Application of Answer Set Programming for Public Health Data Integration and Analysis

  • Monica L. Nogueira
  • Noel P. Greis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6908)


Public health surveillance systems routinely process massive volumes of data to identify health adverse events affecting the general population. Surveillance and response to foodborne disease suffers from a number of systemic and other delays that hinder early detection and confirmation of emerging contamination situations. In this paper we develop an answer set programming (ASP) application to assist public health officials in detecting an emerging foodborne disease outbreak by integrating and analyzing in near real-time temporally, spatially and symptomatically diverse data. These data can be extracted from a large number of distinct information systems such as surveillance and laboratory reporting systems from health care providers, real-time complaint hotlines from consumers, and inspection reporting systems from regulatory agencies. We encode geographic ontologies in ASP to infer spatial relationships that may not be evident using traditional statistical tools. These technologies and ontologies have been implemented in a new informatics tool, the North Carolina Foodborne Events Data Integration and Analysis Tool (NCFEDA). The application was built to demonstrate the potential of situational awareness—created through real-time data fusion, analytics, visualization, and real-time communication—to reduce latency of response to foodborne disease outbreaks by North Carolina public health personnel.


data integration answer set programming public health food safety ontology 


  1. 1.
    Scallan, E., et al.: Foodborne illness acquired in the United States–Major pathogens. Emerg. Infect. Dis. 17(1), 7–15 (2011)Google Scholar
  2. 2.
    Morris, J.G.: How safe is our food? Emerg. Infect. Dis. 17(1), 126–128 (2011)Google Scholar
  3. 3.
    Scharff, R.L.: Health-Related Costs from Foodborne Illness in the United States. The Produce Safety Project at Georgetown University, Washington, D.C (2010)Google Scholar
  4. 4.
    Greis, N.P., Nogueira, M.L.: Food Safety Emerging Public-Private Approaches: A perspective for local, state, and federal government leaders. IBM Center for The Business of Government (2010)Google Scholar
  5. 5.
    Nogueira, M.L., Greis, N.P.: Rule-Based Complex Event Processing for Food Safety and Public Health. In: Bassiliades, N., et al. (eds.) RuleML 2011 - Europe. LNCS, vol. 6826, pp. 376–384. Springer, Heidelberg (in press, 2011) Google Scholar
  6. 6.
    Associated Free Press, Killer bacteria toll rises to 36. AFP, Web, June 13 (2011)Google Scholar
  7. 7.
    Cowell, A.: Germany Faces Criticism Over E. Coli Outbreak., Web, June 7 (2011),
  8. 8.
    Marvin, H.J.P., et al.: A working procedure for identifying emerging food safety issues at an early stage: Implications for European and international risk management practices. Food Control 20, 345–356 (2009)CrossRefGoogle Scholar
  9. 9.
    Yan, X., et al.: From Ontology Selection and Semantic Web to an Integrated Information System for Food-borne Diseases and Food Safety. Software Tools and Algorithms for Biological Systems. In: Arabnia, H.R., Tran, Q.-N. (eds.) Advances in Experimental Medicine and Biology, vol. 696, pp. 741–750 (2011)Google Scholar
  10. 10.
    Gendel, S.M.: Allergen databases and allergen semantics. Regulatory Toxicology and Pharmacology 54, S7–S10 (2009)CrossRefGoogle Scholar
  11. 11.
    Thakur, M., Olafsson, S., Lee, J.-S., Hurburgh, C.R.: Data Mining for recognizing patterns in foodborne disease outbreaks. J. Food Engineering 97, 213–227 (2010)CrossRefGoogle Scholar
  12. 12.
    Regattieri, A., Gamberi, M., Manzini, R.: Tracebility of food products: general framework and experimental evidence. J. Food Engineering 81, 347–356 (2007)CrossRefGoogle Scholar
  13. 13.
    Kleter, G.A., Marvin, H.J.P.: Indicators of emerging hazards and risks to food safety. Food and Chemical Toxology 47, 1022–1039 (2009)CrossRefGoogle Scholar
  14. 14.
    Noy, N., McGuiness, D.: Ontology Development 101: A Guide to Creating Your First Ontology. Technical Report SMI-2001-0880, Stanford University (2001)Google Scholar
  15. 15.
    Cantais, J., Dominguez, D., Gigante, V., Laera, L., Tamma, V.: An example of food ontology for diabetes control. In: Proc. of the International Semantic Web Conference 2005 Workshop on Ontology Patterns for the Semantic Web, Galway, Ireland (November 2005)Google Scholar
  16. 16.
    Marek, V.W., Truszczynski, M.: Stable models and an alternative logic programming paradigm. In: The Logic Programming Paradigm: a 25-Year Perspective, pp. 375–398. Springer, Berlin (1999)Google Scholar
  17. 17.
    Gelfond, M., Lifschitz, V.: The stable model semantics for logic programming. In: Kowalski, R., Bowen, K. (eds.) International Logic Programming Conference and Symposium, pp. 1070–1080. MIT Press, Cambridge (1988)Google Scholar
  18. 18.
    Gelfond, M., Lifschitz, V.: Classical negation in logic programs and disjunctive databases. New Generation Computing 9, 365–385 (1991)CrossRefGoogle Scholar
  19. 19.
    Lin, F., Zhao, Y.: ASSAT: Computing answer sets of a logic program by SAT solvers. Artificial Intelligence 157(1-2), 115–137 (2004)MathSciNetzbMATHCrossRefGoogle Scholar
  20. 20.
    Gebser, M., Kaufmann, B., Neumann, A., Schaub, T.: clasp: A Conflict-Driven Answer Set Solver. In: Baral, C., Brewka, G., Schlipf, J. (eds.) LPNMR 2007. LNCS (LNAI), vol. 4483, pp. 260–265. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  21. 21.
    Lierler, Y.: Cmodels—SAT-based disjunctive answer set solver. In: Baral, C., Greco, G., Leone, N., Terracina, G. (eds.) LPNMR 2005. LNCS (LNAI), vol. 3662, pp. 447–451. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  22. 22.
    Leone, N., Pfeifer, G., Faber, W., Calimeri, F., Dell’Armi, T., Eiter, T., Gottlob, G., Ianni, G., Ielpa, G., Koch, C., Perri, S., Polleres, A.: The DLV System. In: Flesca, S., Greco, S., Leone, N., Ianni, G. (eds.) JELIA 2002. LNCS (LNAI), vol. 2424, pp. 537–540. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  23. 23.
    Janhunen, T., Niemelä, I., Seipel, D., Simons, P., You, J.-H.: Unfolding Partiality and Disjunctions in Stable Model Semantics. ACM Transactions on Computational Logic 7(1), 1–37 (2006)MathSciNetCrossRefGoogle Scholar
  24. 24.
    Anger, C., Gebser, M., Linke, T., Neumann, A., Schaub, T.: The nomore++ System. In: Baral, C., Greco, G., Leone, N., Terracina, G. (eds.) LPNMR 2005. LNCS (LNAI), vol. 3662, pp. 422–426. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  25. 25.
    Truszczynski, M.: Predicate-calculus-based logics for modeling and solving search problems. ACM Transactions on Computational Logic 7(1), 38–83 (2006)MathSciNetCrossRefGoogle Scholar
  26. 26.
    Niemelä, I., Simons, P.: Extending the Smodels System with Cardinality and Weight Constraints. In: Logic-Based Artificial Intelligence, pp. 491–521. Kluwer Academic Publishers, Dordrecht (2000)CrossRefGoogle Scholar
  27. 27.
    Ricca, F., et al.: OntoDLV: An ASP-based System for Enterprise Ontologies. Journal of Logic and Computation 19(4), 643–670 (2008)MathSciNetCrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2011

Authors and Affiliations

  • Monica L. Nogueira
    • 1
  • Noel P. Greis
    • 1
  1. 1.Center for Logistics and Digital Strategy, Kenan-Flagler Business SchoolThe University of North CarolinaChapel HillU.S.A.

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