Abstract
Although current efforts are all aimed at re-defining new ways to harness old data representations, possibly with new schema features, the challenges still open provide evidence of the need for a "diametrically opposite" approach: in fact, all information generated in real contexts is to be understood lacking of any form of schema, where the schema associated with such data is only determined a posteriori based on either a specific application context, or from some data's facets of interest. This solution should still enable recommendation systems to manipulate the aforementioned data semantically. After providing evidence of these limitations from current literature, we propose a new Generalized Semistructured data Model that makes possible queries expressible in any data representation through a Generalised Semistructured Query Language, both relying upon script v2.0 as a MetaModel language manipulating types as terms as well as allowing structural aggregation functions.
- Appleby, S., Bergami, G., and Morgan, G. 2023. Enhancing declarative temporal model mining in relational databases: A preliminary study. In Proceedings of the International Database Engineered Applications Symposium Conference, IDEAS 2023, Heraklion, Crete, Greece, May 5--7, 2023, R. Chbeir, M. Ivanovic, Y. Manolopoulos, and P. Z. Revesz, Eds. ACM, 34--42.Google ScholarDigital Library
- Asperti, A., Ricciotti, W., and Coen, C. S. 2014. Matita tutorial. J. Formaliz. Reason. 7, 2, 91--199.Google Scholar
- Asperti, A., Ricciotti, W., Sacerdoti Coen, C., and Tassi, E. 2009. A compact kernel for the calculus of inductive constructions. Sadhana 34, 1 (Feb), 71--144.Google ScholarCross Ref
- Baader, F., Calvanese, D., McGuinness, D. L., Nardi, D., and Patel-Schneider, P. F. 2010. The Description Logic Handbook: Theory, Implementation and Applications, 2nd ed. Cambridge University press, New York, NY, USA.Google Scholar
- Baazizi, M. A., Colazzo, D., Ghelli, G., and Sartiani, C. 2019. Parametric schema inference for massive JSON datasets. VLDB J. 28, 4, 497--521.Google ScholarDigital Library
- Bergami, G. 2018. A new nested graph model for data integration. Ph.D. thesis, University of Bologna, Italy.Google Scholar
- Bergami, G., Appleby, S., and Morgan, G. 2023. Quickening data-aware conformance checking through temporal algebras. Inf. 14, 3, 173.Google Scholar
- Bergami, G., Bertini, F., and Montesi, D. 2019. On approximate nesting of multiple social network graphs: a preliminary study. In Proceedings of the 23rd International Database Applications & Engineering Symposium, IDEAS 2019, Athens, Greece, June 10--12, 2019, B. C. Desai, D. Anagnostopoulos, Y. Manolopoulos, and M. Nikolaidou, Eds. ACM, 40:1--40:5.Google Scholar
- Bergami, G., Bertini, F., and Montesi, D. 2020. Hierarchical embedding for DAG reachability queries. In IDEAS 2020: 24th International Database Engineering & Applications Symposium, Seoul, Republic of Korea, August 12--14, 2020, B. C. Desai and W. Cho, Eds. ACM, 24:1--24:10.Google ScholarDigital Library
- Bergami, G., Petermann, A., and Montesi, D. 2018. THoSP: an algorithm for nesting property graphs. In Proceedings of the 1st ACM SIGMOD Joint International Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA), Houston, TX, USA, June 10, 2018, A. A. A. Bhattacharya, G. H. L. Fletcher, J. L. Larriba-Pey, S. Roy, and R. West, Eds. ACM, 8:1--8:10.Google Scholar
- Cabibbo, L. 1998. The expressive power of stratified logic programs with value invention. Inf. Comput. 147, 1, 22--56.Google ScholarDigital Library
- Calders, T., Lakshmanan, L. V., Ng, R. T., and Paredaens, J. 2006. Expressive power of an algebra for data mining. ACM Trans. on Database Systems 31, 4, 1169--1214.Google ScholarDigital Library
- Chatterjee, P. 2019. Strengths and weaknesses of relational DBMSs. https://web.archive.org/web/20190908194656/http://www.cems.uwe.ac.uk/~pchatter/2011/dm/readings/rdb_strengths_weaknesses.html. Accessed: 2019-09-08.Google Scholar
- Chen, C., Yan, X., Zhu, F., Han, J., and Yu, P. S. 2008. Graph OLAP: towards online analytical processing on graphs. In Proceedings of the 8th IEEE International Conference on Data Mining (ICDM 2008), December 15--19, 2008, Pisa, Italy. IEEE Computer Society, 103--112.Google Scholar
- Chen, Y., Goldberg, S., Wang, D. Z., and Johri, S. S. 2016. Ontological pathfinding. In Proceedings of the 2016 International Conference on Management of Data. SIGMOD '16. Association for Computing Machinery, New York, NY, USA, 835--846.Google Scholar
- Codd, E. F. 1970. A relational model of data for large shared data banks. Commun. ACM 13, 6, 377--387.Google ScholarDigital Library
- Elmasri, R. and Navathe, S. B. 2015. Fundamentals of Database Systems, 7th ed. Pearson.Google Scholar
- Fionda, V., Pirrò, G., and Gutierrez, C. 2015. NautiLOD: A formal language for the web of data graph. ACM Trans. Web 9, 1, 5:1--5:43.Google ScholarDigital Library
- Ghrab, A., Romero, O., Skhiri, S., Vaisman, A. A., and Zimányi, E. 2015. A framework for building OLAP cubes on graphs. In Advances in Databases and Information Systems - 19th East European Conference, ADBIS 2015, Poitiers, France, September 8--11, 2015, Proceedings, T. Morzy, P. Valduriez, and L. Bellatreche, Eds. Lecture Notes in Computer Science, vol. 9282. Springer, 92--105.Google Scholar
- He, D., Nakandala, S. C., Banda, D., Sen, R., Saur, K., Park, K., Curino, C., Camacho-Rodríguez, J., Karanasos, K., and Interlandi, M. 2022. Query processing on tensor computation runtimes. Proc. VLDB Endow. 15, 11 (jul), 2811--2825.Google ScholarDigital Library
- Henderson-Sellers, B. 2012. On the Mathematics of Modelling, Metamodelling, Ontologies and Modelling Languages. Springer Briefs in Computer Science. Springer.Google Scholar
- Hosoya, H., Vouillon, J., and Pierce, B. C. 2005. Regular expression types for XML. ACM Trans. Program. Lang. Syst. 27, 1, 46--90.Google ScholarDigital Library
- Kühne, T. 2006. Matters of (meta-)modeling. Softw. Syst. Model. 5, 4, 369--385.Google ScholarCross Ref
- Li, J. and Gao, H. 2003. Hierarchical data cube for range queries and dynamic updates. In Advances in Databases and Information Systems, 7th East European Conference, ADBIS 2003, Dresden, Germany, September 3--6, 2003, Proceedings, L. A. Kalinichenko, R. Manthey, B. Thalheim, and U. Wloka, Eds. Lecture Notes in Computer Science, vol. 2798. Springer, 61--75.Google Scholar
- Magnani, M. and Montesi, D. 2006. A unified approach to structured and XML data modeling and manipulation. Data Knowl. Eng. 59, 1, 25--62.Google ScholarDigital Library
- Parra, V. M., Syed, A., Azeem, M., and Halgamuge, M. N. 2016. Pentaho and jaspersoft: A comparative study of business intelligence open source tools processing big data to evaluate performances. In International Journal of Advanced Computer Science and Applications. Vol. 7(10). 3--61.Google Scholar
- Petermann, A., Junghanns, M., Müller, R., and Rahm, E. 2014. Graph-based data integration and business intelligence with BIIIG. Proc. VLDB Endow. 7, 13, 1577--1580.Google ScholarDigital Library
- Petermann, A., Micale, G., Bergami, G., Pulvirenti, A., and Rahm, E. 2017. Mining and ranking of generalized multi-dimensional frequent subgraphs. In Twelfth International Conference on Digital Information Management, ICDIM 2017, Fukuoka, Japan, September 12--14, 2017. IEEE, 236--245.Google Scholar
- Pierce, B. C. 2002. Types and programming languages. MIT Press.Google ScholarDigital Library
- Plump, D. 1999. Term graph rewriting. In Handbook of Graph Grammars and Computing by Graph Transformation. Vol. 2. 3--61.Google Scholar
- Rabbani, K., Lissandrini, M., and Hose, K. 2023. Shactor: Improving the quality of large-scale knowledge graphs with validating shapes. In Companion of the 2023 International Conference on Management of Data. SIGMOD/PODS '23. Association for Computing Machinery, New York, NY, USA, 151--154.Google Scholar
- Raedt, L. D. 2008. Logical and relational learning. Cognitive Technologies. Springer.Google Scholar
- Rensink, A. 2003. Model checking graph grammars. Tech. rep., Department of Computer Science, University of Twente, Netherlands.Google Scholar
- Vaisman, A. and Zimányi, E. 2014. Data Warehouse Systems. Design and Implementation. Springer.Google Scholar
- Vasilyeva, E., Thiele, M., Bornhövd, C., and Lehner, W. 2013. Leveraging flexible data management with graph databases. In First International Workshop on Graph Data Management Experiences and Systems, GRADES 2013, co-located with SIGMOD/PODS 2013, New York, NY, USA, June 24, 2013, P. A. Boncz and T. Neumann, Eds. CWI/ACM, 12.Google ScholarDigital Library
- Zegadło, W. 2023. Exploring the potential of general semi-structured model for managing and querying data.Google Scholar
Recommendations
Semistructured data and XML
Information organization and databasesXML poses a new set of challenges for semistructured data research. The Extensible Markup Language, XML, is a new recommendation from World Wide Web Consortium that will become a universal data exchange format for the Web. XML shares many common ...
Cooperative query answering for semistructured data
ADC '03: Proceedings of the 14th Australasian database conference - Volume 17Semistructured data, in particular XML, has emerged as one of the primary means for information exchange and content management. The power of XML allows authors to structure a document in a way which precisely captures the semantics of the data. This, ...
Query XML Documents Using XTQ Language
WSCS '08: Proceedings of the IEEE International Workshop on Semantic Computing and SystemsUp to now many XML query languages, including XPath and XQuery which become the standard of XML query standard by W3C, have been prosposed. However, since the navigational query approach adopted by XPath can only get homogeneous data, it often incurs ...
Comments