1628960700

In computer science, binary search, also known as half-interval search, logarithmic search, or binary chop, is a search algorithm that finds the position of a target value within a sorted array. Binary search compares the target value to the middle element of the array; if they are unequal, the half in which the target cannot lie is eliminated and the search continues on the remaining half until it is successful. If the search ends with the remaining half being empty, the target is not in the array.

**Time Complexity**: O(log(n)) - since we split search area by two for every next iteration.

TheOriginal Articlecan be found onhttps://github.com

#javascript #algorithms #datastructures #searches

1620466520

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

1652250166

Binary MLM Software Demo | Binary MLM Compensation Plan, MLM Woocommerce Price USA, Philippines : Binary MLM Woocommerce Software is a web application that integrate with the Woo-commerce plugin and helps to manage binary MLM networks. LETSCMS provide worldwide service, such as USA, Hong Kong, China, UK, UAE, Jordan, Saudi Arabia, Pakistan, Philippines, Japan, Singapore, Romania, Vietnam, Canada, Hong Kong, Russia, Hungary, Romania, Poland, Thailand, Laos and many others.

Binary MLM Woo-commerce includes a two legged structure where in a parent Node has two sub nodes where each new distributor or members is placed in either left or right sub-tree. One sub-tree is known as a Power Leg or Profit Leg while the second sub-tree is a Profit Leg or a weak leg.. It is one of the basic Binary MLM plan which is required by all the MLM organizations be it small or large. The binary MLM plan helps admin managing users or sub nodes in a binary network to keep record of their income, expenses etc.

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Vedio : https://www.youtube.com/watch?v=gji5XnnTJNc&list=PLn9cGkS1zw3QMCC-89p5zK39mPtfltkwq&index=5

#mlm_plan #Binary_mlm #binary_mlm_plan #Binary_mlm_wordpress_plugin #Binary_mlm_woo-commerce #binary_mlm_ecommerce #binary_mlm_software #binary_mlm_wp_plugin #biary_mlm_ecommerce #mlm_plan

1620629020

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.

As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).

*This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.*

#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management

1596737580

A week ago we learned about graph data structure. Today we will talk about how we can work with graphs. We will try to find distances between two nodes in a graph. This is one of the main uses of graphs and it’s called graph traversal. There are two main graph algorithms Breadth First Search (BFS) and Depth First Search (DFS) and today we will talk about BFS.

This is how our graph looks like:

**Breadth First Search**

In our example we will work with an adjacency matrix. This is how matrix represents graph above:

We will start with an input node, then visit all its neighbors which is one edge away. And then visit all their neighbors. Point is to determine how close the node is to the root node.

Function which we will write in a moment will return an object with key value pairs where key will represent node and value how far this node is from the root.

First we will loop over the adjacency matrix (2D array), create as many key value pairs as many nodes we have on the graph. Initially we will assign distance to the **infinity** which represents lack of connection between the nodes.

#graph #data-structures #breadth-first-search #javascript #algorithms #algorithms

1603767600

Today, let us touch base on some fundamental concepts like search algorithms.

In simple terms, **searching **is a process of looking up a particular data record in the database or in the collection of items. A search typically answers as true or false whether the particular data in which we are referring is found or not and perform the next action accordingly.

Commonly used algorithms for search are:

- Linear search
- Binary search
- Interpolation search

Let us understand them in detail with some example

*Linear Search Algorithm* is the simplest and easiest form of the search algorithm. In this algorithm, a sequential search is made over all the items one by one to search for the targeted item. Each item is checked in sequence until the match is found. If the match is found, the searched item is returned otherwise the search continues till the end.

To make it easy to understand, let us see understand linear search using a flow diagram

Linear Search — Data Flow

- Does not need sorted array list
- Performs equality comparisons
- The time complexity is O(n)
- Time taken to search elements keeps increasing as the number of elements is increased.

In _Binary search algorithm, _begins with an interval covering the whole array and diving it in half. If the value of the search key is less than the item in the middle of the interval, narrow the interval to the lower half. Otherwise narrow it to the upper half. Repeatedly check until the value is found or the interval is empty.

To make it easy to understand, let us see understand binary search using flow diagram and example as below.

Binary Search — Data Flow

- The array needs to be sorted
- Performs ordering comparisons
- Time complexity to O(log n).
- Search is done to either half of the given list, thus cut down your search to half time

#sorting-algorithms #algorithms #data-structures #search-and-sort #searching-algorithm