi, Maryam Zahmatkeshan and Mahtab Karami
152
Exploiting the Collective Knowledge of Communities of Experts - The Case of Conference Ranking
Federico Cabitza and Angela Locoro
159
Baby Boomers Retirement in Oil and Gas - Challenges of Knowledge Transfer for Organizational
Competitive Advantage
Muhammad Saleem Sumbal, Eric Tsui and W. B. Lee
168
Towards an Ontology for Health Complaints Management
André Oliveira, Filipe Portela, José Machado, António Abelha, José Maia Neves, Suzana Vaz,
Álvaro Silva and Manuel Filipe Santos
174
Empowering Industrial Maintenance Personnel with Situationally Relevant Information using
Semantics and Context Reasoning
David Hästbacka, Pekka Aarnio, Valeriy Vyatkin and Seppo Kuikka
182
Information Systems: Towards a System of Information Systems
Majd Saleh and Marie-Hélène Abel
193
Knowledge Driven Community Self-reliance and Flood Resilience - Study of the Communities in the
Lower Sava Valley, Slovenia
Jernej Agrež and Nadja Damij
201
XIV

16. A Knowledge Management Toolkit based on Open Source
Roberta Mugellesi Dow, Hugo Marée, Raúl Cano Argamasilla, Jose A. Martínez Ontiveros,
Juan F. Prieto and Diogo Bernardino
207
A Complex Network Approach for Museum Services - A Model for Digital Content Management
Filippo Eros Pani, Simone Porru, Matteo Orrù and Simona Ibba
216
Co-Design of Information Systems with Digital Records Management - A Proposal for Research
Sherry L. Xie
222
User Modeling of Skills and Expertise from Resumes
Hua Li, Daniel J. T. Powell, Mark Clark, Tifani O’Brien and Rafael Alonso
229
Knowledge Management Problems in Paediatrics and Paediatrics Neurology Departments - A Case
Study based on the Grounded Theory
Helvi Nyerwanire, Erja Mustonen-Ollila, Antti Valpas and Jukka Heikkonen
234
Sharing Knowledge in Daily Activity: Application in Bio-Imaging
Cong Cuong Pham, Nada Matta, Alexandre Durupt, Benoit Eynard, Marianne Allanic,
Guillaume Ducellier, Marc Joliot and Philippe Boutinaud
242
An Ontology-Driven Knowledge Management System Used in the Patent Library
Wei Ding, Yongji Liu and Jianfeng Zhang
248
Process Extraction from Texts using Semantic Unification
Konstantin Sokolov, Dimitri Timofeev and Alexander Samochadin
254
Hybrid System for Collaborative Knowledge Traceability - An Application to Business Emails
Francois Rauscher, Nada Matta and Hassan Atifi
260
Work-based-Learning in the Digital Age
Roman Senderek and Volker Stich
268
Development and System Assessment of Learning Object Recommendation based on Competency -
RecOAComp
Patrícia Alejandra Behar, Ketia Kellen A. da Silva, Daisy Schneider, Sílvio César Cazella,
Cristina A. W. Torrezzan and Edimara Heis
274
A Knowledge Management Literature Review based on Wiig´s Prognosis of 1997
Zuzana Crhová, Drahomíra Pavelková and Jana Matošková
281
Building a Tool for Analyzing Interactions in a Virtual Learning Environment
Leticia Rocha Machado, Magali Longhi and Patricia Behar
287
The Flows of Concepts
Marcin Skulimowski
292
XV

17. SPECIAL SESSION ON RESEARCH AND DEVELOPMENT ON BUSINESS PROCESS
MANAGEMENT
FULL PAPERS
A Cost-centric Model for Context-aware Simulations of Business Processes
Vincenzo Cartelli, Giuseppe Di Modica and Orazio Tomarchio
303
From a Cloudy View Towards a More Structured Approach for Business Process Related Concepts
Necmettin Ozkan
315
A Survey on Modelling Knowledge-intensive Business Processes from the Perspective of Knowledge
Management
Christoph Sigmanek and Birger Lantow
325
SHORT PAPERS
Exploring the Role of Named Entities for Uncertainty Recognition in Event Detection
Masnizah Mohd and Kiyoaki Shirai
335
Detecting Topics Popular in the Recent Past from a Closed Caption TV Corpus as a Categorized
Chronicle Data
Hajime Mochizuki and Kohji Shibano
342
SPECIAL SESSION ON INFORMATION SHARING ENVIRONMENTS TO FOSTER
CROSS-SECTORIAL AND CROSS-BORDER COLLABORATION BETWEEN PUBLIC
AUTHORITIES
FULL PAPERS
Towards Common Information Sharing - Study of Integration Readiness Levels
Rauno Pirinen
355
Integration of the Finnish National Tax Administration Systems with EU Recapitulative Statement
Data
Raita Melasniemi and Rauno Pirinen
365
A Semantic Framework to Enrich Collaborative Tables with Domain Knowledge
Anna Goy, Diego Magro, Giovanna Petrone, Marco Rovera and Marino Segnan
371
Information Sharing Performance Management - A Semantic Interoperability Assessment in the
Maritime Surveillance Domain
Fernando S. Bryton Dias Marques, Jesús E. Martínez Marín and Olga Delgado Ortega
382
SHORT PAPERS
Cyber Security and Trust - Tools for Multi-agency Cooperation between Public Authorities
Jyri Rajamäki and Juha Knuuttila
397
Success Factors of Information Sharing in the Field of New Media Art
Päivi Meros and Rauno Pirinen
405
XVI

18. 1ST INTERNATIONAL WORKSHOP ON THE DESIGN, DEVELOPMENT AND
USE OF KNOWLEDGE IT ARTIFACTS IN PROFESSIONAL COMMUNITIES AND
AGGREGATIONS
SHORT PAPERS
“Objectivity” and “Situativity” in Knowledge It Artifacts - Incommensurable but Sensible Dimensions
in Different Contexts
Carla Simone
415
KM and KA in International Coooperation - Lesson from the K-Link project in Central Asia
Gianluca Colombo, Alessio Vertemati, Emanuele Panzeri, Eva Grolíková and Philipp Reichmut
421
Knowledge Artifacts: When Society Objectifies Itself in Knowledge
Andrea Cerroni
429
The Misfits in Knowledge Work - Grasping the Essence with the Lens of the IT Knowledge Artefact
Louise Harder Fischer and Lene Pries-heje
436
Mapping the Knowledge Artifact Terrain - A Quantitative Resource for Qualitative Research
Federico Cabitza and Angela Locoro
444
Retrieval, Visualization and Validation of Affinities between Documents
Luis Trigo, Martin Víta, Rui Sarmento and Pavel Brazdil
452
Discovering Communities of Similar R&D Projects
Martin Víta
460
Case based Reasoning as a Tool to Improve Microcredit
Mohammed Jamal Uddin, Giuseppe Vizzari and Stefania Bandini
466
Digital Platorms as Knowledge Artifacts for Clusters of SMEs
Aurelio Ravarini and Luca Cremona
474
AUTHOR INDEX 481
XVII

20. INVITED SPEAKERS

22. KEYNOTE SPEAKERS

24. On Smart Data, Decisions and Processes
Jan Vanthienen
KU Leuven, Belgium
Abstract: Modern, smart businesses use the full power of information and knowledge to reach excellent performance.
Intelligence is not just about the ability to obtain, model and understand information and processes, but also
about smart decisions, the capability to analyze, discover and manage knowledge, the power to adapt to new
situations and events in the networked economy, and the ability to perform effectively according to business
rules and policies in order to innovate and create value.
BRIEF BIOGRAPHY
Jan Vanthienen is full professor of business &
information systems engineering at KU Leuven
(Belgium), Department of Decision Sciences and
Information Management. He is an active researcher
in the area of intelligent business systems (rules,
decisions, processes, analytics). He has published
more than 150 full papers in reviewed international
journals and conference proceedings. Jan is a
founding member of the Leuven Institute for
Research in Information Systems (LIRIS), and a
member of the ACM and the IEEE Computer
Society. He is or was chairholder of the bpost bank
Research Chair on Actionable Customer Analytics,
the Colruyt Research Chair on Smart Marketing
Analytics, the PricewaterhouseCoopers Chair on E-
Business and the Microsoft Research Chair on
Intelligent Environments. He received an IBM
Faculty Award in 2011 on smart decisions, and the
Belgian Francqui Chair 2009 at FUNDP. He is co-
founder and president-elect of the Benelux
Association for Information Systems (BENAIS).
Jan is actively involved in the Decision
Modeling & Notation standard (DMN) at OMG
(Object Management Group). This standard is
designed to complement the Business Process
Modeling & Notation (BPMN) standard, in order to
integrate and distinguish business processes and
business decisions. He is also member of the IEEE
task force on process mining, and co-author of the
Business Process Mining Manifesto.
Vanthienen, J..
On Smart Data, Decisions and Processes.
In Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2015) - Volume 3: KMIS, page 5
ISBN: 978-989-758-158-8
Copyright c 2015 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
5

26. Business Ethics as Personal Ethics
João César das Neves
Universidade Católica Portuguesa (UCP), Portugal
Abstract: Business ethics is today an indispensable trait of any contemporary firm. But this vulgarization has, as
expected, signified a reduction in value. Does the enormous activity related to social responsibility in
modern business marked a real improvement in the ethical attitude of managers? Does it imply, at least, any
noteworthy gain in the moral credibility of companies? How can the contemporaneous enterprise, at the
cutting edge of progress, get in touch with one of the oldest and most determinant characteristics of the
human behaviour?
BRIEF BIOGRAPHY
João César das Neves, born in 1957, married, father
of four, is full professor at Universidade Católica
Portuguesa (UCP). Holds a PhD and BA in
Economics (UCP), MA in Economics (Universidade
Nova of Lisbon, Portugal) and MA in Operations
Research and System Engineering (Universidade
Técnica of Lisbon, Portugal).
Currently he is President of the Scientific
Council of the Catolica Lisbon Scholl of Business
and Economics of UCP. He was from 1991 to 1995
economic advisor of the Portuguese Prime Minister,
in 1990 advisor to the Portuguese Minister of
Finance and in 1990/1991 and 1995/1997 technician
at the Bank of Portugal.
His research interests are poverty and
development, business cycles, Portuguese economic
development, medieval economic tough and Ethics.
Author of more than 50 books, he is a regular
commentator at the Portuguese media.
Neves, J..
Business Ethics as Personal Ethics.
In Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2015) - Volume 3: KMIS, page 7
ISBN: 978-989-758-158-8
Copyright c 2015 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
7

28. Formal Ontology, Patterns and Anti-Patterns for Next-Generation
Conceptual Modeling
Giancarlo Guizzardi
Federal University of Espirito Santo, Brazil and Laboratory for Applied Ontology (LOA),
Institute for Cognitive Science and Technology, Italian National Research Council (CNR), Italy
Abstract: In his ACM Turing Award Lecture entitled “The Humble Programmer”, E. W. Dijkstra discusses the sheer
complexity one has to deal with when programming large computer systems. His article represented an open
call for an acknowledgement of the complexity at hand and for the need of more sophisticated techniques to
master this complexity. This talk advocates the view that we are now in an analogous situation with respect
to Conceptual Modeling. We will experience an increasing demand for building Reference Conceptual
Models in subject domains in reality, as well as employing them to address classes of problems, for which
sophisticated ontological distinctions are demanded.
One of these key problems is Semantic Interoperability. Effective semantic interoperability requires an
alignment between worldviews or, to put it more accurately, it requires the precise understanding of the
relation between the (inevitable) ontological commitments assumed by different conceptual models and the
systems based on them (including sociotechnical systems). This talk advocates the view that an approach
that neglects true ontological distinctions (i.e., Ontology in the philosophical sense) cannot meet these
requirements. The talk discusses the importance of foundational axiomatic theories and principles in the
design of conceptual modeling languages and models. Moreover, it discusses the role played by three types
of complexity management tools: Ontological Design Patterns (ODPs) as methodological mechanisms for
encoding these ontological theories; Ontology Pattern Languages (OPLs) as systems of representation that
take ODPs as higher-granularity modeling primitives; and Ontological Anti-Patterns (OAPs) as structures
that can be used to systematically identify possible deviations between the set of valid state of affairs
admitted by a model (the actual ontological commitment) and the set of state of affairs actually intended by
the stakeholders (the intended ontological commitment).
Finally, the talk elaborates on the need for proper computational tools to support a process of pattern-based
conceptual model creation, analysis, transformation and validation (via model simulation).
BRIEF BIOGRAPHY
Giancarlo Guizzardi holds a PhD (with the highest
distinction) in Computer Science from the
University of Twente, in The Netherlands. He
coordinates the Ontology and Conceptual Modeling
Group (NEMO) at the Federal University of Espírito
Santo in Brazil. He is also an Associate Researcher
at the Laboratory of Applied Ontology (ISTC-CNR),
Trento, Italy. Between 2013 and 2015, he was also a
Visiting Professor at the University of Trento, Italy.
He has been doing research in ontology and
conceptual modeling for the past two decades and
has published over 170 publications in these areas
(including 9 award-wining publications). Over the
years, he has contributed to the ontology and
conceptual modeling communities in roles such as
keynote speaker (e.g., ER), general chair (e.g.,
FOIS), tutorialist (e.g., CAISE, ER) and PC Chair
(e.g., FOIS, EDOC). He is an associate editor of the
Applied Ontology journal and is a member of
editorial boards of a number of other international
journals (e.g., Requirements Engineering). Between
2012 and 2014, he was an elected member of the
Executive Council of the International Association
of Ontologies and its Applications (IAOA) and
currently is a member of its Advisory Board (since
2014). Finally, his experience in ontology-driven
conceptual modeling has also been acquired in a
number of industrial projects in domains such as off-
shore software development, energy, digital
journalism, government, telecommunications,
product recommendation, and complex media
management.
Guizzardi, G..
Formal Ontology, Patterns and Anti-Patterns for Next-Generation Conceptual Modeling.
In Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2015) - Volume 3: KMIS, page 9
ISBN: 978-989-758-158-8
Copyright c 2015 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
9

30. Ontology-based Systems Engineering
The Smart Way of Realizing Complex Systems
Ralf Bogusch
Airbus Defence and Space, Germany
Abstract: Systems engineering constitutes a holistic and interdisciplinary approach to enable the realization of
successful systems that meet customer expectations. Today, stakeholders demand increasingly capable
systems that are growing in complexity. Model-based approaches which involve application of system
modelling for requirements, design, analysis, verification, and validation, are becoming more and more
popular in order to deal with the increase of system complexity. However, model-based systems engineering
is still in the early stage of maturity.
According to the INCOSE Systems Engineering Vision 2025, formal systems modelling based on
knowledge representation will be a standard practice in the future. Advanced simulation capabilities will
enable understanding of complex system behaviour in a virtual environment, immersive technologies will
allow data visualization, semantic web technologies will facilitate data integration, reasoning will aid
decision making, and finally communication technologies will support collaboration across interdisciplinary
teams.
Ontology engineering helps advance model-based systems engineering towards this vision. For example, the
combination of a controlled vocabulary and underlying formalism provides the opportunity to create high-
quality requirements and models, improve semantic interoperability and enable additional analysis. This talk
reports about current experiences gained from the European research project CRYSTAL and the envisioned
work.
BRIEF BIOGRAPHY
Dr. Ralf Bogusch received a MS degree in Technical
Cybernetics from the University of Stuttgart,
Germany, in 1992 and his PhD in Computer-aided
Modelling from the Technical University of Aachen,
Germany, in 2001. After his academic career, he has
practiced application of software and systems
engineering in the aerospace and automotive
industry for fifteen years. His research interests and
published papers cover requirements engineering,
product family management, model-based systems
engineering and model-based testing. Currently he is
an Expert for Validation and Verification Processes,
Methods and Tools at Airbus Defence and Space. In
this role he supports the Airbus Group PLM
(Product Lifecycle Management) strategy, provides
corporate trainings and leads improvement projects.
He has represented Airbus Defence and Space in a
number of EU funded ARTEMIS (Advanced
Research & Technology for EMbedded Intelligence
and Systems) projects on developing ontologies for
systems engineering, pushing interoperability
specifications towards standards and industrializing
reference technology platforms for the development
of safety-critical embedded systems. He received a
Lean Six Sigma Black Belt degree in 2011 and the
Airbus Engineering Award “Top Innovation and
Design” in 2012.
Bogusch, R..
Ontology-based Systems Engineering - The Smart Way of Realizing Complex Systems.
In Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2015) - Volume 3: KMIS, page 11
ISBN: 978-989-758-158-8
Copyright c 2015 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
11

32. PAPERS

34. FULL PAPERS

36. repAIrC: A Tool for Ensuring Data Consistency
By Means of Active Integrity Constraints
Lu´ıs Cruz-Filipe1
, Michael Franz1
, Artavazd Hakhverdyan1
, Marta Ludovico2
, Isabel Nunes2
and Peter Schneider-Kamp1
1Dept. of Mathematics and Computer Science, University of Southern Denmark, Campusvej 55, 5230 Odense M, Denmark
2Faculdade de Ciˆencias da Universidade de Lisboa, Campo Grande, 1749-016 Lisboa, Portugal
lcf@imada.sdu.dk, mf@bfdata.dk, {artavazd19, marta.al.ludovico}@gmail.com, in@fc.ul.pt, petersk@imada.sdu.dk
Keywords: Active Integrity Constraints, Database Repair, Implementation.
Abstract: Consistency of knowledge repositories is of prime importance in organization management. Integrity con-
straints are a well-known vehicle for specifying data consistency requirements in knowledge bases; in partic-
ular, active integrity constraints go one step further, allowing the specification of preferred ways to overcome
inconsistent situations in the context of database management.
This paper describes a tool to validate an SQL database with respect to a given set of active integrity con-
straints, proposing possible repairs in case the database is inconsistent. The tool is able to work with the
different kinds of repairs proposed in the literature, namely simple, founded, well-founded and justified re-
pairs. It also implements strategies for parallelizing the search for them, allowing the user both to compute
partitions of independent or stratified active integrity constraints, and to apply these partitions to find repairs
of inconsistent databases efficiently in parallel.
1 INTRODUCTION
There is a generalized consensus that knowledge
repositories are a key ingredient in the whole pro-
cess of Knowledge Management, cf. (Duhon, 1998;
K¨onig, 2012). Furthermore, being able to rely upon
the consistency of the information they provide is
paramount to any business whatsoever. Databases
and database management systems, by far the most
common framework for knowledge storage and re-
trieval, have been around for many years now, and
have evolved substantially, at pace with information
technology. In this paper, we are focusing on the im-
portant aspect of database consistency.
Typical database management systems allow the
user to specify integrity constraints on the data as
logical statements that are required to be satisfied at
any given point in time. The classical problem is
how to guarantee that such constraints still hold af-
ter updating databases (Abiteboul, 1988), and what
repairs have to be made when the constraints are vio-
lated (Katsuno and Mendelzon, 1991), without mak-
ing any assumptions about how the inconsistencies
came about. Repairing an inconsistent database (Eiter
and Gottlob, 1992) is a highly complex process; also,
it is widely accepted that human intervention is of-
ten necessary to choose an adequate repair. That said,
every progress towards automation in this field is nev-
ertheless important.
In particular, the framework of active integrity
constraints (Flesca et al., 2004; Caroprese and
Truszczy´nski, 2011) was introduced more recently
with the goal of giving operational mechanisms to
compute repairs of inconsistent databases. This
framework has subsequently been extended to con-
sider preferences (Caroprese et al., 2007) and to find
“best” repairs automatically (Cruz-Filipe et al., 2013)
and efficiently (Cruz-Filipe, 2014).
Active integrity constraints (AICs) seem to be a
promising framework for the purpose of achieving re-
liability in information retrieval:
• AICs are expressive enough to encompass the ma-
jority of integrity constraints that are typically
found in practice;
• AICs allow the definition of preferred ways to cal-
culate repairs, through specific actions to be taken
in specific inconsistent situations;
• AICs provide mechanisms to resolve inconsisten-
cies while the database is in use;
• AICs can enhance databases to provide a basis for
self-healing autonomic systems.
Cruz-Filipe, L., Franz, M., Hakhverdyan, A., Ludovico, M., Nunes, I. and Schneider-Kamp, P..
repAIrC: A Tool for Ensuring Data Consistency - By Means of Active Integrity Constraints.
In Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2015) - Volume 3: KMIS, pages 17-26
ISBN: 978-989-758-158-8
Copyright c 2015 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
17

37. To the best of our knowledge, no real-world imple-
mentation of an AIC–enhanced database system ex-
ists today. This paper presents a prototype tool that
implements the tree–based algorithms for comput-
ing repairs presented in (Caroprese and Truszczy´nski,
2011; Cruz-Filipe et al., 2013). While not yet ready
for productive deployment, this implementation can
work successfully with database management sys-
tems working in the SQL framework, and is readily
extendible to other (nearly arbitrary) database man-
agement systems thanks to its modular design.
This paper is structured as follows. Section 2
recapitulates previous work on active integrity con-
straints and repair trees. Section 3 introduces our
tool, repAIrC, and describes its implementation, fo-
cusing on the new theoretical results that were nec-
essary to bridge the gap between theory and practice.
Section 4 then discusses how parallel computation ca-
pabilities are incorporated in repAIrC to make the
search for repairs more efficient. Section 5 summa-
rizes our achievements and gives a brief outlook into
future developments.
2 ACTIVE INTEGRITY
CONSTRAINTS
Active integrity constraints (AICs) were introduced
in (Flesca et al., 2004) and further explored in (Carop-
rese et al., 2009; Caroprese and Truszczy´nski, 2011),
which define the basic concepts and prove complex-
ity bounds for the problem of repairing inconsistent
databases. These authors introduce declarative se-
mantics for different types of repairs, obtaining their
complexity results by means of a translation into re-
vision programming. In practice, however, this does
not yield algorithms that are applicable to real-life
databases; for this reason, a direct operational se-
mantics for AICs was proposed in (Cruz-Filipe et al.,
2013), presenting database-oriented algorithms for
finding repairs. The present paper describes a tool that
can actually execute these algorithms in collaboration
with an SQL database management system.
2.1 Syntax and Declarative Semantics
For the purpose of this work, we can view a database
simply as a set of atomic formulas over a typed
function-free first-order signature Σ, which we will
assume throughout to be fixed. Let At be the set of
closed atomic formulas over Σ. A database I entails
literal L, I |= L, if L ∈ At and L ∈ I, or if L is not a
with a ∈ At and a /∈ I.
An integrity constraint is a clause
L1,...,Lm ⊃ ⊥
where each Li is a literal over Σ, with intended se-
mantics that ∀(L1 ∧ ... ∧ Lm) should not hold. As
is usual in logic programming, we require that if Li
contains a negated variable x, then x already occurs
in L1,...,Li−1. We say that I satisfies integrity con-
straint r, I |= r, if, for every instantiation θ of the vari-
ables in r, it is the case that I |= Lθ for some L in r;
and I satisfies a set η of integrity constraints, I |= η,
if it satisfies each integrity constraint in η.
If I |= η, then I may be updated through update
actions of the form +a and −a, where a ∈ At, stating
that a is to be inserted in or deleted from I, respec-
tively. A set of update actions U is consistent if it
does not contain both +a and −a, for any a ∈ At;
in this case, I can be updated by U, yielding the
database
I ◦U = (I ∪{a | +a ∈ U}){a | −a ∈ U} .
The problem of database repair is to find U such that
I ◦U |= η.
Definition 1. Let I be a database and η a set of in-
tegrity constraints. A weak repair for I,η is a con-
sistent set U of update actions such that: (i) every
action in U changes I; and (ii) I ◦ U |= η. A repair
for I,η is a weak repair U for I,η that is minimal
w.r.t. set inclusion.
The distinction between weak repairs and re-
pairs embodies the standard principle of minimality
of change (Winslett, 1990).
The problem of deciding whether there exists a
(weak) repair for an inconsistent database is NP-
complete (Caroprese and Truszczy´nski, 2011). Fur-
thermore, simply detecting that a database is incon-
sistent does not give any information on how it can be
repaired. In order to address this issue, those authors
proposed active integrity constraints (AICs), which
guide the process of selection of a repair by pairing
literals with the corresponding update actions.
In the syntax of AICs, we extend the notion of
update action by allowing variables. Given an action
α, the literal corresponding to it is lit(α), defined as a
if α = +a and not a if α = −a; conversely, the update
action corresponding to a literal L, ua(L), is +a if
L = a and −a if L = not a. The dual of a is not a,
and conversely; the dual of L is denoted LD. An active
integrity constraint is thus an expression r of the form
L1,...,Lm ⊃ α1 | ... | αk
where the Li (in the body of r, body(r)) are literals
and the αj (in the head of r, head(r)) are update ac-
tions, such that
lit(α1)D
,...,lit(αk)D
⊆ {L1,...,Lm} .
KMIS 2015 - 7th International Conference on Knowledge Management and Information Sharing
18

38. The set lit(head(r))D contains the updatable literals
of r. The non-updatable literals of r form the set
nup(r) = body(r)lit(head(r))D
.
The natural semantics for AICs restricts the notion
of weak repair.
Definition 2. Let I be a database, η a set of AICs
and U be a (weak) repair for I,η . Then U is a
founded (weak) repair for I,η if, for every action
α ∈ U, there is a closed instance r of r ∈ η such that
α ∈ head(r ) and I ◦U |= L for every L ∈ body(r )
lit(α)D .
The problem of deciding whether there exists a
weak founded repair for an inconsistent database is
again NP-complete, while the similar problem for
founded repairs is ΣP
2 -complete. Despite their natural
definition, founded repairs can include circular sup-
port for actions, which can be undesirable; this led
to the introduction of justified repairs (Caroprese and
Truszczy´nski, 2011).
We say that a set U of update actions is closed un-
der r if nup(r) ⊆ lit(U) implies head(r)∩U = /0, and
it is closed under a set η of AICs if it is closed under
every closed instance of every rule in η. In particular,
every founded weak repair for I,η is by definition
closed under η.
A closed update action +a (resp. −a) is a no-effect
action w.r.t. (I,I ◦ U) if a ∈ I ∩ (I ◦ U) (resp. a /∈
I ∪ (I ◦ U)). The set of all no-effect actions w.r.t.
(I,I ◦U) is denoted by ne(I,I ◦U). A set of update
actions U is a justified action set if it coincides with
the set of update actions forced by the set of AICs and
the database before and after applying U (Caroprese
and Truszczy´nski, 2011).
Definition 3. Let I be a database and η a set of
AICs. A consistent set U of update actions is a jus-
tified action set for I,η if it is a minimal set of up-
date actions containing ne(I,I ◦U) and closed un-
der η. If U is a justified action set for I,η , then
U ne(I,I ◦U) is a justified weak repair for I,η .
In particular, it has been shown that justi-
fied repairs are always founded (Caroprese and
Truszczy´nski, 2011). The problem of deciding
whether there exist justified weak repairs or justified
repairs for I,η is again a ΣP
2 -complete problem, be-
coming NP-complete if one restricts the AICs to con-
tain only one action in their head (normal AICs).
2.2 Operational Semantics
The declarative semantics of AICs is not very sat-
isfactory, as it does not capture the operational na-
ture of rules. In particular, the quantification over all
no-effect actions in the definition of justified action
set poses a practical problem. Therefore, an oper-
ational semantics for AICs was proposed in (Cruz-
Filipe et al., 2013), which we now summarize.
Definition 4. Let I be a database and η be a set of
AICs.
• The repair tree for I,η , T I,η , is a labeled
tree where: nodes are sets of update actions;
each edge is labeled with a closed instance of
a rule in η; the root is /0; and for each consis-
tent node n and closed instance r of a rule in η,
if I ◦ n |= r then for each L ∈ body(r) the set
n = n ∪ ua(L)D is a child of n, with the edge
from n to n labeled by r.
• The founded repair tree for I,η , T
f
I,η , is con-
structed as T I,η but requiring that ua(L) occur
in the head of some closed instance of a rule in η.
• The well-founded repair tree for I,η , T
wf
I,η , is
also constructed as T I,η but requiring that ua(L)
occur in the head of the rule being applied.
• The justified repair tree for I,η , T
j
I,η , has
nodes that are pairs of sets of update actions
U,J , with root /0, /0 . For each node n and
closed instance r of a rule in η, if I ◦Un |= r, then
for each α ∈ head(r) there is a descendant n of
n, with the edge from n to n labeled by r, where:
Un = Un ∪{α}; and Jn = (Jn ∪{ua(nup(r))})
Un.
The properties of repair trees are summarized in
the following results, proved in (Cruz-Filipe et al.,
2013).
Theorem 1. Let I be a database and η be a set of
AICs. Then:
1. T I,η is finite.
2. Every consistent leaf of T I,η is labeled by a weak
repair for I,η .
3. If U is a repair for I,η , then there is a branch
of T I,η ending with a leaf labeled by U.
4. If U is a founded repair for I,η , then there is a
branch of T
f
I,η ending with a leaf labeled by U.
5. If U is a justified repair for I,η , then there is a
branch of T
j
I,η ending with a leaf labeled by U.
6. If η is a set of normal AICs and U,J is a leaf of
T
j
I,η with U consistent and U ∩J = /0, then U is
a justified repair for I,η .
Not all leaves will correspond to repairs of the
desired kind; in particular, there may be weak re-
pairs in repair trees. Also, both T
f
I,η and T
j
I,η typi-
cally contain leaves that do not correspond to founded
or justified (weak) repairs – otherwise the problem
repAIrC: A Tool for Ensuring Data Consistency - By Means of Active Integrity Constraints
19

39. of deciding whether there exists a founded or justi-
fied weak repair for I,η would be solvable in non-
deterministic polynomial time. The leaves of the
well-founded repair tree for I,η correspond to a
new type of weak repairs, called well-founded weak
repairs, not considered in the original works on AICs.
2.3 Parallel Computation of Repairs
The computation of founded or justified repairs can
be improved by dividing the set of AICs into indepen-
dent sets that can be processed independently, simply
merging the computed repairs at the end (Cruz-Filipe,
2014). Here, we adapt the definitions given therein
to the first-order scenario. Two sets of AICs η1 and
η2 are independent if the same atom does not occur
in a literal in the body of a closed instance of two
distinct rules r1 ∈ η1 and r2 ∈ η2. If η1 and η2 are
independent, then repairs for I,η1 ∪ η2 are exactly
the unions of a repair for I,η1 and I,η2 ; further-
more, the result still holds if one considers founded,
well-founded or justified repairs.
If an atom occurs in a literal in the body of a closed
instance of a rule in η2 and in an action in the head of
a closed instance of a rule in η1, but not conversely,
then we say that η1 precedes η2. Founded/justified
(but not well-founded) repairs for η1 ∪η2 can be com-
puted in a stratified way, by first repairing I w.r.t. η1,
and then repairing the result w.r.t. η2.
Splitting a set of AICs into independent sets or
stratifying it can be solved using standard algorithms
on graphs, as we describe in Section 4.
3 THE TOOL
The tool repAIrC is implemented in Java, and its sim-
plified UML class diagram can be seen in Figure 1.
Structurally, this tool can be split into four main sepa-
rate components, centered on the four classes marked
in bold in that figure.
• Objects of type AIC implement active integrity
constraints.
• Implementations of interface DB provide the nec-
essary tools to interact with a particular database
management system; currently, we provide func-
tionality for SQL databases supported by JDBC.
• Objects of type RepairTree correspond to con-
crete repair trees; their exact type will be the sub-
class corresponding to a particular kind of repairs.
• Class RunRepairGUI provides the graphical inter-
face to interact with the user.
An important design aspect has to do with ex-
tensibility and modularity. A first prototype focused
on the construction of repair trees, and used simple
text files to mimick databases as lists of propositional
atoms, in the style of (Caroprese and Truszczy´nski,
2011; Cruz-Filipe et al., 2013). Later, parallelization
capabilities were added (as explained in Section 4),
requiring changes only to RepairController – the
class that controls the execution of the whole process.
Likewise, the extension of repAIrC to SQL databases
and the addition of the stratification mechanism only
required localized changes in the classes directly con-
cerned with those processes.
The next subsections detail the implementa-
tion of the classes AIC, DB, RepairTree and
RunRepairTreeGUI.
3.1 Representing Active Integrity
Constraints
In the practical setting, it makes sense to diverge a
little from the theoretical definition of AICs.
• Real-world tables found in DBs contain many
columns, most of which are typically irrelevant
for a given integrity constraint.
• The columns of a table are not static, i.e., columns
are usually added or removed during a database’s
lifecycle.
• The order of columns in a table should not matter,
as they are identified by a unique column name.
To deal pragmatically with these three aspects, we
will write atoms using a more database-oriented
notation, allowing the arguments to be provided
in any order, but requiring that the column names
be provided. The special token $ is used as first
character of a variable. So, for example, the literal
hasInsurance(firstName=$X, type=’basic’)
will match any entry in table hasInsurance having
value basic in column type and any value in column
firstName; this table may additionally have other
columns. Negative literals are preceded by the
keyword NOT, while actions must begin with + or -.
Literals and actions are separated by commas, and the
body and head of an AIC are separated by ->. The
AIC is finished when ; is encountered, thus allowing
constraints to span several lines.
AICs are provided in a text file, which is parsed
by a parser generated automatically using JavaCC
and transformed into objects of type AIC. These
contain a body and a head, which are respectively
List<Literal> and List<Action>; for consistency
with the underlying theory, Literal and Action are
implemented separately, although their objects are
KMIS 2015 - 7th International Conference on Knowledge Management and Information Sharing
20

40. RepairController
RunRepairGUI
SimpleNode
JustifiedNode
SimpleRepairTree
FoundedRepairTree WellFoundedRepairTree
JustifiedRepairTree
AIC
Literal Action
DBMySQL
abstract
RepairTree
abstract
Node
interface
DB
RepairGUI
create
create
call *{List}
*{List} *{List}
*{List}
*{List}
*{Set}
*{Set}
Clause
Preprocess
*{List}
Figure 1: Class diagram for repAIrC.
isomorphic: they contain an object of type Clause
(which consists of the name of a table in the database
and a list of pairs column name/value) and a flag indi-
cating whether they are positive/negated (literals) or
additions/removals (actions).
Example 1. Consider the following active integrity
constraints for an employee database. The first states
that the boss (as specified in the category table) can-
not be a junior employee (i.e., have an entry in the
junior table); the second states that every junior em-
ployee must have some basic insurance (as specified
in the insured table).
junior(X),category(boss,X) ⊃ −junior(X)
junior(X),not insured(X,basic)
⊃ +insured(X,basic)
These are written in the concrete text-based syntax
of the repAIrC tool as
junior(id = $X),
category(type = boss, empId = $X)
-> - junior(id = $X);
junior(id = $X),
NOT insured(empId = $X, type = basic)
-> + insured(empId = $X, type = basic);
respectively, assuming the corresponding column
names for the atributes. Note that, thanks to our usage
of explicit column naming, the column names for the
same variable need not have identical designations.
3.2 Interfacing with the Database
Database operations (queries and updates) are de-
fined in the DB interface, which contains the following
methods.
• getUpdateActions(AIC aic): queries the
database for all the instances of aic that are
not satisfied in its current state, returning a
Collection<Collection<Action>> that con-
tains the corresponding instantiations of the head
of aic.
• update(Collection<Action> actions): ap-
plies all update actions in actions to the database
(void).
• undo(Collection<Action> actions): undoes
the effect of all update actions in actions (void).
• aicsCompatible(Collection<AIC> aics):
checks that all the elements of aics are compati-
ble with the structure of the database.
• disconnect(): disconnects from the database
(void). The connection is established when the
object is originally constructed.
Some of these methods require more detailed
comments. The construction of the repair tree also re-
quires that the database be changed interactively, but
upon conclusion the database should be returned to its
original state. In theory, this would be achievable by
applying the update method with the duals of the ac-
tions that were used to change the database; but this
turns out not to be the case for deletion actions. Since
the AICs may underspecify the entries in the database
(because some fields are left implicit), the implemen-
tation of update must take care to store the values
of all rows that are deleted from the database. In turn,
the undo method will read this information every time
repAIrC: A Tool for Ensuring Data Consistency - By Means of Active Integrity Constraints
21

41. it has to undo a deletion action, in order to find out ex-
actly what entries to re-add.
The method aicsCompatible is necessary be-
cause the AICs are given independently of the
database, but they must be compatible with its struc-
ture – otherwise, all queries will return errors. Includ-
ing this method in the interface allows the AICs to be
tested before any queries are made, thus significantly
reducing the number of exceptions that can occur dur-
ing program execution.
Currently, repAIrC includes an implementation
DBMySQL of DB, which works with SQL databases.
The interaction between repAIrC and the database
is achieved by means of JDBC, a Java database con-
nectivity technology able to interface with nearly
all existing SQL databases. In order to determine
whether an AIC is satisfied by a database, method
getUpdateActions first builds a single SQL query
corresponding to the body of the AIC. This method
builds two separate SELECT statements, one for the
positive and another for the negative literals in the
body of the AIC. Each time a new variable is found,
the table and column where it occurs are stored, so
that future references to the same variable in a positive
literal can be unified by using inner joins. The select
statement for the negative literals is then connected to
the other one using a WHERE NOT EXISTS condition.
Variables in the negative literals must necessarily ap-
pear first in a positive literal in the same AIC; there-
fore, they can then be connected by a WHERE clause
instead of an inner join.
Example 2. The bodies of the integrity constraints in
Example 1 generate the following SQL queries.
SELECT * FROM junior
INNER JOIN dept_emp
ON junior.id=category.empId
WHERE category.type=‘boss’
SELECT * FROM junior
WHERE NOT EXISTS
(SELECT * FROM insured
WHERE insured.empId=junior.id
AND insured.type=‘basic’)
3.3 Implementing Repair Trees
The implementation of the repair trees directly fol-
lows the algorithms described in Section 2. Differ-
ent types of repair trees are implemented using inher-
itance, so that most of the code can be reused in the
more complex trees. The trees are constructed in a
breadth-first manner, and all non-contradictory leaves
that are found are stored in a list. At the end, this list
is pruned so that only the minimal elements (w.r.t. set
inclusion) remain – as these are the ones that corre-
spond to repairs.
While constructing the tree, the database has to be
temporarily updated and restored. Indeed, to calculate
the descendants of a node, we first need to evaluate all
AICs at that node in order to determine which ones are
violated; this requires querying a modified version of
the database that takes into account the update actions
in the current node.
In order to avoid concurrency issues, these up-
dates are performed in a transaction-style way, where
we update the database, perform the necessary SQL
queries, and rollback to the original state, guarantee-
ing that other threads interacting with the database
during this process neither see the modifications nor
lead to inconsistent repair trees. This becomes of
particular interest when the parallel processing tools
described in Section 4 are put into place. Although
this adds some overhead to the execution time, at the
end of that section we discuss why scalability is not a
practically relevant concern.
After finding all the leaves of the repair tree, a
further step is needed in the case one is looking for
founded or justified repairs, as the corresponding trees
may contain leaves that do not correspond to repairs
with the desired property. This step is skipped if all
AICs are normal, in view of the results from (Cruz-
Filipe et al., 2013). For founded repairs, we directly
apply the definition: for each action α, check that
there is an AIC with α in its head and such that all
other literals in its body are satisfied by the database.
For justified repairs, the validation step is less ob-
vious. Directly following the definition requires con-
structing the set of no-effect actions, which is essen-
tially as large as the database, and iterating over sub-
sets of this set. This is obviously not possible to do in
practical settings. Therefore, we use some criteria to
simplify this step.
Lemma 1. If a rule r was not applied in the branch
leading to U, then U is closed under r.
Proof. Suppose that r was never applied and assume
nup(r) ⊆ ne(I,I ◦U). Then necessarily head(r) ∩
ne(I,I ◦U) = /0, otherwise r would be applicable and
U would not be a repair.
By construction, U is also closed for all rules ap-
plied in the branch leading to it.
Let U be a candidate justified weak repair. In or-
der to test it, we need to show that U ∪ ne(I,I ◦U)
is a justified action set (see (Cruz-Filipe et al., 2013)),
which requires iterating over all subsets of U ∪
ne(I,I ◦U) that contain ne(I,I ◦U). Clearly this
can be achieved by iterating over subsets of U.
But if U∗ ⊆ U, then nup(r) ∩ U∗ = /0; this al-
lows us to simplify the closedness condition to: if
nup(r) ⊆ ne(I,I ◦U), then U∗ ∩ head(r) = /0. The
KMIS 2015 - 7th International Conference on Knowledge Management and Information Sharing
22

42. antecedent needs then only be done once (since it only
depends on U), whereas the consequent does not re-
quire consulting the database.
The following result summarizes these properties.
Lemma 2. A weak repair U in a leaf of the justi-
fied repair tree for I,η is a justified weak repair
for I,η iff, for every set U∗ ⊆ U, if nup(r) ⊆
ne(I,I ◦U), then U∗ ∩head(r) = /0.
The different implementations of repair trees use
different subclasses of the abstract class Node; in par-
ticular, nodes of JustifiedRepairTrees must keep
track not only of the sets of update actions being con-
structed, but also of the sets of non-updatable ac-
tions that were assumed. These labels are stored as
Set<Action> using HashSet from the Java library
as implementation, as they are repeatedly tested for
membership everytime a new node is generated.
For efficiency, repair trees maintain internally a
set of the sets of update actions that label nodes con-
structed so far as a Set<Node>. This is used to avoid
generating duplicate nodes with the same label. Since
this set is used mainly for querying, it is again imple-
mented as a HashSet. Nodes with inconsistent labels
are also immediately eliminated, since they can only
produce inconsistent leaves.
3.4 Interfacing with the User
The user interface for repAIrC is implemented us-
ing the standard Java GUI widget toolkit Swing, and
is rather straightforward. On startup, the user is pre-
sented with the dialog box depicted in Figure 2.
The user can then provide credentials to connect
to a database, as well as enter a file containing a set
of AICs. If the connection to the database is success-
ful and the file is successfully parsed, repAIrC in-
vokes the aicsCompatible method required by the
Figure 2: The initial screen for repAIrC.
implementation of the DB interface (see Section 3.2)
and verifies that all tables and columns mentioned in
the set of AICs are valid tables and columns in the
database. If this is not the case, then an error mes-
sage is generated and the user is required to select
new files; otherwise, the buttons for configuration and
computation of repairs become active.
Once the initialization has succeeded, one can
check the database for consistency and obtain differ-
ent types of repairs, computed using the repair tree
described above. As it may be of interest to obtain
also weak repairs, the user is given the possibility of
selecting whether to see only the repairs computed,
or all valid leaves of the repair tree – which typically
include some weak repairs. In both cases the neces-
sary validations are performed, so that leaves that do
not correspond to repairs (in the case of founded or
justified repairs) are never presented.
An example output screen after successful compu-
tation of the repairs for an inconsistent database can
be seen in Figure 3.
4 PARALLELIZATION AND
STRATIFICATION
As described in Section 2.3, it is possible to paral-
lelize the search for repairs of different kinds by split-
ting the set of AICs into independent sets; in the case
of founded or justified repairs, this parallelization can
be taken one step further by also stratifying the set
of AICs. Even though finding partitions and/or strat-
ifications is asymptotically not very expensive (it can
be solved in linear time by the well-known graph al-
gorithms described below), it may still take noticeable
time if the set of AICs grows very large.
Since, by definition, partitions and stratifications
Figure 3: Possible repairs of an inconsistent database.
repAIrC: A Tool for Ensuring Data Consistency - By Means of Active Integrity Constraints
23

43. are independent of the actual database, it makes sense
to avoid repeating their computation unless the set of
AICs changes. For this reason, parallelization capa-
bilities are implemented in repAIrC in a two-stage
process. Inside repAIrC, the user can switch to the
Preprocess tab, which provides options for comput-
ing partitions and stratifications of a set of AICs. This
results in an annotated file which still can be read by
the parser; in the main tab, parallel computation is
automatically enabled whenever the input file is an-
notated in a proper manner.
4.1 Implementation
Computing optimal partitions in the spirit of (Cruz-
Filipe, 2014) is not feasible in a setting where vari-
ables are present, as this would require considering
all closed instances of all AICs – but it is also not de-
sirable, as it would also result in a significant increase
of the number of queries to the database. Instead, we
work with the adapted definition of dependency given
in Section 2. Given a set of AICs, repAIrC constructs
the adjacency matrix for the undirected graph whose
nodes are AICs and such that there is an edge between
r1 to r2 iff r1 and r2 are not independent. A partition is
then computed simply by finding the connected com-
ponents in this graph by a standard graph algorithm.
The partitions computed are then written to a file,
where each partition begins with the line
#PARTITION_BEGIN_[NO]#
where [NO] is the number of the current partition, and
ends with
#PARTITION_END#
and the AICs in each partition are inserted in between,
in the standard format.
To compute the partitions for stratification, we
need to find the strongly connected components of a
similar graph. This is now a directed graph where
there is an edge from r1 to r2 if r1 precedes r2. The im-
plementation is a variant of Tarjan’s algorithm (Tar-
jan, 1972), adapted to give also the dependencies be-
tween the connected components.
The computed stratification is then written to a file
with a similar syntax to the previous one, to which
a dependency section is added, between the special
delimiters
#DEPENDENCIES_BEGIN#
and
#DEPENDENCIES_END#
The dependencies are included in this section as a se-
quence of strings X -> Y, one per line, where X and Y
are the numbers of two partitions and Y precedes X.
Example 3. The two AICs from Example 1 cannot
be parallelized, as they both use the junior table,
but they can be stratified, as only the first one makes
changes to this table. Preprocessing this example by
repAIrC would return the following output.
#PARTITION_BEGIN_1#
junior(id = $X),
category(type = boss, empId = $X)
-> - junior(id = $X);
#PARTITION_END#
#PARTITION_BEGIN_2#
junior(id = $X),
NOT insured(empId = $X, type = basic)
-> + insured(empId = $X, type = basic);
#PARTITION_END#
#DEPENDENCIES_BEGIN#
2 -> 1
#DEPENDENCIES_END#
Imagine a simple scenario where the junior ta-
ble contains a single entry. Then, computing repairs
for this set of AICs can be achieved by first repair-
ing partition 1 (which will generate a tree with only
one node) and then repairing the resulting database
w.r.t. partition 2 (which builds another tree, also with
only one node). By comparison, processing the two
AICs simultaneously would potentially give a tree
with 4 nodes, as both AICs would have to be consid-
ered at each stage.
In general, if there are n entries in the junior ta-
ble, the stratified approach will construct at most n+1
trees with a total of n2 + n nodes (one tree with n
nodes for the first AIC, at most n trees with at most
n nodes for the second AIC). By contrast, process-
ing both AICs together will construct a tree with po-
tentially (2n)! leaves, which by removing duplicate
nodes may still contain 22n nodes.
This example shows that, by stratifying AICs, we
can actually get an exponential decrease on the size of
the repair trees being built – and therefore also on the
total runtime.
In addition to alleviating the exponential blowup
of the repair trees, parallelization and stratifica-
tion also allow for a multi-threaded implementation,
where repair trees are built in parallel in multiple con-
current threads. To ensure that the dependencies be-
tween the partitions are respected, the threads are in-
structed to wait for other threads that compute pre-
ceding partitions. In Example 3, the thread process-
ing partition 2 would be instructed to first wait for the
thread processing partition 1 to finish.
Our empirical evaluation of repAIrC showed that
speedups of a factor of 4 to 7 were observable even
when processing small parallelizable sets of only two
or three AICs. For larger sets of AICs, paralleliza-
tion and stratification are necessary to obtain feasi-
KMIS 2015 - 7th International Conference on Knowledge Management and Information Sharing
24

44. ble runtimes. In one application, which allowed for
15 partitions to be processed independently, the strat-
ified version computed the founded repairs in approx-
imately 1 second, whereas the sequential version did
not terminate within a time limit of 15000 seconds.
This corresponds to a speedup of at least four orders
of magnitude, demonstrating the practical impact of
the contributions of this section.
4.2 Practical Assessment
In the worst case, parallelization and stratification will
have no impact on the construction of the repair tree,
as it is possible to construct a set of AICs with no
independent subsets. However, the worst case is not
the general case, and it is reasonable to believe that
real-life sets of AICs will actually have a high paral-
lelization potential.
Indeed, integrity constraints typically reflect high-
level consistency requirements of the database, which
in turn capture the hierarchical nature of relational
databases, where more complex relations are built
from simpler ones. Thus, when specifying active in-
tegrity constraints there will naturally be a preference
to correct inconsistencies by updating the more com-
plex tables rather than the most primitive ones.
Furthermore, in a real setting we are not so much
interested in repairing a database once, but rather in
ensuring that it remains consistent as its information
changes. Therefore, it is likely that inconsistencies
that arise will be localized to a particular table. The
ability to process independent sets of AICs separately
guarantees that we will not be repeatedly evaluat-
ing those constraints that were not broken by recent
changes, focusing only on the constraints that can ac-
tually become unsatisfied as we attempt to fix the in-
consistency.
For the same reason, scalability of the techniques
we implemented is not a relevant issue: there is no
practical need to develop a tool that is able to fix hun-
dreds of inconsistencies efficiently simultaneously,
since each change to the database will likely only im-
pact a few AICs.
5 CONCLUSIONS AND FUTURE
WORK
We presented a working prototype of a tool, called
repAIrC, to check integrity of real-world SQL
databases with respect to a given set of active in-
tegrity constraints, and to compute different types
of repairs automatically in case inconsistency is de-
tected, following the ideas and algorithms in (Flesca
et al., 2004; Caroprese et al., 2007; Caroprese and
Truszczy´nski, 2011; Cruz-Filipe et al., 2013; Cruz-
Filipe, 2014). This tool is the first implementation of
a concept we believe to have the potential to be inte-
grated in current database management systems.
Our tool currently does not automatically apply
repairs to the database, rather presenting them to the
user. As discussed in (Eiter and Gottlob, 1992), such
a functionality is not likely to be obtainable, as human
intervention in the process of database repair is gener-
ally accepted to be necessary. That said, automating
the generation of a small and relevant set of repairs
is a first important step in ensuring a consistent data
basis in Knowledge Management.
In order to deal with real-world heterogenous
knowledge management systems, we are currently
working on extending and generalizing the notion of
(active) integrity constraints to encompass more com-
plex knowledge repositories such as ontologies, ex-
pert reasoning systems, and distributed knowledge
bases. The design of repAIrC has been with this ex-
tension in mind, and we believe that its modularity
will allow us to generalize it to work with such knowl-
edge management systems once the right theoretical
framework is developed.
On the technical side, we are planning to speed up
the system by integrating a local database cache for
peforming the many update and undo actions during
exploration of the repair trees without the overhead of
an external database connection.
ACKNOWLEDGMENTS
This work was supported by the Danish Council
for Independent Research, Natural Sciences, and by
FCT/MCTES/PIDDAC under centre grant to BioISI
(Centre Reference: UID/MULTI/04046/2013). Marta
Ludovico was sponsored by a grant “Bolsa Universi-
dade de Lisboa / Fundac¸˜ao Amadeu Dias”.
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KMIS 2015 - 7th International Conference on Knowledge Management and Information Sharing
26

46. A Practical Guide to Developing a Knowledge Management Culture
(KMC) in a Non-Profit Organization (NPO)
Tomasz Kampioni and Felicia Ciolfitto
The Law Society of British Columbia, 845 Cambie Street, Vancouver, BC V6Z 4Z9, Canada
tkampioni@lsbc.org, felicia@sfu.ca
Keywords: Knowledge Management Culture, Knowledge Management Project.
Abstract: Knowledge is the most important asset of an organization. Being able to preserve organizational knowledge
determines profitability, sustainability, competitiveness and the ability to grow. No organization can afford to
lose its knowledge base. According to the World Economy Forum, 95 percent of CEOs claim that Knowledge
Management (KM) is a critical factor in an organization’s success; and 80 percent of companies mentioned
in Fortune Magazine have staff assigned specifically to KM. Developing a culture of sharing and creating
knowledge is a long process that requires changing people’s values, beliefs and behaviours. Staff must be
convinced of KM benefits and be engaged in programs and initiatives that support transfer of knowledge.
Many organizations focus on technology as a silver bullet, losing sight of the fact that people as well as
processes are important factors in successful implementation of Knowledge Management Culture (KMC). In
this article we will discuss the concept of a knowledge management culture. We will specifically explore how
a non-profit organization (NPO) assessed its current environment and capitalized on its existing KMC as a
way to leverage its KM program. Creating a KMC is key since technology does not manage knowledge –
people do!
1 INTRODUCTION
Knowledge is a critical asset of any organization. It is
stored in documents, reports, organizational studies,
as well as in people’s heads. When an organization
loses an employee, it also loses any knowledge that
was not captured or transferred to other employees.
In the current competitive job market, staff
retention is one of the biggest challenges faced by
organizations. Dan Schwabel in the article: “The Top
10 Workplace Trends For 2014” points out that 73
percent of workers in the United States are either open
to hearing about or are looking for new employment.
The Bureau of Labor Statistics of United States
reports that people have about eleven jobs between
the ages of 18 and 34. Finally, 18 percent of boomers
will retire within five years (Schawbel, 2013). These
facts alone should encourage organizations to
develop KMC and promote capturing and sharing of
organizational knowledge.
In 2015, millennials will account for 36 percent of
the American workforce. One of the biggest problems
companies will have is succession planning.
Organizations have to develop knowledge transfer
programs and train the Gen X and Gen Y employees
before the boomers retire or they will be in major
trouble.
2 NON-PROFIT ORGANIZATION
The nature of a non-profit organization is to serve the
public for a defined purpose, without being profit
oriented. While the aim of for-profit organizations is
to maximize profits and forward these profits to the
company’s owners and shareholders, non-profit
organizations aim to provide for some aspect of
society’s needs. Despite these differences, both types
of organizations focus on improving staff
productivity, minimizing costs, introducing more
efficient and effective processes, as well as promoting
innovation, collaboration and the reuse of
information. Many organizations are already taking
advantage of KM programs to reach these objectives.
In 2014, the non-profit sector was the third largest
employer in United States. It included two million
non-profit organizations that employed 10.7 million
people and generated $1.9 trillion in revenue. Non-
profit organizations are projecting growth in 2015
Kampioni, T. and Ciolfitto, F..
A Practical Guide to Developing a Knowledge Management Culture (KMC) in a Non-Profit Organization (NPO).
In Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2015) - Volume 3: KMIS, pages 27-38
ISBN: 978-989-758-158-8
Copyright c 2015 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
27

47. that could outpace the corporate sector. However, as
non-profits continue to grow, 90 percent of non-profit
organizations lack formal retention strategies,
succession planning and have no formal career paths
for the employees they would like to retain.
According to Nonprofit HR’s 2015 Nonprofit
Employment Practices Survey staff turnover in the
non-profit sector in 2014 reached 19 percent, and 14
percent of that was voluntary turnover. An increase in
voluntary turnover rate from 11 percent in 2012, and
10 percent in 2013, signals employees’ increased
confidence in the job market. An inability to pay
competitively and to promote staff, as well as
excessive workloads are the greatest retention
challenges faced by non-profits.
Organizations can't stop employees from leaving
unless they plan to entice them to stay. Even though
non-profit organizations are unable to pay
competitively, it turns out that compensation only
ranks 4th on the list of job satisfaction elements
according to 2014 SHRM Employee Satisfaction and
Engagement Survey. The top job satisfaction factor in
the survey was respectful treatment of all employees
at all levels and trust between employees and senior
management. The opportunity to use skills and
abilities in work ranked 6th
and career advancement
opportunities within the organization and having
challenging, interesting and meaningful job were also
very important to employees. Keeping people
engaged and connected to the organization, as well as
providing environment to grow personally and
professionally while working on a variety of projects,
is the key to fostering employee commitment to the
organization’s mission.
The culture of the organization can certainly
contribute to whether an employee stays or leaves.
Non-profits need to make a conscious effort to engage
their employees from the recruitment process though
the reminder of the employment cycle in order to
retain these valuable resources. It is also critical to
provide staff with opportunities to learn new things
and make them feel that they are part of something
bigger.
KMC provides an environment for staff to acquire
new skills, to participate in mentoring and
apprenticeship programs and to work on cross
departmental projects in order to meet the
organizational objectives. Organizations are more
likely to retain employees who feel engaged and have
job satisfaction. KM programs contribute to high
levels of employee engagement, and, therefore,
greater staff retention.
3 ORGANIZATIONAL CULTURE
AND KNOWLEDGE
MANAGEMENT
Culture has been called the DNA of the organization.
It is about patterns of human interactions that are
often deeply ingrained. (Dalkir, 2011).
Organizational culture is composed of three building
blocks: values, beliefs and behavioural norms. Values
hold a central position in organizational culture. They
also reflect a person’s set of beliefs and assumptions
about external and internal environments. In addition,
they serve as the basis for the norms that underlie
behaviour. Organizational culture defines ways in
which people perform tasks, solve problems, resolve
conflicts, and treat customers or employees (Schein
1999). KM involves instilling certain kinds of values
in the organization. These values have at their core a
high appreciation and respect for individual
knowledge, as well as a commitment towards
fostering knowledge interactions through mutual
trust. An organizational culture that promotes KM is
founded on the perception that everyone stands to
gain by sharing and creating knowledge. It is a win-
win culture, in which both individuals and the
organization benefit.
In order to support a KM oriented culture, the
organization must develop shared values that promote
KM. Some of the values such as trust, respect for the
knowledge worker and identification with the
organizational goals, are universal KM values.
(Pasher and Ronen, 2011).
3.1 Misconceptions about Knowledge
Management
As you can imagine, a computer system cannot help
you to transfer tacit knowledge that is deep in
people’s minds into documented, explicit knowledge.
Technology, next to people and processes, is just one
of three components of KM. It is worth remembering
that KM programs should not be branded by their
technology applications. Wiki or Document
Management Systems (DMS) are just tools not brands
and they should never promote a KM program. It is
crucial to ensure that KM is seen as a holistic
approach enabled by dedicated employees, standard
processes and technology tools (O’Dell and Hubert,
2011).
The transfer of tacit knowledge usually occurs
when people work with other people and share their
knowledge. Psychologists have found that in face-to-
face talks, only 7 percent of the meaning is conveyed
KMIS 2015 - 7th International Conference on Knowledge Management and Information Sharing
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48. by the words, while 38 percent is communicated by
intonation and 55 percent through visual cues, and up
to 87 percent of messages are interpreted on a
nonverbal, visual level (Mehrabian, 1972).
It is hard to deny the benefits of face-to-face
communication and transferring knowledge through
working together. KM programs must promote
interactions between employees but also provide
technology and support systems to capture acquired
knowledge. In addition, organizations must reward
employees’ contributions to the ongoing process of
capturing and preserving knowledge. The
participation of staff in KM programs is a key to the
development of a KMC in the organization. The
Pareto principle, also known as the 80–20 rule, states
that, for many events, roughly 80 percent of the
effects come from 20 percent of the causes (Reh
2005). When we look at the content contribution on
Facebook and Twitter, we notice that 80 percent of
content on Facebook is posted by 20 percent of the
users. Only one in five Twitter account holders has
ever posted anything, and 90 percent of content is
posted by 10 percent of the users (Moore 2010). We
should keep in mind these statistics while thinking
about participation rates for KM approaches using
Web 2.0 tools inside the organization. A small group
of people are the core contributors of content. The key
is to change this ratio and have more people creating
and capturing knowledge.
Developing and sustaining a KMC in an
organization is a challenging task that goes beyond
deploying a number of different applications and
systems. It is a complex process that relies on people
interacting with each other through face-to-face
programs, as well as online platforms. It also needs to
be supported by management and incentive programs
to keep the knowledge flowing through the
organization. Establishing KMC requires a project
management approach and all stakeholders must
understand what KMC is and its benefits. A KM
project team must develop a project plan and achieve
a number of milestones before completing the project.
The objective of this article is to provide guidance
on how to establish KMC in an organization. The
article captures the work, research and experiences
that led to introducing KMC in a NPO. However,
before we discuss our journey to KMC, we would like
to focus on the benefits of KMC and answer the
question ‘why’ organizations develop KM programs.
3.2 Benefits of a KMC
KM strategy must provide a balance between the
interactions of people and technology. KM is critical
to efficient operations, and a base for the continuous
development and improvement. A KMC offers
benefits in terms of succession planning and reduces
risk of organizational amnesia. In addition, KMC
provides quick and easy access to information and
consistency across the organization as well as
promotes reusing information and innovation.
3.2.1 Succession Planning
Losing an employee with years of experience can be
very disruptive to the operation of a particular
department, even to the entire organization. Tacit
knowledge that was never captured will be gone
forever. With the right programs in place, people’s
tacit knowledge can be documented and captured
providing a foundation and reference point for new
staff. Succession planning programs allow
organizations to reduce costs and help staff transition
to new positions without significant interruption in
business operations. Some organizations with strong
succession planning programs welcome rotation of
personnel as an opportunity for innovation, and for
bringing new energy and ideas to the organization.
3.2.2 Reducing Risk of Organizational
Amnesia
The National Aeronautic and Space Administration
(NASA) admitted that all the lessons learned and the
innovations that lead to successful landing on the
Moon cannot be found in the collective organizational
memory of NASA. This means that NASA’s
organizational memory cannot be used as a resource
to plan a more effective mission to send another
manned flight to the moon or to Mars (Dalkir, 2011).
Recreating the knowledge that has been lost is an
additional cost to the organization that a KMC could
have been prevented.
3.2.3 Quick and Easy Access to Information
RDMP Communications surveyed 100 UK
executives and found that more than half are unable
to access data they need largely because of "disparity
of data" and the "volume of data." That problem is
only increasing: Gartner Survey Results revealed that
"data volumes are increasing by over 75 percent every
year" (Gartner Press Release, 2014).
International Data Corporation’s (IDC) Content
Technologies Groups director, Susan Feldman (2004)
estimates that knowledge workers typically spend
from 15 to 35 percent of their time searching for
information. These workers typically succeed less
than 50 percent of the time. IDC estimates that 90
A Practical Guide to Developing a Knowledge Management Culture (KMC) in a Non-Profit Organization (NPO)
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