Add to ORKGWindow queries are proving essential to data-stream processing. In this paper, we present an approach for evaluating sliding-window aggregate queries that reduces both space and computation time for query execution. Our approach divides overlapping windows into disjoint panes, computes sub-aggregates over each pane, and “rolls up ” the pane-aggregates to compute window-aggregates. Our experimental study shows that using panes has significant performance benefits. 1
The processing of data streams plays a central role in emerging applications such as pervasive compu...
Abstract: Sliding Window is the most popular data model in processing data streams as it captures fi...
Data stream systems process persistent queries, typically posed over sliding windows and re-evaluate...
Sliding windows are bounded sets which evolve together with an infinite data stream of records. Each...
A windowed query operator breaks a data stream into possibly overlapping subsets of data and compute...
Data stream management systems may be subject to higher input rates than their resources can handle....
Window aggregation is a core operation in data stream processing. Existing aggregation techniques fo...
Aggregate window computations lie at the core of online analyt-ics in both academic and industrial a...
National audienceComputing aggregation over sliding windows, i.e., finite subsets of an unbounded st...
Computing aggregates over windows is at the core of virtually every stream processing job. Typical s...
Continuous queries applied over nonterminating data streams usually specify windows in order to obta...
We consider the problem of resource sharing when processing large numbers of continuous queries. We ...
Sliding Window is the most popular data model in processing data streams as it captures finite and r...
We consider the problem of handling aggregate computations in a scalable fashion in stream databases...
The computation of sliding window aggregates is one of the core functionalities of stream processing...
The processing of data streams plays a central role in emerging applications such as pervasive compu...
Abstract: Sliding Window is the most popular data model in processing data streams as it captures fi...
Data stream systems process persistent queries, typically posed over sliding windows and re-evaluate...
Sliding windows are bounded sets which evolve together with an infinite data stream of records. Each...
A windowed query operator breaks a data stream into possibly overlapping subsets of data and compute...
Data stream management systems may be subject to higher input rates than their resources can handle....
Window aggregation is a core operation in data stream processing. Existing aggregation techniques fo...
Aggregate window computations lie at the core of online analyt-ics in both academic and industrial a...
National audienceComputing aggregation over sliding windows, i.e., finite subsets of an unbounded st...
Computing aggregates over windows is at the core of virtually every stream processing job. Typical s...
Continuous queries applied over nonterminating data streams usually specify windows in order to obta...
We consider the problem of resource sharing when processing large numbers of continuous queries. We ...
Sliding Window is the most popular data model in processing data streams as it captures finite and r...
We consider the problem of handling aggregate computations in a scalable fashion in stream databases...
The computation of sliding window aggregates is one of the core functionalities of stream processing...
The processing of data streams plays a central role in emerging applications such as pervasive compu...
Abstract: Sliding Window is the most popular data model in processing data streams as it captures fi...
Data stream systems process persistent queries, typically posed over sliding windows and re-evaluate...