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Sketch-based querying of distributed sliding-window data streams.
| Content Provider | CiteSeerX |
|---|---|
| Author | Papapetrou, Odysseas Garofalakis, Minos Deligiannakis, Antonios |
| Abstract | While traditional data-management systems focus on evaluating single, adhoc queries over static data sets in a centralized setting, several emerging applications require (possibly, continuous) answers to queries on dynamic data that is widely distributed and constantly updated. Furthermore, such query answers often need to discount data that is “stale”, and operate solely on a sliding window of recent data arrivals (e.g., data updates occurring over the last 24 hours). Such distributed data streaming applications mandate novel algorithmic solutions that are both time- and space-efficient (to manage high-speed data streams), and also communication-efficient (to deal with physical data distribution). In this paper, we consider the problem of complex query answering over distributed, high-dimensional data streams in the sliding-window model. We introduce a novel sketching technique (termed ECM-sketch) that allows effective summarization of streaming data over both time-based and count-based sliding windows with probabilistic |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Sketch-based Querying Distributed Sliding-window Data Stream Novel Sketching Technique High-speed Data Stream Physical Data Distribution Traditional Data-management System Static Data Set Adhoc Query Sliding-window Model Novel Algorithmic Solution Centralized Setting High-dimensional Data Stream Complex Query Answering Query Answer Count-based Sliding Window Data Update Effective Summarization Recent Data Arrival Termed Ecm-sketch Dynamic Data |
| Content Type | Text |
