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How Machine Learning is Taking the Automotive Industry to a New Level

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How Machine Learning is Taking the Automotive Industry to a New Level


Machine learning is taking the automotive industry to a new level by improving user experience and leveraging the power of big data.

Most manufacturing operations in automotive industries are still largely dependent on experience-based human decisions. The emergence of Big Data, in conjunction with machine learning in automotive companies, has paved a way that is helping bring operational and business transformations, thereby leading to an increased level of accuracy in decision-making and improved performance.

The automotive industry continues to face a dynamic set of challenges. Shifting market conditions, increased competition, globalization, cost pressure and volatility are leading to a change in the market landscape. Self driving cars and changing usage models have heightened customer expectations. It is needless to say that the automotive industry is on the brink of a revolution. One area that has demonstrated an opportunity to deliver significant competitive advantage is analytics. The automobile is getting transformed by technologies. AI and machine learning algorithms have found an increasing level of applicability in this industry. The collaboration of Big Data analytics and machine learning has boosted capacity to process large volumes of data, thereby accelerating growth of AI systems. Machine learning in the automotive industry has a remarkable ability to bring out hidden relationships among data sets and make predictions.

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Source: McKinsey

1. Improving Vehicle Performance with the Incorporation of Big Data Analysis

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Machine learning algorithms can accurately incorporate analysis results of customer feedback in social media, for example, text and tweet analytics. This helps in building vehicle and sub-systems performance for guiding future product design. It also helps in detecting failure patterns for establishing a relationship between the failure and causes of failure. Take an example of an automotive company, that found out that cause of failure in several operations in the car is associated with region-specific issues such as inferior fuel quality, climatic conditions, road infrastructure, and so on. This company can make use of machine learning systems for developing region-specific customizations that can improve product reliability.

2. Leveraging Preventive & Predictive Maintenance

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Source: Prometheus Group

Machine learning algorithms can aid in effective planning and execution of predictive maintenance. Predictive maintenance employs monitoring and prediction modelling for determining the condition of the machine and for predicting what is likely to fail and when it is going to happen. Machine learning systems can help in adjusting maintenance interval, where the same maintenance is conducted but shifted backwards or forward in time or mileage. Thus, machine learning systems can enhance predictive maintenance capabilities and help in accurate prediction of future failures instead of diagnosing already existing ones.

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3. Enhancing Overall In-Vehicle User Experience

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Machine learning facilitates personalization and smart personal assistance. It incorporates analysis results and learns traits of user personality, thereby creating user-specific profiles, which can then be leveraged to provide personalization and assistance. 

Machine learning algorithms can be quite useful in solving automotive domain problems, but organizations implementing Big Data analytics and machine learning systems must know how to select the correct algorithm and input/feature vectors for a specific problem domain. Selecting correct feature vectors requires domain experts, and selecting correct algorithms requires experienced data scientists. Once they know how to define the problem domain and business objectives, and validate the selected algorithm in terms of functionality and performance metrics, machine learning systems can accurately demonstrate tangible business benefits.



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TECHNOLOGY

How Blockchain and Big Data Can Work Together

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How Blockchain and Big Data Can Work Together


Big data and blockchain work well together by providing more security and integrity. 

One is transforming data management while the other is changing the nature of transactions altogether. Could they create an even more significant impact on the industries by binding together – big data for blockchain or blockchain for big data?.

Big data technologies first came into the picture at the dawn of this millennium to meet the computational needs of large datasets in the Internet-era. Proprietary applications like BigTable by Google and ZooKeeper at Yahoo showcased the potential of big data. However, the potential could only be tapped into after open-source projects such as the Hadoop File System (HDFS) and Hadoop MapReduce hit the market. Since then, big data has snowballed to transform how companies manage their data in the 21st century. Satoshi Nakamoto, an anonymous mystic individual, introduced the world to blockchain in 2008. It was developed in an attempt to solve the problem of double spending in transactions by eliminating the need for a third party in financial transactions. Blockchain also gave the world its first digital cryptocurrency – the bitcoin. Since then, the concept of blockchain has rapidly evolved to provide robust solutions to problems persisting in a wide array of industries. Now that both big data and blockchain are established as effective tools to tackle issues in different domains, we look forward to – possible methods of integrating both big data and blockchain to deliver even better solutions to specific problems, or as we’ve called it in this article, blockchain for big data and big data for blockchain.

How Big Data Works With Blockchain

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A lot of governments have had trouble with the anonymity clause of blockchain. Despite being favored for its security and infallibility, blockchains are turned down for not being able to track stakeholders in transactions, thus being a preferred choice for illegal trade. Big data applications can help make blockchains trackable by managing structured datasets of wallet addresses and their owner details. This kind of infrastructure can convince governments to adopt blockchain as a platform for transactions that demand speed, safety, reliability, and traceability – thanks to big data for blockchain.

The Close Ties Between Blockchain and Big Data

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Big data is comfortably dealing with huge sets of data, but some issues in its infrastructure have posed a problem in the widespread adoption of the technology. The big data infrastructure is centralized to a server location that offers complete unconditional control of data to the ones who have access to the server. This ‘ownership’ creates a problem when big data infrastructure is to be shared between different companies or even different regional offices of the same company. Besides, having multiple copies at different locations is not a solution because it puts a burden on resources and also creates confusion while determining the most updated data resource. Furthermore, now that big data resources are being traded among different entities, the legitimacy of a data resource poses a concern. With a blockchain for big data, we can create a decentralized data resource to which every one has full access. We can also track updates to the data resource on the blockchain, eliminating the need for and confusion due to multiple copies. Moreover, data transactions can be verified for legitimacy using blockchain concepts like proof-of-work or proof-of-stake and at the same time blockchain can provide a robust financial platform for data transactions between entities.

It is incredible how both of these technologies – big data and blockchain – can together significantly improve the usability of each other. The techniques can help create a hybrid infrastructure on pillars of big data and blockchain. The infrastructure will be flexible for different application types, like its parents – big data and blockchain.

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