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		<title>Bigdata on nyadgar.com</title>
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			<lastBuildDate>Wed, 13 Nov 2024 15:50:12 +0200</lastBuildDate>
		
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				<title>The Beautiful Math of Bloom Filters</title>
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				<pubDate>Wed, 13 Nov 2024 15:50:12 +0200</pubDate>
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				<description>&lt;p&gt;Probabilistic functions can model many algorithms and procedures. They can help us optimize&#xA;procedures to produce the best results. Experienced software engineers know that at some point, almost any&#xA;software will reach some level of non-determinism, where a solution is not absolute but approaches the best results&#xA;if using the optimal configuration. Mathematically, such a solution usually boils down to finding &lt;em&gt;minima&lt;/em&gt;, &lt;em&gt;maxima&lt;/em&gt; or &lt;em&gt;limits&lt;/em&gt;&#xA;of some probabilistic functions.&lt;/p&gt;&#xA;&lt;p&gt;In this article, we will explore the beautiful math behind Bloom filters.&#xA;We will explore the accuracy and trade-offs of Bloom filters and see why a Bloom filter can be an excellent choice for some cases,&#xA;especially in &lt;em&gt;Big Data&lt;/em&gt;, &lt;a href=&#34;https://en.wikipedia.org/wiki/Online_analytical_processing&#34;&gt;OLAP&lt;/a&gt; systems, dealing with huge and fairly static datasets.&lt;/p&gt;</description>
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