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Atomic commitment and resilience in grid database systems

INTERNATIONAL JOURNAL OF GRID AND UTILITY COMPUTING 2005年第1期1卷 46-60页

作者： Goel, Sushant Sharda, Hema Taniar, David RMIT Univ Sch Elect & Comp Engn Melbourne Vic Australia Monash Univ Fac Informat Technol Sch Business Syst Clayton Vic Australia

Atomic Commitment Protocol (ACP) is an important part of any distributed transaction. ACPs have been proposed for homogeneous and heterogeneous distributed database management systems (DBMS). ACPs designed for these DBMS do not meet the requirement of Grid databases. Homogeneous DBMS are synchronous and tightly coupled while heterogeneous DBMS, like multi-database systems require a top layer of multi-database management system to manage distributed transactions. These ACPs either become too restrictive or need some changes in participating DBMS, which may not be acceptable in Grid Environment. In this paper we identify requirements for Grid database systems and then propose an ACP for grid databases, Grid-Atomic Commitment Protocol (Grid-ACP). We then extend Grid-ACP to handle failure of database sites.

关键词： data grid recovery protocols distributed data management atomic commit protocols failure recovery resilient database systems

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data management procedures in the Asthma Clinical Research Network

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CONTROLLED CLINICAL TRIALS 2001年第6期22卷 168S-180S页

作者： Pogash, RM Boehmer, SJ Forand, PE Dyer, AM Kunselman, SJ Penn State Univ Milton S Hershey Med Ctr Coll Med Dept Hlth Evaluat Sci Hershey PA 17033 USA

Well-designed data management processes are essential in ensuring the quality of data collected in multicenter clinical trials. This paper describes the data management processes and systems that were developed by the data coordinating center of the Asthma Clinical Research Network. A combination cf manual and electronic processes has been designed to process clinical trial data from the point of collection to statistical analysis. A distributed database management system consisting of modular applications for separate data processing activities was developed to enter, track, verify, validate, and edit collected data. In addition, processes for monitoring and reporting data quality are discussed. (C) Elsevier Science Inc. 2001.

关键词： data management distributed data management data quality database management system

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On the equivalence of distributed systems with queries and communication

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JOURNAL OF COMPUTER AND SYSTEM SCIENCES 2013年第6期79卷 739-762页

作者： Abiteboul, Serge ten Cate, Balder Katsis, Yannis INRIA Saclay Saclay France ENS Cachan Cachan France Univ Calif Santa Cruz Santa Cruz CA 95064 USA

distributed data management systems consist of peers that store, exchange and process data in order to collaboratively achieve a common goal, such as evaluating some query. We study the equivalence of such systems. We model a distributed system by a collection of Active XML documents, i.e., trees augmented with function calls for performing tasks such as sending, receiving and querying data. As our model is quite general, the equivalence problem turns out to be undecidable. However, we exhibit several restrictions of the model, for which equivalence can be effectively decided. We also study the computational complexity of the equivalence problem, and present an axiomatization of equivalence, in the form of a set of equivalence-preserving rewrite rules allowing us to optimize a system by rewriting it into an equivalent, but possibly more efficient system. (C) 2013 Elsevier Inc. All rights reserved.

关键词： Active XML distributed data management Equivalence Optimization

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Security requirements model for grid data management systems

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1st International Workshop on Critical Information Infrastructures Security

作者： Naqvi, Syed Massonet, Philippe Arenas, Alvaro Centre of Excellence in Information and Communication Technologies (CETIC) Belgium CCLRC Rutherford Appleton Laboratory United Kingdom

ISBN: (纸本)9783540690832

In this paper, we present our ongoing work of a policy-driven approach to security requirements of grid data management systems (GDMS). We analyse the security functionalities of existing GDMS to determine their shortcomings that should be addressed in our work. We identify a comprehensive set of security requirements for GDMS followed by the presentation of our proposed Security Requirements Model. Derivation of security policies from security requirements and their consequent refinement is also presented in this paper. Our approach of addressing modelling issues by providing requirements for expressing security related quality of service is the key step to turn storage systems into knowledge representation systems.

关键词： grid security requirements analysis distributed data management

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Efficient Threshold Monitoring for distributed Probabilistic data

Efficient Threshold Monitoring for Distributed Probabilistic...

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28th IEEE International Conference on data Engineering (ICDE)

作者： Tang, Mingwang Li, Feifei Phillips, Jeff M. Jestes, Jeffrey Univ Utah Sch Comp Salt Lake City UT 84112 USA

ISBN: (纸本)9781467300421

In distributed data management, a primary concern is monitoring the distributed data and generating an alarm when a user specified constraint is violated. A particular useful instance is the threshold based constraint, which is commonly known as the distributed threshold monitoring problem [4], [16], [19], [29]. This work extends this useful and fundamental study to distributed probabilistic data that emerge in a lot of applications, where uncertainty naturally exists when massive amounts of data are produced at multiple sources in distributed, networked locations. Examples include distributed observing stations, large sensor fields, geographically separate scientific institutes/units and many more. When dealing with probabilistic data, there are two thresholds involved, the score and the probability thresholds. One must monitor both simultaneously, as such, techniques developed for deterministic data are no longer directly applicable. This work presents a comprehensive study to this problem. Our algorithms have significantly outperformed the baseline method in terms of both the communication cost (number of messages and bytes) and the running time, as shown by an extensive experimental evaluation using several, real large datasets.

关键词： baseline method Distribute Byte Runtime distributed NETWORKS Messages Constraint distributed data management Threshold Monitoring problem

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distributed Wavelet Thresholding for Maximum Error Metrics 16

Distributed Wavelet Thresholding for Maximum Error Metrics

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ACM SIGMOD International Conference on management of data

作者： Mytilinis, Ioannis Tsoumakos, Dimitrios Koziris, Nectarios Natl Tech Univ Athens Comp Syst Lab Athens Greece Ionian Univ Dept Informat Corfu Greece

ISBN: (纸本)9781450335317

Modern data analytics involve simple and complex computations over enormous numbers of data records. The volume of data and the increasingly stringent response-time requirements place increasing emphasis on the efficiency of approximate query processing. A major challenge over the past years has been the efficient construction of fixed-space synopses that provide a deterministic quality guarantee, often expressed in terms of a maximum error metric. For data reduction, wavelet decomposition has proved to be a very effective tool, as it can successfully approximate sharp discontinuities and provide accurate answers to queries. However, existing polynomial time wavelet thresholding schemes that minimize maximum error metrics are constrained with impractical time and space complexities for large datasets. In order to provide a practical solution to the problem, we develop parallel algorithms that take advantage of key properties of the wavelet decomposition and allocate tasks to multiple workers. To that end, we present i) a general framework for the parallelization of existing dynamic programming algorithms, ii) a parallel version of one such DP-based algorithm and iii) a new parallel greedy algorithm for the problem. To the best of our knowledge, this is the first attempt to scale algorithms for wavelet thresholding for maximum error metrics via a state-of-the-art distributed run-time. Our extensive experiments on both real and synthetic datasets over Hadoop show that the proposed algorithms achieve linear scalability and superior running-time performance compared to their centralized counterparts. Furthermore, our distributed greedy algorithm outperforms the distributed version of the current state-of-the-art dynamic programming algorithm by 2 to 4 times, without compromising the quality of results.

关键词： greedy thresholding distributed data management approximate query processing wavelet synopses

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CINTIA: a distributed, Low-Latency Index for Big Interval data 3

CINTIA: a Distributed, Low-Latency Index for Big Interval Da...

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IEEE International Conference on Big data

作者： Mavlyutov, Ruslan Cudre-Mauroux, Philippe Univ Fribourg eXascale Infolab CH-1700 Fribourg Switzerland

ISBN: (纸本)9781479999255

Intervals have become prominent in data management as they are the main data structure to represent a number of key data types such as temporal or genomic data. Yet, there exists no solution to compactly store and efficiently query big interval data. In this paper we introduce CINTIA-the Checkpoint INTerval Index Array-an efficient data structure to store and query interval data, which achieves high memory locality and outperforms state-of-the art solutions. We also propose a low-latency, Big data system that implements CINTIA on top of a popular distributed file system and efficiently manages large interval data on clusters of commodity machines. Our system can easily be scaled-out and was designed to accommodate large delays between the various components of a distributed infrastructure. We experimentally evaluate the performance of our approach on several datasets and show that it outperforms current solutions by several orders of magnitude in distributed settings.

关键词： Interval data Low-Latency Scalability distributed data management

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Gateways to High-Perfomance and distributed Computing Resources for Global Health Challenges

Gateways to High-Perfomance and Distributed Computing Resour...

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IEEE Canada International Humanitarian Technology Conference (IHTC)

作者： Gesing, Sandra Nabrzyski, Jarek Jha, Shantenu Univ Notre Dame Ctr Res Comp Notre Dame IN 46556 USA Rutgers State Univ Sch Engn New Brunswick NJ USA

ISBN: (纸本)9781479939961

Computational simulations for disease modeling and efficient analysis tools using large data collections have become invaluable tools for Global Health programs fighting infectious diseases. Simulations are used in many ways e. g., from predicting the effectiveness of interventions for certain diseases through Bayesian-based data-model assimilation to genomic analysis of diverse vector species in their different growth states. Even though the approaches and technologies vary, they have several common requirements on the underlying infrastructure. Simulations for infectious diseases, for example, rely on environmental data like weather, geospatial data, biodiversity and transmission complexity. data-intensive applications need efficient distributed data management capabilities facilitating replication services or Software-as-a-Service solutions. Such solutions might follow the paradigm to transfer applications to the data instead of transferring data to where the applications are deployed. In this paper we present our work towards providing a common extensible platform to build the computational investigation environment. This platform will provide an API for developers of science gateways, which can be adapted for specific simulations, various distributed data management technologies and diverse data structures. Furthermore, it will include metadata to increase the quality and the information about the data, its provenance and its context. Such an API will ease the development of new science gateways and the core technologies for both modeling and running the models. Developers can focus on the targeted domain and are relieved from re-developing core features for the underlying infrastructure. These gateways also enable the stakeholders (scientists, policy makers, etc.) in using the sophisticated tools and/or offer a single point of entry to large data collections and data analytics tools.

关键词： Gateways Gateways doorways data Collection Methods Global Health Metadata point of entry distributed data management Developers Key technology infectious diseases

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AFrame: Extending dataFrames for Large-Scale Modern data Analysis

AFrame: Extending DataFrames for Large-Scale Modern Data Ana...

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IEEE International Conference on Big data (Big data)

作者： Sinthong, Phanwadee Carey, Michael J. Univ Calif Irvine Dept Comp Sci Irvine CA 92697 USA

ISBN: (纸本)9781728108582

Analyzing the increasingly large volumes of data that are available today, possibly including the application of custom machine learning models, requires the utilization of distributed frameworks. This can result in serious productivity issues for "normal" data scientists. This paper introduces AFrame, a new scalable data analysis package powered by a Big data management system that extends the data scientists' familiar dataFrame operations to efficiently operate on managed data at scale. AFrame is implemented as a layer on top of Apache AsterixDB, transparently scaling out the execution of dataFrame operations and machine learning model invocation through a parallel, shared-nothing big data management system. AFrame incrementally constructs SQL++ queries and leverages AsterixDB's semistructured data management facilities, user-defined function support, and live data ingestion support. In order to evaluate the proposed approach, this paper also introduces an extensible micro-benchmark for use in evaluating dataFrame performance in both single-node and distributed settings via a collection of representative analytic operations. This paper presents the architecture of AFrame, describes the underlying capabilities of AsterixDB that efficiently support modern data analytic operations, and utilizes the proposed benchmark to evaluate and compare the performance and support for largescale data analyses provided by alternative dataFrame libraries.

关键词： dataFrames distributed data management large-scale data analysis data science benchmark

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ChronoCache: Predictive and Adaptive Mid-Tier Query Result Caching 20

ChronoCache: Predictive and Adaptive Mid-Tier Query Result C...

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ACM SIGMOD International Conference on management of data (SIGMOD)

作者： Glasbergen, Brad Langendoen, Kyle Abebe, Michael Daudjee, Khuzaima Univ Waterloo Cheriton Sch Comp Sci Waterloo ON Canada

ISBN: (纸本)9781450367356

The performance of data-driven, web-scale client applications is sensitive to access latency. To address this concern, enterprises strive to cache data on edge nodes that are closer to users, thereby avoiding expensive round-trips to remote data centers. However, these geo-distributed approaches are limited to caching static data. In this paper we present ChronoCache, a mid-tier caching system that exploits the presence of geo-distributed edge nodes to cache database query results closer to users. ChronoCache transparently learns and leverages client application access patterns to predictively combine query requests and cache their results ahead of time, thereby reducing costly round-trips to the remote database. We show that ChronoCache reduces query response times by up to 2/3 over prior approaches on multiple representative benchmark workloads.

关键词： predictive caching prefetching distributed data management

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