Can you measure water coming out of a fire hose, while it’s hitting your face? No? Then how would you calculate the amount of data that is constantly being churning out of numerous social media platforms? In simple terms, it’s not feasible, unless you opt for streaming algorithms – these are computer programs that execute such on-the-go calculations.
Data flow is constant and humongous – in order to strategically record the essence, the rest of the data is mostly forgotten. A large pool of data scientists is constantly looking for ways to build a better, improved streaming algorithm, but now I guess their search has come to an end, they have invented something incredible to vouch for. This new, best-of-the-lot streaming algorithm performs miraculously by grasping just what it seems to be necessary, ignoring others. It remembers just that which it has seen the most, and that gives it an upper hand.
Streaming algorithms is great, especially for observing a database that needs to be updated constantly. This could be anything from Google nabbing the continuous flow of search queries to AT&T that checks on data packets. Sometimes, it becomes extremely important to answer real-time questions related to data without going through piles of data or remembering every single piece of data you have every laid your hands on.
In simple phases, it is easy, but the case turns out to be complicated when the data gets more intricate. Suppose, instead of doing a simple calculation to find out sum, you are asked to answer which of the numbers appear most frequently? This kind of problems is known as “heavy hitters” or “frequent items” problem. Back in 1980s, Jayadev Misra from University of Texas and David Gries from Cornell University developed an inspiring yet effective algorithm, but it fell short to address the issue of change detection. It could highlight the most frequently searched items but couldn’t pinpoint items that are trending.
In the next 30 years, several budding and veteran data scientists tried to improve Misra and Gries’ algorithm model – and some of them actually proved worthwhile in detecting the trending terms. Majority of these efforts focused their attention on an index. For example, if you are on a lookout to identify frequent search terms, the best way to do it would be by assigning a number to the English language words and then team that number with another number to track how many times the word has been searched. However, this process poses a key drawback – it would take a lot of time to fire up an algorithm that combs through an infinite number of English words.
Irrespective of all the pros and cons, no algorithm runs perfectly always – even the best ones fails to perform well even though for a small percentage of time. However, nowadays scientists are using an ‘expander graph’ – in place of connecting each point with an adjoining block, you can now connect each two-digit block with multiple blocks to form a cluster. Nevertheless, often an expander graph fails to perform perfectly. Either they fail to link two blocks or links wrong blocks together. As a result of this, a new sub-algorithm had to be developed, ‘cluster-preserving’ – it minutely observes an expander graph and determines the points that must be clustered together and points which should be left apart.
As compared to previous works, this algorithm proves that there was always something that wasn’t accurate or not enough memory-intensive in case of older versions. The new algorithm exhibits that if the encoding is done in the right manner, you would be able to store all your frequent items, as well recall them, as and when necessary.
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