| Type of Material: | Thesis |
| Title: | Temporal based approach for the concept drift adaptation in stream data processing |
| Researcher: | Suryawanshi, Shubhangi |
| Guide: | Goswami, Anurag | Patil, Pramod |
| Department: | School of Computer Science Engineering and Technology |
| Publisher: | Bennett University |
| Place: | Greater Noida |
| Year: | 2024 |
| Language: | English |
| Subject: | Computer Science | Computer Science Software Engineering | Engineering and Technology | Computer Science and Applications | Engineering and Technology |
| Dissertation/Thesis Note: | PhD; School of Computer Science Engineering and Technology, Bennett University, Greater Noida; 2024 |
| Fulltext: | Shodhganga |
| 003 | IN-AhILN | |
| 005 | 2026-10-14 14:53:32 | |
| 008 | __ | 261014t2024||||ii#||||g|m||||||||||eng|| |
| 035 | __ | |a(IN-AhILN)th_464164 |
| 040 | __ | |dIN-AhILN|aBENN_201310 |
| 041 | __ | |aeng |
| 100 | __ | |eResearcher|aSuryawanshi, Shubhangi |
| 110 | __ | |aSchool of Computer Science Engineering and Technology|0U-0890|dGreater Noida|bBennett University|ein |
| 245 | __ | |aTemporal based approach for the concept drift adaptation in stream data processing |
| 260 | __ | |aGreater Noida|c2024|bBennett University |
| 300 | __ | |ax; 164p. |
| 502 | __ | |d2024|bPhD|cSchool of Computer Science Engineering and Technology, Bennett University, Greater Noida |
| 518 | __ | |d2024|oDate of Award |
| 518 | __ | |d2018|oDate of Registration |
| 518 | __ | |d2024|oDate of Completion |
| 520 | __ | |aquotOver the past decades, tremendous technological advancement has massively increased the numerous applications such as financial market analysis, email systems, weather forecasting, energy consumption monitoring, fraud detection, web mining, social media monitoring, and real-time sensor data processing that produce vast amounts of data streams at rapid rates with uninterrupted data flows. One significant challenge is the non-static data stream patterns, where the underlying relationships and patterns can evolve. This phenomenon, termed concept drift, can severely impair the performance of previously trained models as they fail to account for these changes in data distribution. In dynamic environments where data continuously evolves, traditional static learning models become obsolete over time. In data streams, vast amounts of fast-changing data are processed sequentially, so handling concept drift becomes especially critical. Different methods have been developed for drift detection and adaptation. The t |
| 650 | __ | |aComputer Science and Applications|2UGC |
| 650 | __ | |aEngineering and Technology|2AIU |
| 653 | __ | |aComputer Science |
| 653 | __ | |aComputer Science Software Engineering |
| 653 | __ | |aEngineering and Technology |
| 700 | __ | |aGoswami, Anurag|eGuide |
| 700 | __ | |aPatil, Pramod|eGuide |
| 856 | __ | |uhttp://shodhganga.inflibnet.ac.in/handle/10603/565473|yShodhganga |
| 905 | __ | |afromsg |
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