ACM Home Page
Please provide us with feedback. Feedback
Event-triggered data and knowledge sharing among collaborating government organizations
Full text PdfPdf (198 KB)
Source
dg.o; Vol. 228 archive
Proceedings of the 8th annual international conference on Digital government research: bridging disciplines & domains table of contents
Philadelphia, Pennsylvania
SESSION: Information sharing table of contents
Pages: 102 - 111  
Year of Publication: 2007
ISBN:1-59593-599-1
Authors
Seema Degwekar  University of Florida
Jeff DePree  University of Florida
Howard Beck  University of Florida, Gainesville, Florida
Carla S. Thomas  University of California, Davis, California
Stanley Y. W. Su  University of Florida
Sponsors
: Center for Technology in Government
: CISCO
: Center for Statistical Ecology and Environmental Statistics
: CIMIC
Publisher
Bibliometrics
Downloads (6 Weeks): 5,   Downloads (12 Months): 70,   Citation Count: 1
Additional Information:

abstract   references   cited by   index terms   collaborative colleagues  

Tools and Actions: Review this Article  

ABSTRACT

Solving complex global problems such as illegal immigration, border control, and terrorism requires government organizations at all levels to share not only data but, more importantly, knowledge pertinent to decision support, problem solving and activity coordination. Responding to an emergency often requires organizational and inter-organizational policies and complex operating procedures to be followed. In this work, we focus on the sharing of data associated with events of interest to collaborating organizations. Condition-action-alternative-action rules, logic/derivation rules, and constraint rules are used to define organizational and inter-organizational policies, regulations, and data and security constraints. Structures of these heterogeneous rules are used to capture organizational processes and operating procedures. A distributed event-triggered knowledge sharing system enables the interoperation of distributed, heterogeneous rules and rule structures on the data associated with each event occurrence so that all data pertinent to the event occurrence can be generated and delivered to relevant organizations. Presented in this paper are: 1) the system architecture and the distributed event and rule processing strategy, 2) algorithms used for the translation of heterogeneous rules and rule structures into web services for their uniform and efficient processing in a web service infrastructure and 3) issues and solutions related to event data aggregation, conflicting rules, and cyclic rules. The developed user interface tool and system are for deployment in the USDA's National Plant Diagnostics Network to strengthen the homeland security protection of this nation's food and agriculture.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

1
 
2
Animal Plant and Health Inspection Service, http://www.aphis.usda.gov.
 
3
 
4
 
5
Beck, H. W. On-line Content Development Tools. http://orb.at.ufl.edu/ObjectEditor.
 
6
Buchmann, A., et al. DREAM: Distributed Reliable Event-Based Application Management. Web Dynamics Adapting to Change in Content, Size, Topology and Use, M. Levene and A. Poulovassilis (eds.), Springer-Verlag, Germany, 2004, 319--352.
 
7
Business Rules Group. Defining Business Rules -- What Are They Really? Final report, http://www.businessrulesgroup.org/first_paper/BRG-whatisBR_3ed.pdf, 2000.
 
8
Business Rules Markup Language, http://xml.coverpages.org/brml.html, 2002.
9
 
10
Degwekar, S. et al. Application of An Event-Trigger-Rule System to Agricultural Homeland Security. International Conference on Knowledge Sharing and Collaborative Engineering, St. Thomas, US Virgin Islands, Nov. 22-24, 2004, 50--56
 
11
Degwekar, S., and Su, S. Knowledge Sharing in a Collaborative Business Environment Workshop on e-Business, USA, 2006, abstract (pp. 60), paper on CD, 12 pgs.
12
 
13
He, B., and Chang, K. A holistic paradigm for large scale schema matching. SIGMOD, France, 2004, 20--25.
 
14
 
15
 
16
National Plant Diagnostic Network, http://www.npdn.org.
 
17
 
18
 
19
Rouvellou, I., et al. Combining Different Business Rules Technologies: A Rationalization. OOPSLA 2000 Workshop on Best-practices in Business Rule Design and Implementation, Minnesota, USA, Oct. 15, 2000.
 
20
Rule Markup Initiative. http://www.ruleml.org, 2000.
 
21
The Rule and Rule Structure Definition Schemas, http://www.cise.ufl.edu/~spd/RuleBase.xsd, http://www.cise.ufl.edu/~spd/RuleStruc.xsd.
 
22
Simple Rule Markup Language, http://xml.coverpages.org/srml.html, 2001.
 
23
 
24
Stack, J. et al. The National Plant Diagnostic Network. Plant Disease, 90, 2, 2006, 128--136.
 
25
Su, S. Y. W., et al. Transnational Information Sharing, Event Notification, Rule Enforcement and Process Coordination. International Journal of Electronic Government Research, 1, 2 (Apr-Jun 2005), 1--26.
 
26
Ullman, J. Principles of Database Systems, 2nd Ed, Computer Science Press, Rockville, MD, 1982.
 
27
 
28
U.S. Congress. Office of Technology Assessment, Harmful Non-Indigenous Species in the United States, OTA-F-565 Washington, DC, U.S. Government Printing Office, 1993, 3--5.
 
29
 
30
 
31


Collaborative Colleagues:
Seema Degwekar: colleagues
Jeff DePree: colleagues
Howard Beck: colleagues
Carla S. Thomas: colleagues
Stanley Y. W. Su: colleagues