Business Intelligence: 6th European Summer School, EBISS 2016, Tours, France, July 3-8, 2016, Tutorial Lectures
Saved in:
| Main Author: | |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Cham
Springer International Publishing AG
2017
|
| Series: | Lecture Notes in Business Information Processing Ser.
v.280 |
| Subjects: | |
| Online Access: | DE-2070s |
| Item Description: | Description based on publisher supplied metadata and other sources |
| Physical Description: | 1 online resource (148 pages) |
| ISBN: | 9783319611648 |
Staff View
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| 505 | 8 | |a Intro -- Preface -- Organization -- Contents -- Declarative Multidimensional Graph Queries -- 1 Introduction -- 2 Graph Data Models -- 3 Subgraph Matching -- 3.1 Graph Similarity -- 3.2 Matching Semantics -- 3.3 Query Classes -- 3.4 Query Languages -- 4 Graph Transformation -- 4.1 Composability -- 4.2 Aggregation -- 5 Multidimensional Graph Queries -- 5.1 Entity-Oriented Multidimensional Queries -- 5.2 Structure-Oriented Multidimensional Queries -- 6 Conclusion -- References -- Computational Approaches to Translation Studies -- 1 Introduction -- 2 Translationese -- 3 Identification of Translationese -- 3.1 Supervised Classification -- 3.2 Features -- 3.3 Results -- 3.4 Unsupervised Classification -- 4 Applications to Machine Translation -- 4.1 Language Models -- 4.2 Translation Models -- 5 Conclusion -- References -- Two Decades of Pattern Mining: Principles and Methods -- 1 Introduction -- 2 Pattern, Language and Dataset -- 2.1 Basic Definitions -- 2.2 Language Sophistication -- 3 Interestingness Measures -- 3.1 Basic Definitions -- 3.2 The Obsession with Frequency -- 4 Constraint-Based Pattern Mining -- 4.1 Principle -- 4.2 From Frequency to Better Interestingness Measures -- 5 Preference-Based Pattern Mining -- 5.1 Principle -- 5.2 Diversity Issue -- 6 Interactive Pattern Mining -- 6.1 Learning a User Preference Model from Patterns -- 6.2 Pattern Sampling -- 7 Conclusion -- References -- Scalability and Realtime on Big Data, MapReduce, NoSQL and Spark -- Abstract -- 1 Introduction -- 2 Big Data Architectures and Scalability -- 2.1 NoSQL and Key-Value Data Stores -- 2.2 Parallel Database Management Systems -- 2.3 Joins, Aggregations and Shuffling -- 2.4 Hadoop and MapReduce -- 3 Spark and Spark-SQL -- 3.1 Shuffling and Other Overheads in Spark -- 3.2 Data Frames and Datasets -- 4 Realtime Scalable Big Data Analytics -- 4.1 Realtime Concerns | |
| 505 | 8 | |a 4.2 The Lambda Architecture -- 4.3 Realtime Data Integration -- 4.4 De-normalization and Predictable Response Time -- 4.5 Session Scalability -- 4.6 Scaling for Approximate Execution Time Bound Guarantees -- 5 Conclusions -- References -- Step by Step Towards Energy-Aware Data Warehouse Design -- 1 Introduction -- 2 Related Work -- 3 Background -- 4 Eco-Query Optimizer Design -- 4.1 Parse -- 4.2 Rewrite -- 4.3 Plan/Optimize -- 4.4 Executor -- 4.5 Our Energy-Aware Query Processing -- 4.6 Power Cost Model -- 4.7 Plans Evaluation -- 4.8 EnerQuery GUI -- 5 Energy Incorporation in Logical and Physical Phases -- 5.1 Logical Design -- 5.2 Physical Design -- 5.3 Summary -- 5.4 Energy at Logical and Physical Phases -- 5.5 Capturing Variability of Logical Design -- 5.6 Scenario 1: Impact of VM on Logical Optimizations -- 5.7 Scenario 2: Impact of VM on Physical Optimizations -- 5.8 Experimental Study -- 6 Conclusion -- References -- Author Index | |
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| contents | Intro -- Preface -- Organization -- Contents -- Declarative Multidimensional Graph Queries -- 1 Introduction -- 2 Graph Data Models -- 3 Subgraph Matching -- 3.1 Graph Similarity -- 3.2 Matching Semantics -- 3.3 Query Classes -- 3.4 Query Languages -- 4 Graph Transformation -- 4.1 Composability -- 4.2 Aggregation -- 5 Multidimensional Graph Queries -- 5.1 Entity-Oriented Multidimensional Queries -- 5.2 Structure-Oriented Multidimensional Queries -- 6 Conclusion -- References -- Computational Approaches to Translation Studies -- 1 Introduction -- 2 Translationese -- 3 Identification of Translationese -- 3.1 Supervised Classification -- 3.2 Features -- 3.3 Results -- 3.4 Unsupervised Classification -- 4 Applications to Machine Translation -- 4.1 Language Models -- 4.2 Translation Models -- 5 Conclusion -- References -- Two Decades of Pattern Mining: Principles and Methods -- 1 Introduction -- 2 Pattern, Language and Dataset -- 2.1 Basic Definitions -- 2.2 Language Sophistication -- 3 Interestingness Measures -- 3.1 Basic Definitions -- 3.2 The Obsession with Frequency -- 4 Constraint-Based Pattern Mining -- 4.1 Principle -- 4.2 From Frequency to Better Interestingness Measures -- 5 Preference-Based Pattern Mining -- 5.1 Principle -- 5.2 Diversity Issue -- 6 Interactive Pattern Mining -- 6.1 Learning a User Preference Model from Patterns -- 6.2 Pattern Sampling -- 7 Conclusion -- References -- Scalability and Realtime on Big Data, MapReduce, NoSQL and Spark -- Abstract -- 1 Introduction -- 2 Big Data Architectures and Scalability -- 2.1 NoSQL and Key-Value Data Stores -- 2.2 Parallel Database Management Systems -- 2.3 Joins, Aggregations and Shuffling -- 2.4 Hadoop and MapReduce -- 3 Spark and Spark-SQL -- 3.1 Shuffling and Other Overheads in Spark -- 3.2 Data Frames and Datasets -- 4 Realtime Scalable Big Data Analytics -- 4.1 Realtime Concerns 4.2 The Lambda Architecture -- 4.3 Realtime Data Integration -- 4.4 De-normalization and Predictable Response Time -- 4.5 Session Scalability -- 4.6 Scaling for Approximate Execution Time Bound Guarantees -- 5 Conclusions -- References -- Step by Step Towards Energy-Aware Data Warehouse Design -- 1 Introduction -- 2 Related Work -- 3 Background -- 4 Eco-Query Optimizer Design -- 4.1 Parse -- 4.2 Rewrite -- 4.3 Plan/Optimize -- 4.4 Executor -- 4.5 Our Energy-Aware Query Processing -- 4.6 Power Cost Model -- 4.7 Plans Evaluation -- 4.8 EnerQuery GUI -- 5 Energy Incorporation in Logical and Physical Phases -- 5.1 Logical Design -- 5.2 Physical Design -- 5.3 Summary -- 5.4 Energy at Logical and Physical Phases -- 5.5 Capturing Variability of Logical Design -- 5.6 Scenario 1: Impact of VM on Logical Optimizations -- 5.7 Scenario 2: Impact of VM on Physical Optimizations -- 5.8 Experimental Study -- 6 Conclusion -- References -- Author Index |
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| dewey-full | 658.4038011 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 658 - General management |
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| dewey-search | 658.4038011 |
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| dewey-tens | 650 - Management and auxiliary services |
| discipline | Informatik Wirtschaftswissenschaften |
| discipline_str_mv | Informatik Wirtschaftswissenschaften |
| format | Electronic eBook |
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| index_date | 2024-07-03T18:57:27Z |
| indexdate | 2025-01-10T17:07:43Z |
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| series2 | Lecture Notes in Business Information Processing Ser. |
| spelling | Marcel, Patrick Verfasser aut Business Intelligence 6th European Summer School, EBISS 2016, Tours, France, July 3-8, 2016, Tutorial Lectures Cham Springer International Publishing AG 2017 ©2017 1 online resource (148 pages) txt rdacontent c rdamedia cr rdacarrier Lecture Notes in Business Information Processing Ser. v.280 Description based on publisher supplied metadata and other sources Intro -- Preface -- Organization -- Contents -- Declarative Multidimensional Graph Queries -- 1 Introduction -- 2 Graph Data Models -- 3 Subgraph Matching -- 3.1 Graph Similarity -- 3.2 Matching Semantics -- 3.3 Query Classes -- 3.4 Query Languages -- 4 Graph Transformation -- 4.1 Composability -- 4.2 Aggregation -- 5 Multidimensional Graph Queries -- 5.1 Entity-Oriented Multidimensional Queries -- 5.2 Structure-Oriented Multidimensional Queries -- 6 Conclusion -- References -- Computational Approaches to Translation Studies -- 1 Introduction -- 2 Translationese -- 3 Identification of Translationese -- 3.1 Supervised Classification -- 3.2 Features -- 3.3 Results -- 3.4 Unsupervised Classification -- 4 Applications to Machine Translation -- 4.1 Language Models -- 4.2 Translation Models -- 5 Conclusion -- References -- Two Decades of Pattern Mining: Principles and Methods -- 1 Introduction -- 2 Pattern, Language and Dataset -- 2.1 Basic Definitions -- 2.2 Language Sophistication -- 3 Interestingness Measures -- 3.1 Basic Definitions -- 3.2 The Obsession with Frequency -- 4 Constraint-Based Pattern Mining -- 4.1 Principle -- 4.2 From Frequency to Better Interestingness Measures -- 5 Preference-Based Pattern Mining -- 5.1 Principle -- 5.2 Diversity Issue -- 6 Interactive Pattern Mining -- 6.1 Learning a User Preference Model from Patterns -- 6.2 Pattern Sampling -- 7 Conclusion -- References -- Scalability and Realtime on Big Data, MapReduce, NoSQL and Spark -- Abstract -- 1 Introduction -- 2 Big Data Architectures and Scalability -- 2.1 NoSQL and Key-Value Data Stores -- 2.2 Parallel Database Management Systems -- 2.3 Joins, Aggregations and Shuffling -- 2.4 Hadoop and MapReduce -- 3 Spark and Spark-SQL -- 3.1 Shuffling and Other Overheads in Spark -- 3.2 Data Frames and Datasets -- 4 Realtime Scalable Big Data Analytics -- 4.1 Realtime Concerns 4.2 The Lambda Architecture -- 4.3 Realtime Data Integration -- 4.4 De-normalization and Predictable Response Time -- 4.5 Session Scalability -- 4.6 Scaling for Approximate Execution Time Bound Guarantees -- 5 Conclusions -- References -- Step by Step Towards Energy-Aware Data Warehouse Design -- 1 Introduction -- 2 Related Work -- 3 Background -- 4 Eco-Query Optimizer Design -- 4.1 Parse -- 4.2 Rewrite -- 4.3 Plan/Optimize -- 4.4 Executor -- 4.5 Our Energy-Aware Query Processing -- 4.6 Power Cost Model -- 4.7 Plans Evaluation -- 4.8 EnerQuery GUI -- 5 Energy Incorporation in Logical and Physical Phases -- 5.1 Logical Design -- 5.2 Physical Design -- 5.3 Summary -- 5.4 Energy at Logical and Physical Phases -- 5.5 Capturing Variability of Logical Design -- 5.6 Scenario 1: Impact of VM on Logical Optimizations -- 5.7 Scenario 2: Impact of VM on Physical Optimizations -- 5.8 Experimental Study -- 6 Conclusion -- References -- Author Index Business-Data processing-Congresses Business Intelligence (DE-588)4588307-5 gnd rswk-swf Entscheidungsunterstützung (DE-588)4202171-6 gnd rswk-swf Data Mining (DE-588)4428654-5 gnd rswk-swf Data-Warehouse-Konzept (DE-588)4406462-7 gnd rswk-swf Betriebliches Informationssystem (DE-588)4069386-7 gnd rswk-swf (DE-588)1071861417 Konferenzschrift 2016 Tours gnd-content Betriebliches Informationssystem (DE-588)4069386-7 s Data-Warehouse-Konzept (DE-588)4406462-7 s Business Intelligence (DE-588)4588307-5 s DE-604 Entscheidungsunterstützung (DE-588)4202171-6 s Data Mining (DE-588)4428654-5 s Zimányi, Esteban Sonstige oth Erscheint auch als Druck-Ausgabe Marcel, Patrick Business Intelligence Cham : Springer International Publishing AG,c2017 9783319611631 |
| spellingShingle | Marcel, Patrick Business Intelligence 6th European Summer School, EBISS 2016, Tours, France, July 3-8, 2016, Tutorial Lectures Intro -- Preface -- Organization -- Contents -- Declarative Multidimensional Graph Queries -- 1 Introduction -- 2 Graph Data Models -- 3 Subgraph Matching -- 3.1 Graph Similarity -- 3.2 Matching Semantics -- 3.3 Query Classes -- 3.4 Query Languages -- 4 Graph Transformation -- 4.1 Composability -- 4.2 Aggregation -- 5 Multidimensional Graph Queries -- 5.1 Entity-Oriented Multidimensional Queries -- 5.2 Structure-Oriented Multidimensional Queries -- 6 Conclusion -- References -- Computational Approaches to Translation Studies -- 1 Introduction -- 2 Translationese -- 3 Identification of Translationese -- 3.1 Supervised Classification -- 3.2 Features -- 3.3 Results -- 3.4 Unsupervised Classification -- 4 Applications to Machine Translation -- 4.1 Language Models -- 4.2 Translation Models -- 5 Conclusion -- References -- Two Decades of Pattern Mining: Principles and Methods -- 1 Introduction -- 2 Pattern, Language and Dataset -- 2.1 Basic Definitions -- 2.2 Language Sophistication -- 3 Interestingness Measures -- 3.1 Basic Definitions -- 3.2 The Obsession with Frequency -- 4 Constraint-Based Pattern Mining -- 4.1 Principle -- 4.2 From Frequency to Better Interestingness Measures -- 5 Preference-Based Pattern Mining -- 5.1 Principle -- 5.2 Diversity Issue -- 6 Interactive Pattern Mining -- 6.1 Learning a User Preference Model from Patterns -- 6.2 Pattern Sampling -- 7 Conclusion -- References -- Scalability and Realtime on Big Data, MapReduce, NoSQL and Spark -- Abstract -- 1 Introduction -- 2 Big Data Architectures and Scalability -- 2.1 NoSQL and Key-Value Data Stores -- 2.2 Parallel Database Management Systems -- 2.3 Joins, Aggregations and Shuffling -- 2.4 Hadoop and MapReduce -- 3 Spark and Spark-SQL -- 3.1 Shuffling and Other Overheads in Spark -- 3.2 Data Frames and Datasets -- 4 Realtime Scalable Big Data Analytics -- 4.1 Realtime Concerns 4.2 The Lambda Architecture -- 4.3 Realtime Data Integration -- 4.4 De-normalization and Predictable Response Time -- 4.5 Session Scalability -- 4.6 Scaling for Approximate Execution Time Bound Guarantees -- 5 Conclusions -- References -- Step by Step Towards Energy-Aware Data Warehouse Design -- 1 Introduction -- 2 Related Work -- 3 Background -- 4 Eco-Query Optimizer Design -- 4.1 Parse -- 4.2 Rewrite -- 4.3 Plan/Optimize -- 4.4 Executor -- 4.5 Our Energy-Aware Query Processing -- 4.6 Power Cost Model -- 4.7 Plans Evaluation -- 4.8 EnerQuery GUI -- 5 Energy Incorporation in Logical and Physical Phases -- 5.1 Logical Design -- 5.2 Physical Design -- 5.3 Summary -- 5.4 Energy at Logical and Physical Phases -- 5.5 Capturing Variability of Logical Design -- 5.6 Scenario 1: Impact of VM on Logical Optimizations -- 5.7 Scenario 2: Impact of VM on Physical Optimizations -- 5.8 Experimental Study -- 6 Conclusion -- References -- Author Index Business-Data processing-Congresses Business Intelligence (DE-588)4588307-5 gnd Entscheidungsunterstützung (DE-588)4202171-6 gnd Data Mining (DE-588)4428654-5 gnd Data-Warehouse-Konzept (DE-588)4406462-7 gnd Betriebliches Informationssystem (DE-588)4069386-7 gnd |
| subject_GND | (DE-588)4588307-5 (DE-588)4202171-6 (DE-588)4428654-5 (DE-588)4406462-7 (DE-588)4069386-7 (DE-588)1071861417 |
| title | Business Intelligence 6th European Summer School, EBISS 2016, Tours, France, July 3-8, 2016, Tutorial Lectures |
| title_auth | Business Intelligence 6th European Summer School, EBISS 2016, Tours, France, July 3-8, 2016, Tutorial Lectures |
| title_exact_search | Business Intelligence 6th European Summer School, EBISS 2016, Tours, France, July 3-8, 2016, Tutorial Lectures |
| title_exact_search_txtP | Business Intelligence 6th European Summer School, EBISS 2016, Tours, France, July 3-8, 2016, Tutorial Lectures |
| title_full | Business Intelligence 6th European Summer School, EBISS 2016, Tours, France, July 3-8, 2016, Tutorial Lectures |
| title_fullStr | Business Intelligence 6th European Summer School, EBISS 2016, Tours, France, July 3-8, 2016, Tutorial Lectures |
| title_full_unstemmed | Business Intelligence 6th European Summer School, EBISS 2016, Tours, France, July 3-8, 2016, Tutorial Lectures |
| title_short | Business Intelligence |
| title_sort | business intelligence 6th european summer school ebiss 2016 tours france july 3 8 2016 tutorial lectures |
| title_sub | 6th European Summer School, EBISS 2016, Tours, France, July 3-8, 2016, Tutorial Lectures |
| topic | Business-Data processing-Congresses Business Intelligence (DE-588)4588307-5 gnd Entscheidungsunterstützung (DE-588)4202171-6 gnd Data Mining (DE-588)4428654-5 gnd Data-Warehouse-Konzept (DE-588)4406462-7 gnd Betriebliches Informationssystem (DE-588)4069386-7 gnd |
| topic_facet | Business-Data processing-Congresses Business Intelligence Entscheidungsunterstützung Data Mining Data-Warehouse-Konzept Betriebliches Informationssystem Konferenzschrift 2016 Tours |
| work_keys_str_mv | AT marcelpatrick businessintelligence6theuropeansummerschoolebiss2016toursfrancejuly382016tutoriallectures AT zimanyiesteban businessintelligence6theuropeansummerschoolebiss2016toursfrancejuly382016tutoriallectures |