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1.Ying Wang, Gaurang Mehta, Rajiv Mayani, Jingxi Lu, Tade Souaiaia, Yangho Chen, Andrew Clark, Hee Jae Yoon, Lin Wan, Oleg V. Yolanda Gil, Varun Ratnakar, Jihie Kim, Pedro Gonzalez-Calero, Paul Groth, Joshua Moody, and Ewa Deelman, Wings: Intelligent Workflow-Based Design of Computational Experiments.
4.Saccone SF, Quan J, Mehta G, Bolze R, Thomas P, Deelman E, Tischfield JA, Rice JP, New tools and methods for direct programmatic access to the dbSNP relational database.
17.Yolanda Gil, Ewa Deelman, Mark Ellisman, Thomas Fahringer, Geoffrey Fox, Dennis Gannon, Carole Goble, Miron Livny, Luc Moreau, Jim Myers, Examining the Challenges of Scientific Workflows, IEEE Computer, IEEE Computer, vol. 18.Gurmeet Singh, Karan Vahi, Arun Ramakrishnan, Gaurang Mehta, Ewa Deelman, Henan Zhao, Rizos Sakellariou, Kent Blackburn, Duncan Brown, Stephen Fairhurst, David Meyers, G.
21.Ewa Deelman, Tevfik Kosar, Carl Kesselman and Miron Livny, What Makes Workflows Work in an Opportunistic Environment?
22.Gurmeet Singh, Carl Kesselman, Ewa Deelman, Optimizing Grid-Based Workflow Execution, Journal of Grid Computing, Vol.
23.Ewa Deelman, Gurmeet Singh, Mei-Hui Su, James Blythe, Yolanda Gil, Carl Kesselman, Gaurang Mehta, Karan Vahi, G.
Mats Rynge, Gideon Juve, Gaurang Mehta, Ewa Deelman, Krista Larson, Burt Holzman, Igor Sfiligoi, Frank WA?rthwein, G. Ashish Nagavaram, Gagan Agrawal, Michael Freitas, Gaurang Mehta, Rajiv Mayani, Ewa Deelman, A Clouda€?based Dynamic Workflow for Mass Spectrometry Data Analysis,, Proceedings of the 7th IEEE International Conference on e-Science (e-Science 2011), December 2011.
Gideon Juve, Ewa Deelman Automating Application Deployment in Infrastructure Clouds, 3rd IEEE International Conference on Cloud Computing Technology and Science (CloudCom) 2011. Taghrid Samak, Dan Gunter, Monte Goode, Ewa Deelman, Gaurang Mehta, Fabio Silva, Karan Vahi, Failure Prediction and Localization in Large Scientific Workflows, 6th Workshop on Workflows in Support of Large-Scale Science (WORKS 11), Seattle, Washington, November 14th, 2011. Taghrid Samak, Dan Gunter, Ewa Deelman, Gideon Juve, Gaurang Mehta, Fabio Silva, Karan Vahi, Online Fault and Anomaly Detection for Large-Scale Scientific Workflows, 13th IEEE International Conference on High Performance Computing and Communications (HPCC 2011), 2011.
Wrangler: Virtual Cluster Provisioning for the Cloud, Gideon Juve and Ewa Deelman, short paper, Proceedings of the 20th International Symposium on High Performance Distributed Computing (HPDC'11), 2011. Mirko Sonntag Dimka Karastoyanova and Ewa Deelman, BPEL4Pegasus: Combining Business and Scientific Workflows, International Conference on Service-Oriented Computing (ICSOC), San Francisco, California, December 2010.
Philip Maechling, Ewa Deelman, Yifeng Cui: Implementing Software Acceptance Tests as Scientific Workflows.
Vijay Kumar, Mary Hall, Jihie Kim, Yolanda Gil, Tahsin Kurc, Ewa Deelman and Joel Saltz, a€?Designing and Parameterizing a Workflow for Optimization: A Case Study in Biomedical Imaginga€?, In Proc. Ann Chervenak, Ewa Deelman, Miron Livny, Mei-Hui Su, Rob Schuler, Shishir Bharathi, Gaurang Mehta, Karan Vahi, Data Placement for Scientific Applications in Distributed Environments, Proceedings of Grid Conference 2007, Austin, Texas, September 2007. Ewa Deelman and Yolanda Gil, Managing Large-Scale Scientific Workflows in Distributed Environments: Experiences and Challenges, Proceedings of Workflows in e-Science, Amsterdam, The Netherlands, 2006. Seung-Hye Jang, Valerie Taylor, Xingfu Wu, Mieke Prajugo, Ewa Deelman, Gaurang Mehta, and Karan Vahi, Performance Prediction-based versus Load-based Site Selection: Quantifying the Difference, the 18th International Conference on Parallel and Distributed Computing Systems (PDCS-2005), Las Vegas, Nevada, 12-14 September 2005.
Jim Blythe, Sonal Jain, Ewa Deelman, Karan Vahi, Yolanda Gil, Anirban Mandal, Ken Kennedy, Task Scheduling Strategies for Workflow-based Applications in Grids, IEEE International Symposium on Cluster Computing and the Grid (CCGrid 2005). Ewa Deelman, James Blythe, Yolanda Gil, Carl Kesselman, Gaurang Mehta, Sonal Patil, Mei-Hui Su, Karan Vahi, Miron Livny, Pegasus: Mapping Scientific Workflows onto the Grid, Across Grids Conference 2004, Nicosia, Cyprus. Ewa Deelman, Raymond Plante, Carl Kesselman, Gurmeet Singh, Mei-Hui Su, Gretchen Greene, Robert Hanisch, Niall Gaffney, Antonio Volpicelli, James Annis, Vijay Sekhri, Tamas Budavari, Maria Nieto-Santisteban, William Oa€™Mullane, David Bohlender, Tom McGlynn, Arnold Rots, Olga Pevunova, Grid-Based Galaxy Morphology Analysis for the National Virtual Observatory, Proceedings of SC 2003. Gurmeet Singh, Shishir Bharathi,Ann Chervenak, Ewa Deelman, Carl Kesselman, Mary Manohar, Sonal Patil, and Laura Pearlman. 4.Ewa Deelman, Miron Livny, Gaurang Mehta, Andy Pavlo, Gurmeet Singh, Mei-Hui Su, Karan Vahi, R. 11.Ewa Deelman, James Blythe, Yolanda Gil, and Carl Kesselman, Workflow Management in GriPhyN, in Grid Resource Management, J.
2.Leila Meshkat, William Allcock, Ewa Deelman, Carl Kesselman, Fault Location in Grids Using Fault Location in Grids Using Bayesian Belief Networks. 22.Gurmeet Singh, Carl Kesselman, Ewa Deelman,A  Optimizing Grid-Based Workflow Execution, Journal of Grid Computing, Vol. 1.Mats Rynge, Gideon Juve, Gaurang Mehta, Ewa Deelman, Krista Larson, Burt Holzman, Igor Sfiligoi, Frank WA?rthwein, G. 2.Ashish Nagavaram, Gagan Agrawal, Michael Freitas, Gaurang Mehta, Rajiv Mayani, Ewa Deelman, A Clouda€?based Dynamic Workflow for Mass Spectrometry Data Analysis,, Proceedings of the 7th IEEE International Conference on e-Science (e-Science 2011), December 2011. 3.Gideon Juve, Ewa Deelman Automating Application Deployment in Infrastructure Clouds, 3rd IEEE International Conference on Cloud Computing Technology and Science (CloudCom) 2011. 4.Mats Rynge, Gideon Juve, Gaurang Mehta, Ewa Deelman, Krista Larson, Burt Holzman, Igor Sfiligoi, Frank WA?rthwein, G. 5.Taghrid Samak, Dan Gunter, Monte Goode, Ewa Deelman, Gaurang Mehta, Fabio Silva, Karan Vahi, Failure Prediction and Localization in Large Scientific Workflows,A  6th Workshop on Workflows in Support of Large-Scale Science (WORKS 11), Seattle, Washington, November 14th, 2011.
7.Taghrid Samak, Dan Gunter, Ewa Deelman, Gideon Juve, Gaurang Mehta, Fabio Silva, Karan Vahi, Online Fault and Anomaly Detection for Large-Scale Scientific Workflows, 13th IEEE International Conference on High Performance Computing and Communications (HPCC 2011), 2011. 33.Gurmeet Singh, Mei-Hui Su, Karan Vahi, Ewa Deelman, Bruce Berriman, John Good, Daniel S. 35.Ann Chervenak, Ewa Deelman, Miron Livny, Mei-Hui Su, Rob Schuler, Shishir Bharathi, Gaurang Mehta, Karan Vahi,A  Data Placement for Scientific Applications in Distributed Environments, Proceedings of Grid Conference 2007, Austin, Texas, September 2007.
54.Jim Blythe, Sonal Jain, Ewa Deelman, Karan Vahi, Yolanda Gil, Anirban Mandal, Ken Kennedy, Task Scheduling Strategies for Workflow-based Applications in Grids, IEEE International Symposium on Cluster Computing and the Grid (CCGrid 2005). 64.Ewa Deelman, Raymond Plante, Carl Kesselman, Gurmeet Singh, Mei-Hui Su, Gretchen Greene, Robert Hanisch, Niall Gaffney, Antonio Volpicelli, James Annis, Vijay Sekhri, Tamas Budavari, Maria Nieto-Santisteban, William Oa€™Mullane, David Bohlender, Tom McGlynn, Arnold Rots, Olga Pevunova, Grid-Based Galaxy Morphology Analysis for the National Virtual Observatory, Proceedings of SC 2003. 66.Gurmeet Singh, Shishir Bharathi,Ann Chervenak, Ewa Deelman, Carl Kesselman, Mary Manohar, Sonal Patil, and Laura Pearlman. Over the centuries many attempts have been made to classify knowledge, and different fields have focused on different dimensions. Within business and KM, two types of knowledge are usually defined, namely explicit and tacit knowledge.


KM and organisational learning theory almost always take root in the interaction and relationship between these two types of knowledge. Without question, the most important distinction within KM is between explicit and tacit knowledge. For this reason, the discussions on this site will, when relevant, use all three categorizations of knowledge but the focus will always be primarily on the explicit-tacit dimension. This type of knowledge is formalized and codified, and is sometimes referred to as know-what (Brown & Duguid 1998). From a managerial perspective, the greatest challenge with explicit knowledge is similar to information. Although this is changing to some limited degree, KM initiatives driven by technology have often had the flaw of focusing almost exclusively on this type of knowledge. Tacit knowledge is also regarded as being the most valuable source of knowledge, and the most likely to lead to breakthroughs in the organization (Wellman 2009).
Using a reference by Polanyi (1966), imagine trying to write an article that would accurately convey how one reads facial expressions.
The exact extent to which IT systems can aid in the transfer and enhancement of tacit knowledge is a rather complicated discussion. Embedded knowledge refers to the knowledge that is locked in processes, products, culture, routines, artifacts, or structures (Horvath 2000, Gamble & Blackwell 2001). The challenges in managing embedded knowledge vary considerably and will often differ from embodied tacit knowledge. Due to the difficulty in effectively managing embedded knowledge, firms that succeed may enjoy a significant competitive advantage. Embedded knowledge is found in: rules, processes, manuals, organizational culture, codes of conduct, ethics, products, etc.
Kumar, Tahsin Kurc, Varun Ratnakar, Jihie Kim, Gaurang Mehta, Karan Vahi, Yoonju Lee Nelson, P. Concurrency and Computation: Practice and Experience, Volume 18, Issue 10 , Pages 1187 a€“ 1199. Bruce Berriman, Scott Callaghan, Experiences Using GlideinWMS and the Corral Frontend Across Cyberinfrastructures, Proceedings of the 7th IEEE International Conference on e-Science (e-Science 2011), December 2011. Bruce Berriman, Scott Callaghan, Experiences Using GlideinWMS and the Corral Frontend Across Cyberinfrastructures, Proceedings of the 7th IEEE International Conference on e-Science (e-Science 2011), December 2011.
Brooks, Ewa Deelman, Monte Good, Gideon Juve, Gaurang Mehta, Priscilla Moaes, Taghrid Samak, Fabio Silva, Martin Swany, Karan Vahi, Online Workflow Management and Performance Analysis with STAMPEDE, Proceedings of the 7th International Conference on Network and Service Management (CNSM 2011), 2011. Bruce Berriman, Gideon Juve, Ewa Deelman, Moira Regelson, Peter Plavchan, The Application of Cloud Computing to Astronomy: A Study of Cost and Performance, Workshop on e-Science challenges in Astronomy and Astrophysics in conjunction with the 6th IEEE International Conference on e-Science (e-Science 2010), December 2010.
Bruce Berriman, Ewa Deelman, Paul Groth, and Gideon Juve, The Application of Cloud Computing to the Creation of Image Mosaics and Management of Their Provenance SPIE Conference 7740: Software and Cyberinfrastructure for Astronomy, 2010. Connecting Scientific Data to Scientific Experiments with Provenance, Third IEEE International Conference on e-Science and Grid Computing (e-Science 2007) Bangalore, India. Wings for Pegasus: Creating Large-Scale Scientific Applications Using Semantic Representations of Computational Workflows, Proceedings of the 19th Annual Conference on Innovative Applications of Artificial Intelligence (IAAI), Vancouver, British Columbia, Canada, July 22-26, 2007.
Virtual Metadata Catalogs: Augmenting Metadata Catalogs with Semantic Representations, Short paper at the Fourth International Semantic Web Conference (ISWC-05), Galway, Ireland, November 7-10, 2005. Planning and Metadata on the Computational Grid, In AAAI Spring Symposium on Semantic Web Services, Palo Alto, California, USA, 2004. Foster and others in the iVDGL project, The Grid2003 Production Grid: Principles and Practice, High Performance Distributed Computing, 2004. Planning and Metadata on the Computational Grid, In AAAI Spring Symposium on Semantic Web Services, 2004.
Bruce Berriman, Scott Callaghan, Experiences Using GlideinWMS and the Corral Frontend Across Cyberinfrastructures,A  Proceedings of the 7th IEEE International Conference on e-Science (e-Science 2011), December 2011. For example, it should be fairly evident that the knowledge captured in a document would need to be managed (i.e. As discussed previously, in fields such as IT there is often a lack of a more sophisticated definition. It is sometimes referred to as know-how (Brown & Duguid 1998) and refers to intuitive, hard to define knowledge that is largely experience based. Gamble & Blackwell (2001) link the lack of focus on tacit knowledge directly to the reduced capability for innovation and sustained competitiveness. It should be quite apparent that it would be near impossible to convey our intuitive understanding gathered from years of experience and practice. This is one reason why experience in a particular field is so highly regarded in the job market.
For now, suffice it to say that successful KM initiatives must place a very strong emphasis on the tacit dimension, focusing primarily on the people involved, and they must understand the limitations imposed by computerized systems.
Knowledge is embedded either formally, such as through a management initiative to formalize a certain beneficial routine, or informally as the organization uses and applies the other two knowledge types. Bruce Berriman, Proceedings of 2nd Workshop on Scientific Cloud Computing (ScienceCloud 2011), 2011. Sadayappan, Gaurang Mehta, Karan Vahi, Ewa Deelman, Varun Ratnakar, Jihie Kim, Yolanda Gil, Mary W. On the Black Art of Designing Computational Workflows, Proceedings of the Second Workshop on Workflows in Support of Large-Scale Science (WORKS'07), in conjunction with the IEEE International Symposium on High Performance Distributed Computing Monterrey, CA, June 2007.
Szymanski, Dynamic load balancing in parallel discrete event simulation for spatially explicit problems. This is the type of knowledge most easily handled by KMS, which are very effective at facilitating the storage, retrieval, and modification of documents and texts. It is considered simpler in nature and cannot contain the rich experience based know-how that can generate lasting competitive advantage.


Formalized routines on the other hand may be easier to implement and management can actively try to embed the fruits of lessons learned directly into procedures, routines, and products. However, it has also been argued that IT can have a disruptive influence on culture and processes, particularly if implemented improperly. Fernandes, Gaurang Mehta, Adaptive Workflow Processing and Execution in Pegasus, Concurrency Practice and Experience, volume 21, issue 16, 2009, pages 1965-1981.
Deelman, Application-level Resource Provisioning on the Grid, Proceedings of e-Science, Amsterdam, The Netherlands, 2006. Deelman, Metadata Catalogs with Semantic Representation, Proceedings of International Provenance Annotation Workshop (IPAW-06), Chicago, IL, 2006.
Katz, Albert Lazzarini, Kent Blackburn, and Scott Koranda, The Pegasus Portal: Web Based Grid Computing, The 20th Annual ACM Symposium on Applied Computing, SAC 2005. Proceedings of the Twelfth Workshop on Parallel and Distributed Simulation, PADS '98, Banff, CA, May 26-29, 1998.
It is hard to communicate and deeply rooted in action, commitment, and involvement (Nonaka 1994). An IT specialist for example will troubleshoot a problem based on his experience and intuition. On this site, I will generally limit tacit knowledge to knowledge embodied in people, and refer separately to embedded knowledge (as defined below), whenever making this distinction is relevant. Compiler-Optimized Simulation of Large-Scale Applications on High Performance Architectures. Simulating Spatially Explicit Problems on High Performance Architectures, Journal of Parallel and Distributed Computing, Vol.
Parallel Simulation of Large Scale Parallel Applications, International Journal of High-Performance Computing Applications. Proceedings of the International Conference on Parallel and Distributed Processing Techniques and Applications, Las Vegas, NV, June 30- July 3, 1997. Proceedings of the 11th Workshop on Parallel and Distributed Simulation, PADS'97, Lockenhaus, Austria, June 10-13, 1997. Livny, Representing Virtual Data: A Catalog Architecture for Location and Materialization Transparency, GriPhyN technical report 2001-13. Simulating Spatially Explicit Problems on High Performance Architectures,A  Journal of Parallel and Distributed Computing, Vol. Parallel Simulation of Large Scale Parallel Applications,A A  International Journal of High-Performance Computing Applications. Adve.A  Improving Lookahead in Parallel Discrete Event Simulations of Large-Scale Applications Using Compiler Analysis. ACM SIGPLAN 1999 Symposium on Principles and Practice of Parallel Programming, Atlanta, Georgia, May 4-6, 1999. Sakellariou, Compiler-Supported Simulation of Highly Scalable Parallel Applications, Proceedings of SC'99, Portland, OR, Nov 13 - 19, 1999.
Williams.A  Applications of Virtual Data in the LIGO Experiment, Proceedings of the International Conference on Parallel Processing and Applied Mathematics, Naleczow, Poland, September 2001,A  Lecture Notes in Computer Science.
Allen, A Virtual Data Grid for LIGO, Lecture Notes in Computer Science 2110 (2001) 3, also Proc.
Saltz, A Systematic Approach to Composing and Optimizing Application Workflows, Proceedings of Workshop on Patterns in High Performance Computing, Urbana-Champaign, 2005.
Saltz,A  A Systematic Approach to Composing and Optimizing Application Workflows, Proceedings of Workshop on Patterns in High Performance Computing, Urbana-Champaign, 2005. Proceeding of the Workshop on Languages, Compilers and Run-Time Systems for Parallel Programming, Kluwer Academic Publishers, Boston, 1995.
Ellisman, Enabling Parallel Scientific Applications with Workflow Tools, Proceedings of Challenges of Large Applications in Distributed Environments (CLADE), Paris, 2006. Ellisman,A  Enabling Parallel Scientific Applications with Workflow Tools, Proceedings of Challenges of Large Applications in Distributed Environments (CLADE), Paris, 2006. Koranda, Mapping Complex Workflows Onto Grid Environments, Journal of Grid Computing, Vol.1, No. Williams Astronomical Image Mosaicking on a Grid: Initial Experiences in Engineering the Grid-Status and Perspectives, B.
High-Performance Remote Access to Climate Simulation Data: A Challenge Problem for Data Grid Technologies.
Su, Montage: a Grid Enabled Engine for Delivering Custom Science-Grade Image Mosaics on Demand, Proceedings of the SPIE Conference on Astronomical Telescopes and Instrumentation, June 2004. Su, The Montage Architecture for Grid-Enabled Science Processing of Large, Distributed Datasets, Proceedings of the Earth Science Technology Conference (ESTC) 2004, June 2004. Williams, Montage A Grid Enabled Image Mosaic Service for the National Virtual Observatory, ADASS XIII, ASP Conference Series Vol XXX, F Ochsenbein M Allen and D Egret eds, 2003. Williams, Montage A Grid Enabled Image Mosaic Service for the National Virtual Observatory,A  ADASS XIII, ASP Conference Series Vol XXX, F Ochsenbein M Allen and D Egret eds, 2003. Zhao, Managing Large-Scale Workflow Execution from Resource Provisioning to Provenance tracking: The CyberShake Example, Proceedings of e-Science, Amsterdam, The Netherlands, 2006. Zhao,A  Managing Large-Scale Workflow Execution from Resource Provisioning to Provenance tracking: The CyberShake Example, Proceedings of e-Science, Amsterdam, The Netherlands, 2006.
Proceedings of the First International Workshop on Software and Performance '98 - WOSP '98, Santa Fe, New Mexico, October 12-16, 1998.



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