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1. |
Record Nr. |
UNINA9910456413003321 |
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Titolo |
End-to-end e-business transaction management made easy [[electronic resource] /] / [Morten Moeller ... et al.] |
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Pubbl/distr/stampa |
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Austin, TX, : IBM, International Technical Support Organization, c2003 |
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Edizione |
[1st ed.] |
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Descrizione fisica |
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Collana |
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Altri autori (Persone) |
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Disciplina |
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Soggetti |
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Electronic commerce - Management |
Business enterprises - Computer networks - Management |
Electronic books. |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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"December 2003." |
"SG24-6080-00." |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Sommario/riassunto |
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This IBM Redbooks publication will help you install, tailor, and configure the new IBM Tivoli Monitoring for Transaction Performance Version 5.2, which will assist you in determining the business performance of your e-business transactions in terms of responsiveness, performance, and availability. The major enhancement in Version 5.2 is the addition of state-of-the-art industry strength monitoring functions for J2EE applications hosted by WebSphere Application Server or BEA Weblogic. In addition, the architecture of Web Transaction Monitoring (WTP) has been redesigned to provide for even easier deployment, increased scalability, and better performance. Also, the reporting functions has been enhanced by the addition of ETL2s for the Tivoli Enterprise Date Warehouse. This new version of IBM Tivoli Monitoring for Transaction Performance provides all the capabilities of previous versions of IBM Tivoli Monitoring for Transaction Performance, including the Enterprise Transaction Performance (ETP) functions used to add transaction performance monitoring capabilities to the Tivoli Management Environment (with the exception of reporting through Tivoli Decision Support). The reporting functions have been migrated to the Tivoli Enterprise Date Warehouse environment. Please note that the |
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additional material referenced in the text is not available from IBM. |
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2. |
Record Nr. |
UNINA9910892013003321 |
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Titolo |
Colonia |
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Pubbl/distr/stampa |
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Köln, : Universitäts- und Stadtbibliothek, 2019 |
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Descrizione fisica |
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Disciplina |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Periodico |
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Note generali |
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Gesehen am 22. August 2019 |
1819 - 1820 nicht digitalisiert |
Reproduktion |
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3. |
Record Nr. |
UNINA9910983485603321 |
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Titolo |
Advances in Artificial Intelligence and Machine Learning in Big Data Processing : First International Conference, AAIMB 2023, Chennai, India, August 17–18, 2023, Proceedings, Part-II / / edited by R. Geetha, Nhu-Ngoc Dao, Saeed Khalid |
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Pubbl/distr/stampa |
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Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025 |
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ISBN |
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Edizione |
[1st ed. 2025.] |
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Descrizione fisica |
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1 online resource (342 pages) |
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Collana |
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Communications in Computer and Information Science, , 1865-0937 ; ; 2203 |
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Disciplina |
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Soggetti |
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Artificial intelligence |
Machine learning |
Expert systems (Computer science) |
Data mining |
Artificial Intelligence |
Machine Learning |
Knowledge Based Systems |
Data Mining and Knowledge Discovery |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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-- Artificial Intelligence and Data Analytics. -- Feature Fusion Based Bayesian model detection in the prognosis of glioma - A Survey. -- AI Voice Assistant Using Python. -- Research Output on Artificial Intelligence in India: A Scientometric study. -- Analyzing Hand Gestures using Object Detection and Processing it into Local Language. -- A Review of an Automated Model for Sexist Language Detection and Replacement of Sexist Terms. -- Machine Learning. -- Detection of DoS Attack using Machine learning in Software Defined Network. -- Regression Model Approach towards Concrete Compressive Strength Prediction and Evaluation. -- An Enhanced Artificial Neural Network Model for type 2 Diabetes Classification 84 using SMOTE and SMOTETomek with Effective Feature Selection Methods. -- Machine Learning based Traffic Congestion and Accident Prevention Analysis. |
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-- A Comparative Evaluation of Machine Learning Methods for Predicting Chronic Kidney Disease. -- Detecting Implicit Aspects of Customer Experience in the Hotel Industry Using a Machine Learning Algorithm. -- Vehicle Insurance Claim Prediction. -- Ensemble Learning Models for Detecting Spam Over Social Networks Using RFE. -- An Intelligent Machine Learning Framework for Melanoma Classification System: A Critique. -- Online Network Intrusion Detection System for IOT structure using Machine Learning Techniques. -- An Analysis of Machine Learning Tools and Algorithms. -- Ensemble Learning-Based Android Malware Detection. -- Handling Imbalanced Data for Credit Card Fraudulent Detection: A Machine Learning Approach. -- An Enhanced Learning Model Based on an Improved Random Forest Classifier and an Integrated Attribute Selector for Healthcare Datasets. -- Engineering Open Access Research Analysis of Machine Learning Publications in India. -- Optimizing Coronary Illness Prediction Using Hyperparameter Tuning Through Machine Learning. -- Detection and Classification of Intracranial Tumor in Machine Learning using Fuzzy C-Means Algorithm. -- A survey of Anomaly Detection in Video Surveillance. -- Prediction and comparison of ML Algorithm for Heart Disease. -- Comparative Analysis of Classifiers for Chat Classification: A Study on Random Forest, Support Vector Machine and Multinomial Naive Bayes with Bag of Words and TF-IDF. |
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Sommario/riassunto |
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This book constitutes the refereed proceedings of the First International Conference on Advances in Artificial Intelligence & Machine Learning in Big Data Processing, AAIMB 2023, held in Chennai, India, during August 17–18, 2023. The 51 full papers presented were carefully reviewed and selected from 183 submissions. They were organized in the following topical sections: Part I- artificial intelligence and data analytics; deep learning. Part II- artificial intelligence and data analytics; machine learning. |
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