The CQL continuous query language: semantic foundations and query execution

Arasu, Arvind; Babu, Shivnath; Widom, Jennifer

doi:10.1007/s00778-004-0147-z

Cited by 920 publications

(783 citation statements)

References 40 publications

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“…The main purpose of Niagarino is to serve as an easy-to-use and extensible research platform for streaming applications such as the one presented in the paper. The concepts embodied by Niagarino can be traced back to a series of pioneering data stream management systems, such as Aurora [2], Borealis [1], and STREAM/CQL [5]. In particular, Niagarino is an offshoot of NiagaraST [13], with which it shares the most common ground.…”

Section: Niagarino Overviewmentioning

confidence: 99%

Run-Time and Task-Based Performance of Event Detection Techniques for Twitter

Weiler

Grossniklaus

Scholl

2015

Lecture Notes in Computer Science

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Abstract. Twitter's increasing popularity as a source of up to date news and information about current events has spawned a body of research on event detection techniques for social media data streams. Although all proposed approaches provide some evidence as to the quality of the detected events, none relate this task-based performance to their runtime performance in terms of processing speed or data throughput. In particular, neither a quantitative nor a comparative evaluation of these aspects has been performed to date. In this paper, we study the runtime and task-based performance of several state-of-the-art event detection techniques for Twitter. In order to reproducibly compare run-time performance, our approach is based on a general-purpose data stream management system, whereas task-based performance is automatically assessed based on a series of novel measures.

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Section: Niagarino Overviewmentioning

confidence: 99%

Run-Time and Task-Based Performance of Event Detection Techniques for Twitter

Weiler

Grossniklaus

Scholl

2015

Lecture Notes in Computer Science

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“…Window-processing [8] is one of the most common techniques, which extracts windows from the stream the last data stream elements. Such extraction can be sequencebased (i.e., a given number of tuples) or time-based (i.e., all the tuples which occur during a given time interval).…”

Section: Stream Algorithmsmentioning

confidence: 99%

StreamEB: Stream Edge Bundling.

2013

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Abstract. Graph streams have been studied extensively, such as for data mining, while fairly limitedly for visualizations. Recently, edge bundling promises to reduce visual clutter in large graph visualizations, though mainly focusing on static graphs.This paper presents a new framework, namely StreamEB, for edge bundling of graph streams, which integrates temporal, neighbourhood, data-driven and spatial compatibility for edges. Amongst these metrics, temporal and neighbourhood compatibility are introduced for the first time. We then present force-directed and tree-based methods for stream edge bundling.

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“…One very powerful approach is represented by the CQL Continuous Query Language implemented in the Stanford STREAM system [4]. In STREAM, data is modeled both as timestamped streams and as conventional relations.…”

Section: Techniques For the Subscription Matchermentioning

confidence: 99%

The Architecture of the Cornell Knowledge Broker

Demers

Gehrke

Riedewald

2004

Intelligence and Security Informatics

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Abstract.Intelligence applications have to process massive amounts of data in order to extract relevant information. This includes archived historical data as well as continuously arriving new data. We propose a novel architecture that addresses this problem -the Cornell Knowledge Broker. It will not only support knowledge discovery, but also security, privacy, information exchange, and collaboration.

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The CQL continuous query language: semantic foundations and query execution

Cited by 920 publications

References 40 publications

Run-Time and Task-Based Performance of Event Detection Techniques for Twitter

Run-Time and Task-Based Performance of Event Detection Techniques for Twitter

StreamEB: Stream Edge Bundling.

The Architecture of the Cornell Knowledge Broker

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