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Algorithms / Computational complexity theory / P / NP

  • Glossary

Algorithms / Computational complexity theory / P / NP

This glossary provides explanations of various keywords to help you understand the concepts necessary for successful data utilization and digital transformation (DX).
This time, we will explain the concepts of algorithms and computational complexity theory, which are necessary concepts when discussing the performance of computer programs and the security of cryptography.

What is algorithm/computational complexity theory?

An algorithm is a set of steps or rules that show the sequence of actions a person or computer should take to solve a particular problem.

Computational complexity theory is the study of the inherent difficulty of a particular problem, based on the execution cost of algorithms used to solve that problem (such as the amount of computation and memory required to obtain the solution).
For example, problems that can be solved in polynomial time using a Turing machine are sometimes classified as class P, and problems that can be solved in polynomial time using a non-deterministic Turing machine are sometimes classified as class NP.

These are used as a means of discussing the excellence and practicality of the implementation of "specific computer programs," as a business perspective to determine whether an "IT system" can scale when the amount of data or the number of users increases, to determine whether "that problem" can be practically solved by a computer, or as a means of considering the security of "cryptographic technology."

What is an "algorithm"?

An algorithm is a set of procedures or rules used by humans or computers to solve a particular problem (for example, sorting numbers in ascending order). For example,

  • "You have 10,000 numbers in a computer, and you want to sort them in ascending order" (problem). "You can sort them by processing the data in this way" (procedure) is an example of an algorithm.
  • Given timetables and railway network information, the question is, "How can we find the shortest route from station A to station B?" (Problem). An example of an algorithm is, "You can find the answer by checking the timetable in these steps" (Procedure).

These "procedures for solving a problem" are referred to as, for example, "algorithms for sorting," "algorithms for finding the shortest path," and "algorithms for finding all prime numbers less than or equal to 1000."

Example of an algorithm: Sorting algorithm

Next, I will explain what sorting algorithms are by using them as an example.

"Rearranging" is a problem we often encounter in everyday life. Arranging elementary school students in order of height from shortest to oldest is one such problem, as are tasks like sorting hotels by price from cheapest to most expensive or sorting Excel data by purchase amount from most expensive to least expensive.

These problems are essentially the same in that they involve sorting by magnitude, but how should such problems generally be solved?

Example of a sorting algorithm (finding the king)

Imagine you're asked to "create a program that sorts 100 numbers in ascending order." If you're told to "figure out the steps to solve it yourself" without any prior knowledge, it can be surprisingly difficult. You might not come up with a good solution, or you might create something incorrect (the sorting might fail, the process might not stop, or the data might be different from the input data, etc.).

If you implement a program simply and without overthinking it, and it works well—for example, if you have an elementary school student create it using programming—it might be achieved by "finding the biggest/smallest things in order," sometimes called "finding the king."

  • A: Find the shortest person among everyone.
    • Put that person at the front of the line.
  • B: From the remaining people after excluding that one, find the shortest person.
    • Place that person "next" in the list.
  • C: Repeat step B until everyone is lined up.

Alternatively, the following analogy might make it easier to understand why "this method works for rearranging."

  • Find the strongest person among everyone and make them the king.
  • Find the strongest one (the king) from "everyone except that person".
    • "The next king" will be found.
  • Repeating the process of "finding the next king" completes the sorting in order of strength.

In other words, by repeatedly performing the process of "finding the largest/smallest item (finding the king)," we can "generally solve" various sorting problems.

An "algorithm" is a set of steps for solving a problem. This example is an algorithm based on the idea that "if we find the largest/smallest values in order, we'll end up with a sorted state."

Regarding how to implement the process of "finding the biggest/smallest (finding the king)," an example that anyone can easily imagine is "finding the best one using a tournament system." A simple example in programming would be "first, select one item, and then compare it with all the others using a loop, and the remaining item will naturally be the smallest."

The good and bad points of the "King Search" algorithm

However, this algorithm has a problem: it's not very efficient. For example, if you use this method to arrange everyone in a class in order of height from shortest to tallest, you'll have to repeatedly compare the heights from the beginning each time you find the next person. This results in the same people being compared multiple times, and many people will likely complain, "How many times am I going to have to compare heights unnecessarily?"

This means that the amount of computation required to sort data of the same size is unnecessarily large, and therefore the algorithm has poor performance.

  • Some algorithms have excellent processing performance, while others do not.

However, while the "King Search" algorithm has low processing power, its strength lies in its simple concept and ease of understanding, as explained above. An easy-to-understand algorithm is easier to write as a program, and its simplicity and clarity make it easier to verify that it is bug-free.

  • Algorithms can be compared not only in terms of "processing performance," but also from other perspectives such as "ease of understanding."

An example of a more efficient algorithm is "Merge sort".

Next, let's look at an example of a "highly performing sorting algorithm." I'll introduce "merge sort," a sorting algorithm known for its refined and elegant procedure. Notice how the number of comparisons required to sort is significantly reduced.

Incidentally, the person credited with first conceiving this algorithm is none other than John von Neumann, the genius scientist who devised the very computer architecture we use today (the von Neumann architecture). This algorithm is said to have been conceived in 1945. It seems that legendary geniuses are "amazing" no matter what they do.

The approach is to sort all the data into "groups of two," then rearrange each group, and then repeatedly "combine the sorted groups in pairs" until the entire dataset is sorted.

  • Let's rearrange the following eight numbers.
    • 54283176
  • Divide them into groups of two and rearrange them.
    • Divide them into two portions.
      • 54 | 28 | 31 | 76
    • We will sort the group.
      • 45 | 28 | 13 | 67
  • Merge two groups at a time.
    • The groups are divided into sets of two.
      • (45 | 28) | (13 | 67)
    • Combine two groups into one:
      The key point is that the elements within each group are already sorted. By simply comparing the first elements of each group, you can find the smallest element overall.
      • 2458 | 1367
        • | 45 | 28
        • 2 | 45 | 8
        • 24 | 5 | 8
        • 245 | | 8
        • 2458
    • The merging process is repeated until the entire system becomes a single group.
      • 2458 | 1367
      • 12345678

​What I want you to grasp, even in a rough sense, is that while the "King Search" method repeatedly compares the magnitude of the same numbers, this algorithm eliminates that kind of inefficiency.

There are many other sorting algorithms.

I introduced "King Search" (or selection sort) as an example of a simple, straightforward algorithm, and "Merge Sort" as an example of a simple, elegant, and highly efficient algorithm, but there are many other sorting algorithms as well.

In actual programming libraries, the "quicksort" algorithm is often used, and there are many other algorithms as well, such as "heapsort" and "base sort," which can sort quickly on the premise that sorting is done without comparison.

How much of a performance difference will there be due to the difference in algorithms?

Next, let's examine the performance differences between "King Search" (selection sort) and "Merge Sort." If you find the explanation difficult, feel free to skim the parts involving mathematical formulas.

If there are N data points: "King Search" (selection sort)

The underlying idea of the algorithm was to repeatedly search for the "king."

  • To find the "king" from N data points, "N-1 comparison operations" are required. This is because a comparison is performed once between the king and everyone else.
  • Since the process involves finding the smallest value and then repeating the "finding the king" process from the remaining values, it initially searches for the king with N values, then with N-1 values, and so on.
  • Then the total number of comparison operations performed during the sorting process will be "(N-1) + (N-2) + ,,, + 1".

Using the formula for the sum of a sequence, we get "N × (N-1) × (1/2)". Therefore, when there are N input data points, the total number of comparison operations is "1/2 × N²- 1/2 × N".

If there are N data points: "Merge sort"

Let's consider the same thing with merge sort.

  • The initial sorting requires 1/2 × N comparison operations if there are N data points.
    • In this example, there are 8 data points, so the answer is "4 times".
    　
  • In the subsequent "merge" process, the number of comparison operations will vary depending on the data, but even in the most cases it will be less than the total number of data points, so at most it will be N-1 times.
    • In this example, there are 8 data points, so the answer is "at most 7 times".
    　
  • The number of phases in which the merge process is performed is calculated as "(log 2 N) - 1" times, taking into account that the amount of data doubles with each merge.
    • If there are 8 data points, since "8 is 2 to the power of 3", it's 3-1 = "2 times", and in the example above, it is indeed merged twice: "2 to 4" and "4 to 8".
    　　

And since the initial "1/2 × N times" is also included in "at most N-1 times," to simplify things, the total number of comparison operations when there are N data points is roughly "N log 2 N" times.

Specifically, how much of a performance difference is there?

Some people might notice the significant difference just by looking at the formula, but since it's generally difficult to understand, let's calculate and confirm how much of a difference it makes with a specific number of data points.

  N=10 N=100 N=1000 N=1 million
Searching for the King
(Selection sort)		 45th
45 microseconds		 4950 times
4.9 milliseconds		 499,500 times
499 milliseconds		 499999500000 times
138 hours
Merge sort 33 times
33 microseconds		 664 times
0.6 milliseconds		 9966 times
9.6 milliseconds		 19,931,569 times
19.9 seconds

The number of operations refers to the number of comparison operations performed, as described above, and the processing time assuming that 1 million comparison operations can be performed per second (1 megabyte per second). This is a very conservative assumption considering the processing power of current computers. You can see that as the amount of data increases, the difference in processing time becomes enormous.

While "1 million data points" might seem like a huge amount of data at first glance, if it's "1 million 32-bit integer data points" (4 bytes each), that's only 4 megabytes of data. This would be a difficult amount of data for a 20th-century computer to process, but for today's computers (and smartphones), it's a perfectly manageable amount.

In other words, a program with a superior algorithm can complete the task in 20 seconds, but a naive implementation can take a staggering 138 hours. This demonstrates the significant difference in processing time depending on the quality of the algorithm. This highlights the importance of implementing the task with an appropriate algorithm, especially when dealing with large amounts of data.

Why is there such a big difference?

Why is there such a difference in processing time? The key lies in "this part" of the calculation time formula for each algorithm.

  • King Search (Selection Sort): 1/2 × N 2- 1/2 × N times
  • Merge sort: N log 2 N times

The highlighted portion is the part where the "increasing force" is greatest in response to an increase in N. The "increasing force of N" and the "increasing force of N log 2 N" are far weaker than the "increasing force of N²", so they can be considered negligible errors when "roughly considering the performance of the algorithm".

You might find "log 2 N" difficult to visualize, but the relationship "N > log 2 N > 1" holds true. Therefore, if we add "N log 2 N" to this, we get "N 2 > N log 2 N > N > log 2 N > 1," meaning it's "clearly larger than N, but clearly smaller than N squared."

In other words, the reason why the practicality of comparing and finding the king (selection sort) becomes questionable when the amount of data increases is because there is a big difference between the increase in N² and the increase in N log 2 N that accompanies the increase in N.

The "computational complexity order" of an algorithm

The "strongest component" extracted from such a computational complexity formula is called the "algorithm order (computational complexity order)," and is written in notation such as "O(N²)."

  • A search for the king (selection sort) with a computational complexity of "1/2 × N ²- 1/2 × N".
    • The algorithm's computational complexity is O(N²).
  • Merge sort with a computational complexity of "N log 2 N"
    • The algorithm's computational complexity is O(N log N).

You might think this is too simplified, but by doing so, we can classify and group the performance of the many algorithms in the world based on their essential performance differences. This allows us to "rank" the algorithms by their performance, making it easier to understand what kind of performance each algorithm has across different algorithms.

For example, even just from reading this far, you should now be able to imagine how the difference between the "O(N log N)" and "O(N 2)" algorithms becomes apparent when the amount of data being processed increases significantly, based on the examples of the two algorithms.

The "essential difficulty" of that problem

As examples of algorithms for solving "sorting problems (sorting)," we introduced "selection sort (king search)" and "merge sort." Naturally, the ideas for achieving sorting are not limited to just these two types, and there are countless other algorithms besides these two.

So, does an algorithm exist that performs better than merge sort? Order notation provides a clear guideline for this as well. In sorting problems that involve comparing elements and rearranging them, it has been theoretically proven that O(NlogN) is the lower bound, and nothing better exists.

In other words, unfortunately, we can't find a new algorithm that is any more groundbreakingly fast. It's a sad fact, but on the other hand, it frees us from the agony of searching for an ideal algorithm that doesn't exist, and we can accept that. By choosing an O(NlogN) algorithm that has characteristics that suit our circumstances, we have generally done our best.

Conversely, one could say that the inherent difficulty of a "sorting problem" is "O(N log N)".

Complexity class P/NP

There is a field that discusses this "essential difficulty in solving a problem," and that is "computational complexity theory." When classifying problems and algorithms into "complexity classes" based on their difficulty, the terms "P" or "NP" (NP-complete/NP-hard) are often used.

"P" refers to a class of problems that can be solved in polynomial time, essentially problems that are practically solvable or can be solved efficiently. In contrast, "NP" (more accurately, NP-hard) refers to a class of problems that cannot be solved in polynomial time, essentially problems that are practically unsolvable.

*More accurately, P stands for "a problem that can be solved in polynomial time by a deterministic Turing machine," and NP stands for "a problem that can be solved in polynomial time by a non-deterministic Turing machine." Furthermore, "NP-hard" and "NP-complete" also have different meanings, but explaining them in detail would become too complicated, so a simplified explanation is provided.

What does it mean to be "solvable in polynomial time"? It means that if the number of data points is N, then the problem can be solved in a time range of N to the power of n, such as "N² + log 2 N" or "N⁶ + 3N²- N". In contrast, "NP" refers to problems that are "not" polynomial, such as those where the computational complexity increases exponentially, like "2N".

While something like "N 1000" is polynomial time, and therefore not necessarily practically solvable for "P", "2N" is distinguished by the fact that its computational complexity explodes exponentially, making it "hopelessly unsolvable except for very small amounts of data". If you try putting "2N" into the "comparison of selection sort and merge sort" mentioned earlier, you'll get a good sense of how hopeless it is.

  N=10 N=100 N=1000 N=1 million
Searching for the King
(Selection sort)		 45th
45 microseconds		 4950 times
4.9 milliseconds		 499,500 times
499 milliseconds		 499999500000 times
138 hours
Merge sort 33 times
33 microseconds		 664 times
0.6 milliseconds		 9966 times
9.6 milliseconds		 19,931,569 times
19.9 seconds
The "2N" algorithm 1024th time
1 millisecond		 1267650600228230000000000000000 times
401,969,368,413,315 years		 Please understand. Please understand.

For comparison, 400 trillion years is equivalent to 13.8 billion years since the birth of the universe. This shows that even if we had been calculating continuously since the very beginning of the universe, it would still be an enormous amount of computation, just with only 100 data points.

Thus, "NP" (NP-hard) is a complexity class that classifies problems or algorithms that, while theoretically solvable by computation, should be considered impractically unsolvable.

An algorithm that is also related to whether the business will scale.

Furthermore, the perspectives we have discussed so far are important not only when implementing algorithms and using them in real systems, but also, above all, when "providing business using IT systems." This is because "whether it can handle an increase in N" is also about "whether the IT system or business can scale."

For example, let's say your company provides a cloud service. When the amount of data being processed increases 100-fold, or when the number of users increases 100-fold, the question of "how will the amount of computation required for processing change?" is a crucial business concern.

  • O(1)
    • This algorithm has the ideal property of maintaining a constant computational cost even as the amount of data increases.
    • For example, the computational complexity of "searching for or adding data using a hash table" is "O(1)".
  • O(logN)
    • This is also a situation that "scales well."
    • For example, situations like "the amount of data increases 100-fold, but the computational complexity only increases 10-fold (in the case of log 10 N)" or "the amount of data increases 256-fold, but the computational complexity only increases 16-fold (in the case of log 2 N)" are examples of this.
    • Even if the amount of data increases significantly, the computational cost (implementation cost) does not increase as much, which is a favorable characteristic for business.
    • This includes the computational complexity of "searching (searching) sorted data."
  • O(N)
    • This is the case where the computational complexity increases in proportion to the amount of data.
    • The intuitive statement, "If the data is 10 times larger, the processing load is also 10 times larger," makes the cost structure easy for users to understand.
    • This includes computational complexity for tasks such as "searching for unsorted data" and "sorting operations that do not rely on comparison (such as base sort)."
  • O(NlogN)
    • This is a case where the increase in computational complexity is greater than the increase in data volume, but the scaling is still relatively good.
    • As already mentioned, this includes the computational complexity of sorting algorithms.
  • O(N2)
    • It can be practically solved with a certain amount of data, but processing becomes difficult as the amount of data increases.
    • As N increases, it becomes practically impossible to process the data, so it's important to take precautions to avoid such a situation in business.
    • This includes "uninspired sorting algorithms such as selection sort (king search)."
    • Furthermore, the situation becomes even more difficult when the complexity reaches O(N3) or O(N6).
  • O(2N) etc.
    • This refers to problems or algorithms that are commonly known as "NP" (more accurately, NP-hard).
    • This happens when you are unable to solve the problem through sufficient ingenuity, and essentially, you are left with no choice but to exhaustively search for possible solutions.
    • It can only be practically solved when "N is a very small number." The fact that it "becomes NP" is something that must be extremely careful about from a business perspective.
    • Examples of problems that result in "NP"
      • The Traveling Salesperson Problem: The problem of finding the shortest route to visit all N cities.
      • The Knapsack Problem: A problem that asks you to find the combination of items that maximizes the total price while keeping the total weight limit at X kg.
    • When you want to solve some kind of optimization problem, or any other problem that is highly valuable to solve in practical terms, problems in this complexity class often belong to this category, and unfortunately, there are times when they simply cannot be solved.
    • However, while finding the "optimal solution" results in an "NP" complexity, there are algorithms that can find an "approximate solution" in "P" complexity, making it practically possible to solve the problem in some cases.
  • "Unsolvable" Problems
    • Furthermore, there are even more disheartening situations where "there is no guaranteed way to obtain the solution by following a set procedure."
    • It's not that it's impossible to find an answer even if you try to solve it; rather, there might be cases where it can be solved in specific circumstances or where individual solutions are found by chance, but there simply isn't a method that guarantees a solution for any given input using a predetermined procedure.
    • For example, the problem of determining whether an arbitrary program, given as input, has the property of terminating or never terminating (the halting problem) is known to have "no generally solvable method."

If the computational load becomes too large, the cost of providing the service will increase, and if it takes too long to complete, customers will complain that it is "unusable."

You would need to either strive to implement it with a more scalable and high-performance algorithm (if such an algorithm exists), or you would need to devise a business strategy, such as how to market the service, so that it is only used in ways that do not involve excessive amounts of data.

Algorithms in competition with other companies

Let's say a competitor implements a solution using an O(N²) algorithm, while your company implements it using a superior O(logN) algorithm. When the amount of data to be processed (or the number of users) increases 100 times, the competitor's cost of providing the solution increases 10,000 times, while your company's cost increases only 10 times, resulting in a significant difference.

If we can maintain this cost structure and launch a major offensive by "comparing products based on data volume" to draw them into the battle, we can put only our competitors in a situation where it will be difficult to maintain their services.

While algorithms might seem like a technical topic only relevant to engineers, there are benefits to being aware of them at a business level as well.

"Safety" is "essentially impossible to calculate."

Up to this point, we've discussed cases where "calculating" or "being able to calculate" creates value. Next, let's talk about the opposite: when "not being able to calculate" creates value.

In recent times, "security" has become an extremely important topic. In IT, "cryptography" is a crucial element for ensuring "safety and security." The usefulness of cryptography lies in protecting data safety and security by making it impossible for third parties who do not know the secret information, such as passwords, to "reverse the ciphertext back into the original data (plaintext)."

By the way, what do you think it means for a cryptographic technique to be secure? You might think it means that there is no way to decrypt the code, but that's not actually the case.

  • The public perception of secure encryption technology
    • It is impossible to recover the "plaintext" (the data before encryption) when only the "ciphertext" is available.

To use a physical security analogy, it's like saying it's safe because it's impossible to break in due to thorough security measures.

In modern times, when encrypting data, it is common to use "publicly available encryption technologies" such as "AES encryption." Since these are public technologies, the "processing steps used for encryption" are not secret but publicly available, and therefore, the "method for converting ciphertext back into plaintext" is also not a secret from the start.

  • Real-world cryptography
    • The method for recovering the "plaintext" when only the "ciphertext" is available has been known from the beginning and is publicly available.

"A cryptographic encryption is secure" means that "the method of decryption is (technically) known," but "it is practically impossible to do so because the computational complexity would explode."

  • Actual "Secure Cryptographic Technology"
    • It can convert input plaintext into ciphertext using "practical computational complexity".
    • If you know secret information such as a password, you can convert the ciphertext back into plaintext using "practical computational complexity".
    • While algorithms exist to convert ciphertext back to plaintext without a password, the security of cryptography lies in the fact that "it is not possible to convert back to plaintext with practical computational power."

To use the analogy from the earlier discussion about physical security, it's like saying "anyone can get in," but "it's so difficult to get inside that everyone runs out of energy," so in practice, it's impossible to get in.

In other words, in the modern era, "cryptographic technology is secure" doesn't mean "there's no way to decipher it," but rather that "known decryption methods would require too much computation to be practically effective," which is a matter of computational complexity and algorithms.

Let's consider how to interact with algorithms.

As already mentioned, discussions about algorithms and computational complexity are not only essential expertise for engineers when programming, but also important from a business perspective when a company uses IT for any purpose.

Points to consider when hiring personnel

As we've seen with the sorting algorithm example, the performance of an IT system can differ significantly depending on whether it's implemented with an appropriate algorithm or not. In an improper implementation, even a slight increase in data volume can cause abnormal increases in computational complexity and memory consumption, leading to system failures.

When hiring engineers or individuals responsible for IT-based business design, it's advisable to verify whether they possess a solid foundation in computer science, including algorithms. Alternatively, it's important to check their academic knowledge, such as that gained from the Information Processing Engineer Examination, which is sometimes overlooked. Study and training Ken-san Perhaps what I've shown you today is what can happen if you proceed with system development without doing this first.

Examine the IT systems that support your company's business from an algorithmic perspective.

If you provide your own IT systems or cloud services that support your business, it might be a good idea to investigate how the functionality of your services will change as the amount of data and the number of users increase.

If performance declines as the amount of data increases, you should consider reimplementing it with a better algorithm. If a superior algorithm doesn't exist, you'll need to structure the business itself based on the assumption that the amount of data won't increase. Conversely, if you've found that it scales well, the cost burden won't increase significantly even if the amount of data or users increases, so a proactive business strategy at that point can be expected to yield good results.

Superior algorithms can become an asset that generates a company's competitive advantage.

For example, if you want to optimize the delivery route of a package using an IT system, the algorithm for finding the optimal solution is often a problem of class NP, making it difficult to solve problems of practical scale. However, even in such cases, there are algorithms that can find an "approximate solution" in a practical amount of computation time.

For example, if another company is spending 10 billion yen annually on shipping costs because it uses a poorly thought-out algorithm to create its delivery plans, but your company can do it for 8 billion yen by using a superior approximation algorithm, then the difference in algorithms can become a difference in competitiveness.

Just as we consider data, excellent pre-trained machine learning models, or our own proprietary LLMs as assets, shouldn't "superior algorithms tailored to our company's specific circumstances" also be assets that generate a company's competitive advantage?

In reality, we end up using a combination of things that "already exist."

While it is desirable to use IT systems implemented with superior algorithms, it has become increasingly impractical to develop all IT systems for in-house use from scratch these days.

Even if we develop our own solutions for critical aspects of our business, it has become more practical to utilize existing packaged software and cloud services—in other words, to "effectively combine and use" things that "already exist in the world."

In that case, instead of developing in-house, companies will search for existing software and cloud services that "already exist" in the world and "seem to be implemented with superior algorithms," and combine them appropriately according to their needs. When this happens, the following occurs.

  • Cloud service A is very popular in the field because it's easy to use, but it has been discovered that it has a problem where processing becomes extremely slow when the amount of data increases.
  • Cloud service B has excellent processing performance when the amount of data increases, but its usage costs are high, making full-scale use difficult.
  • Cloud service C offers relatively good processing performance for basic functions and is cost-effective, but it lacks certain features and performance is poor for non-essential functions, making it difficult to use comprehensively.

In this situation, it is difficult to use only one cloud service, and it is desirable to use a combination of multiple cloud services effectively.

"Connecting" technology plays a vital role as a means of effectively combining and utilizing existing elements.

Even if the "parts that are important to the company" are implemented as a proprietary system with optimal specifications and algorithms, other parts will often be cleverly utilized by making use of "what already exists," such as existing cloud services, as needed.

Furthermore, problems may arise later with cloud services that you initially thought were good, forcing you to subsequently use other cloud services in combination. Business circumstances may also change, altering what is important in data processing (for example, a business that previously dealt with a small number of customers may become one that needs to process data from hundreds of thousands of customers without delay).

This means that not only will you need to be able to "effectively combine and utilize multiple cloud services," but you will also need to be able to "switch the combination of cloud services you use each time, depending on the situation," through trial and error in the business environment.

Please utilize "connecting" technology

There are ways to efficiently develop these various "integration processes" using only a GUI. These are "connecting" technologies such as "DataSpider" and "HULFT Square," also known as "EAI," "ETL," and "iPaaS." By utilizing these, it is possible to smoothly and efficiently integrate old and new systems.

Can be used with GUI only

Unlike regular programming, there is no need to write code. By placing and configuring icons on the GUI, you can achieve integration with a wide variety of systems, data, and cloud services.

Being able to develop using a GUI is also an advantage

​No-code development using only a GUI might seem like a simplified and compromised approach compared to full-fledged programming. However, the ability to develop solely with a GUI enables "on-the-ground personnel to proactively work on implementing integrated processes themselves." It is the people on the ground who understand the business best.

Full-scale processing can be implemented

There are many products that claim to allow development using only a GUI, but some people may have a negative impression of such products as being too simple.

Indeed, it's common to encounter situations where "it's easy to create but can only do simple things," "it crashes when you try to process a substantial amount of data," or "it lacks the high reliability and stable operation capabilities to support business operations, leading to serious problems."

"DataSpider" and "HULFT Square" are easy to use, but they also allow for the creation of processing at a level comparable to full-fledged programming. They have high processing power similar to full-fledged programming, such as being internally converted to Java and executed, and have a proven track record of supporting corporate IT for many years. Consideration has already been given to whether they can be practically used even when the amount of data increases as business use progresses. They combine the advantages of "GUI only" with full-fledged capabilities.

No need to operate in-house as it is iPaaS

DataSpider can be operated securely on a system under your own management. With HULFT Square, a cloud service (iPaaS), this "connecting" technology itself can be used as a cloud service without the need for in-house operation, eliminating the hassle of in-house implementation and system operation.

Related keywords (for further understanding)

  • EAI
    • It is a concept of "connecting" systems by data integration, and is a means of freely connecting various data and systems. It is a concept that has been used since long before the cloud era as a way to effectively utilize IT.
  • ETL
    • In the recent trend of actively working on data utilization, the majority of the work is not the data analysis itself, but rather the collection and preprocessing of data scattered in various places, from on-premise to cloud.
  • iPaaS
    • A cloud service that "connects" various clouds with external systems and data simply by operating on a GUI is called iPaaS.

Are you interested in "iPaaS" and "connecting" technologies?

Try out our products that allow you to freely connect various data and systems, from on-premise IT systems to cloud services, and make successful use of IT.

The ultimate "connecting" tool: data integration software "DataSpider" and data integration platform "HULFT Square"

"DataSpider," data integration tool developed and sold by our company, is a "connecting" tool with a long history of success. "HULFT Square," a data integration platform, is a "connecting" cloud service developed using DataSpider technology.

Another feature is that development can be done using only the GUI (no code) without writing code like in regular programming, so business staff who have a good understanding of their company's business can take the initiative to use it.

Try out DataSpider/ HULFT Square 's "connecting" technology:

There are many simple collaboration tools on the market, but this tool can be used with just a GUI, is easy enough for even non-programmers to use, and has "high development productivity" and "full-fledged performance that can serve as the foundation for business (professional use)."

It can smoothly solve the problem of "connecting disparate systems and data" that hinders successful IT utilization. We regularly hold free trial versions and hands-on sessions where you can try it out for free, so we hope you will give it a try.


Why not try a PoC to see if "HULFT Square" can transform your business?

Why not try verifying how "connecting" can be utilized in your business, the feasibility of solving problems using data integration, and the benefits that can be obtained?

  • I want to automate data integration with SaaS, but I want to confirm the feasibility of doing so.
  • We want to move forward with data utilization, but we have issues with system integration
  • I want to consider data integration platform to achieve DX.

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