Monitoring Streams - A New Class of Data Management Applications

By Donald Carney, Ugur Çetintemel, Mitch Cherniack, Christian Convey, Sangdong Lee, Greg Seidman, Michael Stonebraker, Nesime Tatbul, S. Zdonik. Very Large Data Bases Conference, 2002

988 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/5d5d591ddb815accc0d5eec83583a12bd58197c2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computer Networks and Communications

In Signal Processing

Citations in the corpus

TinyDB: an acquisitional query processing system for sensor networks (2005, methodology): “2002] and stream query processing [Motwani et al. 2003; Carney et al. 2002], include some notion of data quality.”
Operator Scheduling in a Data Stream Manager (2003, methodology): “In this paper, we will only deal with latency-based QoS graphs; for a discussion of other types of QoS graphs and how they are utilized, please refer to [2, 8].”
Streaming Queries over Streaming Data (2002, background): “STREAM[ BW01 ] considers the relation of materialized views to continuous queries in the context of selfmaintenance.”
High-availability algorithms for distributed stream processing (2005, background): “Stream-processing engines (SPEs) [1] [3] [5] [6] [16] [18] are designed to support a new class of data processing applications , called stream-based applications, where data is pushed to the system in the form of streams of tuples and queries are continuously executed over these streams.”
Towards Expressive Publish/Subscribe Systems (2006, background): “Traditionally this is performed by sharing operators and query results [7, 8, 11, 20].”
Evaluating window joins over unbounded streams (2003, background): “[28] proposed a stream monitoring system, Aurora, which also targeted to address the new class of problems that arise in dealing with the massive number of sensor data inputs and continuous queries.”
Scalable Distributed Stream Processing (2003, background): “We begin in Section 2 with a brief description of our centralized stream processing system, Aurora [4].”
Processing flows of information: From data stream to complex event processing (2012, background): “An orthogonal way of classifying windows considers the way their bounds move, ¨ resulting in the following classes [Carney et al. 2002; Golab and Ozsu 2003].”
Publish/Subscribe in a Mobile Environment (2001, background): “Recently, for example, data stream systems [2, 6 ,15] and sensor networks [13] have garnered attention in the research community.”
Processing Sliding Window Multi-Joins in Continuous Queries over Data Streams (2003, methodology): “As surveyed in [4, 15], there has been a great deal of recent interest in developing novel data management techniques and adapting traditional database technology to the data stream model; Cougar [32], Aurora [7], TelegraphCQ [8, 22], and STREAM [4, 23] are some examples.”
Linear Road: A Stream Data Management Benchmark (2004, methodology): “Aurora uses a workflow-like boxes-and-arrows model for constructing queries over stream data [2].”
Efficient elastic burst detection in data streams (2003, background): “The Aurora project[4] considers the systems aspect of monitoring data streams.”
Live Video Analytics at Scale with Approximation and Delay-Tolerance (2017, background): “Aurora, Medusa, and Borealis [19, 33, 37] and follow-up works [78, 79, 81, 82, 88] use QoS graphs to capture lag and sampling rate but they consider them separately and do not trade-off between them, a key aspect in our solution.”