Aurora: a new model and architecture for data stream management

Abadi, Daniel J.; Carney, Don; Çetintemel, Uǧur; Cherniack, Mitch; Convey, Christian; Lee, Sang−Don; Stonebraker, Michael; Tatbul, Nesime; Zdonik, Stanley B.

doi:10.1007/s00778-003-0095-z

Cited by 1,205 publications

(921 citation statements)

References 23 publications

Supporting

Mentioning

919

Contrasting

Unclassified

Order By: Relevance

“…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

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…Some approaches have used load shedding, eviction and discarding methods to alleviate the load, and could be applicable in these scenarios [1,9].…”

Section: Results Delivery For Constrained Consumersmentioning

confidence: 99%

Reactive Processing of RDF Streams of Events

Calbimonte

Aberer

2015

The Semantic Web: ESWC 2015 Satellite Events

View full text Add to dashboard Cite

Abstract. Events on the Web are increasingly being produced in the form of data streams, and are present in many different scenarios and applications such as health monitoring, environmental sensing or social networks. The heterogeneity of event streams has raised the challenges of integrating, interpreting and processing them coherently. Semantic technologies have shown to provide both a formal and practical framework to address some of these challenges, producing standards for representation and querying, such as RDF and SPARQL. However, these standards are not suitable for dealing with streams for events, as they do not include the concpets of streaming and continuous processing. The idea of RDF stream processing (RSP) has emerged in recent years to fill this gap, and the research community has produced prototype engines that cover aspects including complex event processing and stream reasoning to varying degrees. However, these existing prototypes often overlook key principles of reactive systems, regarding the event-driven processing, responsiveness, resiliency and scalability. In this paper we present a reactive model for implementing RSP systems, based on the Actor model, which relies on asynchronous message passing of events. Furthermore, we study the responsiveness property of RSP systems, in particular for the delivery of streaming results.

show abstract

“…In contrast to such stream processor architectures as [2,7,3,5,8,10] our stream definition incorporates positive temporal tuples and negative tuples. Let T be a discrete time domain.…”

Section: Tuple Lifetimementioning

confidence: 99%

“…[2,3]) and the other comprising events whose lifetimes also depend on the facts that will appear later (e.g. [4,5,6,7]). The second approach to data stream processing is more general.…”

Section: Introductionmentioning

confidence: 99%

Query Processing Using Negative and Temporal Tuples in Stream Query Engines

Gorawski

Chrószcz

2012

Advances in Software Engineering Techniques

View full text Add to dashboard Cite

Abstract.In this paper, we analyze how stream monotonicity classification can be adopted for the introduced developed model, which processes both temporal and negative events. As we show, information about stream monotonicity can be easily used to optimize individual stream operators as well as a full query plan. Comparing our stream engine with such engines as CEDR, STREAM and PIPES we demonstrate how a primary key constraint can be used in different types of the developed stream schemes. We implemented all of the above techniques in StreamAPAS.

show abstract

Aurora: a new model and architecture for data stream management

Cited by 1,205 publications

References 23 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

Reactive Processing of RDF Streams of Events

Query Processing Using Negative and Temporal Tuples in Stream Query Engines

Contact Info

Product

Resources

About