A Survey of Multi-Dimensional Indexes: Past and Future Trends

Li, Mingxin; Wang, Hancheng; Dai, Haipeng; Li, Meng; Chai, Chengliang; Gu, Rong; Chen, Feng; Chen, Zhiyuan; Li, Shuaituan; Liu, Qizhi; Chen, Guihai

doi:10.1109/tkde.2024.3364183

Cited by 2 publications

References 120 publications

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A systematic review of deep learning applications in database query execution

Milicevic,

Babovic

2024

J Big Data

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Modern database management systems (DBMS), primarily designed as general-purpose systems, face the challenging task of efficiently handling data from diverse sources for both analytical services and online transactional processing (OLTP). The volume of data has grown significantly, with distributions ranging from linear to highly skewed, sometimes involving very slow changes or rapid, intensive updates. Recent research in this field has been significantly influenced by advances in machine learning (ML), particularly deep learning (DL), and these developments have led to the application of various ML algorithms to enhance the efficiency of different parts of the query execution engine. While previous research studies were mostly focused on identifying drawbacks to individual components, such as the query optimizer, there is a notable lack of studies examining the applicability and effectiveness of various machine learning approaches across multiple aspects of the query execution engine. This article aims to provide a systematic review of approaches that apply deep learning models at various levels within the query execution engine. We categorize these approaches into three groups based on how such models are applied: improving performance of index structures and consequently data manipulation algorithms, query optimization tasks, and externally controlling query optimizers through parameter tuning. Furthermore, we discuss the key challenges associated with implementing deep learning algorithms in DBMS.

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A systematic review of deep learning applications in database query execution

Milicevic,

Babovic

2024

J Big Data

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A Framework for IoT Data Collection and Fusion in Infectious Diseases Surveillance

Jillahi,

Thandekkattu

2024

Advances in Business Information Systems and Analytics

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Although IoT has proven to be a vital instrument for data collection, epidemiological data collection has presented a significant challenge to the AI community. Thus, data for epidemiological research from multiple sources needs to be integrated to gain a holistic insight into epidemiological incidences. Hence, the authors aim to provide a framework for collecting, aggregating, and fusing data from diverse IoT sources for epidemiological research. Thus, a wireless network is designed for energy efficiency and communication efficiency. In addition, the CoAP mechanism for data transfer to a cloud-based service for aligning, de-duplicating, aggregating, and mapping data from several sources is presented. Subsequently, the researchers trained three machine learning models to predict disease incidence, vector abundance, and host population using both synthesized data using the proposed framework and data captured using traditional means. They assessed the performance of the two models by measuring their accuracy and learning rate. Results show the superiority of the proposed framework.

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A Survey of Multi-Dimensional Indexes: Past and Future Trends

Cited by 2 publications

References 120 publications

A systematic review of deep learning applications in database query execution

A systematic review of deep learning applications in database query execution

A Framework for IoT Data Collection and Fusion in Infectious Diseases Surveillance

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