| |
|
|
|
|
|
|
|
|
1. |
Record Nr. |
UNISA996466303503316 |
|
|
Titolo |
Knowledge Science, Engineering and Management [[electronic resource] ] : 4th International Conference, KSEM 2010, Belfast, Northern Ireland, UK, September 1-3, 2010, Proceedings / / edited by Yaxin Bi, Mary Anne Williams |
|
|
|
|
|
|
|
Pubbl/distr/stampa |
|
|
Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2010 |
|
|
|
|
|
|
|
|
|
ISBN |
|
|
|
|
|
|
Edizione |
[1st ed. 2010.] |
|
|
|
|
|
Descrizione fisica |
|
1 online resource (XIII, 618 p. 156 illus.) |
|
|
|
|
|
|
Collana |
|
Lecture Notes in Artificial Intelligence ; ; 6291 |
|
|
|
|
|
|
Classificazione |
|
|
|
|
|
|
Disciplina |
|
|
|
|
|
|
Soggetti |
|
Data mining |
Computer communication systems |
Artificial intelligence |
Application software |
Information storage and retrieval |
Database management |
Data Mining and Knowledge Discovery |
Computer Communication Networks |
Artificial Intelligence |
Information Systems Applications (incl. Internet) |
Information Storage and Retrieval |
Database Management |
Belfast <2010> |
|
|
|
|
|
|
|
|
Lingua di pubblicazione |
|
|
|
|
|
|
Formato |
Materiale a stampa |
|
|
|
|
|
Livello bibliografico |
Monografia |
|
|
|
|
|
Note generali |
|
Bibliographic Level Mode of Issuance: Monograph |
|
|
|
|
|
|
Nota di bibliografia |
|
Includes bibliographical references and index. |
|
|
|
|
|
|
Nota di contenuto |
|
Mining Video Data: Learning about Activities -- Ontology Languages and Engineering -- Theory of Belief Functions for Data Analysis and Machine Learning Applications: Review and Prospects -- Modeling Ontological Concepts of Locations with a Heterogeneous Cardinal Direction Model -- Finding Minimal Rare Itemsets and Rare Association Rules -- Background Knowledge Integration in Clustering Using Purity Indexes -- A Comparison of Merging Operators in Possibilistic Logic -- |
|
|
|
|
|
|
|
|
|
Modelling and Reasoning in Metamodelling Enabled Ontologies -- Towards Encoding Background Knowledge with Temporal Extent into Neural Networks -- A Fuzzy Description Logic with Automatic Object Membership Measurement -- Decomposition-Based Optimization for Debugging of Inconsistent OWL DL Ontologies -- A Concept Hierarchy Based Ontology Mapping Approach -- Composing Cardinal Direction Relations Basing on Interval Algebra -- Retrieval Result Presentation and Evaluation -- Autonomy: Life and Being -- A Method of Social Collaboration and Knowledge Sharing Acceleration for e-Learning System: The Distance Learning Network Scenario -- A Comparative Study of Target-Based Evaluation of Traditional Craft Patterns Using Kansei Data -- Optimization of Multiple Related Negotiation through Multi-Negotiation Network -- Reasoning Activity for Smart Homes Using a Lattice-Based Evidential Structure -- Knowledge Modelling to Support Inquiry Learning Tasks -- Building the Knowledge Base to Support the Automatic Animation Generation of Chinese Traditional Architecture -- Discovery of Relation Axioms from the Web -- An Improved Piecewise Aggregate Approximation Based on Statistical Features for Time Series Mining -- Incorporating Duration Information in Activity Recognition -- A Graphical Model for Risk Analysis and Management -- Towards Awareness Services Usage Characterization: Clustering Sessions in a Knowledge Building Environment -- Adjusting Class Association Rules from Global and Local Perspectives Based on Evolutionary Computation -- Probabilistic Declarative Process Mining -- Making Ontology-Based Knowledge and Decision Trees Interact: An Approach to Enrich Knowledge and Increase Expert Confidence in Data-Driven Models -- A Novel Initialization Method for Semi-supervised Clustering -- Constructing and Mapping Fuzzy Thematic Clusters to Higher Ranks in a Taxonomy -- Anticipation as a Strategy: A Design Paradigm for Robotics -- Modular Logic Programming for Web Data, Inheritance and Agents -- Automatic Collecting Technique of Low Frequency Electromagnetic Signals and Its Application in Earthquake Study -- Improving Search in Tag-Based Systems with Automatically Extracted Keywords -- Towards a Framework for Trusting the Automated Learning of Social Ontologies -- PlayPhysics: An Emotional Games Learning Environment for Teaching Physics -- A SOM-Based Technique for a User-Centric Content Extraction and Classification of Web 2.0 with a Special Consideration of Security Aspects -- Modularizing Spatial Ontologies for Assisted Living Systems -- Towards Scalable Instance Retrieval over Ontologies -- MindDigger: Feature Identification and Opinion Association for Chinese Movie Reviews -- The Impact of Latency on Online Classification Learning with Concept Drift -- Efficient Reasoning with RCC-3D -- Automated Ontology Generation Using Spatial Reasoning -- Facilitating Experience Reuse: Towards a Task-Based Approach -- Behavioural Rule Discovery from Swarm Systems -- Knowledge Discovery Using Bayesian Network Framework for Intelligent Telecommunication Network Management -- Combining Logic and Probabilities for Discovering Mappings between Taxonomies -- An Ontology-Based Semantic Web Service Space Organization and Management Model -- Genetic Algorithm-Based Multi-objective Optimisation for QoS-Aware Web Services Composition -- Knowledge Merging under Multiple Attributes -- Feature Selection Based on Mutual Information and Its Application in Hyperspectral Image Classification -- Static, Dynamic and Semantic Dimensions: Towards a Multidisciplinary Approach of Social Networks Analysis -- Knowledge Based Systems and Metacognition in Radar -- Maximus-AI: Using Elman Neural Networks for Implementing a SLMR Trading Strategy -- A Formalism for Causal Explanations with an Answer Set Programming |
|
|
|
|
|
|
|
|
|
|
|
Translation -- Earthquake Prediction Based on Levenberg-Marquardt Algorithm Constrained Back-Propagation Neural Network Using DEMETER Data -- Affinity Propagation on Identifying Communities in Social and Biological Networks -- Semantic Decomposition of Indicators and Corresponding Measurement Units -- Engineering Knowledge for Assistive Living -- Large-Scale, Exhaustive Lattice-Based Structural Auditing of SNOMED CT. |
|
|
|
|
|
|
2. |
Record Nr. |
UNINA9910584482303321 |
|
|
Autore |
L'Esteve Ron |
|
|
Titolo |
The Azure Data Lakehouse Toolkit : Building and Scaling Data Lakehouses on Azure with Delta Lake, Apache Spark, Databricks, Synapse Analytics, and Snowflake / / by Ron L'Esteve |
|
|
|
|
|
|
|
Pubbl/distr/stampa |
|
|
Berkeley, CA : , : Apress : , : Imprint : Apress, , 2022 |
|
|
|
|
|
|
|
ISBN |
|
|
|
|
|
|
|
|
Edizione |
[1st ed. 2022.] |
|
|
|
|
|
Descrizione fisica |
|
1 online resource (467 pages) |
|
|
|
|
|
|
Disciplina |
|
|
|
|
|
|
Soggetti |
|
Microsoft Azure (Computing platform) |
Cloud computing |
Electronic data processing |
Databases |
|
|
|
|
|
|
|
|
Lingua di pubblicazione |
|
|
|
|
|
|
Formato |
Materiale a stampa |
|
|
|
|
|
Livello bibliografico |
Monografia |
|
|
|
|
|
Note generali |
|
|
|
|
|
|
Nota di bibliografia |
|
|
|
|
|
|
Nota di contenuto |
|
Part I: Getting Started -- Chapter 1: The Data Lakehouse Paradigm -- Part II: Data Platforms -- Chapter 2: Snowflake -- Chapter 3: Databricks -- Chapter 4: Synapse Analytics -- Part III: Apache Spark ELT -- Chapter 5: Pipelines and Jobs -- Chapter 6: Notebook Code -- Part IV: Delta Lake.-Chapter 7: Schema Evolution -- Chapter 8: Change Feed -- Chapter 9: Clones -- Chapter 10: Live Tables -- Chapter 11: Sharing -- Part V: Optimizing Performance -- Chapter 12: Dynamic Partition Pruning for Querying Star Schemas -- Chapter 13: Z-Ordering & Data Skipping -- Chapter 14: Adaptive Query Execution -- Chapter 15: Bloom Filter Index -- Chapter 16: Hyperspace -- Part VI: Advanced |
|
|
|
|
|
|
|
|
|
|
|
Capabilities -- Chapter 17: Auto Loader -- Chapter 18: Python Wheels -- Chapter 19: Security & Controls. |
|
|
|
|
|
|
Sommario/riassunto |
|
Design and implement a modern data lakehouse on the Azure Data Platform using Delta Lake, Apache Spark, Azure Databricks, Azure Synapse Analytics, and Snowflake. This book teaches you the intricate details of the Data Lakehouse Paradigm and how to efficiently design a cloud-based data lakehouse using highly performant and cutting-edge Apache Spark capabilities using Azure Databricks, Azure Synapse Analytics, and Snowflake. You will learn to write efficient PySpark code for batch and streaming ELT jobs on Azure. And you will follow along with practical, scenario-based examples showing how to apply the capabilities of Delta Lake and Apache Spark to optimize performance, and secure, share, and manage a high volume, high velocity, and high variety of data in your lakehouse with ease. The patterns of success that you acquire from reading this book will help you hone your skills to build high-performing and scalable ACID-compliant lakehouses using flexible and cost-efficient decoupled storage and compute capabilities. Extensive coverage of Delta Lake ensures that you are aware of and can benefit from all that this new, open source storage layer can offer. In addition to the deep examples on Databricks in the book, there is coverage of alternative platforms such as Synapse Analytics and Snowflake so that you can make the right platform choice for your needs. After reading this book, you will be able to implement Delta Lake capabilities, including Schema Evolution, Change Feed, Live Tables, Sharing, and Clones to enable better business intelligence and advanced analytics on your data within the Azure Data Platform. What You Will Learn Implement the Data Lakehouse Paradigm on Microsoft’s Azure cloud platform Benefit from the new Delta Lake open-source storage layer for data lakehouses Take advantage of schema evolution, change feeds, live tables, and more Write functional PySpark code for data lakehouse ELT jobs Optimize Apache Spark performance through partitioning, indexing, and other tuning options Choose between alternatives such as Databricks, Synapse Analytics, and Snowflake. |
|
|
|
|
|
|
|
| |