Multi-Query Optimization of Incrementally Evaluated Sliding-Window Aggregations

Shein, Anatoli U.; Chrysanthis, Panos K.

doi:10.1109/tkde.2020.3029770

Cited by 4 publications

(3 citation statements)

References 37 publications

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“…Inspired by scheduling in systems, those algorithms employ scheduling techniques in order to prioritize the order of correlating the pairs in an attempt to first correlate the promising pairs that have a high likelihood of containing correlated windows. Furthermore, we utilize incremental computations in order to avoid re-computations [48]. This is achieved by having our proposed novel algorithm based on a utility function that incorporates the knowledge of incremental computations, scheduling, and caching information to correlate two synchronized windows of pairs of data streams.…”

Section: Hypotheses Objective and Approachmentioning

confidence: 99%

Optimizing Operators for Temporal and Spatiotemporal Data

Alseghayer

2024

2024 25th IEEE International Conference on Mobile Data Management (MDM)

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Without their support I would not have been able to finish this eventful and rewarding journey. Your belief in me, support, humor, and positiveness kept me going through the path until the end. This dissertation is dedicated for you. Above all, I would like to thank my academic advisor Panos K. Chrysanthis for his admirable guidance and support during my PhD years. His incremental step-wise research style, holistic view of research problems, and story telling talent have shaped me to be the scientific researcher I am. I also would like to thank Constantinos Costa for his technical support, dedication to shape this work, and high availability for feedback. I would like to thank the rest of the committee members for their valuable feedback and support. I would like also to thank my colleagues and co-authors of several papers Daniel Mossé and Daniel Petrov. Working with you has been a pleasure and a very rewarding experience. Moreover, I would like to extend my gratitude to the rest of the Advanced Data Management Technologies Lab members and my colleagues and friends at PITT that I have known during this journey, for you have been available for feedback and personal support when I needed

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Section: Hypotheses Objective and Approachmentioning

confidence: 99%

Optimizing Operators for Temporal and Spatiotemporal Data

Alseghayer

2024

2024 25th IEEE International Conference on Mobile Data Management (MDM)

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“…Based on data of such different origins, an evident need is to integrate these sources, whether historical or stream, structured or not [Asano et al 2019]. Several works have been developed in order to promote query mechanisms capable of integrating streaming data, addressing aspects of semantic optimization [Cappuzzo et al 2020;Alkhamisi and Saleh 2020], continuous queries with sliding windows [Shein and Chrysanthis 2020], time alignment of queries [Tu et al 2020] and aspects related to scalability [Stonebraker and Ilyas 2018].…”

Section: Introductionmentioning

confidence: 99%

Integrating Heterogeneous Stream and Historical Data Sources using SQL

Amará

Ströele

Braga

et al. 2022

jidm

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Applications capable of integrating data from historical and streaming sources can make the most contextualized and enriched decision-making. However, the complexity of data integration over heterogeneous data sources can be a hard task for querying in this context. Approaches that facilitate data integration, abstracting details and formats of the primary sources can meet these needs. This work presents a framework that allows the integration of streaming and historical data in real-time, abstracting syntactic aspects of queries through the use of SQL as a standard language for querying heterogeneous sources. The framework was evaluated through an experiment using relational datasets and real data produced by sensors. The results point to the feasibility of the approach.

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“…Based on data of such different origins, an evident need is to integrate these sources [Asano et al 2019, Tatbul 2010]. Several works have been developed in order to promote query mechanisms capable of integrating streaming data, addressing aspects of semantic optimization [Cappuzzo et al 2020, Alkhamisi andSaleh 2020], continuous queries with sliding windows [Shein and Chrysanthis 2020], time alignment of queries [Tu et al 2020] and aspects related to scalability [Stonebraker and Ilyas 2018].…”

Section: Introductionmentioning

confidence: 99%

Stream and Historical Data Integration using SQL as Standard Language

Amará

Ströele

Braga

et al. 2021

Anais Do XXXVI Simpósio Brasileiro De Banco De Dados (SBBD 2021)

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The complexity imposed by data heterogeneity makes it difficult to integrate 'streaming x streaming' and 'streaming x historical' data types. For practical analysis, the enrichment and contextualization process based on historical and streaming data would benefit from approaches that facilitate data integration, abstracting details and formats of the primary sources. This work presents a framework that allows the integration of streaming data and historical data in real-time, abstracting syntactic aspects of queries through the use of SQL as a standard language for querying heterogeneous sources. The framework was evaluated through an experiment using a relational database and real data produced by sensors. The results point to the feasibility of the approach.

show abstract

Multi-Query Optimization of Incrementally Evaluated Sliding-Window Aggregations

Cited by 4 publications

References 37 publications

Optimizing Operators for Temporal and Spatiotemporal Data

Optimizing Operators for Temporal and Spatiotemporal Data

Integrating Heterogeneous Stream and Historical Data Sources using SQL

Stream and Historical Data Integration using SQL as Standard Language

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