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» k-Anonymity in Databases with Timestamped Data
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KDD
2012
ACM
221views Data Mining» more  KDD 2012»
12 years 1 months ago
Fast mining and forecasting of complex time-stamped events
Given huge collections of time-evolving events such as web-click logs, which consist of multiple attributes (e.g., URL, userID, timestamp), how do we find patterns and trends? Ho...
Yasuko Matsubara, Yasushi Sakurai, Christos Falout...
ICDE
2007
IEEE
149views Database» more  ICDE 2007»
15 years 6 days ago
Optimizing Timestamp Management in Data Stream Management Systems
It has long been recognized that multi-stream operators, such as union and join, often have to wait idly in a temporarily blocked state, as a result of skews between the timestamp...
Yijian Bai, Hetal Thakkar, Haixun Wang, Carlo Zani...
IDEAS
2005
IEEE
106views Database» more  IDEAS 2005»
14 years 4 months ago
Using the Lock Manager to Choose Timestamps
Our goal is to support transaction-time functionality that enables the coexistence of ordinary, non-temporal tables with transaction-time tables. In such a system, each transactio...
David B. Lomet, Richard T. Snodgrass, Christian S....
ICDE
1998
IEEE
70views Database» more  ICDE 1998»
15 years 6 days ago
Point-Versus Interval-Based Temporal Data Models
The association of timestamps with various data items such as tuples or attribute values is fundamental to the management of time-varying information. Using intervals in timestamp...
Michael H. Böhlen, Renato Busatto, Christian ...
VLDB
2006
ACM
122views Database» more  VLDB 2006»
14 years 11 months ago
A secure distributed framework for achieving k-anonymity
k-anonymity provides a measure of privacy protection by preventing re-identification of data to fewer than a group of k data items. While algorithms exist for producing k-anonymous...
Wei Jiang, Chris Clifton