Dylan  Iqbal

Dylan Iqbal

1628957100

JavaScript Algorithms and Data Structures: Searches - Jump Search

Like Binary Search, Jump Search (or Block Search) is a searching algorithm for sorted arrays. The basic idea is to check fewer elements (than linear search) by jumping ahead by fixed steps or skipping some elements in place of searching all elements.

For example, suppose we have an array arr[] of size n and block (to be jumped) of size m. Then we search at the indexes arr[0], arr[m], arr[2 * m], ..., arr[k * m] and so on. Once we find the interval arr[k * m] < x < arr[(k+1) * m], we perform a linear search operation from the index k * m to find the element x.

What is the optimal block size to be skipped? In the worst case, we have to do n/m jumps and if the last checked value is greater than the element to be searched for, we perform m - 1 comparisons more for linear search. Therefore the total number of comparisons in the worst case will be ((n/m) + m - 1). The value of the function ((n/m) + m - 1) will be minimum when m = √n. Therefore, the best step size is m = √n.

Complexity

Time complexity: O(√n) - because we do search by blocks of size √n.

References

 

The Original Article can be found on https://github.com

#javascript #algorithms #datastructures #searches

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JavaScript Algorithms and Data Structures: Searches - Jump Search
Siphiwe  Nair

Siphiwe Nair

1620466520

Your Data Architecture: Simple Best Practices for Your Data Strategy

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

Gerhard  Brink

Gerhard Brink

1620629020

Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

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.

Introduction

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

Graphs Data Structure: Breadth First Search

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:

Image for post

Breadth First Search

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

Image for post

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

Alayna  Rippin

Alayna Rippin

1603767600

Search Algorithms

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

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

Image for post

Linear Search — Data Flow

Points to note:

  • 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.

Binary Search Algorithm

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.

Image for post

Binary Search — Data Flow

Points to note:

  • 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

Dylan  Iqbal

Dylan Iqbal

1628957100

JavaScript Algorithms and Data Structures: Searches - Jump Search

Like Binary Search, Jump Search (or Block Search) is a searching algorithm for sorted arrays. The basic idea is to check fewer elements (than linear search) by jumping ahead by fixed steps or skipping some elements in place of searching all elements.

For example, suppose we have an array arr[] of size n and block (to be jumped) of size m. Then we search at the indexes arr[0], arr[m], arr[2 * m], ..., arr[k * m] and so on. Once we find the interval arr[k * m] < x < arr[(k+1) * m], we perform a linear search operation from the index k * m to find the element x.

What is the optimal block size to be skipped? In the worst case, we have to do n/m jumps and if the last checked value is greater than the element to be searched for, we perform m - 1 comparisons more for linear search. Therefore the total number of comparisons in the worst case will be ((n/m) + m - 1). The value of the function ((n/m) + m - 1) will be minimum when m = √n. Therefore, the best step size is m = √n.

Complexity

Time complexity: O(√n) - because we do search by blocks of size √n.

References

 

The Original Article can be found on https://github.com

#javascript #algorithms #datastructures #searches