Skip to main content
SpringerLink
Log in

An AI Approach to Temporal Indeterminacy in Relational Databases

  • Conference paper
  • First Online:
Advances in Artificial Intelligence - IBERAMIA 2018 (IBERAMIA 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11238))

Included in the following conference series:

  • 1267 Accesses

Abstract

Time is pervasive of the human way of approaching reality, so that it has been widely studied in many research areas, including Artificial Intelligence (AI) and relational Temporal Databases (TDB). Indeed, while thousands of TDB papers have been devoted to the treatment of determinate time, only few approaches have faced temporal indeterminacy (i.e., “don’t know exactly when” indeterminacy). In this paper, we propose a new AI-based methodology to approach temporal indeterminacy in relational DBs. We show that typical AI techniques, such as studying the semantics of the representation formalism, and adopting symbolic manipulation techniques based on such a semantics, are very important in the treatment of indeterminate time in relational databases.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Indeed, the most common way of presenting the semantics of a temporal database is the one in BCDM, in which each tuple is paired with all the chronons when it holds. In BCDM, temporal databases directly associate times with tuples, so that the semantics of Example 1 above would be modeled as follows: {<John, fever, high, {10,11,12}>, <Mary, fever, moderate, {11, 12, 13}>.

  2. 2.

    Notably, it is possible to show that it is not possible to define correct algebraic operators closed with respect to the representational model also in case one admits the possibility that facts in the TDBs do not necessarily occur, i.e., imposing t1 ≤ t2 ≤ t3 ≤t4 in the representational model. We cannot show such a generalization here, for the sake of space constraints.

References

  1. Snodgrass, R.T.: Developing Time-Oriented Database Applications in SQL. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2000)

    Google Scholar 

  2. Snodgrass, R.T.: The TSQL2 Temporal Query Language. Kluwer (1995)

    Google Scholar 

  3. Wu, Y., Jajodia, S., Wang, X.S.: Temporal database bibliography update. In: Etzion, O., Jajodia, S., Sripada, S. (eds.) Temporal Databases: Research and Practice. LNCS, vol. 1399, pp. 338–366. Springer, Heidelberg (1998). https://doi.org/10.1007/BFb0053709

    Chapter  Google Scholar 

  4. Liu, L., Özsu, M.T. (eds.): Encyclopedia of Database Systems. Springer, Heidelberg (2009)

    MATH  Google Scholar 

  5. Dyreson, C.: Temporal indeterminacy. In: Liu, L., Ozsu, M.T. (eds.) Encyclopedia of Database Systems, pp. 2973–2976. Springer, Boston (2009)

    Google Scholar 

  6. Jensen, C.S., Snodgrass, R.T.: Semantics of time-varying information. Inf. Syst. 21, 311–352 (1996)

    Article  Google Scholar 

  7. Dyreson, C.E., Snodgrass, R.T.: Supporting valid-time indeterminacy. ACM Trans. Database Syst. (TODS) 23, 1–57 (1998)

    Article  Google Scholar 

  8. Dekhtyar, A., Ross, R., Subrahmanian, V.: Probabilistic temporal databases, I: algebra. ACM Trans. Database Syst. (TODS) 26, 41–95 (2001)

    Article  Google Scholar 

  9. Anselma, L., Terenziani, P., Snodgrass, R.T.: Valid-time indeterminacy in temporal relational databases: a family of data models. In: Proc. TIME, pp. 139–145. IEEE (2010)

    Google Scholar 

  10. Anselma, L., Terenziani, P., Snodgrass, R.T.: Valid-time indeterminacy in temporal relational databases: semantics and representations. IEEE Trans. Knowl. Data Eng. 25, 2880–2894 (2013)

    Article  Google Scholar 

  11. Anselma, L., Piovesan, L., Terenziani, P.: A 1NF temporal relational model and algebra coping with valid-time temporal indeterminacy. J. Intell. Inf. Syst. 47, 345–374 (2016)

    Article  Google Scholar 

  12. Hobbs, J.R., Pan, F.: An ontology of time for the semantic web. ACM Trans. Asian Lang. Inf. Process. 3, 66–85 (2004)

    Article  Google Scholar 

  13. Baumann, R., Loebe, F., Herre, H.: Axiomatic theories of the ontology of time in GFO. Appl. Ontol. 9, 171–215 (2014)

    Google Scholar 

  14. Ermolayev, V., Batsakis, S., Keberle, N., Tatarintseva, O., Antoniou, G.: Ontologies of time: review and trends. IJCSA 11, 57–115 (2014)

    Google Scholar 

  15. McKenzie Jr., L.E., Snodgrass, R.T.: Evaluation of relational algebras incorporating the time dimension in databases. ACM Comput. Surv. 23, 501–543 (1991)

    Article  Google Scholar 

  16. Anselma, L., Bottrighi, A., Montani, S., Terenziani, P.: Extending BCDM to cope with proposals and evaluations of updates. IEEE Trans. Knowl. Data Eng. 25, 556–570 (2013)

    Article  Google Scholar 

  17. Anselma, L., Stantic, B., Terenziani, P., Sattar, A.: Querying now-relative data. J. Intell. Inf. Syst. 41, 285–311 (2013)

    Article  Google Scholar 

  18. Anselma, L., Piovesan, L., Sattar, A., Stantic, B., Terenziani, P.: A comprehensive approach to “now” in temporal relational databases: semantics and representation. IEEE Trans. Knowl. Data Eng. 28, 2538–2551 (2016)

    Article  Google Scholar 

  19. Terenziani, P.: Irregular indeterminate repeated facts in temporal relational databases. IEEE Trans. Knowl. Data Eng. 28, 1075–1079 (2016)

    Article  Google Scholar 

  20. Terenziani, P.: Nearly periodic facts in temporal relational databases. IEEE Trans. Knowl. Data Eng. 28, 2822–2826 (2016)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dipartimento di Informatica, Università di Torino, Corso Svizzera 185, 10149, Turin, Italy

    Luca Anselma

  2. DISIT, Università del Piemonte Orientale “A. Avogadro”, Alessandria, Italy

    Luca Piovesan & Paolo Terenziani

Authors
  1. Luca Anselma
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Luca Piovesan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Paolo Terenziani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Luca Anselma .

Editor information

Editors and Affiliations

  1. Universidad Nacional del Sur, Bahía Blanca, Buenos Aires, Argentina

    Guillermo R. Simari

  2. University of Madeira, Funchal, Portugal

    Eduardo Fermé

  3. Universidad Nacional de Piura, Castilla-Piura, Peru

    Flabio Gutiérrez Segura

  4. Universidad Nacional de Trujillo, Trujillo, Peru

    José Antonio Rodríguez Melquiades

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Anselma, L., Piovesan, L., Terenziani, P. (2018). An AI Approach to Temporal Indeterminacy in Relational Databases. In: Simari, G., Fermé, E., Gutiérrez Segura, F., Rodríguez Melquiades, J. (eds) Advances in Artificial Intelligence - IBERAMIA 2018. IBERAMIA 2018. Lecture Notes in Computer Science(), vol 11238. Springer, Cham. https://doi.org/10.1007/978-3-030-03928-8_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-03928-8_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-03927-1

  • Online ISBN: 978-3-030-03928-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation