Database Tuning Advisor for Microsoft SQL Server 2005

Agrawal, Sanjay; Chaudhuri, Surajit; Kollár, Ľubor; Marathe, A.; Narasayya, Vivek; Syamala, Manoj

doi:10.1016/b978-012088469-8.50097-8

Cited by 113 publications

(26 citation statements)

References 3 publications

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“…2 K-medoids is an iterative greedy algorithm that chooses k cluster centers, assigns all points to the closest center and iteratively refines the points in each cluster. 3 Hierarchical clustering is a top-down approach where all points start in a single cluster and the algorithm recursively splits the points into k disjoint clusters. Lemma 2 tells us that when the parameter γ tends to zero, a summary that covers strictly more tokens will always have a better value for the objective G, independent of the representativity of each summary.…”

Section: A New Objective Functionmentioning

confidence: 99%

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Revenue maximization for query pricing

et al. 2019

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Buying and selling of data online has increased substantially over the last few years. Several frameworks have already been proposed that study query pricing in theory and practice. The key guiding principle in these works is the notion of arbitrage-freeness where the broker can set different prices for different queries made to the dataset, but must ensure that the pricing function does not provide the buyers with opportunities for arbitrage. However, little is known about revenue maximization aspect of query pricing. In this paper, we study the problem faced by a broker selling access to data with the goal of maximizing her revenue. We show that this problem can be formulated as a revenue maximization problem with single-minded buyers and unlimited supply, for which several approximation algorithms are known. We perform an extensive empirical evaluation of the performance of several pricing algorithms for the query pricing problem on real-world instances. In addition to previously known approximation algorithms, we propose several new heuristics and analyze them both theoretically and experimentally. Our experiments show that algorithms with the best theoretical bounds are not necessarily the best empirically. We identify algorithms and heuristics that are both fast and also provide consistently good performance when valuations are drawn from a wide variety of distributions.

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Section: A New Objective Functionmentioning

confidence: 99%

“…Execution time is scaled using the total running time when using optimal indexes to show the comparative slowdown. than a decade [3]. All experiments in this section are run on a m5a.8xlarge AWS EC2 instance using a single core.…”

Section: Use Casesmentioning

confidence: 99%

Revenue maximization for query pricing

et al. 2019

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“…For each workload, we tune the physical database design separately using the summarized workload obtained (a) by our summarization queries, and (b) by Workload Compression. We use the Index Tuning Wizard [4] that is part of Microsoft SQL Server 2000 to perform physical-design tuning. We measure the quality of summarization by the optimizer-estimated cost of the entire (i.e., original) workload on the tuned database.…”

Section: Methodsmentioning

confidence: 99%

Primitives for Workload Summarization and Implications for SQL

Chaudhuri

Ganesan

Narasayya

2003

Proceedings 2003 VLDB Conference

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“…L. Haas From a systems research perspective, there are several approaches one might take to this challenge. Perhaps the simplest is to start by building an "Integration Advisor", on the model of today's physical design advisors [49,50]. This tool would lead the user through the various integration steps, asking for input on the desiderata, and then recommend the appropriate engines, and perhaps even generate the configuration information needed.…”

Section: The "Big I" Challengementioning

confidence: 99%

Beauty and the Beast: The Theory and Practice of Information Integration

Haas

2006

Lecture Notes in Computer Science

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Abstract. Information integration is becoming a critical problem for businesses and individuals alike. Data volumes are sky-rocketing, and new sources and types of information are proliferating. This paper briefly reviews some of the key research accomplishments in information integration (theory and systems), then describes the current state-of-the-art in commercial practice, and the challenges (still) faced by CIOs and application developers. One critical challenge is choosing the right combination of tools and technologies to do the integration. Although each has been studied separately, we lack a unified (and certainly, a unifying) understanding of these various approaches to integration. Experience with a variety of integration projects suggests that we need a broader framework, perhaps even a theory, which explicitly takes into account requirements on the result of the integration, and considers the entire end-to-end integration process.

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Database Tuning Advisor for Microsoft SQL Server 2005

Cited by 113 publications

References 3 publications

Revenue maximization for query pricing

Revenue maximization for query pricing

Primitives for Workload Summarization and Implications for SQL

Beauty and the Beast: The Theory and Practice of Information Integration

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