D2IA: Stream Analytics on User-Defined Event Intervals
Nowadays, modern Big Stream Processing Solutions (e.g. Spark, Flink) are working towards ultimate frameworks for streaming analytics. In order to achieve this goal, they started to offer extensions of SQL that incorporate stream-oriented primitives such as windowing and Complex Event Processing (CEP...
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2019
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| Online Access: | https://bspace.buid.ac.ae/handle/1234/2921 https://dl.acm.org/doi/abs/10.1007/978-3-030-21290-2_22 https://doi.org/10.1016/j.is.2020.101679 |
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| _version_ | 1862980619002183680 |
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| author | Awad, Ahmed |
| author2 | Tommasini, Riccardo Kamel, Mahmoud Della Valle, Emanuele Sakr, Sherif |
| author2_role | author author author author |
| author_facet | Awad, Ahmed Tommasini, Riccardo Kamel, Mahmoud Della Valle, Emanuele Sakr, Sherif |
| author_role | author |
| dc.creator.none.fl_str_mv | Awad, Ahmed Tommasini, Riccardo Kamel, Mahmoud Della Valle, Emanuele Sakr, Sherif |
| dc.date.none.fl_str_mv | 2019 2025-05-06T08:01:26Z 2025-05-06T08:01:26Z |
| dc.identifier.none.fl_str_mv | Awad, A. et al. (2022) “D2IA: User-defined interval analytics on distributed streams,” Information Systems, 104, p. 1. 0306-4379 https://bspace.buid.ac.ae/handle/1234/2921 https://dl.acm.org/doi/abs/10.1007/978-3-030-21290-2_22 https://doi.org/10.1016/j.is.2020.101679 |
| dc.publisher.none.fl_str_mv | Springer Nature Switzerland AG |
| dc.relation.none.fl_str_mv | Information Systemsv104 (Feb 2022): 1 |
| dc.subject.none.fl_str_mv | Big Stream Processing · Complex event processing · User-defined event intervals |
| dc.title.none.fl_str_mv | D2IA: Stream Analytics on User-Defined Event Intervals |
| dc.type.none.fl_str_mv | Article |
| description | Nowadays, modern Big Stream Processing Solutions (e.g. Spark, Flink) are working towards ultimate frameworks for streaming analytics. In order to achieve this goal, they started to offer extensions of SQL that incorporate stream-oriented primitives such as windowing and Complex Event Processing (CEP). The former enables stateful com putation on infinite sequences of data items while the latter focuses on the detection of events pattern. In most of the cases, data items and events are considered instantaneous, i.e., they are single time points in a discrete temporal domain. Nevertheless, a point-based time semantics does not satisfy the requirements of a number of use-cases. For instance, it is not possible to detect the interval during which the temperature increases until the temperature begins to decrease, nor all the relations this interval subsumes. To tackle this challenge, we present D2IA; a set of novel abstract operators to define analytics on user-defined event inter vals based on raw events and to efficiently reason about temporal rela tionships between intervals and/or point events. We realize the imple mentation of the concepts of D2IA on top of Esper, a centralized stream processing system, and Flink, a distributed stream processing engine for big data. |
| id | budr_44fcc88dd2d96a21e16eb62b9f688ac9 |
| identifier_str_mv | Awad, A. et al. (2022) “D2IA: User-defined interval analytics on distributed streams,” Information Systems, 104, p. 1. 0306-4379 |
| network_acronym_str | budr |
| network_name_str | The British University in Dubai repository |
| oai_identifier_str | oai:bspace.buid.ac.ae:1234/2921 |
| publishDate | 2019 |
| publisher.none.fl_str_mv | Springer Nature Switzerland AG |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | D2IA: Stream Analytics on User-Defined Event IntervalsAwad, AhmedTommasini, RiccardoKamel, MahmoudDella Valle, EmanueleSakr, SherifBig Stream Processing · Complex event processing · User-defined event intervalsNowadays, modern Big Stream Processing Solutions (e.g. Spark, Flink) are working towards ultimate frameworks for streaming analytics. In order to achieve this goal, they started to offer extensions of SQL that incorporate stream-oriented primitives such as windowing and Complex Event Processing (CEP). The former enables stateful com putation on infinite sequences of data items while the latter focuses on the detection of events pattern. In most of the cases, data items and events are considered instantaneous, i.e., they are single time points in a discrete temporal domain. Nevertheless, a point-based time semantics does not satisfy the requirements of a number of use-cases. For instance, it is not possible to detect the interval during which the temperature increases until the temperature begins to decrease, nor all the relations this interval subsumes. To tackle this challenge, we present D2IA; a set of novel abstract operators to define analytics on user-defined event inter vals based on raw events and to efficiently reason about temporal rela tionships between intervals and/or point events. We realize the imple mentation of the concepts of D2IA on top of Esper, a centralized stream processing system, and Flink, a distributed stream processing engine for big data.Springer Nature Switzerland AG2025-05-06T08:01:26Z2025-05-06T08:01:26Z2019ArticleAwad, A. et al. (2022) “D2IA: User-defined interval analytics on distributed streams,” Information Systems, 104, p. 1.0306-4379https://bspace.buid.ac.ae/handle/1234/2921https://dl.acm.org/doi/abs/10.1007/978-3-030-21290-2_22https://doi.org/10.1016/j.is.2020.101679Information Systemsv104 (Feb 2022): 1oai:bspace.buid.ac.ae:1234/29212025-08-13T13:11:18Z |
| spellingShingle | D2IA: Stream Analytics on User-Defined Event Intervals Awad, Ahmed Big Stream Processing · Complex event processing · User-defined event intervals |
| title | D2IA: Stream Analytics on User-Defined Event Intervals |
| title_full | D2IA: Stream Analytics on User-Defined Event Intervals |
| title_fullStr | D2IA: Stream Analytics on User-Defined Event Intervals |
| title_full_unstemmed | D2IA: Stream Analytics on User-Defined Event Intervals |
| title_short | D2IA: Stream Analytics on User-Defined Event Intervals |
| title_sort | D2IA: Stream Analytics on User-Defined Event Intervals |
| topic | Big Stream Processing · Complex event processing · User-defined event intervals |
| url | https://bspace.buid.ac.ae/handle/1234/2921 https://dl.acm.org/doi/abs/10.1007/978-3-030-21290-2_22 https://doi.org/10.1016/j.is.2020.101679 |