Linguistic summaries of graph databases in customer relationship management (CRM)

• Autorzy: Bartczak Monika , Niewiadomski Adam
• Języki publikacji: EN

Abstrakty

EN

The paper concentrates on data models that differ from the traditional relational one by Codd (1970). In particular, we are interested in processing graph databases (graph datasets) without any pre-configured structure, in which graph nodes may represent different objects and graph edges - relations between them. In this approach, the linguistic summarization methods for graph datasets are introduced, and diferences for these methods with respect to traditional relational approach are shown, commented and improved in comparison to the preceding proposition (Strobin, Niewiadomski, 2016). The novelty of the paper is mostly the new form for summaries: Multi-Subject linguistic summaries of graph databases, previously introduced for relational databases (Superson, 2018).

Słowa kluczowe

EN

graph data model; graph databases; linguistic summarization of graph datasets; customer relationship management; Neo4j; data mining; data; fuzzy representations of data

PL

grafowy model danych; grafowa baza danych; lingwistyczne podsumowania grafowych zbiorów danych; zarządzanie relacjami z klientami; Neo4j; eksploracja danych; dane; rozmyta reprezentacja danych

• Wydawca: Wydawnictwo Politechniki Łódzkiej
• Czasopismo: Journal of Applied Computer Science
• Rocznik: 2019
• Tom: Vol. 27, nr 1
• Strony: 7--26
• Opis fizyczny: Bibliogr. 19 poz., il. kolor., 1 rys.

Twórcy

autor

Bartczak Monika

• Faculty of Technical Physics, Information Technology and Applied Mathematics, Lodz University of Technology, ul. Wólczańska 215, 90-924 Łódź, Poland

autor

Niewiadomski Adam

• Faculty of Technical Physics, Information Technology and Applied Mathematics, Lodz University of Technology, ul. Wólczańska 215, 90-924 Łódź, Poland

Bibliografia

• [1] Codd, E. F., A Relational Model of Data for Large Shared Data Banks, Communications of the ACM, Vol. 13, No. 6, 1970, pp. 377-387.
• [2] Kacprzyk, J. and Yager, R. R., Linguistic summaries of data using fuzzy logic, International Journal of General Systems, Vol. 30, 2001, pp. 133-154.
• [3] Niewiadomski, A., Methods for the Linguistic Summarization of Data: Applications of Fuzzy Sets and Their Extensions, Academic Publishing House EXIT, 2008.
• [4] Yager, R. R., A new approach to the summarization of data, Information Sciences, Vol. 28, 1982, pp. 69-86.
• [5] Bartczak, M., Podsumowania lingwistyczne grafowych baz danych w zarządzaniu relacjami z klientem (CRM), Master’s thesis, Instytut Informatyki, Politechnika Łódzka, 2018, (in Polish).
• [6] Strobin, L. and Niewiadomski, A., Linguistic Summaries of Graph Datasets Using Ontologies: An Application to Semantic Web, In: Computational Collective Intelligence 7th International Conference ICCCI 2015, 2015, pp. 380- 389.
• [7] NoSQL, http://www.leavcom.com/pdf/NoSQL.pdf, Accessed: 07.12.2019.
• [8] Chang, F., Dean, J., Ghemawat, S., Hsieh, W., Wallach, D., Burrows, M., Chandra, T., Fikes, A., and Gruber, R., Bigtable:A Distributed Storage System for Structured Data, https://static.googleusercontent.com/m edia/research.google.com/pl//archive/bigtable-osdi06.pdf, Accessed: 07.12.2019.
• [9] Kacprzyk, J., Yager, R. R., and Zadrożny, S., Fuzzy linguistic summaries of databases for an effcient business data analysis and decision support, In: Knowledge Discovery for Business Information Systems, edited by W. Abramowicz and J. ˙ Zurada, Kluwer Academic Publisher, B. V. and Boston, 2001, pp. 129-152.
• [10] Niewiadomski, A., A type-2 fuzzy approach to linguistic summarization of data, IEEE Transactions on Fuzzy Systems, Vol. 16, No. 1, 2008, pp. 198- 213.
• [11] Niewiadomski, A., Ochelska, J., and Szczepaniak, P. S., Interval-valued linguistic summaries of databases, Control and Cybernetics, Vol. 35, No. 2, 2006, pp. 415-444.
• [12] Strobin, L. and Niewiadomski, A., Integration of Multiple Graph Datasets and Their Linguistic Summaries: An Application to Linked Data, In: Artificial Intelligence and Soft Computing 2016, Part I, 2016, pp. 333-343.
• [13] Niewiadomski, A. and Superson, I., Multi-Subject Type-2 Linguistic Summaries of Relational Databases, In: Frontiers of higher order Fuzzy Sets, edited by A. Sadeghian and H. Tahayori, Springer-Verlag, 2015.
• [14] NoSQL, https://www.grupa-tense.pl/blog/czy-nosql-to-przyszlosc-baz-danych, Accessed: 07.12.2019.
• [15] Słotwiński, D., Grafowe bazy danych - przegląd technologii, https://ai.ia.agh.edu.pl/_media/pl:dydaktyka:ztb:2010: projekty:gdb:grafowe_bazy_danych.pdf, 2010, (in Polish).
• [16] Strobin, L. and Niewiadomski, A., Wielokrotne przyspieszenie działania aplikacji poprzez zastosowanie technologii nierelacyjnych baz danych, Economic Studies. University of Economics in Katowice, , No. 199, 2014, (in Polish).
• [17] Kacprzyk, J., Yager, R. R., and Zadrożny, S., A fuzzy logic based approach to linguistic summaries of databases, International Journal of Applied Mathematics and Computer Sciences, Vol. 10, 2000, pp. 813-834.
• [18] Strobin, L., Wyszukiwanie zale˙zno˙sci semantycznych w grafowych bazach danych z zastosowaniem logiki rozmytej i algorytm˙ow genetycznych, Ph.D. thesis, Politechnika Łódzka, 2017.
• [19] Superson, I. and Niewiadomski, A., Pozyskiwanie wiedzy z relacyjnych baz danych: wielopodmiotowe podsumowania lingwistyczne, Economic Studies. University of Economics in Katowice, 2014, pp. 301-304, (in Polish).

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021)