Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Buchtitelbild

2018 | OriginalPaper | Buchkapitel

Graph BI & Analytics: Current State and Future Challenges

verfasst von : Amine Ghrab, Oscar Romero, Salim Jouili, Sabri Skhiri

Erschienen in: Big Data Analytics and Knowledge Discovery

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

In an increasingly competitive market, making well-informed decisions requires the analysis of a wide range of heterogeneous, large and complex data. This paper focuses on the emerging field of graph warehousing. Graphs are widespread structures that yield a great expressive power. They are used for modeling highly complex and interconnected domains, and efficiently solving emerging big data application. This paper presents the current status and open challenges of graph BI and analytics, and motivates the need for new warehousing frameworks aware of the topological nature of graphs. We survey the topics of graph modeling, management, processing and analysis in graph warehouses. Then we conclude by discussing future research directions and positioning them within a unified architecture of a graph BI and analytics framework.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Nächstes Kapitel Community Detection in Who-calls-Whom Social Networks
Literatur
1.
2.
García-Solaco, M., Saltor, F., Castellanos, M.: In: Bukhres, O.A., Elmagarmid, A.K. (eds.) Object-Oriented Multidatabase Systems, pp. 129–202. Prentice Hall International (UK) Ltd, Hertfordshire, UK (1995)
3.
4.
Akoglu, L., Tong, H., Koutra, D.: Graph based anomaly detection and description: a survey. Data Mining Knowl. Discov. 29(3), 626–688 (2015)MathSciNetCrossRef
5.
Van Vlasselaer, V., Bravo, C., Caelen, O., Eliassi-Rad, T., Akoglu, L., Snoeck, M., Baesens, B.: Apate: a novel approach for automated credit card transaction fraud detection using network-based extensions. Decis. Support Syst. 75, 38–48 (2015)CrossRef
6.
Dasgupta, K., Singh, R., Viswanathan, B., Chakraborty, D., Mukherjea, S., Nanavati, A.A., Joshi, A.: Social ties and their relevance to churn in mobile telecom networks. In: Proceedings of the 11th International Conference on Extending Database Technology, EDBT 2008. Advances in database technology, New York, USA, pp. 668–677. ACM (2008)
7.
Duan, L., Da Xu, L.: Business intelligence for enterprise systems: a survey. IEEE Trans. Industr. Inform. 8(3), 679–687 (2012)CrossRef
8.
Lim, E.P., Chen, H., Chen, G.: Business intelligence and analytics: Research directions. ACM Trans. Manag. Inf. Syst. 3(4), 17 (2013)CrossRef
9.
Cuzzocrea, A., Bellatreche, L., Song, I.Y.: Data warehousing and OLAP over big data: Current challenges and future research directions. In: Proceedings of the Sixteenth International Workshop on Data Warehousing and OLAP, pp. 67–70. ACM (2013)
10.
11.
Shi, C., Li, Y., Zhang, J., Sun, Y., Philip, S.Y.: A survey of heterogeneous information network analysis. IEEE Trans. Knowl. Data Eng. 29(1), 17–37 (2017)CrossRef
12.
Chen, C., Yan, X., Zhu, F., Han, J., Yu, P.S.: Graph OLAP: a multi-dimensional framework for graph data analysis. Knowl. Inf. Syst. 21(1), 41–63 (2009)CrossRef
13.
Hannachi, L., Benblidia, N., Boussaid, O., Bentayeb, F.: Community cube: a semantic framework for analysing social network data. Int. J. Metadata Semant. Ontol. 10(3), 155–169 (2015)CrossRef
14.
Angles, R., Arenas, M., Barceló, P., Hogan, A., Reutter, J., Vrgoč, D.: Foundations of modern query languages for graph databases. ACM Comput. Surv. 50(5), 68 (2017)CrossRef
15.
Hölsch, J., Schmidt, T., Grossniklaus, M.: On the performance of analytical and pattern matching graph queries in neo4j and a relational database. In: Ioannidis, Y.E., Stoyanovich, J., Orsi, G. (eds.) Proceedings of the Workshops of the EDBT/ICDT 2017 Joint Conference (EDBT/ICDT 2017), Venice, Italy, March 21–24, 2017. Volume 1810 of CEUR Workshop Proceedings, CEUR-WS.org (2017)
16.
17.
Berlingerio, M., Coscia, M., Giannotti, F., Monreale, A., Pedreschi, D.: Multidimensional networks: foundations of structural analysis. World Wide Web 16(5–6), 567–593 (2013)CrossRef
18.
Zhao, P., Li, X., Xin, D., Han, J.: Graph cube: On warehousing and OLAP multidimensional networks. In: Proceedings of the 2011 ACM SIGMOD International Conference on Management of data, pp. 853–864. ACM (2011)
19.
Wang, Z., Fan, Q., Wang, H., Tan, K.l., Agrawal, D., El Abbadi, A.: Pagrol: Prallel Graph OLAP over large-scale attributed graphs. In: 2014 IEEE 30th International Conference on Data Engineering (ICDE), pp. 496–507. IEEE (2014)
20.
21.
Nebot, V., Berlanga, R.: Building data warehouses with semantic web data. Decis. Support Syst. 52(4), 853–868 (2012)CrossRef
22.
Kämpgen, B., Harth, A.: Transforming statistical linked data for use in OLAP systems. In: Proceedings of the 7th International Conference on Semantic Systems, pp. 33–40. ACM (2011)
23.
Beheshti, S.M.R., Benatallah, B., Motahari-Nezhad, H.R.: Scalable graph-based olap analytics over process execution data. Distrib. Parallel Databases 34(3), 379–423 (2016)CrossRef
24.
Varga, J., Vaisman, A.A., Romero, O., Etcheverry, L., Pedersen, T.B., Thomsen, C.: Dimensional enrichment of statistical linked open data. Web Semant. Sci. Serv. Agents World Wide Web 40, 22–51 (2016)CrossRef
25.
Nath, R.P.D., Hose, K., Pedersen, T.B., Romero, O.: SETL: a programmable semantic extract-transform-load framework for semantic data warehouses. Inf. Syst. 68, 17–43 (2017)CrossRef
26.
Lee, K., Lee, K.: Escaping your comfort zone: a graph-based recommender system for finding novel recommendations among relevant items. Expert Syst. with Appl. 42(10), 4851–4858 (2015)CrossRef
27.
Demesmaeker, F., Ghrab, A., Nijssen, S., Skhiri, S.: Discovering interesting patterns in large graph cubes. In: 2017 IEEE International Conference on Big Data (Big Data), pp. 3322–3331 (2017)
28.
Bleco, D., Kotidis, Y.: Entropy-based selection of graph cuboids. In: Proceedings of the Fifth International Workshop on Graph Data-management Experiences & Systems, vol. 2. ACM (2017)
29.
Lumsdaine, A., Gregor, D., Hendrickson, B., Berry, J.: Challenges in parallel graph processing. Parallel Process. Lett. 17(01), 5–20 (2007)MathSciNetCrossRef
30.
Batarfi, O., El Shawi, R., Fayoumi, A.G., Nouri, R., Barnawi, A., Sakr, S., et al.: Large scale graph processing systems: survey and an experimental evaluation. Cluster Comput. 18(3), 1189–1213 (2015)CrossRef
31.
Denis, B., Ghrab, A., Skhiri, S.: A distributed approach for graph-oriented multidimensional analysis. In: 2013 IEEE International Conference on Big Data, pp. 9–16, October 2013
32.
Malewicz, G., Austern, M.H., Bik, A.J., Dehnert, J.C., Horn, I., Leiser, N., Czajkowski, G.: PREGEL: a system for large-scale graph processing. In: Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data, pp. 135–146. ACM (2010)
33.
Low, Y., Bickson, D., Gonzalez, J., Guestrin, C., Kyrola, A., Hellerstein, J.M.: Distributed graphlab: a framework for machine learning and data mining in the cloud. Proc. VLDB Endow. 5(8), 716–727 (2012)CrossRef
34.
Gonzalez, J.E., Low, Y., Gu, H., Bickson, D., Guestrin, C.: Powergraph: Distributed graph-parallel computation on natural graphs. In: OSDI, vol. 12, p. 2 (2012)
35.
Gonzalez, J.E., Xin, R.S., Dave, A., Crankshaw, D., Franklin, M.J., Stoica, I.: Graphx: Graph processing in a distributed dataflow framework. OSDI. 14, 599–613 (2014)
36.
Zaharia, M., Chowdhury, M., Franklin, M.J., Shenker, S., Stoica, I.: Spark: cluster computing with working sets. In: Proceedings of the 2nd USENIX Conference on Hot Topics in Cloud Computing, HotCloud 2010, Berkeley, CA, USA, p. 10 (2010)
37.
Junghanns, M., Petermann, A., Gómez, K., Rahm, E.: Gradoop: Scalable graph data management and analytics with hadoop. arXiv preprint arXiv:​1506.​00548 (2015)
38.
Carbone, P., Katsifodimos, A., Ewen, S., Markl, V., Haridi, S., Tzoumas, K.: Apache FLINK: Stream and batch processing in a single engine. In: Bulletin of the IEEE Computer Society Technical Committee on Data Engineering, vol. 36(4) (2015)
Metadaten
Titel
Graph BI & Analytics: Current State and Future Challenges
verfasst von
Amine Ghrab
Oscar Romero
Salim Jouili
Sabri Skhiri
Copyright-Jahr
2018
Buch
Big Data Analytics and Knowledge Discovery

Print ISBN: 978-3-319-98538-1

Electronic ISBN: 978-3-319-98539-8

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-98539-8_1