Abstract
The Big Data paradigm has recently come on scene in a quite pervasive manner. Sifting through massive amounts of this kind of data, parsing them, transferring them from a source to a target database, and analyzing them to improve business decision-making processes is too complex for traditional approaches. In this respect, there have been recent proposals that enrich data while exchanging them, such as the Data Posting framework. This framework requires the ability of using domain relations and count constraints, which may be difficult to manage for non-expert users. In this paper, we propose Smart Data Posting, a framework using intuitive constructs that are automatically translated in the standard Data Posting framework. In particular, we allow the use of smart mapping rules extended with additional selection criteria and the direct use of tuple generating dependencies and equality generating dependences. We present a complexity analysis of the framework and describe the architecture of a system for advanced search, tailored for Big Data, that implements the Smart Data Posting framework.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Agrawal, D., et al.: Challenges and opportunities with big data. A community white paper developed by leading researchers across the United States (2012)
Arenas, M., Barceló, P., Fagin, R., Libkin, L.: Locally consistent transformations and query answering in data exchange. In: Beeri, C., Deutsch, A. (eds.) PODS, pp. 229–240. ACM (2004)
Calautti, M., Greco, S., Molinaro, C., Trubitsyna, I.: Rewriting-based check of chase termination. In: Proceedings of the 9th Alberto Mendelzon International Workshop on Foundations of Data Management, Lima, Peru (2015)
Calautti, M., Greco, S., Molinaro, C., Trubitsyna, I.: Exploiting equality generating dependencies in checking chase termination. PVLDB 9(5), 396–407 (2016)
Caroprese, L., Trubitsyna, I., Zumpano, E.: A framework for prioritized reasoning based on the choice evaluation. In: Proceedings of ACM Symposium on Applied Computing (SAC), Seoul, Korea, 11–15 March 2007, pp. 65–70 (2007)
Caroprese, L., Zumpano, E.: Aggregates and priorities in P2P data management systems. In: Proceedings of 15th International Database Engineering and Applications Symposium, IDEAS 2011, Lisbon, Portugal, pp. 1–7 (2011)
Caroprese, L., Zumpano, E.: Computing a deterministic semantics for P2P deductive databases. In: Proceedings of 21st International Database Engineering & Applications Symposium, IDEAS 2017, Bristol, United Kingdom, pp. 184–191 (2017)
Cassavia, N., Masciari, E., Pulice, C., Saccà, D.: Discovering user behavioral features to enhance information search on big data. TIIS 7(2), 7:1–7:33 (2017)
Deutsch, A., Nash, A., Remmel, J.B.: The chase revisited. In: Proceedings of the Twenty-Seventh ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, PODS 2008, Vancouver, BC, Canada, 9–11 June 2008, pp. 149–158 (2008)
The Economist: Drowning in numbers - digital data will flood the planet - and help us understand it better. The Economist, November 2011
Fagin, R., Kolaitis, P.G., Popa, L.: Data exchange: getting to the core. ACM Trans. Database Syst. 30(1), 174–210 (2005)
Greco, G., Greco, S., Trubitsyna, I., Zumpano, E.: Optimization of bound disjunctive queries with constraints. TPLP 5(6), 713–745 (2005)
Greco, S., Molinaro, C., Trubitsyna, I., Zumpano, E.: NP datalog: a logic language for expressing search and optimization problems. TPLP 10(2), 125–166 (2010)
Greco, S., Spezzano, F., Trubitsyna, I.: Stratification criteria and rewriting techniques for checking chase termination. PVLDB 4(11), 1158–1168 (2011)
Greco, S., Spezzano, F., Trubitsyna, I.: Checking chase termination: cyclicity analysis and rewriting techniques. IEEE Trans. Knowl. Data Eng. 27(3), 621–635 (2015)
Greco, S., Trubitsyna, I., Zumpano, E.: On the semantics of logic programs with preferences. J. Artif. Intell. Res. 30, 501–523 (2007)
Lohr, S.: The age of big data. nytimes.com, February 2012
Masciari, E., Saccà, D., Trubitsyna, I.: Simple user assistance by data posting. In: Proceedings of the 2nd IEEE International Conference on Artificial Intelligence and Knowledge Engineering, AIKE 2019, pp. 1–8 (2019, to appear)
Masciari, E., Saccà, D., Trubitsyna, I.: Simplified data posting in practice. In: Proceedings of 23rd International Database Engineering and Applications Symposium, IDEAS 2019, Athens, Greece (2019, to appear)
Meier, M., Schmidt, M., Lausen, G.: On chase termination beyond stratification. PVLDB 2(1), 970–981 (2009)
Onet, A.: The chase procedure and its applications in data exchange. In: Data Exchange, Integration, and Streams, pp. 1–37 (2013)
Redmond, E., Wilson, J.R.: Seven Databases in Seven Weeks: A Guide to Modern Databases and the NoSQL Movement. Pragmatic Bookshelf, Raleigh (2012)
Saccà, D., Serra, E., Guzzo, A.: Count constraints and the Inverse OLAP problem: definition, complexity and a step toward aggregate data exchange. In: Lukasiewicz, T., Sali, A. (eds.) FoIKS 2012. LNCS, vol. 7153, pp. 352–369. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-28472-4_20
Sakama, C., Inoue, K.: Prioritized logic programming and its application to commonsense reasoning. Artif. Intell. 123(1–2), 185–222 (2000)
Special Report: Big data. Nature, September 2008
Acknowledgements
All authors have been supported by MISE Project Data Alliance (D-ALL).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Masciari, E., Saccà, D., Trubitsyna, I. (2019). An Effective System for User Queries Assistance. In: Cuzzocrea, A., Greco, S., Larsen, H., Saccà, D., Andreasen, T., Christiansen, H. (eds) Flexible Query Answering Systems. FQAS 2019. Lecture Notes in Computer Science(), vol 11529. Springer, Cham. https://doi.org/10.1007/978-3-030-27629-4_33
Download citation
DOI: https://doi.org/10.1007/978-3-030-27629-4_33
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-27628-7
Online ISBN: 978-3-030-27629-4
eBook Packages: Computer ScienceComputer Science (R0)