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Big Data Management and Analytics
Hinweis:
Die Vorlesungszeit beginnt am 16.10.2017, vorher erscheinen hier keine Inhalte.
The Flip Side of the Coin
Recording from:
30.01.2018
Lecturer:
Prof. Dr. Matthias Schubert
Node Importance and Neighborhoods
Recording from:
23.01.2018
Lecturer:
Prof. Dr. Matthias Schubert
Community Detection
Recording from:
16.01.2018
Lecturer:
Prof. Dr. Matthias Schubert
Text Processing and High-Dimensional Data
Recording from:
09.01.2018
Lecturer:
Prof. Dr. Matthias Schubert
Text Processing and High-Dimensional Data
Recording from:
19.12.2017
Lecturer:
Prof. Dr. Matthias Schubert
Teil:2 Stream Analytics
Recording from:
19.12.2017
Lecturer:
Prof. Dr. Matthias Schubert
Stream Analytics
Recording from:
12.12.2017
Lecturer:
Prof. Dr. Matthias Schubert
Stream Processing
(00:00:00)
>
Apache Flink
(00:00:34)
>
Introduction to Apache Flink
(00:01:54)
>
Flink Software Stack
(00:02:42)
>
System Legacy
(00:02:43)
>
Architecture
(00:03:13)
>
Dataflow Graphs
(00:04:34)
>
Intermediate Data Streams
(00:08:32)
>
Latency and Throughput
(00:11:54)
>
Control Events and Fault Tolerance
(00:12:57)
>
Stateful Streams Processing
(00:14:12)
>
Stream Windows
(00:14:17)
>
Batch Processing
(00:15:49)
>
Query Optimization
(00:16:26)
>
Memory Management
(00:17:33)
>
Batch Iterations
(00:17:36)
>
API Examples
(00:18:04)
>
API Examples
(00:19:44)
>
Outlook
(00:25:30)
>
Maintaining Histograms
(00:26:51)
>
Maintaining Histograms
(00:29:05)
>
Maintaining Histograms
(00:31:25)
>
Maintaining Histograms
(00:33:18)
>
Maintaining Histograms
(00:34:08)
>
Stream Applications and Algorithms
(00:36:45)
>
Maintaining Histograms
(00:37:16)
>
Stream Applications and Algorithms
(00:38:48)
>
Maintaining Histograms
(00:42:58)
>
Maintaining Histograms
(00:43:55)
>
Maintaining Histograms
(00:49:24)
>
Maintaining Histograms
(00:52:34)
>
Change Detection
(00:56:39)
>
Change Detection
(00:58:09)
>
Change Detection
(00:59:42)
>
Change Detection
(01:03:03)
>
Change Detection
(01:05:00)
>
Change Detection
(01:05:18)
>
Change Detection
(01:05:41)
>
Change Detection
(01:09:06)
>
Change Detection
(01:09:49)
>
Frequent ItemsetMining
(01:14:50)
>
Frequent ItemsetMining
(01:17:16)
>
Frequent ItemsetMining
(01:18:55)
>
Frequent ItemsetMining
(01:24:34)
>
Frequent ItemsetMining
(01:27:39)
>
Frequent ItemsetMining
(01:33:23)
>
Frequent ItemsetMining
(01:37:14)
>
Clustering fromData Streams
(01:44:33)
>
Clustering fromData Streams
(01:48:39)
>
Clustering fromData Streams
(01:49:58)
>
Clustering fromData Streams
(01:51:06)
>
Clustering fromData Streams
(01:54:49)
>
Clustering fromData Streams
(01:57:08)
>
Clustering fromData Streams
(02:02:23)
>
Clustering fromData Streams
(02:05:25)
>
Clustering fromData Streams
Recording from:
05.12.2017
Apache Flink
Recording from:
28.11.2017
Lecturer:
Prof. Dr. Matthias Schubert
Stream Processing
(00:00:00)
>
Apache Spark
(00:00:27)
>
Motivation
(00:01:12)
>
Motivation
(00:02:53)
>
Apache Spark
(00:04:13)
>
RDD Transformations and actions
(00:04:41)
>
RDD Transformations and actions
(00:10:24)
>
Apache Spark
(00:12:45)
>
RDD Transformations and actions
(00:12:47)
>
Architecture
(00:14:08)
>
RDD narrow and wide dependencies
(00:16:02)
>
Lettercountexamples
(00:16:07)
>
Shuffle reduceByKey
(00:16:18)
>
Lettercountexamples
(00:18:39)
>
Shuffle reduceByKey
(00:19:06)
>
Shuffle groupByKey
(00:20:09)
>
Other relevant spark projects
(00:22:19)
>
Other relevant spark projects
(00:22:33)
>
Sources
(00:23:27)
>
Example application: Facebook
(00:24:23)
>
Example application: Twitter
(00:27:00)
>
Example application: CERN
(00:33:55)
>
Stream Processing
(00:35:01)
>
Stream Processing
(00:35:50)
>
Stream Processing
(00:41:29)
>
Stream Processing
(00:44:40)
>
Stream Processing
(00:46:19)
>
Stream Processing
(00:48:28)
>
Stream Processing
(00:49:32)
>
Stream Processing
(00:52:32)
>
Stream Processing
(00:55:27)
>
Stream Processing
(00:56:03)
>
Stream Processing
(00:58:11)
>
Stream Processing
(01:00:25)
>
Stream Processing
(01:03:42)
>
Stream Processing
(01:12:19)
>
Stream Processing
(01:13:41)
>
Stream Processing
(01:19:38)
>
Stream Processing
(01:21:46)
>
Stream Processing
(01:29:43)
>
Stream Processing
(01:32:43)
>
Stream Processing
(01:33:18)
>
Stream Processing
(01:37:04)
>
Stream Processing
(01:37:44)
>
Stream Processing
(01:38:29)
>
Stream Processing
(01:39:04)
>
Stream Processing
(01:40:17)
>
Stream Processing
(01:41:18)
>
Stream Processing
(01:41:40)
>
Stream Processing
(01:44:42)
>
Stream Processing
(01:46:42)
>
Stream Processing
(01:50:02)
>
Stream Processing
(01:52:58)
>
Stream Processing
(01:55:11)
>
Stream Processing
(01:59:00)
>
Stream Processing
(02:02:13)
>
Stream Processing
(02:03:05)
>
Stream Processing
(02:03:54)
>
Stream Processing
(02:05:37)
>
Stream Processing
(02:08:54)
>
Stream Processing
Recording from:
21.11.2017
Apache Spark
(00:00:00)
>
Batch Processing Systems
(00:00:59)
>
Outline
(00:02:52)
>
NoSQL and RBDMS
(00:09:20)
>
NoSQL and Batch Systems
(00:10:42)
>
Distributed File Systems
(00:12:27)
>
Distributed File Systems
(00:12:29)
>
Distributed File Systems
(00:12:50)
>
Distributed File Systems
(00:16:09)
>
Distributed File Systems
(00:16:10)
>
Hadoop Distributed File System (HDFS)
(00:17:51)
>
HDFS-Architecture
(00:18:12)
>
Data Storage Operations on HDFS
(00:22:21)
>
Partitioning the input data
(00:22:28)
>
Partitioning the input data
(00:28:07)
>
Partitioning the input data
(00:28:13)
>
HDFS Robustness
(00:28:20)
>
Overview
(00:28:57)
>
MapReduce
(00:29:04)
>
MapReduce
(00:29:56)
>
MapReduce
(00:30:18)
>
MapReduce
(00:33:46)
>
MapReduce
(00:34:11)
>
MapReduce
(00:38:25)
>
MapReduce
(00:42:00)
>
MapReduce
(00:42:08)
>
MapReduce
(00:42:10)
>
MapReduce
(00:42:34)
>
MapReduce
(00:42:36)
>
MapReduce
(00:42:41)
>
MapReduce
(00:42:44)
>
MapReduce
(00:43:03)
>
Big Data in Hadoop
(00:44:32)
>
Apache Spark
(00:44:49)
>
Motivation
(00:46:58)
>
Motivation
(00:51:26)
>
Resilient Distributed Dataset (RDD)
(00:54:43)
>
Resilient Distributed Dataset (RDD)
(00:57:54)
>
RDD Transformations and actions
(01:05:46)
>
RDD Transformations and actions
(01:07:48)
>
Architecture
(01:10:12)
>
RDD narrow and wide dependencies
(01:13:27)
>
Shuffle
(01:17:34)
>
Lettercount examples
(01:23:27)
>
Shuffle reduceByKey
(01:24:46)
>
Shuffle groupByKey
(01:27:03)
>
Lettercount examples
(01:29:11)
>
RDD Persistence
(01:29:39)
>
RDD Persistence
(01:31:26)
>
RDD Persistence
(01:35:18)
>
RDD Persistence
(01:40:01)
>
Shared variables
(01:44:30)
>
Shared variables
(01:46:56)
>
Shared variables
(01:48:57)
>
Shared variables
(01:50:58)
>
Shared variables
(01:53:57)
>
Other relevant spark projects
(02:02:54)
>
Other relevant spark projects
(02:05:29)
>
Sources
Recording from:
14.11.2017
Lecturer:
Prof. Dr. Matthias Schubert
Batch Systems
(00:00:02)
>
Outline
(00:00:52)
>
NoSQL and RBDMS
(00:02:43)
>
NoSQL and Batch Systems
(00:05:52)
>
Distributed File Systems
(00:14:01)
>
Hadoop Distributed File System (HDFS)
(00:17:09)
>
HDFS-Architecture
(00:18:22)
>
Data Storage Operations on HDFS
(00:19:27)
>
Partitioning the input data
(00:29:51)
>
HDFS Robustness
(00:30:55)
>
Data Disk Failure, Heartbeats and Re-replication
(00:34:57)
>
Cluster Rebalancing
(00:38:04)
>
Data Integrity
(00:40:38)
>
Metadata Disk Failure
(00:43:57)
>
Snapshots
(00:46:01)
>
Overview
(00:46:32)
>
MapReduce
(01:53:01)
>
Big Data in Hadoop
Recording from:
07.11.2017
Lecturer:
Prof. Dr. Matthias Schubert
NoSQL Databases
Recording from:
24.10.2017
Lecturer:
Prof. Dr. Matthias Schubert
Data Science: The Big Picture
Recording from:
17.10.2017
Lecturer:
Prof. Dr. Matthias Schubert
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