Introduction
Industry analysts describe Big Data in terms of three V’s: volume, velocity, variety. The data is too big to process with current tools; it arrives too fast for optimal storage and indexing; and it is too heterogeneous to fit into a rigid schema. There is a huge pressure on database researchers to study, explain, and solve the technical challenges in big data, but we find no inspiration in the three Vs. Volume is surely nothing new for us, streaming databases have been extensively studied over a decade, while data integration and semistructured has studied heterogeneity from all possible angles.
This work was partially supported by NSF IIS-1115188, IIS-0915054 and IIS-1247469.
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
Preview
Unable to display preview. Download preview PDF.
References
Ceri, S., Gottlob, G., Tanca, L.: What you always wanted to know about datalog (and never dared to ask). IEEE Trans. Knowl. Data Eng. 1(1), 146–166 (1989)
Bu, Y., Howe, B., Balazinska, M., Ernst, M.D.: The haloop approach to large-scale iterative data analysis. VLDB J. 21(2), 169–190 (2012)
Upadhyaya, P., Kwon, Y., Balazinska, M.: A latency and fault-tolerance optimizer for online parallel query plans. In: SIGMOD Conference, pp. 241–252 (2011)
Dean, J., Ghemawat, S.: Mapreduce: Simplified data processing on large clusters. In: OSDI, pp. 137–150 (2004)
Beame, P., Koutris, P., Suciu, D.: Communication steps for parallel query processing. In: PODS (2013)
Suri, S., Vassilvitskii, S.: Counting triangles and the curse of the last reducer. In: WWW, pp. 607–614 (2011)
Ganguly, S., Silberschatz, A., Tsur, S.: Parallel bottom-up processing of datalog queries. J. Log. Program. 14(1&2), 101–126 (1992)
Afrati, F.N., Ullman, J.D.: Optimizing joins in a map-reduce environment. In: EDBT, pp. 99–110 (2010)
Veldhuizen, T.L.: Leapfrog triejoin: a worst-case optimal join algorithm. CoRR abs/1210.0481 (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Suciu, D. (2013). Big Data Begets Big Database Theory. In: Gottlob, G., Grasso, G., Olteanu, D., Schallhart, C. (eds) Big Data. BNCOD 2013. Lecture Notes in Computer Science, vol 7968. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39467-6_1
Download citation
DOI: https://doi.org/10.1007/978-3-642-39467-6_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39466-9
Online ISBN: 978-3-642-39467-6
eBook Packages: Computer ScienceComputer Science (R0)