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Models and issues in data stream systems
TLDR
In this overview paper we motivate the need for and research issues arising from a new model of data processing in which data arrives in multiple, continuous, rapid, time-varying data streams. Expand
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STREAM: The Stanford Stream Data Manager
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Distributed top-k monitoring
TLDR
We study a useful class of queries that continuously report the k largest values obtained from distributed data streams ("top-k monitoring queries"), which are of particular interest because they can be used to reduce the overhead incurred while running other types of monitoring queries. Expand
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Load shedding for aggregation queries over data streams
TLDR
We present algorithms that determine at what points in a query plan should load shedding be performed and what amount of load should be shed at each point in order to minimize the degree of inaccuracy introduced into query answers. Expand
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Query Processing, Approximation, and Resource Management in a Data Stream Management System
TLDR
This paper describes our ongoing work developing the Stanford Stream Data Manager (STREAM), a system for executing continuous queries over multiple continuous data streams. Expand
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Sampling from a moving window over streaming data
TLDR
We introduce the problem of sampling from a moving window of recent items from a data stream and develop two algorithms for this problem. Expand
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STREAM: The Stanford Data Stream Management System
TLDR
We are building a general-purpose prototype Data Stream Management System that supports a large class of declarative continuous queries over continuous streams and traditional stored data sets. Expand
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Chain: operator scheduling for memory minimization in data stream systems
TLDR
We present Chain scheduling, an operator scheduling strategy for data stream systems that is near-optimal in minimizing run-time memory usage for any collection of single-stream queries involving selections, projections, and foreign-key joins with stored relations. Expand
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STREAM: the stanford stream data manager (demonstration description)
TLDR
STREAM is a general-purpose relational Data Stream Management System (DSMS) with a declarative query language and flexible query execution plans. Expand
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Query Processing, Resource Management, and Approximation ina Data Stream Management System
TLDR
This paper describes our ongoing work developing the Stanford Stream Data Manager (STREAM), a system for executing continuous queries over multiple continuous data streams. Expand
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