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117
The CQL Continuous Query Language: Semantic Foundations and Query Execution
- VLDB Journal
, 2003
"... CQL, a Continuous Query Language, is supported by the STREAM prototype Data Stream Management System at Stanford. CQL is an expressive SQL-based declarative language for registering continuous queries against streams and updatable relations. We begin by presenting an abstract semantics that relie ..."
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Cited by 339 (4 self)
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CQL, a Continuous Query Language, is supported by the STREAM prototype Data Stream Management System at Stanford. CQL is an expressive SQL-based declarative language for registering continuous queries against streams and updatable relations. We begin by presenting an abstract semantics that relies only on "black box" mappings among streams and relations.
The design of the borealis stream processing engine
- In CIDR
, 2005
"... Borealis is a second-generation distributed stream processing engine that is being developed at Brandeis University, Brown University, and MIT. Borealis inherits core stream processing functionality from Aurora [14] and distribution functionality from Medusa [51]. Borealis modifies and extends both ..."
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Cited by 242 (10 self)
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Borealis is a second-generation distributed stream processing engine that is being developed at Brandeis University, Brown University, and MIT. Borealis inherits core stream processing functionality from Aurora [14] and distribution functionality from Medusa [51]. Borealis modifies and extends both systems in non-trivial and critical ways to provide advanced capabilities that are commonly required by newly-emerging stream processing applications. In this paper, we outline the basic design and functionality of Borealis. Through sample real-world applications, we motivate the need for dynamically revising query results and modifying query specifications. We then describe how Borealis addresses these challenges through an innovative set of features, including revision records, time travel, and control lines. Finally, we present a highly flexible and scalable QoS-based optimization model that operates across server and sensor networks and a new fault-tolerance model with flexible consistency-availability trade-offs.
Memory-Limited Execution of Windowed Stream Joins
, 2004
"... We address the problem of computing approximate answers to continuous sliding-window joins over data streams when the available memory may be insufficient to keep the entire join state. One approximation scenario is to provide a maximum subset of the result, with the objective of losing as few ..."
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Cited by 80 (1 self)
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We address the problem of computing approximate answers to continuous sliding-window joins over data streams when the available memory may be insufficient to keep the entire join state. One approximation scenario is to provide a maximum subset of the result, with the objective of losing as few result tuples as possible. An alternative scenario is to provide a random sample of the join result, e.g., if the output of the join is being aggregated.
Stream: The stanford data stream management system
, 2004
"... Traditional database management systems are best equipped to run onetime queries over finite stored data sets. However, many modern applications such as network monitoring, financial analysis, manufacturing, and sensor networks require long-running, or continuous, queries over continuous unbounded ..."
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Cited by 68 (0 self)
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Traditional database management systems are best equipped to run onetime queries over finite stored data sets. However, many modern applications such as network monitoring, financial analysis, manufacturing, and sensor networks require long-running, or continuous, queries over continuous unbounded
Processing flows of information: from data stream to complex event processing
- ACM COMPUTING SURVEYS
, 2011
"... A large number of distributed applications requires continuous and timely processing of information as it flows from the periphery to the center of the system. Examples include intrusion detection systems which analyze network traffic in real-time to identify possible attacks; environmental monitori ..."
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Cited by 60 (11 self)
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A large number of distributed applications requires continuous and timely processing of information as it flows from the periphery to the center of the system. Examples include intrusion detection systems which analyze network traffic in real-time to identify possible attacks; environmental monitoring applications which process raw data coming from sensor networks to identify critical situations; or applications performing online analysis of stock prices to identify trends and forecast future values. Traditional DBMSs, which need to store and index data before processing it, can hardly fulfill the requirements of timeliness coming from such domains. Accordingly, during the last decade, different research communities developed a number of tools, which we collectively call Information flow processing (IFP) systems, to support these scenarios. They differ in their system architecture, data model, rule model, and rule language. In this article, we survey these systems to help researchers, who often come from different backgrounds, in understanding how the various approaches they adopt may complement each other. In particular, we propose a general, unifying model to capture the different aspects of an IFP system and use it to provide a complete and precise classification of the systems and mechanisms proposed so far.
Static optimization of conjunctive queries with sliding windows over infinite streams
- In SIGMOD
, 2004
"... We define a framework for static optimization of sliding window conjunctive queries over infinite streams. When computational resources are sufficient, we propose that the goal of optimization should be to find an execution plan that minimizes resource usage within the available resource constraints ..."
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Cited by 53 (1 self)
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We define a framework for static optimization of sliding window conjunctive queries over infinite streams. When computational resources are sufficient, we propose that the goal of optimization should be to find an execution plan that minimizes resource usage within the available resource constraints. When resources are insufficient, on the other hand, we propose that the goal should be to find an execution plan that sheds some of the input load (by randomly dropping tuples) to keep resource usage within bounds while maximizing the output rate. An intuitive approach to load shedding suggests starting with the plan that would be optimal if resources were sufficient and adding "drop boxes " to this plan. We find this to be often times suboptimal – in many instances the optimal partial answer plan results from adding drop boxes to plans that are not optimal in the unlimited resource case. In view of this, we use our framework to investigate an approach to optimization that unifies the placement of drop boxes and the choice of the query plan from which to drop tuples. The effectiveness of our optimizer is experimentally validated and the results show the promise of this approach. 1.
Load shedding in stream databases: a control-based approach
- In Proc. Int. Conf. on Very Large Data Bases (VLDB
, 2006
"... has to meet various Quality-of-Service (QoS) requirements. In many data stream applications, processing delay is the most critical quality requirement since the value of query results decreases dramatically over time. The ability to remain within a desired level of delay is significantly hampered un ..."
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Cited by 36 (10 self)
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has to meet various Quality-of-Service (QoS) requirements. In many data stream applications, processing delay is the most critical quality requirement since the value of query results decreases dramatically over time. The ability to remain within a desired level of delay is significantly hampered under situations of overloading, which are common in data stream systems. When overloaded, DSMSs employ load shedding in order to meet quality requirements and keep pace with the high rate of data arrivals. Data stream applications are extremely dynamic due to bursty data arrivals and time-varying data processing costs. Current approaches ignore system status information in decision-making and consequently are unable to achieve desired control of quality under dynamic load. In this paper, we present a quality management framework that leverages well studied feedback control techniques. We discuss the design and implementation of such a framework in a real DSMS- the Borealis stream manager. Our data management framework is built on the advantages of system identification and rigorous controller analysis. Experimental results show that our solution achieves significantly fewer QoS (delay) violations with the same or lower level of data loss, as compared to current strategies utilized in DSMSs. It is also robust and bears negligible computational overhead. 1.
Approximately detecting duplicates for streaming data using stable bloom filters
- In SIGMOD
, 2006
"... Traditional duplicate elimination techniques are not applicable to many data stream applications. In general, precisely eliminating duplicates in an unbounded data stream is not feasible in many streaming scenarios. Therefore, we target at approximately eliminating duplicates in streaming environmen ..."
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Cited by 34 (2 self)
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Traditional duplicate elimination techniques are not applicable to many data stream applications. In general, precisely eliminating duplicates in an unbounded data stream is not feasible in many streaming scenarios. Therefore, we target at approximately eliminating duplicates in streaming environments given a limited space. Based on a well-known bitmap sketch, we introduce a data structure, Stable Bloom Filter, and a novel and simple algorithm. The basic idea is as follows: since there is no way to store the whole history of the stream, SBF continuously evicts the stale elements so that SBF has room for those more recent ones. After finding some properties of SBF analytically, we show that a tight upper bound of false positive rates is guaranteed. In our empirical study, we compare SBF to alternative methods. The results show that our method is superior in terms of both accuracy and time efficiency when a fixed small space and an acceptable false positive rate are given. 1.
Loadstar: A load shedding scheme for classifying data streams
- In SDM
, 2005
"... We consider the problem of resource allocation in mining multiple data streams. Due to the large volume and the high speed of streaming data, mining algorithms must cope with the effects of system overload. How to realize maximum mining benefits under resource constraints becomes a challenging task. ..."
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Cited by 24 (8 self)
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We consider the problem of resource allocation in mining multiple data streams. Due to the large volume and the high speed of streaming data, mining algorithms must cope with the effects of system overload. How to realize maximum mining benefits under resource constraints becomes a challenging task. In this paper, we propose a load shedding scheme for classifying multiple data streams. We focus on the following problems: i) how to classify data that are dropped by the load shedding scheme? and ii) how to decide when to drop data from a stream? We introduce a quality of decision (QoD) metric to measure the level of uncertainty in classification when exact feature values of the data are not available because of load shedding. A Markov model is used to predict the distribution of feature values and we make classification decisions using the predicted values and the QoD metric. Thus, resources are allocated among multiple data streams to maximize the quality of classification decisions. Furthermore, our load shedding scheme is able to learn and adapt to changing data characteristics in the data streams. Experiments on both synthetic data and real-life data show that our load shedding scheme is effective in improving the overall accuracy of classification under resource constraints.
Sole: scalable on-line execution of continuous queries on spatio-temporal data streams
- VLDB JOURNAL
, 2008
"... This paper presents the Scalable On-Line Execution algorithm (SOLE, for short) for continuous and on-line evaluation of concurrent continuous spatiotemporal queries over data streams. Incoming spatiotemporal data streams are processed in-memory against a set of outstanding continuous queries. The S ..."
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Cited by 24 (4 self)
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This paper presents the Scalable On-Line Execution algorithm (SOLE, for short) for continuous and on-line evaluation of concurrent continuous spatiotemporal queries over data streams. Incoming spatiotemporal data streams are processed in-memory against a set of outstanding continuous queries. The SOLE algorithm utilizes the scarce memory resource efficiently by keeping track of only the significant objects. In-memory stored objects are expired (i.e., dropped) from memory once they become insignificant. SOLE is a scalable algorithm where all the continuous outstanding queries share the same buffer pool. In addition, SOLE is presented as a spatio-temporal join between two input streams, a stream of spatio-temporal objects and a stream of spatio-temporal queries. To cope with intervals of high arrival rates of objects and/or queries, SOLE utilizes a load-shedding approach where some of the stored objects are dropped from memory. SOLE is implemented as a pipelined query operator that can be combined with traditional query operators in a query execution plan to support a wide variety of continuous queries. Performance experiments based on a real implementation of SOLE inside a prototype of a data stream management system show the scalability and efficiency of SOLE in highly dynamic environments.
