Source:http://www.igi-global.com/journals/abstract-announcement/131884

The contents of the latest issue of:

International Journal of Organizational and Collective Intelligence (IJOCI)

Indexed by DBLP, Inspec… (more)

Volume 6, Issue 3, July – September 2016
Indexed by: INSPEC
Published: Quarterly in Print and Electronically
ISSN: 1947-9344; EISSN: 1947-9352;
Published by IGI Global Publishing, Hershey, USA
www.igi-global.com/ijoci

Editor-in-Chief: Victor Chang (Xi’an Jiaotong Liverpool University, China) and Dickson K.W. Chiu (The University of Hong Kong, Hong Kong)

Note: The International Journal of Organizational and Collective Intelligence (IJOCI) has an Open Access option, which allows individuals and institutions unrestricted access to its published content. Unlike traditional subscription-based publishing models, open access content is available without having to purchase or subscribe to the journal in which the content is published. All IGI Global manuscripts are accepted based on a double-blind peer review editorial process.

ARTICLE 1

A Case Study on Data Quality, Privacy, and Evaluating the Outcome of Entity Resolution Processes

Pei Wang (University of Arkansas at Little Rock, Little Rock, AR, USA), Daniel Pullen (University of Arkansas at Little Rock, Little Rock, AR, USA), Fan Liu (University of Arkansas at Little Rock, Little Rock, AR, USA), William C. Decker (University of Arkansas at Little Rock, Little Rock, AR, USA), Ningning Wu (University of Arkansas at Little Rock, Little Rock, AR, USA), John R. Talburt (University of Arkansas at Little Rock, Little Rock, AR, USA)

This paper presents ongoing research conducted through collaboration between the University of Arkansas at Little Rock and the Arkansas Department of Education to develop an entity resolution and identity management system. The process includes a multi-phase approach consisting of data-quality analysis, selection of entity-identity attributes for entity resolution, defined a rule set using the open source entity-resolution system named OYSTER and used entropy approach to identify the potential false positive and false negative. The research is the first known of its kind to evaluate privacy-enhancing, entity-resolution rule sets in a state education agency.

To obtain a copy of the entire article, click on the link below.
www.igi-global.com/article/a-case-study-on-data-quality-privacy-and-evaluating-the-outcome-of-entity-resolution-processes/157316

To read a PDF sample of this article, click on the link below.
www.igi-global.com/viewtitlesample.aspx?id=157316

ARTICLE 2

The Role of Stories and Simulations in the Lessons Learned Process

Kimiz Dalkir (McGill University, Montreal, Canada)

One of the major challenges of any organizational lessons learned system is how to ensure that this content is actually implemented: that employees can find and learn from them. While we are guided by a number of theories on how newly acquired knowledge can become institutionalized such that it becomes “the way things are done,” there is very little theory or evidence-based practice to guide us on specific implementation strategies. This paper presents specific strategies that were used to ensure that lessons learned became embedded in the organization through digital storytelling and simulation environments. Organizational stories are often very well suited to capturing and conveying complex tacit knowledge. The role of information and communication technologies such as digital libraries will be discussed and recommendation on how to best ensure individuals, groups and the organization itself can learn and continuously improve through the institutionalization of digital storytelling and simulation.

To obtain a copy of the entire article, click on the link below.
www.igi-global.com/article/the-role-of-stories-and-simulations-in-the-lessons-learned-process/157317

To read a PDF sample of this article, click on the link below.
www.igi-global.com/viewtitlesample.aspx?id=157317

ARTICLE 3

RSSMSO Rapid Similarity Search on Metric Space Object Stored in Cloud Environment

Raghavendra S. (University Visvesvaraya College of Engineering, Bangalore, India), Nithyashree K. (University Visvesvaraya College of Engineering, Bangalore, India), Geeta C.M. (University Visvesvaraya College of Engineering, Bangalore, India), Rajkumar Buyya (University of Melbourne, Melbourne, Australia), Venugopal K. R. (University Visvesvaraya College of Engineering, Bangalore, India), S. S. Iyengar (Florida International University, Miami, FL, USA), L. M. Patnaik (National Institute of Advanced Studies, Bangalore, India)

This paper involves a cloud computing environment in which the dataowner outsource the similarity search service to a third party service provider. Privacy of the outsourced data is important because they may be confidential data. The data should be made available to the authorized client groups, but not to be revealed to the service provider in which the data is stored. Given this scenario, the paper presents a technique called RSSMSO which has build phase, query phase, data transformation and search phase. The build phase and the query phase are about uploading the data and querying the data respectively; the data transformation phase transforms the data before submitting it to the service provider for similarity queries on the transformed data; search phase involves searching similar object with respect to query object. The RSSMSO technique provides enhanced query accuracy with low communication cost. Experiments have been carried out on real data sets which exhibits that the proposed work is capable of providing privacy and achieving accuracy at a low cost in comparison with FDH

To obtain a copy of the entire article, click on the link below.
www.igi-global.com/article/rssmso-rapid-similarity-search-on-metric-space-object-stored-in-cloud-environment/157318

To read a PDF sample of this article, click on the link below.
www.igi-global.com/viewtitlesample.aspx?id=157318

ARTICLE 4

Public Financial Information Management for Benefits Maximization: Insights from Organization Theories

Yaotai Lu (Florida Atlantic University, Boca Raton, Florida, USA)

Information management is an essential part in the public budgetary process. This paper analyzes the theoretical basis, tools, and consequences of information management throughout a budget cycle. Budgeters and decision makers need necessary financial data related to all types of revenues and expenditures, economic conditions, and agency needs, among other factors. From an organization theory perspective, budget agencies face a great number of uncertainties and constraints throughout each phase of a budget cycle. Using appropriate budgeting techniques and approaches, they collect, analyze, and use necessary information to make rational budgetary decisions regarding revenue raising and resource distribution. They intend to attain such goals and objectives as cutting inefficient expenditure, achieving more output and outcome with less input, and attaining oriented societal consequences. Extensive efforts in budget reforms have resulted in considerable productivity in government administration, but at a low level. Continuous efforts are needed for further improvement of performance.

To obtain a copy of the entire article, click on the link below.
www.igi-global.com/article/public-financial-information-management-for-benefits-maximization/157319

To read a PDF sample of this article, click on the link below.
www.igi-global.com/viewtitlesample.aspx?id=157319

ARTICLE 5

A Proposed Framework for Cloud Computing adoption

Victor I. C. Chang (Xi’an Jiaotong Liverpool University, Southampton, UK)

This paper presents a review related to Cloud Computing focusing on Cloud business requirements. From the review the author recommends a number of methods managing Cloud services and evaluating its service performance, including the use of a pair of the Hexagon Models. Three organizational challenges of Cloud adoption are identified: (i) Organizational Sustainability; (ii) Portability and (iii) Linkage. The Cloud Computing Adoption Framework (CCAF) is designed to deal with these challenges by helping organizations to achieve good Cloud designs, deployment and services. How these three challenges are addressed by the CCAF is demonstrated using case studies. Services implemented by CCAF are reviewed using the Hexagon Models for comparison. This paper provides recommendations to help organizations, researchers and practitioners to understand Cloud business context, to measure their risk and return analysis, to migrate their services to Cloud from all types and to connect and integrate different services as a single service.

To obtain a copy of the entire article, click on the link below.
www.igi-global.com/article/a-proposed-framework-for-cloud-computing-adoption/157320

To read a PDF sample of this article, click on the link below.
www.igi-global.com/viewtitlesample.aspx?id=157320

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For full copies of the above articles, check for this issue of the International Journal of Organizational and Collective Intelligence (IJOCI) in your institution’s library. This journal is also included in the IGI Global aggregated “InfoSci-Journals” database: www.igi-global.com/isj.

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CALL FOR PAPERS

Mission of IJOCI:

The mission of the International Journal of Organizational and Collective Intelligence (IJOCI) is to provide researchers and practitioners in the communities of computer and information sciences with a forum to advance the practice and understanding of computing theories and empirical analyses for realizing “organizational intelligence and collective intelligence”, i.e., intelligent computing for organizational and collective information from not only technical but also institutional and social aspects.

Indices of IJOCI:

• ACM Digital Library
• Bacon’s Media Directory
• Cabell’s Directories
• DBLP
• Google Scholar
• INSPEC
• JournalTOCs
• Library & Information Science Abstracts (LISA)
• MediaFinder
• The Standard Periodical Directory
• Ulrich’s Periodicals Directory

Coverage of IJOCI:

Topics to be discussed in this journal include (but are not limited to) the following:

• Artificial intelligence for organizational management
• Classification and clustering
• Collaboration and communication systems
• Corporate management systems
• Data mining and knowledge bases for organizational management
• Decision making theory and modeling
• Decision science
• Decision support systems and crisis management systems
• Expert systems
• Game theoretic and information economic analysis
• Genetic algorithms and evolutionary computing
• Global enterprise systems
• Information content security
• Intellectual property management
• Intelligent agents and multi-agent systems
• Intelligent Web-based systems
• Knowledge discovery
• Machine and computer vision
• Machine learning
• Metadata and multimedia information systems
• Monitoring and planning
• Neural networks, bayesian networks,and fuzzy techniques and systems
• Optimization
• Organizational systems, middleware, applications, and experiences
• Robotics for intelligent organizations
• Security and access control
• Self-organizing and complex systems
• Semantic Web architecture and applications
• Service computing
• Signal and time series processing
• Soft computing in organizations

Interested authors should consult the journal’s manuscript submission guidelines www.igi-global.com/calls-for-papers/international-journal-organizational-collective-intelligence/1140