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# JavaScript Algorithms and Data Structures: Levenshtein Distance

The Levenshtein distance is a string metric for measuring the difference between two sequences. Informally, the Levenshtein distance between two words is the minimum number of single-character edits (insertions, deletions or substitutions) required to change one word into the other.

## Definition

Mathematically, the Levenshtein distance between two strings a and b (of length |a| and |b| respectively) is given by where

where is the indicator function equal to 0 when and equal to 1 otherwise, and is the distance between the first i characters of a and the first j characters of b.

Note that the first element in the minimum corresponds to deletion (from a to b), the second to insertion and the third to match or mismatch, depending on whether the respective symbols are the same.

## Example

For example, the Levenshtein distance between kitten and sitting is 3, since the following three edits change one into the other, and there is no way to do it with fewer than three edits:

1. kitten → sitten (substitution of "s" for "k")
2. sitten → sittin (substitution of "i" for "e")
3. sittin → sitting (insertion of "g" at the end).

## Applications

This has a wide range of applications, for instance, spell checkers, correction systems for optical character recognition, fuzzy string searching, and software to assist natural language translation based on translation memory.

## Dynamic Programming Approach Explanation

Let’s take a simple example of finding minimum edit distance between strings ME and MY. Intuitively you already know that minimum edit distance here is 1 operation, which is replacing E with Y. But let’s try to formalize it in a form of the algorithm in order to be able to do more complex examples like transforming Saturday into Sunday.

To apply the mathematical formula mentioned above to ME → MY transformation we need to know minimum edit distances of ME → M, M → MY and M → M transformations in prior. Then we will need to pick the minimum one and add one operation to transform last letters E → Y. So minimum edit distance of ME → MY transformation is being calculated based on three previously possible transformations.

To explain this further let’s draw the following matrix:

• Cell (0:1) contains red number 1. It means that we need 1 operation to transform M to an empty string. And it is by deleting M. This is why this number is red.
• Cell (0:2) contains red number 2. It means that we need 2 operations to transform ME to an empty string. And it is by deleting E and M.
• Cell (1:0) contains green number 1. It means that we need 1 operation to transform an empty string to M. And it is by inserting M. This is why this number is green.
• Cell (2:0) contains green number 2. It means that we need 2 operations to transform an empty string to MY. And it is by inserting Y and M.
• Cell (1:1) contains number 0. It means that it costs nothing to transform M into M.
• Cell (1:2) contains red number 1. It means that we need 1 operation to transform ME to M. And it is by deleting E.
• And so on...

This looks easy for such small matrix as ours (it is only 3x3). But here you may find basic concepts that may be applied to calculate all those numbers for bigger matrices (let’s say a 9x7 matrix for Saturday → Sunday transformation).

According to the formula you only need three adjacent cells (i-1:j), (i-1:j-1), and (i:j-1) to calculate the number for current cell (i:j). All we need to do is to find the minimum of those three cells and then add 1 in case if we have different letters in i's row and j's column.

You may clearly see the recursive nature of the problem.

Let's draw a decision graph for this problem.

You may see a number of overlapping sub-problems on the picture that are marked with red. Also there is no way to reduce the number of operations and make it less than a minimum of those three adjacent cells from the formula.

Also you may notice that each cell number in the matrix is being calculated based on previous ones. Thus the tabulation technique (filling the cache in bottom-up direction) is being applied here.

Applying this principle further we may solve more complicated cases like with Saturday → Sunday transformation.

## References

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

#javascript #algorithms #datastructures

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## 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

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## 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

1621986060

## Basics of Data Structure Algorithms

If I ask you what is your morning routine, what will you answer? Let me answer it for you. You will wake up in the morning, freshen up, you’ll go for some exercise, come back, bath, have breakfast, and then you’ll get ready for the rest of your day.

If you observe closely these are a set of rules that you follow daily to get ready for your work or classes. If you skip even one step, you will not achieve your task, which is getting ready for the day.

These steps do not contain the details like, at what time you wake up or which toothpaste did you use or did you go for a walk or to the gym, or what did you have in your breakfast. But all they do contain are some basic fundamental steps that you need to execute to perform some task. This is a very basic example of algorithms. This is an algorithm for your everyday morning.

In this article, we will be learning algorithms, their characteristics, types of algorithms, and most important the complexity of algorithms.

#### What are Data Structure Algorithms?

Algorithms are a finite set of rules that must be followed for problem-solving operations. Algorithms are step-by-step guides to how the execution of a process or a program is done on a machine to get the expected output.

• Do not contain complete programs or details. They are just logical solutions to a problem.
• Algorithms are expressible in simple language or flowchart.

#### Characteristics of an Algorithm in Data Structure

No one would follow any written instructions to follow a daily morning routine. Similarly, you cannot follow anything available in writing and consider it as an algorithm. To consider some instructions as an algorithm, they must have some specific characteristics :

1. Input: An algorithm, if required, should have very well-defined inputs. An algorithm can have zero or more inputs.

2. Output: Every algorithm should have one or more very well-defined outputs. Without an output, the algorithm fails to give the result of the tasks performed.

3. Unambiguous: The algorithm should be unambiguous and it should not have any confusion under any circumstances. All the sentences and steps should be clear and must have only one meaning.

4. Finiteness: The steps in the algorithm must be finite and there should be no infinite loops or steps in the algorithm. In simple words, an algorithm should always end.

5. Effectiveness: An algorithm should be simple, practically possible, and easy to understand for all users. It should be executable upon the available resources and should not contain any kind of futuristic technology or imagination.

6. Language independent: An algorithm must be in plain language so that it can be easily implemented in any computer language and yet the output should be the same as expected.

#### Data flow of the Algorithm in Data Structure

1. Problem: To write a solution you need to first identify the problem. The problem can be an example of the real-world for which we need to create a set of instructions to solve it.

2. Algorithm: Design a step-by-step procedure for the above problem and this procedure, after satisfying all the characteristics mentioned above, is an algorithm.

3. Input: After creating the algorithm, we need to give the required input. There can be zero or more inputs in an algorithm.

4. Processing unit: The input is now forwarded to the processing unit and this processing unit will produce the desired result according to the algorithm.

5. Output: The desired or expected output of the program according to the algorithm.

#### Why do we need Data Structure Algorithm?

Suppose you want to cook chole ( or chickpeas) for lunch. Now you cannot just go to the kitchen and set utensils on gas and start cooking them. You must have soaked them for at least 12 hours before cooking, then chop desired vegetables and follow many steps after that to get the delicious taste, texture, and nutrition.

This is the need for algorithms. To get desired output, you need to follow some specific set of rules. These rules do not contain details like in the above example, which masala you are using or which salt you are using, or how many chickpeas you are soaking. But all these rules contain a basic step-by-step guide for best results.

We need algorithms for the following two reasons :

1. Performance: The result should be as expected. You can break the large problems into smaller problems and solve each one of them to get the desired result. This also shows that the problem is feasible.

2. Scalability: When you have a big problem or a similar kind of smaller problem, the algorithm should work and give the desired output for both problems. In our example, no matter how many people you have for lunch the same algorithm of cooking chickpeas will work every single time if followed correctly.

Let us try to write an algorithm for our lunch problem :

1. Soak chickpeas in the night so that they are ready till the next afternoon.

2. Chop some vegetables that you like.

3. Set up a utensil on gas and saute the chopped vegetables.

4. Add water and wait for boiling.

5. Add chickpeas and wait until you get the desired texture.

6. Chickpeas are now ready for your lunch.

The real-world example that we just discussed is a very close example of the algorithm. You cannot just start with step 3 and start cooking. You will not get the desired result. To get the desired result, you need to follow the specific order of rules. Also, each instruction should be clear in an algorithm as we can see in the above example.

#algorithms in data structure #data structure algorithms #algorithms

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## 4 Tips To Become A Successful Entry-Level Data Analyst

Companies across every industry rely on big data to make strategic decisions about their business, which is why data analyst roles are constantly in demand. Even as we transition to more automated data collection systems, data analysts remain a crucial piece in the data puzzle. Not only do they build the systems that extract and organize data, but they also make sense of it –– identifying patterns, trends, and formulating actionable insights.

If you think that an entry-level data analyst role might be right for you, you might be wondering what to focus on in the first 90 days on the job. What skills should you have going in and what should you focus on developing in order to advance in this career path?

Let’s take a look at the most important things you need to know.

#data #data-analytics #data-science #data-analysis #big-data-analytics #data-privacy #data-structures #good-company

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## SKP's Algorithms and Data Structures

Continuing on the Quick Revision of Important Questions for My Interviews. These Are Good Puzzles or Questions Related to Data Structures.

My Article Series on Algorithms and Data Structures in a Sort of ‘Programming Language Agnostic Way’. Few of the Algorithms and Data Structures in C, Few in C++, and Others in Core Java. Assorted Collection for Learning, Revising, Revisiting, Quick Refresh, and a Quick Glance for Interviews. You May Even Include them Directly for Professional or Open Source Efforts. Have Included Explanation Only for Few of These! Hope these turn out to be Really Helpful as per the Author’s Intention.

#### Data Structure — Interview Questions

#java #core java #data structures #dijkstra #core java basics #data structure using java #algorithms and data structures #java code examples #linked list in java #circular linked list