Techniques for update handling in the enhanced client-server DBMS

Delis, Alex; Roussopoulos, Nick

doi:10.1109/69.687978

Cited by 7 publications

(4 citation statements)

References 38 publications

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“…We attempt to fill this gap by providing a stochastic model for content evolution, which allows a client to make judicious requests for current data. Other work in related areas (e.g., [3,5,10]) has considered various alternatives for pushing updated data from a server to a cache on the client side. Lazy replica-update policies using replication graphs have also been discussed in, for example, [4].…”

Section: Introductionmentioning

confidence: 99%

Managing periodically updated data in relational databases

Gal

Eckstein

2001

J. ACM

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Recent trends in information management involve the periodic transcription of data onto secondary devices in a networked environment, and the proper scheduling of these transcriptions is critical for efficient data management. To assist in the scheduling process, we are interested in modeling data obsolescence, that is, the reduction of consistency over time between a relation and its replica. The modeling is based on techniques from the field of stochastic processes, and provides several stochastic models for content evolution in the base relations of a database, taking referential integrity constraints into account. These models are general enough to accommodate most of the common scenarios in databases, including batch insertions and life spans both with and without memory. As an initial "proof of concept" of the applicability of our approach, we validate the insertion portion of our model framework via experiments with real data feeds. We also discuss a set of transcription protocols which make use of the proposed stochastic model.

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Section: Introductionmentioning

confidence: 99%

Managing periodically updated data in relational databases

Gal

Eckstein

2001

J. ACM

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“…The server can maintain (partial) information about the contents of the clients' cache or simply broadcast all changes. Updates have been studied for many other caching architectures (e.g., see Franklin [1996], Delis and Roussopoulos [1998], and Amiri et al [2003]); studying update performance for semantic caching is an interesting avenue for future work.…”

Section: General Query Processingmentioning

confidence: 99%

Performance and overhead of semantic cache management

Jónsson

Arinbjarnar

Þórsson

et al. 2006

ACM Trans. Internet Technol.

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The emergence of query-based online data services and e-commerce applications has prompted much recent research on data caching. This article studies semantic caching, a caching architecture for such applications, that caches the results of selection queries. The primary contribution of this article is to revisit the performance and overhead of semantic caching using a modern database server and modern hardware. Initially, the performance study focuses on simple workloads and demonstrates several benefits of semantic caching, including low overhead, insensitivity to the physical layout of the database, reduced network traffic, and the ability to answer some queries without contacting the server. With moderately complex workloads, careful coding of remainder queries is required to maintain efficient query processing at the server. Using very complex workloads, we demonstrate that semantic caching works well in a range of applications, especially in network-constrained environments.

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“…While the discussion in Ref. [21][22][23] ) have considered various alternatives for pushing updated data from a server to cached data on the client side. In this framework, an update is ''pushed'' to a replication site whenever updated data exceeds a predetermined interval of values or whenever a query requires current data.…”

Section: Related Workmentioning

confidence: 99%

Scheduling of Data Transcription in Periodically Connected Databases

Gal

Eckstein

Stoumbos

2003

Stochastic Analysis and Applications

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significantly out of date, which may also be costly (in terms of lost opportunities, for example). Thus, the best transcription policies should make on optimal trade-off between these costs. Our approach to evaluating this trade-off is to use modeling techniques from the field of stochastic processes. The paper presents a general model for data insertions on the server side, using compound nonhomogeneous Poisson processes, and compares several transcription policies in terms of both transcription cost and obsolescence cost. The comparisons use a validation data set from a real data feed, and our models were calibrated using a separate training set from the same feed. We find that transcription policies based on a nonhomogeneous Poisson arrival model often outperform simpler policies.

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Techniques for update handling in the enhanced client-server DBMS

Cited by 7 publications

References 38 publications

Managing periodically updated data in relational databases

Managing periodically updated data in relational databases

Performance and overhead of semantic cache management

Scheduling of Data Transcription in Periodically Connected Databases

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