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Project knowledge management based on social networks

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Project knowledge management based on social networks

  1. 1. USING SOCIAL NETWORK ANALYSIS FOR SOFTWARE PROJECT MANAGEMENT ICEMI 2014, HONG KONG 15-16 FEBRUARY 2014 PROF. PANOS FITSILIS (FITSILIS@TEILAR.GR) TECHNOLOGICAL EDUCATION INSTITUTE OF THESSALY
  2. 2. CONTENTS • Contemporary Trends on Project managements • How SNA can be used in the context of Software Engineering and Software Project Management • The ONSOCIAL project
  3. 3. TYPICAL PROJECT MANAGEMENT APPROACHES • Project Management Institute – Body of Knowledge • www.pmi.org • Integration, scope, time, cost, quality, HR, communication, Process • PRINCE • www.prince2.com • IPMA Competence Baseline • www.ipma.ch • Technical, behavioral, contextual • Agile methods • XP, Scrum, Crystal Reports, etc. People
  4. 4. WHAT ARE THE INTANGIBLES IN SPM? DEFINITION OF INTANGIBLES The factors not shown in the traditional project analysis, but which are of critical importance for the project and the organization’s future success. Using Social Networking to discover the intangibles How we select our team? How we decide on our team composition? What knowledge we are missing? What requirements to include in our release? Which tests to execute?
  5. 5. SOCIAL NETWORKS AND KNOWLEDGE MANAGEMENT • Why Social Networks in KMS? People Knowledge Management Processes Content Knowledge Management involves people, technology, and processes in Overlapping parts.
  6. 6. TRANSFORMING TACIT KNOWLEDGE TO EXPLICIT KNOWLEDGE TACIT KNOWLEDGE SOCIAL NETWORK ANALYSIS EXPLICIT KNOWLEDGE Potential for knowledge extraction
  7. 7. Software Project Ontology Personnel information … Personnel knowledge evaluation 5 Social network Knowledge management Team selection
  8. 8. SOCIAL NETWORK ANALYSIS Social network analysis [SNA] is the mapping and measuring of relationships and flows between people, groups, organizations, computers or other information/knowledge processing entities. The nodes in the network are the people and groups while the links show relationships or flows between the nodes. We measure Social Network in terms of: • 1. Degree Centrality: • The number of direct connections a node has. • 2. Betweenness Centrality: • A node with high betweenness has great influence over what flows in the network indicating important links and single point of failure. • 3. Closeness Centrality: • The measure of closeness of a node which are close to everyone else.
  9. 9. DIMENSION FOR TEAM SELECTION Projects Roles Locations Model Resources Knowledge Tasks Agents
  10. 10. NETWORK OF CONNECTIONS/1
  11. 11. NETWORK OF CONNECTIONS/2
  12. 12. COLLABORATION NET FOR AGENTS (AGENT X AGENT) Project A Project B Project C
  13. 13. NETWORK THAT RELATES TEAM MEMBERS, PROJECTS AND KNOWLEDGE Project team members Knowledge/skills Projects
  14. 14. PROJECT NETWORK PRESENTING CLASSES KNOWLEDGE AND PROJECTS Knowledge required by each project Knowledge/skills Projects
  15. 15. ONSOCIAL PROJECT TECHNICAL ARCHITECTURE
  16. 16. PROBLEMS NEED TO BE ADDRESSED • Data extraction from different Social Networks • Sparse data usage (profiles are empty) • Unique identification of profiles • Creating/keeping current enterprise data corpus • Selecting most appropriate algorithm for matching profiles.
  17. 17. CONCLUSION • We have presented • How social network analysis can be used in order to improve software project management • Different scenarios that can help improve project analysis • Project selection • Team and knowledge analysis • Requirements management • Improving testing • Based on solid theoretical framework (graph theory)

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