Big Data Parallel Programming (7.5 credits) - Revision history

Slawek at 10:36, 14 February 2025

2025-02-14T10:36:43Z

Slawek at 10:36, 14 February 2025

2025-02-14T10:36:25Z

Slawek at 10:31, 14 February 2025

2025-02-14T10:31:30Z

Slawek at 10:31, 14 February 2025

2025-02-14T10:31:10Z

Slawek at 15:37, 25 January 2022

2022-01-25T15:37:36Z

Slawek: Created page with "{{CourseTemplate |CourseCode=DT8013 |CourseShortDescription=Advanced topics in Data Mining |CourseLevel=Advanced |CourseUrl=http://tinyurl.com/DT8013 |isNativeISlab=True |Subj..."

2022-01-25T15:36:24Z

Created page with "{{CourseTemplate |CourseCode=DT8013 |CourseShortDescription=Advanced topics in Data Mining |CourseLevel=Advanced |CourseUrl=http://tinyurl.com/DT8013 |isNativeISlab=True |Subj..."

New page

{{CourseTemplate
|CourseCode=DT8013
|CourseShortDescription=Advanced topics in Data Mining
|CourseLevel=Advanced
|CourseUrl=http://tinyurl.com/DT8013
|isNativeISlab=True
|SubjectArea=Computer Science
}}

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 '''Course responsible''': [https://www.hh.se/english/information-english/search-staff.html?person=55480748-9205-444D-B590-7A751BBF4510 Professor Carlos Silla]
 '''Other teachers involved''': [https://hh.se/staff_en/slawomir Professor Slawomir Nowaczyk] and [https://www.hh.se/information/sok-personal.html?person=270BC1D4-8B53-4761-9135-435FE5D5E03A Mahmoud Rahat]
 Processing huge amounts of data is at the core of data mining, deep learning and real-time autonomous decision making. All these are in turn at the core of modern artificial intelligence applications. Data can reside more or less permanently in the cloud and accessed via distributed file systems and / or be streamed in real time from multiple sensors at very high rates. Access to data as well as processing is done using very well engineered frameworks where both storage and processing are distributed and processing is done in parallel. The purpose of this course is to introduce you to this infrastructure including parallel programming for the implementation of these frameworks. This should enable you to judge how to choose a framework for your applications, identify pros and cons, suggest improvements and even implement improvements.

← Older revision		Revision as of 10:36, 14 February 2025
Line 9:		Line 9:

	'''Course responsible''': [https://www.hh.se/english/information-english/search-staff.html?person=55480748-9205-444D-B590-7A751BBF4510 Professor Carlos Silla]		'''Course responsible''': [https://www.hh.se/english/information-english/search-staff.html?person=55480748-9205-444D-B590-7A751BBF4510 Professor Carlos Silla]
		+	'''Other teachers involved''': [https://hh.se/staff_en/slawomir Professor Slawomir Nowaczyk] and [https://www.hh.se/information/sok-personal.html?person=270BC1D4-8B53-4761-9135-435FE5D5E03A Mahmoud Rahat]

	Processing huge amounts of data is at the core of data mining, deep learning and real-time autonomous decision making. All these are in turn at the core of modern artificial intelligence applications. Data can reside more or less permanently in the cloud and accessed via distributed file systems and / or be streamed in real time from multiple sensors at very high rates. Access to data as well as processing is done using very well engineered frameworks where both storage and processing are distributed and processing is done in parallel. The purpose of this course is to introduce you to this infrastructure including parallel programming for the implementation of these frameworks. This should enable you to judge how to choose a framework for your applications, identify pros and cons, suggest improvements and even implement improvements.		Processing huge amounts of data is at the core of data mining, deep learning and real-time autonomous decision making. All these are in turn at the core of modern artificial intelligence applications. Data can reside more or less permanently in the cloud and accessed via distributed file systems and / or be streamed in real time from multiple sensors at very high rates. Access to data as well as processing is done using very well engineered frameworks where both storage and processing are distributed and processing is done in parallel. The purpose of this course is to introduce you to this infrastructure including parallel programming for the implementation of these frameworks. This should enable you to judge how to choose a framework for your applications, identify pros and cons, suggest improvements and even implement improvements.

@@ Line 8: / Line 8: @@
 }}
-'';Course responsible''': [https://www.hh.se/english/information-english/search-staff.html?person=55480748-9205-444D-B590-7A751BBF4510 Professor Carlos Silla]
+'''Course responsible''': [https://www.hh.se/english/information-english/search-staff.html?person=55480748-9205-444D-B590-7A751BBF4510 Professor Carlos Silla]
 Processing huge amounts of data is at the core of data mining, deep learning and real-time autonomous decision making. All these are in turn at the core of modern artificial intelligence applications. Data can reside more or less permanently in the cloud and accessed via distributed file systems and / or be streamed in real time from multiple sensors at very high rates. Access to data as well as processing is done using very well engineered frameworks where both storage and processing are distributed and processing is done in parallel. The purpose of this course is to introduce you to this infrastructure including parallel programming for the implementation of these frameworks. This should enable you to judge how to choose a framework for your applications, identify pros and cons, suggest improvements and even implement improvements.

@@ Line 7: / Line 7: @@
 |SubjectArea=Computer Science
 }}
 Processing huge amounts of data is at the core of data mining, deep learning and real-time autonomous decision making. All these are in turn at the core of modern artificial intelligence applications. Data can reside more or less permanently in the cloud and accessed via distributed file systems and / or be streamed in real time from multiple sensors at very high rates. Access to data as well as processing is done using very well engineered frameworks where both storage and processing are distributed and processing is done in parallel. The purpose of this course is to introduce you to this infrastructure including parallel programming for the implementation of these frameworks. This should enable you to judge how to choose a framework for your applications, identify pros and cons, suggest improvements and even implement improvements.

← Older revision		Revision as of 15:37, 25 January 2022
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	\|SubjectArea=Computer Science		\|SubjectArea=Computer Science
	}}		}}
		+
		+	Processing huge amounts of data is at the core of data mining, deep learning and real-time autonomous decision making. All these are in turn at the core of modern artificial intelligence applications. Data can reside more or less permanently in the cloud and accessed via distributed file systems and / or be streamed in real time from multiple sensors at very high rates. Access to data as well as processing is done using very well engineered frameworks where both storage and processing are distributed and processing is done in parallel. The purpose of this course is to introduce you to this infrastructure including parallel programming for the implementation of these frameworks. This should enable you to judge how to choose a framework for your applications, identify pros and cons, suggest improvements and even implement improvements.