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Big Data Analytics and Knowledge Discovery : 25th International Conference, DaWaK 2023, Penang, Malaysia, August 28–30, 2023, Proceedings / / edited by Robert Wrembel, Johann Gamper, Gabriele Kotsis, A Min Tjoa, Ismail Khalil
| Big Data Analytics and Knowledge Discovery : 25th International Conference, DaWaK 2023, Penang, Malaysia, August 28–30, 2023, Proceedings / / edited by Robert Wrembel, Johann Gamper, Gabriele Kotsis, A Min Tjoa, Ismail Khalil |
| Autore | Wrembel Robert |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (407 pages) |
| Disciplina |
001.422
005.7 005.745 |
| Altri autori (Persone) |
GamperJohann
KotsisGabriele TjoaA. Min KhalilIsmail |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Quantitative research
Data mining Application software Artificial intelligence Data Analysis and Big Data Data Mining and Knowledge Discovery Computer and Information Systems Applications Artificial Intelligence |
| ISBN |
9783031398315
3031398319 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- From an Interpretable Predictive Model to a Model Agnostic Explanation (Abstract of Keynote Talk) -- Contents -- Data Quality -- Using Ontologies as Context for Data Warehouse Quality Assessment -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 3.1 Running Example -- 3.2 Data Warehouse Formal Specification -- 3.3 Context Formal Specification -- 4 Data Warehouse to Ontology Mapping -- 5 Context-Based Data Quality Rules -- 6 Experimentation -- 6.1 Implementation -- 6.2 Validation -- 7 Conclusions and Future Work -- References -- Preventing Technical Errors in Data Lake Analyses with Type Theory -- 1 Introduction -- 2 Related Works -- 3 Type-Theoretical Framework -- 4 Conclusion -- References -- EXOS: Explaining Outliers in Data Streams -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 4 The Proposed Algorithm: EXOS -- 4.1 Estimator -- 4.2 Temporal Neighbor Clustering -- 4.3 Outlying Attribute Generators -- 5 Evaluation -- 5.1 Experimental Setup -- 5.2 Results and Analysis -- 6 Conclusions -- References -- Motif Alignment for Time Series Data Augmentation -- 1 Introduction -- 2 Preliminaries -- 2.1 Matrix Profile -- 2.2 Pan-Matrix Profile -- 2.3 DTW Alignment for Time Series Data Augmentation -- 3 Proposed Method -- 3.1 Motif Mapping -- 3.2 Time Series Augmentation -- 4 Experimental Evaluation -- 4.1 Setup -- 4.2 Aligning Time Series Using MotifDTW -- 4.3 Performance Gain -- 5 Conclusion -- References -- State-Transition-Aware Anomaly Detection Under Concept Drifts -- 1 Introduction -- 2 Related Works -- 3 Problem Definition -- 3.1 Terminology -- 3.2 Problem Statement -- 4 State-Transition-Aware Anomaly Detection -- 4.1 Reconstruction and Latent Representation Learning -- 4.2 Drift Detection in the Latent Space -- 4.3 State Transition Model -- 5 Experiment -- 5.1 Experiment Setup -- 5.2 Performance.
6 Conclusion -- References -- Anomaly Detection in Financial Transactions Via Graph-Based Feature Aggregations -- 1 Introduction -- 2 Related Work -- 2.1 Graph Embedding -- 2.2 Anomaly Detection -- 3 Problem Formalization -- 4 Proposed Method -- 4.1 PFA: Proximal Feature Aggregation -- 4.2 AFA: Anomaly Feature Aggregation -- 5 Experiment -- 5.1 Experimental Setup -- 5.2 Effectiveness Evaluation -- 5.3 Scalability Evaluation -- 6 Conclusion -- References -- The Synergies of Context and Data Aging in Recommendations -- 1 Introduction -- 2 ALBA: Adding Aging to LookBack Apriori -- 3 Context Modeling -- 4 Evaluation -- 4.1 Contexts -- 4.2 Methodology -- 4.3 Fitbit Validation -- 4.4 Auditel Validation -- 5 Conclusions and Future Work -- References -- Advanced Analytics and Pattern Discovery -- Hypergraph Embedding Based on Random Walk with Adjusted Transition Probabilities -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 3.1 Notation -- 3.2 Hypergraph Projection -- 3.3 Random Walk and Stationary Distribution -- 3.4 Skip-Gram -- 4 Proposed Method -- 4.1 Random Walk -- 5 Experiment -- 5.1 Transition Probabilities in Steady State -- 5.2 Node Label Estimation -- 5.3 Parameter Dependence of F1 Score -- 6 Conclusion -- References -- Contextual Shift Method (CSM) -- 1 Introduction -- 2 Contextual Shifts -- 3 Contextual Shift Method -- 4 Experiments -- 5 Conclusion -- References -- Utility-Oriented Gradual Itemsets Mining Using High Utility Itemsets Mining -- 1 Introduction -- 2 Preliminary Definitions -- 3 High Utility Gradual Itemsets Mining -- 3.1 Database Encoding -- 3.2 High Utility Gradual Itemsets Extraction -- 4 Experimental Study -- 5 Conclusion -- References -- Discovery of Contrast Itemset with Statistical Background Between Two Continuous Variables -- 1 Introduction -- 2 Contrast ItemSB -- 3 Experimental Results -- 4 Conclusions -- References. DBGAN: A Data Balancing Generative Adversarial Network for Mobility Pattern Recognition -- 1 Introduction -- 2 Related Work -- 3 Background -- 3.1 Reproducing Kernel Hilbert Space Embeddings -- 3.2 Attention Mechanism -- 3.3 Generative Adversarial Network -- 4 DBGAN Mobility Pattern Classification Model -- 4.1 Attributes of Travel Trajectories Utilized for Classification -- 4.2 Sequences to Images with Kernel Embedding -- 4.3 Classification Using Self Attention-Based Generative Adversarial Network -- 5 Evaluation -- 6 Conclusion -- References -- Bitwise Vertical Mining of Minimal Rare Patterns -- 1 Introduction -- 2 Background and Related Works -- 3 Our RP-VIPER Algorithm -- 4 Evaluation -- 5 Conclusions -- References -- Inter-item Time Intervals in Sequential Patterns -- 1 Introduction -- 2 Related Work -- 3 Representing Time in Sequences -- 3.1 Preliminaries -- 3.2 Integrating Intervals in Sequences -- 4 Experiments -- 4.1 Datasets and Models -- 4.2 Results -- 5 Conclusion -- References -- Fair-DSP: Fair Dynamic Survival Prediction on Longitudinal Electronic Health Record -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Fair Dynamic Survival Model -- 3.2 Individual Fairness -- 3.3 Group Fairness -- 4 Experiments -- 4.1 Quantitative Analysis -- 4.2 Sensitivity Study -- 5 Conclusions -- References -- Machine Learning -- DAT@Z21: A Comprehensive Multimodal Dataset for Rumor Classification in Microblogs -- 1 Introduction -- 2 Related Works -- 2.1 Fake Health News Datasets -- 2.2 Fake News Datasets -- 3 Data Collection -- 3.1 News Articles and Ground Truth Collection -- 3.2 Preparing the Tweets Collection -- 3.3 Tweets Collection -- 4 Rumor Classification Using DAT@Z21 -- 4.1 Baselines -- 4.2 Experiment Settings -- 4.3 Experimental Results -- 5 Conclusion and Perspectives -- References. Dealing with Data Bias in Classification: Can Generated Data Ensure Representation and Fairness? -- 1 Introduction -- 2 Related Work -- 3 Measuring Discrimination -- 4 Problem Formulation -- 5 Methodology -- 6 Evaluation -- 6.1 Comparing Pre-processors -- 6.2 Investigating the Fairness-Agnostic Property -- 7 Conclusion -- 8 Discussion and Future Work -- A Proof of Time Complexity -- References -- Random Hypergraph Model Preserving Two-Mode Clustering Coefficient -- 1 Introduction -- 2 Preliminaries -- 3 Extending the Hyper dK-Series to the Case of dv = 2.5+ -- 4 Experiments -- 5 Conclusion -- References -- A Non-overlapping Community Detection Approach Based on -Structural Similarity -- 1 Introduction -- 2 Preliminaries -- 3 A Hierarchical Clustering Approach Based on -Structural Similarity -- 4 Experiments -- 5 Conclusion and Future Work -- A Appendix a -- B Appendix B -- References -- Improving Stochastic Gradient Descent Initializing with Data Summarization -- 1 Introduction -- 2 Definitions -- 2.1 Input Data Set -- 2.2 LR Model -- 3 System and Algorithms -- 3.1 Gamma Summarization () -- 3.2 Mini-batch SGD -- 3.3 Mini-batch SGD Initialization Using Gamma -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Experimental Results -- 5 Related Work -- 6 Conclusions -- References -- Feature Analysis of Regional Behavioral Facilitation Information Based on Source Location and Target People in Disaster -- 1 Introduction -- 2 Related Work -- 3 Basic Concept of RBF Tweet Classification -- 3.1 Extraction of BF Tweets -- 3.2 RBF Tweet Extraction and Classification -- 4 Analysis of RBF Tweets -- 4.1 Training and Test Data -- 4.2 Research Question -- 4.3 Results and Discussion of Research Questions -- 5 Conclusion -- References -- Exploring Dialog Act Recognition in Open Domain Conversational Agents -- 1 Introduction -- 2 Related Works. 3 Proposed Dialog Act Taxonomy -- 3.1 Data Sources -- 4 Proposed Dialog Act Classifier -- 4.1 Experimental Setup -- 4.2 Performance Evaluation -- 4.3 Generalizability of Model -- 5 Conclusion -- References -- UniCausal: Unified Benchmark and Repository for Causal Text Mining -- 1 Introduction -- 2 Related Work -- 2.1 Tasks -- 2.2 Datasets -- 2.3 Other Large Causal Resources -- 3 Methodology -- 3.1 Creation of UniCausal -- 3.2 Baseline Model -- 4 Experiments -- 4.1 Baseline Performance -- 4.2 Impact of Datasets -- 4.3 Adding CauseNet to Investigate the Importance of Linguistic Variation in Examples -- 5 Conclusion -- References -- Deep Learning -- Accounting for Imputation Uncertainty During Neural Network Training -- 1 Introduction -- 2 Related Works -- 3 Contributions -- 3.1 Single-Hotpatching -- 3.2 Multiple-Hotpatching -- 4 Experiments -- 4.1 Experimental Protocol -- 4.2 Results -- 5 Discussion and Conclusion -- References -- Supervised Hybrid Model for Rumor Classification: A Comparative Study of Machine and Deep Learning Approaches -- 1 Introduction -- 2 Related Work -- 3 Datasets and Preprocessing -- 4 Implementation -- 4.1 Traditional ML Approaches -- 4.2 DL Approaches -- 4.3 The Ensemble Stack ML Model -- 4.4 The Hybrid ML-DL Model -- 5 Results and Analysis -- 6 Conclusion and Future Work -- References -- Attention-Based Counterfactual Explanation for Multivariate Time Series -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Notation -- 3.2 Proposed Method -- 4 Experiments -- 4.1 Datasets -- 4.2 Baseline Methods -- 4.3 Experimental Result -- 5 Conclusion -- References -- DRUM: A Real Time Detector for Regime Shifts in Data Streams via an Unsupervised, Multivariate Framework -- 1 Introduction -- 2 Related Work -- 3 DRUM -- 4 Evaluation -- 5 Conclusion -- References. Hierarchical Graph Neural Network with Cross-Attention for Cross-Device User Matching. |
| Record Nr. | UNINA-9910741143403321 |
Wrembel Robert
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| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Big Data Analytics and Knowledge Discovery : 24th International Conference, DaWaK 2022, Vienna, Austria, August 22–24, 2022, Proceedings / / edited by Robert Wrembel, Johann Gamper, Gabriele Kotsis, A Min Tjoa, Ismail Khalil
| Big Data Analytics and Knowledge Discovery : 24th International Conference, DaWaK 2022, Vienna, Austria, August 22–24, 2022, Proceedings / / edited by Robert Wrembel, Johann Gamper, Gabriele Kotsis, A Min Tjoa, Ismail Khalil |
| Edizione | [1st ed. 2022.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 |
| Descrizione fisica | 1 online resource (275 pages) |
| Disciplina |
005.7
005.745 |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Quantitative research
Data mining Application software Artificial intelligence Data Analysis and Big Data Data Mining and Knowledge Discovery Computer and Information Systems Applications Artificial Intelligence |
| ISBN | 3-031-12670-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | An Integration of TextGCN and Autoencoder into Aspect-based Sentiment Analysis -- OpBerg: Discovering causal sentences using optimal alignments -- Text-based Causal Inference on Irony and Sarcasm Detection -- Sarcastic RoBERTa: a RoBERTa-based deep neural network detecting sarcasm on Twitter -- A Fast NDFA-Based Approach to Approximate Pattern-Matching for Plagiarism Detection in Blockchain-Driven NFTs -- On Decisive Skyline Queries -- Safeness: Suffix Arrays driven Materialized View Selection Framework for Large-Scale Workloads -- A Process Warehouse for Process Variants Analysis -- Feature Selection Algorithms -- Unsupervised Features Ranking via Coalitional Game Theory for Categorical Data -- Multi-label Online Streaming Feature Selection Algorithms via Extending Alpha Investing Strategy -- Feature Selection Under Fairness and Performance Constraints -- Time Series Processing -- Interpretable Input-Output Hidden Markov Model-Based Deep Reinforcement Learning for the Predictive Maintenance of Turbofan Engines -- Pathology Data Prioritisation: A Study Using Multi-Variate Time Series -- Outlier/Anomaly detection of univariate time series: A dataset collection and benchmark -- Automatic Machine Learning-based OLAP Measure Detection for Tabular Data -- Discovering Overlapping Communities based on Cohesive Subgraph Models over Graph Data -- Discovery of Keys for Graphs -- OPTIMA: Framework Selecting Optimal Virtual Model to Query Large Heterogeneous Data -- . Q-VIPER: Quantitative Vertical Bitwise Algorithm to Mine Frequent Patterns -- Enhanced Sliding Window-Based Periodic Pattern Mining from Dynamic Streams -- Explainable Recommendations for Wearable Sensor Data Machine Learning -- SLA-Aware Cloud Query Processing with Reinforcement Learning-based MultiObjective Re-Optimization -- Distance Based K-Means Clustering -- Grapevine Phenology Prediction: A Comparison of Physical and Machine Learning Models. |
| Record Nr. | UNINA-9910585793603321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Data architecture : a primer for the data scientist : big data, data warehouse and data vault / / W. H. Inmon, Dan Linstedt ; Steven Elliot, executive editor ; Mark Rogers, designer
| Data architecture : a primer for the data scientist : big data, data warehouse and data vault / / W. H. Inmon, Dan Linstedt ; Steven Elliot, executive editor ; Mark Rogers, designer |
| Autore | Inmon W. H. |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Amsterdam, Netherlands : , : Morgan Kaufmann, , 2015 |
| Descrizione fisica | 1 online resource (378 p.) |
| Disciplina | 005.745 |
| Soggetto topico |
Data warehousing
Big data |
| ISBN | 0-12-802091-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Title Page; Copyright; Dedication; Contents; Preface; About the authors; 1.1 - Corporate data; The Totality of Data Across the Corporation; Dividing Unstructured Data; Business Relevancy; Big Data; The Great Divide; The Continental Divide; The Complete Picture; 1.2 - The data infrastructure; Two Types of Repetitive Data; Repetitive Structured Data; Repetitive Big Data; The Two Infrastructures; What's being Optimized?; Comparing the Two Infrastructures; 1.3 - The "great divide"; Classifying Corporate Data; The "Great Divide"; Repetitive Unstructured Data; Nonrepetitive Unstructured Data
Different Worlds1.4 - Demographics of corporate data; 1.5 - Corporate data analysis; 1.6 - The life cycle of data - understanding data over time; 1.7 - A brief history of data; Paper Tape and Punch Cards; Magnetic Tapes; Disk Storage; Database Management System; Coupled Processors; Online Transaction Processing; Data Warehouse; Parallel Data Management; Data Vault; Big Data; The Great Divide; 2.1 - A brief history of big data; An Analogy - Taking the High Ground; Taking the High Ground; Standardization with the 360; Online Transaction Processing Enter Teradata and Massively Parallel ProcessingThen Came Hadoop and Big Data; IBM and Hadoop; Holding the High Ground; 2.2 - What is big data?; Another Definition; Large Volumes; Inexpensive Storage; The Roman Census Approach; Unstructured Data; Data in Big Data; Context in Repetitive Data; Nonrepetitive Data; Context in Nonrepetitive Data; 2.3 - Parallel processing; 2.4 - Unstructured data; Textual Information Everywhere; Decisions Based on Structured Data; The Business Value Proposition; Repetitive and Nonrepetitive Unstructured Information; Ease of Analysis; Contextualization Some Approaches to ContextualizationMapReduce; Manual Analysis; 2.5 - Contextualizing repetitive unstructured data; Parsing Repetitive Unstructured Data; Recasting the Output Data; 2.6 - Textual disambiguation; From Narrative into an Analytical Database; Input into Textual Disambiguation; Mapping; Input/Output; Document Fracturing/Named Value Processing; Preprocessing a Document; Emails - A Special Case; Spreadsheets; Report Decompilation; 2.7 - Taxonomies; Data Models and Taxonomies; Applicability of Taxonomies; What is a Taxonomy?; Taxonomies in Multiple Languages Dynamics of Taxonomies and Textual DisambiguationTaxonomies and Textual Disambiguation - Separate Technologies; Different Types of Taxonomies; Taxonomies - Maintenance Over Time; 3.1 - A brief history of data warehouse; Early Applications; Online Applications; Extract Programs; 4GL Technology; Personal Computers; Spreadsheets; Integrity of Data; Spider-Web Systems; The Maintenance Backlog; The Data Warehouse; To an Architected Environment; To the CIF; DW 2.0; 3.2 - Integrated corporate data; Many Applications; Looking Across the Corporation; More Than One Analyst; ETL Technology The Challenges of Integration |
| Record Nr. | UNINA-9910787905603321 |
|
Inmon W. H.
|
||
| Amsterdam, Netherlands : , : Morgan Kaufmann, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Data architecture : a primer for the data scientist : big data, data warehouse and data vault / / W. H. Inmon, Dan Linstedt ; Steven Elliot, executive editor ; Mark Rogers, designer
| Data architecture : a primer for the data scientist : big data, data warehouse and data vault / / W. H. Inmon, Dan Linstedt ; Steven Elliot, executive editor ; Mark Rogers, designer |
| Autore | Inmon W. H. |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Amsterdam, Netherlands : , : Morgan Kaufmann, , 2015 |
| Descrizione fisica | 1 online resource (378 p.) |
| Disciplina | 005.745 |
| Soggetto topico |
Data warehousing
Big data |
| ISBN | 0-12-802091-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Title Page; Copyright; Dedication; Contents; Preface; About the authors; 1.1 - Corporate data; The Totality of Data Across the Corporation; Dividing Unstructured Data; Business Relevancy; Big Data; The Great Divide; The Continental Divide; The Complete Picture; 1.2 - The data infrastructure; Two Types of Repetitive Data; Repetitive Structured Data; Repetitive Big Data; The Two Infrastructures; What's being Optimized?; Comparing the Two Infrastructures; 1.3 - The "great divide"; Classifying Corporate Data; The "Great Divide"; Repetitive Unstructured Data; Nonrepetitive Unstructured Data
Different Worlds1.4 - Demographics of corporate data; 1.5 - Corporate data analysis; 1.6 - The life cycle of data - understanding data over time; 1.7 - A brief history of data; Paper Tape and Punch Cards; Magnetic Tapes; Disk Storage; Database Management System; Coupled Processors; Online Transaction Processing; Data Warehouse; Parallel Data Management; Data Vault; Big Data; The Great Divide; 2.1 - A brief history of big data; An Analogy - Taking the High Ground; Taking the High Ground; Standardization with the 360; Online Transaction Processing Enter Teradata and Massively Parallel ProcessingThen Came Hadoop and Big Data; IBM and Hadoop; Holding the High Ground; 2.2 - What is big data?; Another Definition; Large Volumes; Inexpensive Storage; The Roman Census Approach; Unstructured Data; Data in Big Data; Context in Repetitive Data; Nonrepetitive Data; Context in Nonrepetitive Data; 2.3 - Parallel processing; 2.4 - Unstructured data; Textual Information Everywhere; Decisions Based on Structured Data; The Business Value Proposition; Repetitive and Nonrepetitive Unstructured Information; Ease of Analysis; Contextualization Some Approaches to ContextualizationMapReduce; Manual Analysis; 2.5 - Contextualizing repetitive unstructured data; Parsing Repetitive Unstructured Data; Recasting the Output Data; 2.6 - Textual disambiguation; From Narrative into an Analytical Database; Input into Textual Disambiguation; Mapping; Input/Output; Document Fracturing/Named Value Processing; Preprocessing a Document; Emails - A Special Case; Spreadsheets; Report Decompilation; 2.7 - Taxonomies; Data Models and Taxonomies; Applicability of Taxonomies; What is a Taxonomy?; Taxonomies in Multiple Languages Dynamics of Taxonomies and Textual DisambiguationTaxonomies and Textual Disambiguation - Separate Technologies; Different Types of Taxonomies; Taxonomies - Maintenance Over Time; 3.1 - A brief history of data warehouse; Early Applications; Online Applications; Extract Programs; 4GL Technology; Personal Computers; Spreadsheets; Integrity of Data; Spider-Web Systems; The Maintenance Backlog; The Data Warehouse; To an Architected Environment; To the CIF; DW 2.0; 3.2 - Integrated corporate data; Many Applications; Looking Across the Corporation; More Than One Analyst; ETL Technology The Challenges of Integration |
| Record Nr. | UNINA-9910816227103321 |
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Inmon W. H.
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| Amsterdam, Netherlands : , : Morgan Kaufmann, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Data warehouse : teoria e pratica della progettazione / Matteo Golfarelli, Stefano Rizzi
| Data warehouse : teoria e pratica della progettazione / Matteo Golfarelli, Stefano Rizzi |
| Autore | Golfarelli, Matteo |
| Edizione | [2. ed.] |
| Pubbl/distr/stampa | Milano : McGraw Hill, 2006 |
| Descrizione fisica | xvi, 447 p. ; 24 cm + 1 CD-ROM |
| Disciplina |
005.745
658.4038 |
| Altri autori (Persone) | Rizzi, Stefanoauthor |
| Collana | Workbooks |
| Soggetto topico |
Data warehousing
Aziende - Archivi di dati - Progettazione Database design |
| ISBN | 9788838662911 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | ita |
| Record Nr. | UNISALENTO-991003073919707536 |
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Golfarelli, Matteo
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| Milano : McGraw Hill, 2006 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. del Salento | ||
| ||
Data warehouse design : modern principles and methodologies / Matteo Golfarelli, Stefano Rizzi ; translated by Claudio Pagliarani
| Data warehouse design : modern principles and methodologies / Matteo Golfarelli, Stefano Rizzi ; translated by Claudio Pagliarani |
| Autore | Golfarelli, Matteo |
| Pubbl/distr/stampa | New York : McGraw-Hill, c2009 |
| Descrizione fisica | xxi, 458 p. : ill. ; 24 cm |
| Disciplina | 005.745 |
| Altri autori (Persone) | Rizzi, Stefanoauthor |
| Soggetto topico |
Data warehousing
Database design |
| ISBN |
9780071610391
0071610391 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISALENTO-991003596389707536 |
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Golfarelli, Matteo
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| New York : McGraw-Hill, c2009 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. del Salento | ||
| ||
Data warehouse designs : achieving ROI with market basket analysis and time variance / / Fon Silvers
| Data warehouse designs : achieving ROI with market basket analysis and time variance / / Fon Silvers |
| Autore | Silvers Fon |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Boca Raton, Fla. : , : CRC Press, , 2012 |
| Descrizione fisica | 1 online resource (286 p.) |
| Disciplina |
005.74
005.745 |
| Soggetto topico |
Business intelligence - Computer programs
Data warehousing |
| Soggetto genere / forma | Electronic books. |
| ISBN |
0-429-10803-6
1-4665-1666-6 1-283-59614-8 9786613908599 1-4398-7077-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Front Cover; Dedication; Contents; Preface; Acknowledgments; The Author; Chapter 1: Data Warehouse ROI; Chapter 2: What Is Market Basket Analysis?; Chapter 3: How Does Market Basket Analysis Produce ROI?; Chapter 4: Why Is Market Basket Analysis Difficult?; Chapter 5: Market Basket Analysis Solution Definition; Chapter 6: Market Basket Architecture and Database Design; Chapter 7: ETL into a Market Basket Datamart; Chapter 8: What Is Time Variance?; Chapter 9: How Does Time Variance Produce ROI?; Chapter 10: Why Is Time Variance Difficult?; Chapter 11: Time Variant Solution Definition
Chapter 12: Time Variant Database DefinitionChapter 13: ETL into a Time Variant Data Warehouse; Chapter 14: Market Basket Analysis in a Time Variant Data Warehouse; References |
| Record Nr. | UNINA-9910457444503321 |
|
Silvers Fon
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| Boca Raton, Fla. : , : CRC Press, , 2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Data warehouse designs : achieving ROI with market basket analysis and time variance / / Fon Silvers
| Data warehouse designs : achieving ROI with market basket analysis and time variance / / Fon Silvers |
| Autore | Silvers Fon |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Boca Raton, Fla. : , : CRC Press, , 2012 |
| Descrizione fisica | 1 online resource (286 p.) |
| Disciplina |
005.74
005.745 |
| Soggetto topico |
Business intelligence - Computer programs
Data warehousing |
| ISBN |
0-429-10803-6
1-4665-1666-6 1-283-59614-8 9786613908599 1-4398-7077-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Front Cover; Dedication; Contents; Preface; Acknowledgments; The Author; Chapter 1: Data Warehouse ROI; Chapter 2: What Is Market Basket Analysis?; Chapter 3: How Does Market Basket Analysis Produce ROI?; Chapter 4: Why Is Market Basket Analysis Difficult?; Chapter 5: Market Basket Analysis Solution Definition; Chapter 6: Market Basket Architecture and Database Design; Chapter 7: ETL into a Market Basket Datamart; Chapter 8: What Is Time Variance?; Chapter 9: How Does Time Variance Produce ROI?; Chapter 10: Why Is Time Variance Difficult?; Chapter 11: Time Variant Solution Definition
Chapter 12: Time Variant Database DefinitionChapter 13: ETL into a Time Variant Data Warehouse; Chapter 14: Market Basket Analysis in a Time Variant Data Warehouse; References |
| Record Nr. | UNINA-9910778816003321 |
|
Silvers Fon
|
||
| Boca Raton, Fla. : , : CRC Press, , 2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Data warehouse designs : achieving ROI with market basket analysis and time variance / / Fon Silvers
| Data warehouse designs : achieving ROI with market basket analysis and time variance / / Fon Silvers |
| Autore | Silvers Fon |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Boca Raton, Fla. : , : CRC Press, , 2012 |
| Descrizione fisica | 1 online resource (286 p.) |
| Disciplina |
005.74
005.745 |
| Soggetto topico |
Business intelligence - Computer programs
Data warehousing |
| ISBN |
0-429-10803-6
1-4665-1666-6 1-283-59614-8 9786613908599 1-4398-7077-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Front Cover; Dedication; Contents; Preface; Acknowledgments; The Author; Chapter 1: Data Warehouse ROI; Chapter 2: What Is Market Basket Analysis?; Chapter 3: How Does Market Basket Analysis Produce ROI?; Chapter 4: Why Is Market Basket Analysis Difficult?; Chapter 5: Market Basket Analysis Solution Definition; Chapter 6: Market Basket Architecture and Database Design; Chapter 7: ETL into a Market Basket Datamart; Chapter 8: What Is Time Variance?; Chapter 9: How Does Time Variance Produce ROI?; Chapter 10: Why Is Time Variance Difficult?; Chapter 11: Time Variant Solution Definition
Chapter 12: Time Variant Database DefinitionChapter 13: ETL into a Time Variant Data Warehouse; Chapter 14: Market Basket Analysis in a Time Variant Data Warehouse; References |
| Record Nr. | UNINA-9910800098903321 |
|
Silvers Fon
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||
| Boca Raton, Fla. : , : CRC Press, , 2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Data warehousing and analytics : fueling the data engine / / David Taniar and Wenny Rahayu
| Data warehousing and analytics : fueling the data engine / / David Taniar and Wenny Rahayu |
| Autore | Taniar David |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (642 pages) |
| Disciplina | 005.745 |
| Collana | Data-Centric Systems and Applications |
| Soggetto topico |
Quantitative research
Data warehousing |
| ISBN | 3-030-81979-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996464383603316 |
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Taniar David
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| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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