Contextual data stream processing overview, architecture, and frameworks survey
Other Title(s)
عرض لتقنيات معالجة و تحليل البيانات المتدفقة في الحال
Joint Authors
al-Kafrawi, Passent Muhammad
Banawym, Muhammad
Source
The Egyptian Journal of Language Engineering
Issue
Vol. 9, Issue 1 (30 Apr. 2022), pp.12-21, 10 p.
Publisher
Egyptian Society of Language Engineering
Publication Date
2022-04-30
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Topics
Abstract EN
Event stream processing (ESP) is a data processing methodology which tackle online processing for a variety of events.
Recently stream processing witnessed a huge interest in both academic research and corporate use cases.
As a consequence, for the extremely huge data sources recently generated and diversely used.
Data sources vary from social media feeds, news articles, internal business transactions, IoT devices logs, .
.
.
etc.
Academically, a lot of research papers discuss how to deal with enormous cloud of events with different data structures such as text, video, logs, transactions, … etc.
Also, research is concerned with different streaming platforms technologies; and evaluates the weakness and strength points of each.
Researchers studied aside how to best utilize the platform within different use cases.
From corporate point of view, decision makers ask about how to best utilize those events with minimal delay in order to 1) uncover insights in real-time, 2) mine textual events, 3) recommend decisions.
This requires a mix of machine learning, stream and batch processing technologies which are typically optimized independently.
However, combining all technologies by building a scalable real-world application is a challenge.
In this paper, we shall discuss state of the art event stream processing technologies by summarizing definition, data flow architectures, textual use cases, frameworks and architecture best practice.
Furthermore, we would discuss how to combine event stream processing with textual events and sentiment analysis to enhance a recommendation model outcome.
American Psychological Association (APA)
Banawym, Muhammad& al-Kafrawi, Passent Muhammad. 2022. Contextual data stream processing overview, architecture, and frameworks survey. The Egyptian Journal of Language Engineering،Vol. 9, no. 1, pp.12-21.
https://search.emarefa.net/detail/BIM-1393805
Modern Language Association (MLA)
Banawym, Muhammad& al-Kafrawi, Passent Muhammad. Contextual data stream processing overview, architecture, and frameworks survey. The Egyptian Journal of Language Engineering Vol. 9, no. 1 (2022), pp.12-21.
https://search.emarefa.net/detail/BIM-1393805
American Medical Association (AMA)
Banawym, Muhammad& al-Kafrawi, Passent Muhammad. Contextual data stream processing overview, architecture, and frameworks survey. The Egyptian Journal of Language Engineering. 2022. Vol. 9, no. 1, pp.12-21.
https://search.emarefa.net/detail/BIM-1393805
Data Type
Journal Articles
Language
English
Notes
-
Record ID
BIM-1393805