001     257352
005     20230726100244.0
020 _ _ |a 978-3-031-08411-9
024 7 _ |2 doi
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024 7 _ |2 K10Plus
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037 _ _ |a DZNE-2023-00421
082 _ _ |a 620.00285
100 1 _ |a Apke, Alexander
|b 0
245 _ _ |a Algorithm Design
250 _ _ |a 1st ed. 2022
260 _ _ |a Cham
|b Springer International Publishing
|c 2022
295 1 0 |a [Ebook] Computational Life Sciences : Data Engineering and Data Mining for Life Sciences / Dörpinghaus, Jens ; Weil, Vera ; Schaaf, Sebastian ; Apke, Alexander 1st ed. 2022, Cham : Springer International Publishing, 2022,
300 _ _ |a 79-98
336 7 _ |2 ORCID
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336 7 _ |0 7
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336 7 _ |2 DRIVER
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336 7 _ |2 DataCite
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336 7 _ |0 PUB:(DE-HGF)7
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490 0 _ |a Studies in Big Data
|v 112
520 _ _ |a In this chapter, we have a closer look at algorithms and the challenges that lie in designing “good” algorithms. We show how a real world problem can be modeled in a way that an algorithm can understand and solve it. Further, we introduce the Big O Notation as a concept for quantifying the efficiency of algorithms. We will know the difference between the complexity classes P and NP and discover some very important basic paradigms in designing efficient algorithms.
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588 _ _ |a Dataset connected to K10Plus
650 _ 0 |a Engineering—Data processing
650 _ 0 |a Bioinformatics
650 _ 0 |a Big data
650 _ 0 |a Computational intelligence
700 1 _ |a Weil, Vera
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700 1 _ |0 P:(DE-2719)2810719
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773 _ _ |a 10.1007/978-3-031-08411-9_4
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914 1 _ |y 2022
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980 _ _ |a contb
980 _ _ |a VDB
980 _ _ |a I:(DE-2719)1013007
980 _ _ |a I:(DE-2719)1011001
980 _ _ |a UNRESTRICTED


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