Continuous Queries Over Data Streams

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Continuous Queries Over Data Streams

Continuous Queries Over Data Streams written by Jürgen Krämer and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with categories.

Continuous Queries Over Data Streams

Continuous Queries Over Data Streams written by Arvind Arasu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006 with categories.

A Temporal Foundation For Continuous Queries Over Data Streams

A Temporal Foundation For Continuous Queries Over Data Streams written by Jürgen Krämer and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with categories.

Processing Continuous Queries Over Streaming Data With Limited System Resources

Processing Continuous Queries Over Streaming Data With Limited System Resources written by Brian Babcock and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006 with categories.

Processing Of Continuous Queries Over Infinite Data Streams

Processing Of Continuous Queries Over Infinite Data Streams written by Ehsan Vossough and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with Data stream and object architectures categories.

Processing Exact Results For Queries Over Data Streams

Processing Exact Results For Queries Over Data Streams written by Abhirup Chakraborty and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with Data mining categories.

In a growing number of information-processing applications, such as network-traffic monitoring, sensor networks, financial analysis, data mining for e-commerce, etc., data takes the form of continuous data streams rather than traditional stored databases/relational tuples. These applications have some common features like the need for real time analysis, huge volumes of data, and unpredictable and bursty arrivals of stream elements. In all of these applications, it is infeasible to process queries over data streams by loading the data into a traditional database management system (DBMS) or into main memory. Such an approach does not scale with high stream rates. As a consequence, systems that can manage streaming data have gained tremendous importance. The need to process a large number of continuous queries over bursty, high volume online data streams, potentially in real time, makes it imperative to design algorithms that should use limited resources. This dissertation focuses on processing exact results for join queries over high speed data streams using limited resources, and proposes several novel techniques for processing join queries incorporating secondary storages and non-dedicated computers. Existing approaches for stream joins either, (a) deal with memory limitations by shedding loads, and therefore can not produce exact or highly accurate results for the stream joins over data streams with time varying arrivals of stream tuples, or (b) suffer from large I/O-overheads due to random disk accesses. The proposed techniques exploit the high bandwidth of a disk subsystem by rendering the data access pattern largely sequential, eliminating small, random disk accesses. This dissertation proposes an I/O-efficient algorithm to process hybrid join queries, that join a fast, time varying or bursty data stream and a persistent disk relation. Such a hybrid join is the crux of a number of common transformations in an active data warehouse. Experimental results demonstrate that the proposed scheme reduces the response time in output results by exploiting spatio-temporal locality within the input stream, and minimizes disk overhead through disk-I/O amortization. The dissertation also proposes an algorithm to parallelize a stream join operator over a shared-nothing system. The proposed algorithm distributes the processing loads across a number of independent, non-dedicated nodes, based on a fixed or predefined communication pattern; dynamically maintains the degree of declustering in order to minimize communication and processing overheads; and presents mechanisms for reducing storage and communication overheads while scaling over a large number of nodes. We present experimental results showing the efficacy of the proposed algorithms.

Adaptive Query Processing In Data Stream Management Systems

Adaptive Query Processing In Data Stream Management Systems written by Shivnath Babu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with categories.

Distributed Query Processing Over Fluctuating Streams

Distributed Query Processing Over Fluctuating Streams written by Roland Kotto Kombi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.

In a Big Data context, stream processing has become a very active research domain. In order to manage ephemeral data (Velocity) arriving at important rates (Volume), some specific solutions, denoted data stream management systems (DSMSs),have been developed. DSMSs take as inputs some queries, called continuous queries,defined on a set of data streams. Acontinuous query generates new results as long as new data arrive in input. In many application domains, data streams haveinput rates and distribution of values which change over time. These variations may impact significantly processingrequirements for each continuous query.This thesis takes place in the ANR project Socioplug (ANR-13-INFR-0003). In this context, we consider a collaborative platformfor stream processing. Each user can submit multiple continuous queries and contributes to the execution support of theplatform. However, as each processing unit supporting treatments has limited resources in terms of CPU and memory, asignificant increase in input rate may cause the congestion of the system. The problem is then how to adjust dynamicallyresource usage to processing requirements for each continuous query ? It raises several challenges : i) how to detect a need ofreconfiguration ? ii) when reconfiguring the system to avoid its congestion at runtime ?In this work, we are interested by the different processing steps involved in the treatment of a continuous query over adistributed infrastructure. From this global analysis, we extract mechanisms enabling dynamic adaptation of resource usage foreach continuous query. We focus on automatic parallelization, or auto-parallelization, of operators composing the executionplan of a continuous query. We suggest an original approach based on the monitoring of operators and an estimation ofprocessing requirements in near future. Thus, we can increase (scale-out), or decrease (scale-in) the parallelism degree ofoperators in a proactive many such as resource usage fits to processing requirements dynamically. Compared to a staticconfiguration defined by an expert, we show that it is possible to avoid the congestion of the system in many cases or to delay itin most critical cases. Moreover, we show that resource usage can be reduced significantly while delivering equivalentthroughput and result quality. We suggest also to combine this approach with complementary mechanisms for dynamic adaptation of continuous queries at runtime. These differents approaches have been implemented within a widely used DSMS and have been tested over multiple and reproductible micro-benchmarks.

Dynamic Optimization And Migration Of Continuous Queries Over Data Streams

Dynamic Optimization And Migration Of Continuous Queries Over Data Streams written by Yali Zhu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006 with Querying (Computer science) categories.

Abstract: Continuous queries process real-time streaming data and output results in streams for a wide range of applications. Due to the fluctuating stream characteristics, a streaming database system needs to dynamically adapt query execution. This dissertation proposes novel solutions to continuous query adaptation in three core areas, namely dynamic query optimization, dynamic plan migration and partitioned query adaptation. Runtime query optimization needs to efficiently generate plans that satisfy both CPU and memory resource constraints. Existing work focus on minimizing intermediate query results, which decreases memory and CPU usages simultaneously. However, doing so cannot assure that both resource constraints are being satisfied, because memory and CPU can be either positively or negatively correlated. This part of the dissertation proposes efficient optimization strategies that utilize both types of correlations to search the entire query plan space in polynomial time when a typical exhaustive search would take at least exponential time. Extensive experimental evaluations have demonstrated the effectiveness of the proposed strategies. Dynamic plan migration is concerned with on-the-fly transition from one continuous plan to a semantically equivalent yet more efficient plan. It is a must to guarantee the continuation and repeatability of dynamic query optimization. However, this research area has been largely neglected in the current literature. The second part of this dissertation proposes migration STRategies that dynamically migrate continuous queries while guaranteeing the integrity of the query results, meaning there are no missing, duplicate or incorrect results. The extensive experimental evaluations show that the proposed strategies vary significantly in terms of output rates and memory usages given distinct system configurations and stream workloads. Partitioned query processing is effective to process continuous queries with large stateful operators in a distributed system. Dynamic load redistribution is necessary to balance uneven workload across machines due to changing stream properties. However, existing solutions generally assume static query plans without runtime query optimization. This part of the dissertation evaluates the benefits of applying query optimization in partitioned query processing and shows dramatic performance improvement of more than 300%. Several load balancing strategies are then proposed to consider the heterogeneity of plan shapes across machines caused by dynamic query optimization. The effectiveness of the proposed strategies is analyzed through extensive experiments using a cluster.

Shared Query Processing In Data Streaming Systems

Shared Query Processing In Data Streaming Systems written by Saileshwar Krishnamurthy and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006 with categories.