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KMO 2014

Call for Papers

KMO 2014: The 9th International Conference on Knowledge Management in Organizations
Theme: Knowledge Management to Improve Innovation and Competitiveness through big data
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September 2nd–5th, 2014 in Santa Maria University, Vitacura Campus
Av. Santa Maria 6400, Vitacura, Santiago, Chile

 Collocated Events:

  1. The 3rd International Workshop on Learning Technology for Education in Cloud (LTEC'14)


The conference is preceded by one day free tutorials for participants who wish to learn state of the art of research relating the topics of KMO and LTEC. . The tutorials will be held on the 2nd September, 2014. The conference itself commences on the 3rd to the 5th September, 2014.

Big Data is the core that drives new research in many areas, from environmental to social science and knowledge management. Knowledge is the true destination in the pursuit of data.  When data is turned into knowledge, the enterprise is better positioned to respond and innovate in all phases of its operation to gain competitive advantages and even build entirely new business models.

This growing capability of firms to derive meaning from data means that knowledge management systems can provide the platform for companies to improve their most valuable asset. Knowledge management systems should form a crucial part of big data investment. Knowledge Management has the ability to integrate and leverage information from multiple perspectives. Big Data is uniquely positioned to take advantage of KM processes and procedures. These processes and procedures enable KM to provide a rich structure to enable decisions to be made on a multitude and variety of data. Organizations do not make decisions based on one factor; the total picture is what should drive decisions.  KM enables organizations to take the total picture Big Data provides, and along with leveraging tools that provide processing speed to break up the data into subsets for analysis

Developing a superior capacity to take advantage of big data will enhance competitive advantage through knowledge management that will lead to innovation. Knowledge management systems should form a crucial part of big data investment because it has the ability to process the type of knowledge that big data analytics can transform and exploit. Turning big data into useful knowledge for real-time analytics poses many new challenges to the life cycle maintenance of knowledge in Big Data analytics.

The ninth KMO conference intends to bring together researchers and developers from industry and the academic world to discuss and research into how best to promote the role of knowledge management for innovation using big data. It aims to shed light on recent advances in big data and innovation and how knowledge management using big data can improve innovation and competitiveness. KMO 2014 provides the ideal opportunity to present your research and experiences in the fields of computer science, information technology, Knowledge management, innovation and big data.  Topics in KMO include, but are not limited to: 

Big Data Computing for Knowledge Management

Mobile Data Communications

Business models on Big Data applications

Social networks analysis

Supply chain of big data and data products

Service Science

Real-time data mining in mobile internet

Web 2.0 and Data Mining

Algorithms for developing user profiles

Management and Business Intelligence

Information security and knowledge protection

Innovative business models

Innovation and Knowledge

Web Services, Grid Services and Service-Oriented Computing

Knowledge Management and supply chains

Knowledge Representation

Benefits and Challenges in Adopting KM in the Public Sector

Knowledge Evaluation

Innovation Management in the Public Sector Through KM

KM in the Cloud

Re-thinking Knowledge Management

KM in Education

Innovative Processes and Models

KM Tools and Techniques.

Innovation in Education and Training

The role of KM in Tourism

KM and Sustainable Competitive Advantage

Social Networks Analysis

KM, HR & Organizational Culture

Social Networks Extraction and Construction

KM & Organizational Structures

Knowledge Management and Knowledge Networks

Value Creation through Knowledge

Knowledge Quality Estimation and Uncertainty Handling

Knowledge Measurement & Evaluation

Data and Knowledge Interoperability and Exchange

Semantic web data management

Large-scale network data analysis

Transferring Critical Knowledge to Maintain Competitiveness

The Role of Semantic Web in Software and Service Development

Organizational semiotics

Large data stream processing on cloud

Best practices & communities of practice

Large incremental datasets on cloud

Intelligent information systems

Open source real-time computing system for data mining

Intellectual capital

Modeling of service, industrial, and environmental processes

Business Process Management

Predictive analytics

Requirements Engineering

Privacy preserving on cloud

Competitive and Business Intelligence

Security & privacy in Big Data

Knowledge Acquisition and Discovery (AI, Data Mining, Text & Web Mining

Social media analytics

Knowledge Organization (Meta Data, Taxonomies & Ontology)

Theoretical development of Big Data

Social Media and Social Network Technologies

Volume, velocity and variety of Big Data on cloud


Practical examples of services innovation

Cloud computing, peer-to-peer, parallel and distributed databases

Big data and innovation

Semantic and Entity-Based Information Retrieval

Data and Knowledge Modeling

Machine Learning for IR

Use Cases and Applications in Knowledge and Big Data analytics

Data mining theory, methods, and applications

Business forecasting

Data warehousing and business intelligence

Big Data theory

Big data analytics

Big Data applications

Knowledge management in innovative applications, such as healthcare information and network security intelligence

Big Data processing tools

Knowledge Creation

Big Data visualization

KM Implementation Challenges & Opportunities

Big Data management

Organizational Memory

Big data and smart city

KM & Data Security

Case studies of big data mining applications for providing online customer support

Knowledge Assets

Big Data sharing Knowledge Analytics Framework and Architectue

Knowledge Measurement & Evaluation

Big data for knowledge management

Customer Knowledge in Innovation

Capture of big data for knowledge management

Knowledge Sharing

Big data and knowledge extraction

Customer Knowledge Management

Dynamic Knowledge Integration and Visualization

Managing Knowledge for Global and Collaborative Innovations

Knowledge Creation through Crowdsourcing

Co-production of Knowledge

Social computing and knowledge  management

Knowledge Management for Social Change and Innovation

New algorithmic approaches to Big Data

Knowledge Management in the Cloud

Big data and knowledge sharing

The Impacts of Knowledge Management in the Organization

Privacy Preserving Big Data Collection / Analytics


Big data on cloud

Intelligent and Multi-agent Control Systems

And so on.

Knowledge asset valuation models

In addition to the conference, there will be pre conference tutorials relating to the state of the art in the topics of the conference. Invitation for submission to the tutorial can be found in :
Instructions for Authors
Papers reporting original and unpublished research results pertaining to the above topics are solicited (Proceedings will be published by Springer). Full paper submission deadline is March 19, 2014. These papers will follow an academic review process. Full paper manuscripts must be in English with a maximum length of 12 pages (using the Springer template). Submissions should include the title, author(s), affiliation(s), e-mail address(es), tel/fax numbers, abstract, and postal address(es) on the first page. All papers are blind reviewed.
  • Review Process:
KMO 2014 welcomes the submission of papers with preference to the topics listed in the call for papers. All submitted papers will undergo a thorough review process; each paper will be refereed by at least three experts in the field, based on relevance, originality, significance, quality and clarity.
  • Submitting Papers:
All papers must be formatted according to the Springer template, with a maximum length of 12 pages, including figures and references. All proposed papers must be submitted in electronic form (WORD format) using the Paper Submission Page

  • Publication:
Accepted papers will be included in the KMO 2014 Proceedings. At least one of the authors will be required to register and attend the symposium to present the paper in order to include the paper in the conference proceedings. All accepted papers will be published by Springer Verlag. The attachment must be in Word .doc format.
  • Special Issue:
Extended and improved versions of selected papers will be including in a special issue of International Journal of Web Engineering and Technology (IJWET) published by Inderscience.
  • Important Dates:
Submission of tutorial   January 10,  2014
Submission of paper   March 19,  2014
Author notification   April 3,  2014
Early Registration   April 18, 2014
Camera ready   April 26,  2014
Conference date    2nd-5th September,  2014


General Conference Chair
Professor Lorna Uden Staffordshire University, UK
Conference Chair
Professor Lorna Uden Staffordshire University, England, UK
Program Chairs
Professor Darcy Fuenzaliza Oshee Universidad Santa Maria, Chile
Professor I Hsien, National University of Kaohsiung, Taiwan
Local Chairs
Professor Dario Liberona Universidad Santa Maria, Chile
Professor Walter Fraser  Universidad Santa Maria, Chile
Conference Officials
Dr. Darcy Fuenzalida Oshee - Universidad Santa Maria, Valparaiso, Chile
Dr. Dario Liberona - Universidad Santa Maria, Chile
Dr. Derrick Ting - National University of Kaohsiung, Taiwan
Dr. Eric Kin-Wai Lau - City University, Hong Kong
Dr. Flavius Frasincar - Erasmus University Rotterdam, the Netherlands
Dr. Guandong Xu - University of Technology Sydney, Australia
Dr. Javier Bajo Perez - Universidad Politécnica de Madrid, Spain
Dr. Li Weigang - University of Brasilia, Brasilia, Brazil.
Dr. Luka Pavlič - University of Maribor, Slovenia
Dr. Marja Naaranoja - Vaasa University of Applied Sciences, Finland
Dr. Marjan Hericko - University of Maribo, Slovenia
Dr. Michitaka Kosaka - Japan Advanced Institute of Science and Technology(JAIST), Japan
Dr. Paul Wu - SIM University, Singpore.
Dr. Remy Magnier-Watanabe - University of Tsukuba, Tokyo, Japan.
Dr. Richard Self - University of Derby, England
Dr. Senoo Dai - Tokyo Institute of Technology, Japan
Dr. Shahrokh Nikou  - Abo  Akademi University, Finland
Dr. Takao Terano - Tokyo Institute of Technology, Japan
Dr. Victor Hugo Medina Garcia - Universidad Distrital Francisco José de Caldas, Colombia
Dr. Walter Fraser - Universidad Santa Maria, Valparaiso, Chile
Dr. William Wang - Auckland University of Technology, New Zealand
Dr. Wu He - Old Dominion University, USA
Universidad Santa MaríaCatenaria  IEEE  IEEE Computational INtelligence Society