Modeling and Management of Big Data: Challenges and opportunities

Future Generation Computer Systems

Volumn 63, Issue , 2016, Pages 96-99

  Gil, David ^a   Song, Il Yeol ^b  

^a UNIVERSITY OF ALICANTE   (Spain)

^b Drexel University   (United States)

Author keywords

Conceptual modeling Big Data; Ecosystem; Integrate analyze visualize

Indexed keywords

EID: 84941254248     PISSN: 0167739X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.future.2015.07.019     Document Type: Editorial

References (32)

1. [1] Aufaure, M.-A., Chiky, R., Curé, O., Khrouf, H., Kepeklian, G., From Business Intelligence to semantic data stream management. Future Gener. Comput. Syst. 63 (2016), 100–107.
2. [2] Sangupamba Mwilu, O., Prat, N., Comyn-Wattiau, I., Design science research contribution to business intelligence in the cloud — a systematic literature review. Future Gener. Comput. Syst. 63 (2016), 108–122.
3. [3] Merino, J., Caballero, I., Rivas, B., Serrano, M., Piattini, M., A Data Quality in Use model for Big Data. Future Gener. Comput. Syst. 63 (2016), 123–130.
4. [4] Maté, A., Peral, J., Ferrández, A., Gil, D., Trujillo, J., A hybrid integrated architecture for energy consumption prediction. Future Gener. Comput. Syst. 63 (2016), 131–147.
5. [5] Gadiraju, K.K., Verma, M., Davis, K.C., Talaga, P.G., Benchmarking performance for migrating a relational application to a parallel implementation. Future Gener. Comput. Syst. 63 (2016), 148–156.
6. [6] Cukier, K., Data, data everywhere: a special report on managing information. Economist Newspaper, 2010.
7. [7] Labrinidis, A., Jagadish, H., Challenges and opportunities with big data. Proc. VLDB Endow. 5:12 (2012), 2032–2033.
8. [8] D. Laney, 3-d data management: Controlling data volume. Velocity and Variety, META Group Original Research Note, 2001.
9. [9] Fan, W., Bifet, A., Mining big data: current status, and forecast to the future. ACM sIGKDD Explorations Newsletter 14:2 (2013), 1–5.
10. [10] Buyya, R., Yeo, C.~S., Venugopal, S., Broberg, J., Brandic, I., Cloud computing and emerging it platforms: vision, hype, and reality for delivering computing as the 5th utility. Future Gener. Comput. Syst. 25:6 (2009), 599–616.
11. [11] Gubbi, J., Buyya, R., Marusic, S., Palaniswami, M., Internet of things (iot): a vision, architectural elements, and future directions. Future Gener. Comput. Syst. 29:7 (2013), 1645–1660.
12. [12] Dobre, C., Xhafa, F., Intelligent services for big data science. Future Gener. Comput. Syst. 37 (2014), 267–281.
13. [13] Franklin, M.~J., Making sense of big data with the berkeley data analytics stack. SSDBM, 2013, 1.
14. [14] Doan, A., Halevy, A., Ives, Z., Principles of Data Integration. 2012, Elsevier.
15. [15] Embley, D.~W., Liddle, S.~W., Big data conceptual modeling to the rescue. Conceptual Modeling, 2013, Springer, 1–8.
16. [16] Chen, P.~P.-S., The entity-relationship model toward a unified view of data. ACM Trans. Database Syst. (TODS) 1:1 (1976), 9–36.
17. [17] Halevy, A., Best-effort modeling of structured data on the web. Conceptual Modeling–ER 2011, 2011, Springer 32-32.
18. [18] Halevy, A., Rajaraman, A., Ordille, J., Data integration: the teenage years. Proceedings of the 32nd International Conference on Very Large Data Bases, 2006, 9–16 VLDB Endowment.
19. [19] Cafarella, M.~J., Halevy, A., Madhavan, J., Structured data on the web. Commun. ACM 54:2 (2011), 72–79.
20. [20] Cafarella, M.~J., Halevy, A., Wang, D.~Z., Wu, E., Zhang, Y., Webtables: exploring the power of tables on the web. Proceedings of the VLDB Endowment 1:1 (2008), 538–549.
21. [21] Gonzalez, H., Halevy, A.~Y., Jensen, C.~S., Langen, A., Madhavan, J., Shapley, R., Shen, W., Goldberg-Kidon, J., Google fusion tables: web-centered data management and collaboration. Proceedings of the 2010 ACM SIGMOD International Conference on Management of data, 2010, ACM, 1061–1066.
22. [22] Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z., Dbpedia: A Nucleus for a Web of Open Data. 2007, Springer.
23. [23] Alani, H., Kim, S., Millard, D.~E., Weal, M.~J., Hall, W., Lewis, P.~H., Shadbolt, N.~R., Automatic ontology-based knowledge extraction from web documents. IEEE Intel. Syst. 18:1 (2003), 14–21.
24. [24] Venetis, P., Halevy, A., Madhavan, J., Paşca, M., Shen, W., Wu, F., Miao, G., Wu, C., Recovering semantics of tables on the web. Proc. VLDB Endow. 4:9 (2011), 528–538.
25. [25] Halevy, A., Norvig, P., Pereira, F., The unreasonable effectiveness of data. IEEE Intel. Syst. 24:2 (2009), 8–12.
26. [26] He, B., Chang, K.~C.-C., Statistical schema matching across web query interfaces. Proceedings of the 2003 ACM SIGMOD International Conference on Management of Data, 2003, ACM, 217–228.
27. [27] Kaisler, S., Armour, F., Espinosa, J.A., Money, W., Big data: Issues and challenges moving forward. System Sciences (HICSS), 2013 46th Hawaii International Conference, 2013, IEEE, 995–1004.
28. [28] Chen, C.P., Zhang, C.-Y., Data-intensive applications, challenges, tech- niques and technologies: A survey on big data. Inform. Sci. 275 (2014), 314–347.
29. [29] Petcu, D., Macariu, G., Panica, S., Craciun, C., Portable cloud applicationsfrom theory to practice. Future Gener. Comput. Syst. 29:6 (2013), 1417–1430.
30. [30] Han, J., Haihong, E., Le, G., Du, J., Survey on nosql database. Pervasive Computing and Applications (ICPCA), 2011 6th International Conference, 2011, IEEE, 363–366.
31. [31] Chen, H., Chiang, R.H., Storey, V.C., Business intelligence and analytics: From big data to big impact. MIS Quart. 36:4 (2012), 1165–1188.
32. [32] Graves, A., Techniques to reduce cluttering of rdf visualizations. Future Gener. Comput. Syst. 53 (2015), 152–156.