Implementing data cubes efficiently
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Totals
Data Mining and Knowledge Discovery
ROLAP implementations of the data cube
ACM Computing Surveys (CSUR)
The Akamai network: a platform for high-performance internet applications
ACM SIGOPS Operating Systems Review
Making every bit count in wide-area analytics
HotOS'13 Proceedings of the 14th USENIX conference on Hot Topics in Operating Systems
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To date, much research in data-intensive computing has focused on batch computation. Increasingly, however, it is necessary to derive knowledge from big data streams. As a motivating example, consider a content delivery network (CDN) such as Akamai [4], comprising thousands of servers in hundreds of globally distributed locations. Each of these servers produces a stream of log data, recording for example every user it serves, along with each video stream they access, when they play and pause streams, and more. Each server also records network- and system-level data such as TCP connection statistics. In aggregate, the servers produce billions of lines of log data from over a thousand locations daily.