تفاصيل مارك
| 000 -LEADER |
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| fixed length control field |
08782namaa22004331i 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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EG-GICUC |
| 005 - أخر تعامل مع التسجيلة |
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20260111125030.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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251226s2025 ua a|||frm||| 000 0 eng d |
| 040 ## - CATALOGING SOURCE |
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| Original cataloguing agency |
EG-GICUC |
| Language of cataloging |
eng |
| Transcribing agency |
EG-GICUC |
| Modifying agency |
EG-GICUC |
| Description conventions |
rda |
| 041 0# - LANGUAGE CODE |
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| Language code of text/sound track or separate title |
eng |
| Language code of summary or abstract |
eng |
| -- |
ara |
| 049 ## - Acquisition Source |
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| Acquisition Source |
Deposit |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
004.678 |
| 092 ## - LOCALLY ASSIGNED DEWEY CALL NUMBER (OCLC) |
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| Classification number |
004.678 |
| Edition number |
21 |
| 097 ## - Degree |
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| Degree |
M.Sc |
| 099 ## - LOCAL FREE-TEXT CALL NUMBER (OCLC) |
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| Local Call Number |
Cai01.20.04.M.S.2025.Al.E |
| 100 0# - MAIN ENTRY--PERSONAL NAME |
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| Authority record control number or standard number |
Alaa Adel Abd Elhafez, |
| Preparation |
preparation. |
| 245 10 - TITLE STATEMENT |
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| Title |
Enhancing internet of things (IOT) data analytics / |
| Statement of responsibility, etc. |
by Alaa Adel Abd Elhafez ; Supervision Prof. Dr. Hatem Elkadi, Prof. Dr. Osama Ismael. |
| 246 15 - VARYING FORM OF TITLE |
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| Title proper/short title |
تحسين تحليلات البيانات لانترنت الاشياء |
| 264 #0 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
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| Date of production, publication, distribution, manufacture, or copyright notice |
2025. |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
124 Leaves : |
| Other physical details |
illustrations ; |
| Dimensions |
30 cm. + |
| Accompanying material |
CD. |
| 336 ## - CONTENT TYPE |
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| Content type term |
text |
| Source |
rda content |
| 337 ## - MEDIA TYPE |
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| Media type term |
Unmediated |
| Source |
rdamedia |
| 338 ## - CARRIER TYPE |
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| Carrier type term |
volume |
| Source |
rdacarrier |
| 502 ## - DISSERTATION NOTE |
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| Dissertation note |
Thesis (M.Sc)-Cairo University, 2025. |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| Bibliography, etc. note |
Bibliography: pages 103-111. |
| 520 #3 - SUMMARY, ETC. |
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| Summary, etc. |
The Internet of Things' (IoT) explosive expansion has resulted in a huge volume of ongoing data <br/>streams produced by networked smart devices. Despite being information-rich, these data streams <br/>suffer greatly from redundancy. Significant difficulties arise from this redundancy, such as the need <br/>for a lot of storage, longer processing times, and the possibility of machine learning models <br/>overfitting. In order to provide real-time, resource-efficient, and scalable analytics in smart settings <br/>like automated homes, smart cities, and environmental monitoring systems, these problems must <br/>be resolved. This thesis addresses duplication in IoT data streams by introducing a concept known <br/>as Cluster-Based Similarity Elimination (CBSE). By reducing duplicate data at the feature and <br/>record levels prior to classification, the suggested approach seeks to greatly increase classification <br/>speed, computational effectiveness, and model performance. Conventional techniques, including <br/>feature selection, are unable to fulfill the processing demands of real-time systems and only <br/>partially address the redundancy issue. To handle the problem holistically, CBSE, on the other <br/>hand, incorporates an extra redundancy removal phase in between preprocessing and classification. <br/>The CBSE model consists of three phases: <br/>1. Preprocessing Phase – Includes basic cleaning, formatting, and normalization to prepare raw <br/>sensor data. <br/>2. Redundancy Elimination Phase – Uses clustering techniques to detect and eliminate similar <br/>records. This is the core innovation of CBSE and plays a pivotal role in reducing dataset size. <br/>3. Classification Phase – Applies machine learning models to the optimized dataset. Multiple <br/>classification algorithms, including Decision Trees, Naive Bayes, were evaluated to ensure <br/>generalizability. <br/>A number of tests were carried out utilizing real-world IoT datasets, including parts of the Austrian <br/>weather dataset, which combines information from several sensors like temperature, humidity, <br/>wind speed, and rainfall, in order to verify the efficacy of CBSE. Increased processing times were <br/>directly linked to the high degree of redundancy, which in certain situations resulted in a 40% <br/>increase in computing time. The exploratory setup included comparing classification execution <br/>and computational costs before and after applying the CBSE model. Comes about illustrated an <br/>emotional advancement in execution time and classification productivity. Particularly, CBSE was <br/>able to diminish the real-time classification execution time to fair 9% of the initial, representing a <br/>94% reduction in handling time for Irregular Timberland classifier. In spite of this significant <br/>reduction in information volume, the classification exactness remained steady or indeed made <br/>strides in a few cases, owing to the removal of clamor and repetitive information focuses. <br/>Assessment measurements included execution time, classification precision, accuracy, review, and <br/>F1-score over different machine learning models. According to these measurements, CBSE <br/>optimizes framework execution while maintaining foresight control. For instance, using CBSE <br/>with an arbitrary timberland classifier resulted in faster reaction times with essentially no impact <br/>on accuracy, which made it suitable for setup in real-time systems. <br/>The key contribution of this thesis is The development and implementation of a redundancy <br/>preprocessing stage integrated into the IoT data classification pipeline. <br/>In conclusion, the CBSE model advances the field of IoT analytics by offering a scalable and <br/>effective solution for data minimization and classification optimization. By removing redundant <br/>data before it reaches the classification stage, CBSE enhances both computational efficiency and <br/>analytical effectiveness, thereby supporting real-time decision-making in IoT applications. This <br/>research has direct implications for industries relying on fast, accurate, and efficient data analytics, <br/>and lays a foundation for future work on intelligent IoT systems. |
| 520 #3 - SUMMARY, ETC. |
|---|
| Summary, etc. |
تقدم هذه الدراسة طريقة جديدة لتقليل التكرار في تدفقات البيانات المستمرة من أجهزة إنترنت الأشياء من خلال الاستفادة من خوارزمية إزالة التشابه القائمة على المجموعة. حيث تركز الدراسة على ضرورة تقنيات معالجة البيانات الفعّالة في بيئات إنترنت الأشياء، خاصة وأن البيانات المكررة يمكن أن تؤدي إلى الإفراط في ملاءمة النموذج وزيادة المتطلبات الحسابية. وحيث أن الأساليب الحالية بما فيها من اختيار الميزات تعالج جزءًا من هذه المشكلات إلا أنها غير كافية للمتطلبات المتزايدة لأجهزة إنترنت الأشياء. وبالتالي تقدم هذه الدراسة منهجية إزالة التشابه القائمة على المجموعة (CBSE)،والتي تقلل بشكل كبير من حجم البيانات من خلال إزالة الميزات والسجلات المكررة من خلال تقنيات تقليل السجلات المقترحة. تم استخدام خوارزميات تصنيف مختلفة ومقاييس تقييم للتحقق من صحة هذه الطريقة مما نتج عنه تحسينات كبيرة في الطرق التقليدية. حيث يقلل نموذج CBSE من وقت تنفيذ التصنيف في الوقت الفعلي إلى 9٪ فقط من الأصل، مما يُظهر انخفاضًا ملحوظًا بنسبة 94٪ في وقت المعالجة مقارنة بالطرق التقليدية. كما تتضمن تسهم الدراسة في تطوير منهجية CBSE التي تدمج مرحلة التقليل قبل عملية التصنيف، مما يؤدي إلى تكثيف مجموعة البيانات بشكل فعال وتعزيز كفاءة ودقة مراحل التجميع والتصنيف. بالإضافة إلى ذلك، فإن التخفيض الكبير في وقت التنفيذ الذي تحققه المنهجية المقترحة أمر بالغ الأهمية لتحليلات بيانات إنترنت الأشياء في الوقت الفعلي، مما يوفر للشركات حلاً عمليًا وقابلًا للتطوير يحافظ على الموارد الحسابية. بشكل عام، تسهم الدراسة في تحقيق تقدم كبير في تحليلات بيانات إنترنت الأشياء في الوقت الفعلي من خلال تحسين حجم البيانات وتحسين كفاءة التصنيف ومعالجة الإفراط في التجهيز وتعزيز الكفاءة الحسابية لتلبية المتطلبات المتزايدة لبيئات المدن الذكية |
| 530 ## - ADDITIONAL PHYSICAL FORM AVAILABLE NOTE |
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| Issues CD |
Issues also as CD. |
| 546 ## - LANGUAGE NOTE |
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| Text Language |
Text in English and abstract in Arabic & English. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Internet of things |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
إنترنت الأشياء |
| 653 #1 - INDEX TERM--UNCONTROLLED |
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| Uncontrolled term |
Data Minimization |
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IoT Data Analytics |
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Real-Time Data Analytics, |
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Machine Learning |
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Classification Task |
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Data Optimization |
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تقليل البيانات |
| -- |
تحليلات بيانات إنترنت الأشياء |
| 700 0# - ADDED ENTRY--PERSONAL NAME |
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| Personal name |
Hatem Elkadi |
| Relator term |
thesis advisor. |
| 700 0# - ADDED ENTRY--PERSONAL NAME |
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| Personal name |
Osama Ismael |
| Relator term |
thesis advisor. |
| 900 ## - Thesis Information |
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| Grant date |
01-01-2025 |
| Supervisory body |
Hatem Elkadi |
| -- |
Osama Ismael |
| Universities |
Cairo University |
| Faculties |
Faculty of Computers and Artificial Intelligence |
| Department |
Department of Information Systems |
| 905 ## - Cataloger and Reviser Names |
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| Cataloger Name |
Shimaa |
| Reviser Names |
Eman Ghareb |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha item type |
Thesis |
| Edition |
21 |
| Suppress in OPAC |
No |