Sketch-based Querying of Distributed Sliding-Window Data Streams
Odysseas Papapetrou, Minos Garofalakis, Antonios Deligiannakis

TL;DR
This paper introduces ECM-sketch, a novel probabilistic data structure for efficient, accurate, and composable querying of distributed high-dimensional data streams within sliding windows, enabling real-time analytics.
Contribution
It presents the ECM-sketch technique for summarizing distributed streaming data over sliding windows, supporting complex queries with probabilistic guarantees, and demonstrates its effectiveness through experiments.
Findings
ECM-sketch effectively summarizes distributed data streams.
Supports point, inner-product, and frequency queries.
Validated with real-world data sets showing high accuracy.
Abstract
While traditional data-management systems focus on evaluating single, ad-hoc 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…
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Taxonomy
TopicsData Management and Algorithms · Advanced Database Systems and Queries · Peer-to-Peer Network Technologies
