Chang Ge

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We address the problem of maintaining data structures used by memory-resident data warehouses that store sliding windows. We propose a framework that eagerly expires data from the sliding window to save space and/or satisfy data retention policies, but lazily maintains the associated data structures to reduce maintenance overhead. Using a dictionary as an(More)
A) Temporal Aggregation  Aggregation grouped by time B) Time Slicing  Consistent snapshot at a particular time C) Temporal Join  Correlating tables on temporal dimension(s) Workloads require both multi-dimensional and time-ordered access, but often there is a dominant dimension: slice one dimension, evaluate other
In this paper, we show how we use Nvidia GPUs and host CPU cores for faster query processing in a DB2 database using BLU Acceleration (DB2's column store technology). Moreover, we show the benefits and problems of using hardware accelerators (more specifically GPUs) in a real commercial Relational Database Management System(RDBMS).We investigate the effect(More)
Bi-temporal databases support system (transaction) and application time, enabling users to query the history as recorded today and as it was known in the past. In this paper, we study windows over both system and application time, i.e., <i>bi-temporal windows.</i> We propose a two-dimensional index that supports one-time and continuous queries over fixed(More)
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