Big Data Management and Analytics

Hinweis:

Die Vorlesungszeit beginnt am 16.10.2017, vorher erscheinen hier keine Inhalte.

The Flip Side of the Coin

Datum: 30.01.2018

Dozent(in): Prof. Dr. Matthias Schubert

Node Importance and Neighborhoods

Datum: 23.01.2018

Dozent(in): Prof. Dr. Matthias Schubert

Community Detection

Datum: 16.01.2018

Dozent(in): Prof. Dr. Matthias Schubert

Text Processing and High-Dimensional Data

Datum: 09.01.2018

Dozent(in): Prof. Dr. Matthias Schubert

Text Processing and High-Dimensional Data

Datum: 19.12.2017

Dozent(in): Prof. Dr. Matthias Schubert

Teil:2 Stream Analytics

Datum: 19.12.2017

Dozent(in): Prof. Dr. Matthias Schubert

Stream Analytics

Datum: 12.12.2017

Dozent(in): 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

Datum: 05.12.2017

Apache Flink

Datum: 28.11.2017

Dozent(in): 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

Datum: 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

Datum: 14.11.2017

Dozent(in): 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

Datum: 07.11.2017

Dozent(in): Prof. Dr. Matthias Schubert

NoSQL Databases

Datum: 24.10.2017

Dozent(in): Prof. Dr. Matthias Schubert

Data Science: The Big Picture

Datum: 17.10.2017

Dozent(in): Prof. Dr. Matthias Schubert

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