Real-Time &amp; Stream Data Management

Wingerath, Wolfram; Ritter, Norbert; Gessert, Felix

doi:10.1007/978-3-030-10555-6

Cited by 14 publications

(3 citation statements)

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“…The backend of this application essentially consists of two working parts: the FireStore cloud database, which is used to store the necessary information (i.e. faces, names, IDs), and the Amazon AWS Rekognition system to power the face recognition aspect of the application [8]. Our app returns the detected face into the Rekognition system, which sends back the student ID of the detected face.…”

Section: Face Detectionmentioning

confidence: 99%

A Mobile Application to Mark Attendance using a Combined Backend of the Firestore Database and Amazon AWS Services

Jiang¹,

Sun²

2023

Computer Science and Machine Learning Trends 2023

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Since the beginning of the COVID-19 pandemic, education largely shifted away from the physical classroom and towards more digitally oriented platforms. This simplified classroom attendance problems greatly, as newly created programming scripts could easily track the students in a meeting room via their names. However, with the recent growing return to in person education, it has become apparent that the problem of attendance within the context of a non-virtual classroom environment has yet to be solved in an efficacious automated fashion. In larger classrooms, the severity of this problem becomes exacerbated even further, as teachers are forced to allocate valuable time for the purpose of marking attendance. The flourishing world of machine-learning based algorithms were the first solutions that we considered, and within the context of the premise, we concluded that facial recognition would likely be the most feasible and effective approach that we could use. This paper develops a mobile application to apply real time face recognition for the purpose of the above stated problem, using a combined backend of the Firestore database and Amazon AWS services. Applying our application to in person classrooms, the results show that our solutions are immensely effective in both saving time and reducing error.

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Section: Face Detectionmentioning

confidence: 99%

A Mobile Application to Mark Attendance using a Combined Backend of the Firestore Database and Amazon AWS Services

Jiang¹,

Sun²

2023

Computer Science and Machine Learning Trends 2023

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“…The disadvantage of such systems is that they depend on sophisticated infrastructures [11] which often do not exist on unmanned vehicles. A more suitable approach for processing streams on-board is the use of a DSMS [6][7] [8]. In the early years, major issues of such systems were the limited memory and the use of blocking operators on continuous streams [12].…”

Section: Related Workmentioning

confidence: 99%

“…An alternative approach is the use of a data stream management system (DSMS) that is specialized to operate on a continuously changing data [6] [7] [8]. In contrast to a conventional database system a DSMS defines a query language, entities, operators and execution plans for processing data streams.…”

Section: Introductionmentioning

confidence: 99%

Window Operators for Processing Spatio-Temporal Data Streams on Unmanned Vehicles

Werner

Brinkhoff

2020

AGILE GIScience Ser.

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Abstract. Unmanned aerial and submersible vehicles are used in an increasing number of applications especially for data collection in misanthropic environments. During a mission, such vehicles generate multiple spatio-temporal data streams suitable to be processed by data stream management systems (DSMS). The main approach of a DSMS is limiting the elements of a stream by using sliding and tilting windows with time intervals as temporal condition. However, due to varying vehicle speed and limited on-board resources, such temporal windows do not provide adequate support for spatio-temporal problems. For solving this problem, we propose a set of six new spatio-temporal window operators in this paper. This set comprises of sliding distance, tilting distance, tilting waypoint, session distance, jumping distance and an area window to limit stream elements based on spatial conditions. Each of the listed operators provides an individual behaviour to support sophisticated applications like spatial interpolation and forecasting. An evaluation based on an example trajectory shows the benefit of the presented operators for spatio-temporal applications.

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