Skip to main content

Managing Temporal and Versioning Aspects of JSON-Based Big Data via the τJSchema Framework

  • Conference paper
  • First Online:
Book cover Big Data and Smart Digital Environment (ICBDSDE 2018)

Part of the book series: Studies in Big Data ((SBD,volume 53))

Included in the following conference series:

Abstract

Several modern applications (e.g., Internet of Things, online social networks), which exploit big data, require a complete history of all changes performed on these data and their schemas (or structures). However, although schema versioning has long been advocated to be the best solution for this issue, currently there are no available technical supports, provided by existing big data management systems (especially NoSQL DBMSs), for handling temporal evolution and versioning aspects of big data. In [14], for a disciplined and systematic approach to the temporal management of JSON-based big data in NoSQL databases, we have proposed the use of a framework, named τJSchema (temporal JSON Schema). It allows defining and validating temporal JSON documents that obey to a temporal JSON schema. A τJSchema schema is composed of a conventional (i.e., non-temporal) JSON schema annotated with a set of temporal logical and temporal physical characteristics. Moreover, since these two components could evolve over time to respond to new applications’ requirements, we have extended τJSchema, in [17], to support versioning of conventional JSON schemas. In this work, we complete the figure by extending our framework to also support versioning of temporal logical and physical characteristics. Indeed, we propose a technique for temporal characteristics versioning, and provide a complete set of low-level change operations for the maintenance of these characteristics; for each operation, we define its arguments and its operational semantics. Thus, with this extension, τJSchema will provide a full support of temporal versioning of JSON-based big data at both instance and schema levels.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover 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

References

  1. Chen, C.P., Zhang, C.Y.: Data-intensive applications, challenges, techniques and technologies: a survey on Big Data. Inf. Sci. 275, 314–347 (2014)

    Article  Google Scholar 

  2. Information Resources Management Association: Big Data: Concepts, Methodologies, Tools, and Applications. IGI Global, Hershey, PA, USA (2016)

    Book  Google Scholar 

  3. Cuzzocrea, A.: Temporal aspects of big data management: state-of-the-art analysis and future research directions. In: Proceedings of the 22nd International Symposium on Temporal Representation and Reasoning (TIME 2015), Kassel, Germany, pp. 180–185, 23–25 September 2015

    Google Scholar 

  4. Snodgrass, R.T. (ed.), Ahn, I., Ariav, G., et al.: The TSQL2 Temporal Query Language. Kluwer Academic Publishers, Norwell (1995)

    Google Scholar 

  5. Brahmia, Z., Grandi, F., Oliboni, B., Bouaziz, R.: Schema versioning. In: Khosrow-Pour, M. (ed.) Encyclopedia of Information Science and Technology, 3rd edn., pp. 7651–7661. IGI Global, Hershey (2015)

    Chapter  Google Scholar 

  6. NoSQL Databases. http://www.nosql-database.org/

  7. Tiwari, S.: Professional NoSQL. John Wiley & Sons, Inc., Indianapolis (2011)

    Google Scholar 

  8. Pokorný, J.: NoSQL databases: a step to database scalability in web environment. Int. J. Web Inf. Syst. 9(1), 69–82 (2013)

    Article  Google Scholar 

  9. Sharma, S., Tim, U.S., Wong, J., et al.: A brief review on leading big data models. Data Sci. J. 13, 138–157 (2014)

    Article  Google Scholar 

  10. Gudivada, V.N., Rao, D., Raghavan, V.V.: NoSQL systems for big data management. In: Proceedings of the 2014 IEEE World Congress on Services (SERVICES 2014), Anchorage, AK, USA, pp. 190–197, 27 June–2 July 2014

    Google Scholar 

  11. Sharma, S., Tim, U.S., Gadia, S.K., et al.: Classification and comparison of NoSQL big data models. Int. J. Big Data Intell. 2(3), 201–221 (2015)

    Article  Google Scholar 

  12. Corbellini, A., Mateos, C., Zunino, A., et al.: Persisting big-data: The NoSQL landscape. Inf. Syst. 63, 1–23 (2017)

    Article  Google Scholar 

  13. Davoudian, A., Chen, L., Liu, M.: A survey on NoSQL stores. ACM Comput. Surv. 51(2), 43 (2018). Article 40

    Article  Google Scholar 

  14. Brahmia, S., Brahmia, Z., Grandi, F., Bouaziz, R.: τJSchema: a framework for managing temporal JSON-based NoSQL databases. In: Proceedings of the 27th International Conference on Database and Expert Systems Applications (DEXA 2016), Part II, Porto, Portugal, 5–8 September, pp. 167–181 (2016)

    Google Scholar 

  15. Internet Engineering Task Force: JSON Schema: core definition and terminology, Internet-Draft, 4 August 2013. https://tools.ietf.org/html/draft-zyp-json-schema-04

  16. Internet Engineering Task Force: The JavaScript Object Notation (JSON) Data Interchange Format, Internet Standards Track document, March 2014

    Google Scholar 

  17. Brahmia, S., Brahmia, Z., Grandi, F., Bouaziz, R.: Temporal JSON schema versioning in the τJSchema framework. J. Digit. Inf. Manag. 15(4), 179–202 (2017)

    Google Scholar 

  18. Brahmia, S., Brahmia, Z., Grandi, F., Bouaziz, R.: Online Appendix of the Paper: Managing Temporal and Versioning Aspects of JSON-based Big Data via the τJSchema Framework, 12 p., 10 October 2018. http://www-db.disi.unibo.it/~fgrandi/papers/ICBDSDE2018paperAppendix.pdf

  19. Florescu, D., Fourny, G.: JSONiq: the History of a query language. IEEE Internet Comput. 17(5), 86–90 (2013)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Zouhaier Brahmia .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Brahmia, S., Brahmia, Z., Grandi, F., Bouaziz, R. (2019). Managing Temporal and Versioning Aspects of JSON-Based Big Data via the τJSchema Framework. In: Farhaoui, Y., Moussaid, L. (eds) Big Data and Smart Digital Environment. ICBDSDE 2018. Studies in Big Data, vol 53. Springer, Cham. https://doi.org/10.1007/978-3-030-12048-1_5

Download citation

Publish with us

Policies and ethics