Facilitating SQL Query Composition and Analysis

Zolaktaf, Zainab; Milani, Mostafa; Pottinger, Rachel

doi:10.1145/3318464.3380602

Cited by 15 publications

(5 citation statements)

References 33 publications

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“…, where n denotes the number of inputs to be joined. While in relational database management systems (RDBMSs), the number of tables and, hence, the number of joins in structured query language (SQL) queries is relatively low [16,17], the simple concept of triples in the resource description framework (RDF) leads to a higher number of joins in SPARQL queries. For example, there are reports of more than 50 joins are in a practical setting [18].…”

Section: Motivationmentioning

confidence: 99%

Using Machine Learning and Routing Protocols for Optimizing Distributed SPARQL Queries in Collaboration

Warnke,

Fischer,

Groppe

2023

Computers

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Due to increasing digitization, the amount of data in the Internet of Things (IoT) is constantly increasing. In order to be able to process queries efficiently, strategies must, therefore, be found to reduce the transmitted data as much as possible. SPARQL is particularly well-suited to the IoT environment because it can handle various data structures. Due to the flexibility of data structures, however, more data have to be joined again during processing. Therefore, a good join order is crucial as it significantly impacts the number of intermediate results. However, computing the best linking order is an NP-hard problem because the total number of possible linking orders increases exponentially with the number of inputs to be combined. In addition, there are different definitions of optimal join orders. Machine learning uses stochastic methods to achieve good results even with complex problems quickly. Other DBMSs also consider reducing network traffic but neglect the network topology. Network topology is crucial in IoT as devices are not evenly distributed. Therefore, we present new techniques for collaboration between routing, application, and machine learning. Our approach, which pushes the operators as close as possible to the data source, minimizes the produced network traffic by 10%. Additionally, the model can reduce the number of intermediate results by a factor of 100 in comparison to other state-of-the-art approaches.

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Section: Motivationmentioning

confidence: 99%

Using Machine Learning and Routing Protocols for Optimizing Distributed SPARQL Queries in Collaboration

Warnke,

Fischer,

Groppe

2023

Computers

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“…Therefore ResTune restricts its calculation to reserved SQL keywords [74]. To support larger vocabulary, [30,76] resort to representation learning which is frequently used in NLP. Representation learning produces dense vectors capturing nuanced relationships of unstructured data.…”

Section: Related Workmentioning

confidence: 99%

“…We adopt representation learning techniques [8] to extract informative encoding of queries and generalize across workloads. We choose LSTM, which have been used successfully for SQL query analysis [29,30,76]. We use a standard LSTM encoderdecoder network [59] to ease the burden of collecting labeling data.…”

Section: Workload Featurizationmentioning

confidence: 99%

Towards Dynamic and Safe Configuration Tuning for Cloud Databases

Zhang,

Wu,

et al. 2022

Preprint

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Configuration knobs of database systems are essential to achieve high throughput and low latency. Recently, automatic tuning systems using machine learning methods (ML) have shown to find better configurations compared to experienced database administrators (DBAs). However, there are still gaps to apply the existing systems in production environments, especially in the cloud. First, they conduct tuning for a given workload within a limited time window and ignore the dynamicity of workloads and data. Second, they rely on a copied instance and do not consider the availability of the database when sampling configurations, making the tuning expensive, delayed, and unsafe. To fill these gaps, we propose OnlineTune, which tunes the online databases safely in changing cloud environments. To accommodate the dynamicity, OnlineTune embeds the environmental factors as context feature and adopts contextual Bayesian Optimization with context space partition to optimize the database adaptively and scalably. To pursue safety during tuning, we leverage the black-box and the white-box knowledge to evaluate the safety of configurations and propose a safe exploration strategy via subspace adaptation.We conduct evaluations on dynamic workloads from benchmarks and real-world workloads. Compared with the state-of-the-art methods, OnlineTune achieves 14.4%~165.3% improvement on cumulative performance while reducing 91.0%~99.5% unsafe configuration recommendations. CCS CONCEPTS• Information systems → Autonomous database administration; • Computing methodologies → Machine learning.

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“…Query composition. ML algorithms can be used to predict query properties like answer size, run-time, and error class [9]. These algorithms can therefore be prone to unintended memorization of query content alongside the attacks which aim at disclosing training document samples.…”

Section: Search Tasksmentioning

confidence: 99%

Privacy in Open Search: A Review of Challenges and Solutions

Sousa,

Guetl,

Kern

2021

Preprint

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Privacy is of worldwide concern regarding activities and processes that include sensitive data. For this reason, many countries and territories have been recently approving regulations controlling the extent to which organizations may exploit data provided by people. Artificial intelligence areas, such as machine learning and natural language processing, have already successfully employed privacy-preserving mechanisms in order to safeguard data privacy in a vast number of applications. Information retrieval (IR) is likewise prone to privacy threats, such as attacks and unintended disclosures of documents and search history, which may cripple the security of users and be penalized by data protection laws. This work aims at highlighting and discussing open challenges for privacy in the recent literature of IR, focusing on tasks featuring user-generated text data. Our contribution is threefold: firstly, we present an overview of privacy threats to IR tasks; secondly, we discuss applicable privacy-preserving mechanisms which may be employed in solutions to restrain privacy hazards; finally, we bring insights on the tradeoffs between privacy preservation and utility performance for IR tasks.

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Facilitating SQL Query Composition and Analysis

Cited by 15 publications

References 33 publications

Using Machine Learning and Routing Protocols for Optimizing Distributed SPARQL Queries in Collaboration

Using Machine Learning and Routing Protocols for Optimizing Distributed SPARQL Queries in Collaboration

Towards Dynamic and Safe Configuration Tuning for Cloud Databases

Privacy in Open Search: A Review of Challenges and Solutions

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