Ettu

Kul, Gökhan; Luong, Duc Thanh Anh; Xie, Ting; Coonan, Patrick; Chandola, Varun; Kennedy, Oliver; Upadhyaya, Shambhu

doi:10.1145/2872518.2888608

Cited by 28 publications

(7 citation statements)

References 7 publications

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“…We opt for online clustering to identify query classes in near realtime using the context-query parse tree. Similar work with relational queries can be found in [ 49 , 50 ]. Each of the context-query classes is attached with expected performance values (e.g.,

), and SLA parameters (e.g.,

) in each learning cycle (i.e., at the end of each learning window).…”

Section: Reactive Context Cache Selectionmentioning

confidence: 90%

Adaptive Context Caching for IoT-Based Applications: A Reinforcement Learning Approach

Weerasinghe

Zaslavsky

Loke

et al. 2023

Sensors

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Making internet-of-things (IoT)-based applications context-aware demands large amounts of raw data to be collected, interpreted, stored, and reused or repurposed if needed from many domains and applications. Context is transient but interpreted data can be distinguished from IoT data in many aspects. Managing context in cache is a novel area of research that has been given very little attention. Performance metric-driven adaptive context caching (ACOCA) can have a profound impact on the performance and cost efficiency of context-management platforms (CMPs) when responding to context queries in realtime. Our paper proposes an ACOCA mechanism to maximize both the cost and performance efficiency of a CMP in near realtime. Our novel mechanism encompasses the entire context-management life cycle. This, in turn, distinctively addresses the problems of efficiently selecting context for caching and managing the additional costs of context management in the cache. We demonstrate that our mechanism results in long-term efficiencies for the CMP that have not been observed in any previous study. The mechanism employs a novel, scalable, and selective context-caching agent implemented using the twin delayed deep deterministic policy gradient method. It further incorporates an adaptive context-refresh switching policy, a time-aware eviction policy, and a latent caching decision management policy. We point out in our findings that the additional complexity of adaptation introduced to the CMP through ACOCA is significantly justified, considering the cost and performance gains achieved. Our algorithm is evaluated using a real-world inspired heterogeneous context-query load and a data set based on parking-related traffic in Melbourne, Australia. This paper presents and benchmarks the proposed scheme against traditional and context-aware caching policies. We demonstrate that ACOCA outperforms the benchmarks in both cost and performance efficiency, i.e., up to 68.6%, 84.7%, and 67% more cost efficient compared to traditional data caching policies to cache context, redirector mode, and context-aware adaptive data caching under real-world-like circumstances.

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), and SLA parameters (e.g.,

) in each learning cycle (i.e., at the end of each learning window).…”

Section: Reactive Context Cache Selectionmentioning

confidence: 90%

Adaptive Context Caching for IoT-Based Applications: A Reinforcement Learning Approach

Weerasinghe

Zaslavsky

Loke

et al. 2023

Sensors

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show abstract

“…In contrast, our framework is more general in the sense that we also incorporate query execution statistics. Ettu [18,19] presents a promising approach that clusters workload queries based on query syntax but it has restrictive assumptions with respect to defining query similarity which is based only on the subtree similarities of the query syntax. While the framework is scalable, it has restrictive assumptions with respect to defining query similarity which is based only on the subtree similarities of the query syntax.…”

Section: Related Workmentioning

confidence: 99%

“…The insight here is to train a model specifically for SQL queries (similar to Word2Vec). Most closely related to our setting is the query log summarization framework, Ettu [18,19]. Ettu summarizes query logs by parsing the syntax tree of queries and performing hierarchical clustering where the distance metric between queries is based on the number of common subexpressions.…”

Section: Introductionmentioning

confidence: 99%

“…While [15] does not suffer from quadratic complexity, it requires expensive preprocessing to train the machine learning model. The approach described in [19] ignores query execution statistics, templated queries, and stored procedures. Additionally, input workloads are often skewed in some way, and it is critical to ensure that the summary exhibits the same kind of characteristics as the input workload; this is not possible without some notion of representativity.…”

Section: Introductionmentioning

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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“…Another critical challenge is the performance bottlenecks that arise as data volumes grow. Kul et al (2018) noted that the performance of SQL databases can degrade significantly with increased data size, leading to slower query response times and longer processing times. This degradation can impede an organization's ability to derive timely insights from their data.…”

Section: Introductionmentioning

confidence: 99%

Optimizing SQL Databases Forbig Data Workloads: Techniques and Best Practices

Uzzaman,

Jim,

Nishat

et al. 2024

AJBAIS

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In the era of big data, SQL databases face significant challenges in handling vast volumes of data efficiently. This article explores optimization techniques and best practices for enhancing the performance and scalability of SQL databases in handling big data workloads. The study addresses the significant challenges faced by traditional SQL databases, including scalability issues, performance bottlenecks, resource constraints, and data integration complexities. Through a comprehensive methodology involving literature review, case studies, expert interviews, and performance testing, the research identifies effective strategies such as indexing, partitioning, sharding, and caching. Findings from case studies in e-commerce and financial services sectors demonstrate substantial improvements in query performance and resource utilization, validating the practical benefits of these optimization techniques. The study underscores the importance of a multifaceted approach to database optimization, integrating both theoretical and practical insights to address the complexities of big data environments. By staying informed and adopting the latest optimization strategies, database administrators and IT professionals can ensure their SQL databases remain efficient, scalable, and capable of managing the increasing demands of large-scale data processing, ultimately enabling organizations to derive valuable insights from their data.

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Ettu

Cited by 28 publications

References 7 publications

Adaptive Context Caching for IoT-Based Applications: A Reinforcement Learning Approach

Adaptive Context Caching for IoT-Based Applications: A Reinforcement Learning Approach

Revenue maximization for query pricing

Optimizing SQL Databases Forbig Data Workloads: Techniques and Best Practices

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