Microsoft CEP server and online behavioral targeting

Ali, Mohamed; Gerea, Ciprian; Raman, Balan Sethu; Sezgin, Beysim; Tarnavski, T.; Verona, T.; Wang, P.; Zabback, Peter; Ananthanarayan, Asvin; Kirilov, A.; Lü, Mo; Raizman, Alex; Krishnan, Rahul; Schindlauer, Roman; Grabs, Torsten; Bjeletich, Sharon; Chandramouli, Badrish; Goldstein, Jonathan; Bhat, Sathyajith; Liu, Ying; Nicola, VF; Wang, X.; Maier, David; Grell, S.; Nano, Olivier; Santos, Ivo

doi:10.14778/1687553.1687590

Cited by 73 publications

(84 citation statements)

References 2 publications

(2 reference statements)

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“…The fundamental difference between the research [2,20,9] and commercial products [27,29,4, 16] mentioned in the introduction and our exoengine architecture is that it is not yet another SPE. Rather, it provides a platform for facilitating application development, deployment, integration, and management of existing or new-to-be-built SPEs.…”

Section: Related Workmentioning

confidence: 99%

“…Stream processing supports such applications using a model whereby data arrives continuously at the stream processing engine (SPE) and triggers the evaluation of queries stored in the SPE. Within the last decade, data stream processing has gone from a research idea (e.g., Aurora [2], STREAM [20], and TelegraphCQ [9]) to a widespread solution, with several commercial products already available [27,29,16,4].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Virtualizing Stream Processing

Duller¹,

Rellermeyer²,

Alonso³

et al. 2011

Middleware 2011

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Abstract. Stream processing systems have evolved into established solutions as standalone engines but they still lack flexibility in terms of large-scale deployment, integration, extensibility, and interoperability. In the last years, a substantial ecosystem of new applications has emerged that can potentially benefit from stream processing but introduces different requirements on how stream processing solutions can be integrated, deployed, extended, and federated. To address these needs, we present an exoengine architecture and the associated ExoP platform. Together, they provide the means for encapsulating components of stream processing systems as well as automating the data exchange between components and their distributed deployment. The proposed solution can be used, e.g., to connect heterogeneous streaming engines, replace operators at runtime, and migrate operators across machines with a negligible overhead.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Virtualizing Stream Processing

Duller¹,

Rellermeyer²,

Alonso³

et al. 2011

Middleware 2011

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show abstract

“…We implemented both methods in Microsoft CEP [27] to compare them. We saw that HWM does not track progress as closely as Probing, especially for disordered inputs.…”

Section: Reachable(x [X]) :-Source(x) Reachable(x [X|p]) :-Link(ymentioning

confidence: 99%

“…In this section, we evaluate and improve upon the proposed techniques by examining their efficacy in the context of Microsoft CEP [27], a streaming product based on the CEDR research project [2]. This system uses the valid-time-interval approach mentioned in earlier examples and described in Goldstein et al [2], and fully implements speculation with out-of-order input over an algebra more expressive than standard SQL (SPJ with GB, aggregation, and union).…”

Section: Evaluation and Improvementmentioning

confidence: 99%

On-the-fly progress detection in iterative stream queries

2009

Self Cite

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Multiple researchers have proposed cyclic query plans for evaluating iterative queries over streams or rapidly changing input. The Declarative Networking community uses cyclic plans to evaluate Datalog programs that track reachability and other graph traversals on networks. Cyclic query plans can also evaluate pattern-matching and other queries based on event sequences.An issue with cyclic queries over dynamic inputs is knowing when the query result has progressed to a certain point in the input, since the number of iterations is data dependent. One option is a -strictly staged‖ computation, where the query plan quiesces between inputs. This option introduces significant latency, and may also -underload‖ inter-operator buffers. An alternative is to settle for soft guarantees, such as -eventual consistency‖. Such imprecision can make it difficult, for example, to know when to purge state from stateful operators.We propose a third option in which cyclic queries run continuously, but detect progress -on the fly‖ by means of a Flying Fixed-Point (FFP) operator. FFP sits on the cyclic loop and circulates speculative predictions on forward progress, which it then validates. FFP is always able to track progress for a class of queries we term strongly convergent. A key advantage of FFP is that it works with existing algebra operators, thereby inheriting their capabilities, such as windowing and dealing with out-oforder input. Also, for stream systems that explicitly model inputevent lifetimes, we know exactly which values are in the query result at each point in time.A key implementation decision is the method for speculating. Using the high-water mark of data events minimizes the number of speculative punctuations. Probing operators on the cyclic loop to determine their external progress circulates many more speculative messages, but tracks actual output progress more closely. We show how a hybrid approach limits predictions while coming close the progress-tracking ability of Probing.

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“…On one hand, Microsoft SQL Server StreamInsight (StreamInsight, for brevity) [1,2,3,4] is a platform for stream query processing. StreamInsight monitors stream data to extract meaningful patterns and trends.…”

Section: Introductionmentioning

confidence: 99%

Geospatial stream query processing using Microsoft SQL Server StreamInsight

et al. 2010

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Microsoft SQL Server spatial libraries contain several components that handle geometrical and geographical data types. With advances in geo-sensing technologies, there has been an increasing demand for geospatial streaming applications. Microsoft SQL Server StreamInsight (StreamInsight, for brevity) is a platform for developing and deploying streaming applications that run continuous queries over high-rate streaming events. With its extensibility infrastructure, StreamInsight enables developers to integrate their domain expertise within the query pipeline in the form of user defined modules. This demo utilizes the extensibility infrastructure in Microsoft StreamInsight to leverage its continuous query processing capabilities in two directions. The first direction integrates SQL spatial libraries into the continuous query pipeline of StreamInsight. StreamInsight provides a well-defined temporal model over incoming events while SQL spatial libraries cover the spatial properties of events to deliver a solution for spatiotemporal stream query processing. The second direction extends the system with an analytical refinement and prediction layer. This layer analyzes historical data that has been accumulated and summarized over the years to refine, smooth and adjust the current query output as well as predict the output in the near future. The demo scenario is based on transportation data in Los Angeles County.

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Microsoft CEP server and online behavioral targeting

Cited by 73 publications

References 2 publications

Virtualizing Stream Processing

Virtualizing Stream Processing

On-the-fly progress detection in iterative stream queries

Geospatial stream query processing using Microsoft SQL Server StreamInsight

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