Big Data

Agile in the World of Technology {0}

By Daniel A.

Agile development is a methodology which is practiced in businesses, either practiced within the business or can be used more efficiently through another business. Businesses need to use the best resources in their disposal to produce services or products in which they can better fulfill the customer’s satisfaction. With so many methodologies to choose from it becomes a question on what would best suit the situation. No single methodology is the best at every situation, but there are many methodologies that can be flexible, but better suited for the issue at hand. Agile development is a great methodology if it is applied appropriately.

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Food database in food industry {0}

By Titan T.

Database management systems are becoming important for commercial and domestic users because these help in managing data efficiently, storing large size of information and carrying out multiple tasks at a time. Use of databases in food industry is the focus of this paper and it will help in finding out trends in food databases, applications of food databases, how these are making food business more competitive and successful.

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Data Mining in Pharmaceutical Research & Development {0}

By Ryan T.

The pharmaceutical industry has always relied heavily on data. That data consists of historical clinical trial results, cellular, genetic, microbial, molecular, proteomic, and metabolic data. With most, if not all, of this data being stored electronically and so much to sift through data mining has been highly advantageous in pharmaceutical research and development (Elvridge, 2016). Several proprietary and nonproprietary tools are available to researchers each with their own distinct differences. The pharmaceutical companies utilizing big data ranges from large companies to small firms since data mining effectively reduces the barrier of entry. This goes without saying, but big data mining does comes with risks and limitations when it comes to the pharmaceutical industry. Overall, the benefits far outweigh the risks though as developers and researchers continue improving their products.

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Data mining in Tourism {0}

By Billy S.

Nowadays, travel and tourism have grown into a large industry throughout the world. As technology has played a major role in our day to day life, it tends to affects the behavior of travelers since it was easy enough to find information on the spot. Lately, travelers have changed how they travel where they prefer to roam freely and use technology as a guide rather than strict their schedule to a plan (1). As a result, tourism industry requires quick and up to date information from many locations around the world. Therefore, data mining has become a necessity to obtain accurate data and information; from popular travel destination, places of interests and popular cultural attractions. However, globalization has changed the behavior of traveler where it made an impact on their cultural criteria, social criteria, personal criteria, and psychological criteria(1).

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Data Mining for Logistics and Transportation {0}

By Danielle N.

Big Data has made an immense impact on logistics and transportation sector by changing how businesses operate and has become highly critical and beneficial for companies to utilize. From personally customers’ experiences on the web to managing supply chains more efficiently, Big Data has changed the way businesses keep up as technology advances. Data mining is a concept used to analyze data from different sources and is utilize to summarize meaningful information. This information is an important factor that can be used to increase revenue, cuts costs, or both. It is a useful analytical tool for analyzing data and businesses are able to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified (Wu 2012).

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Data Mining in the Finance Industry {0}

By Brian Lam

In an industry that relies substantially on information, financial services institutions have long adopted the practice of gathering and analyzing data to improve business activities and decisions. In following more recent trends towards the automation of this process, financial businesses have begun turn their attention towards data mining and its benefits. Businesses such as banks, credit-card companies, insurance companies, etc. are implementing data mining tools and software within their companies to reap the benefits it has to offer. There are numerous applications of data mining within the finance industry, including illegal stock trading detection and customer relationship management. Although, as valuable as data mining can be for a company, it still has its challenges and limitations.

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Machine Learning in Engineering {0}

Hawkins T.

When discussing machine learning in the engineering industry, people have a few misconceptions of the benefits or uses of machine learning or AI in this field. A lot of science fiction imagery is introduced by today’s movies, media, and expansion of technology. This area however, isn’t only concerned with self sufficient robots as we had thought. A lot of machine learning in this industry is concerned with efficiency in the decision making process, and making well informed decisions with the help of machine learning to make these decisions. The application of these machine learning systems can be beneficial, as they can allow you to input lots of data into the system, that could not only help in decision making, but also solution implementation. This can be done, when machine intelligence units are able to learn from the data they are given, and evaluate it through a series of algorithms that could eventually produce a model or framework that an engineer can use as a supplement to his knowledge, to make the most informed decision.

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Machine Learning in the Construction Industry {0}

By Stephanie C.

Technology has changed drastically in the past twenty years and continues to grow today. Many industries are using up-to-date technology in order to get ahead in the game. In particular, the construction industry has been using a method of data analysis, known as machine learning, to increase productivity and safety, reduce costs, and much more. “Machine learning is a subset of artificial intelligence” that allows computers to learn from previous data without intervention from humans, and it does this by creating algorithms. These algorithms are grouped into two main categories: Supervised learning and unsupervised learning (“Machine Learning,” n.d.). Supervised machine-learning algorithms require the data to be labeled in order for decisions to be made, while unsupervised machine-learning only require the raw data and nothing more. The construction industry can greatly benefit from machine learning, but it also faces challenges along the way.

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Machine Learning: Business Products and Services {0}

By Alex C.

Business products and services is something that companies purchase for their own productions or operations. This includes many things such as component parts, raw materials, and any service that assists in the operation of a firm. It is hard to distinguish between a consumer and business product, but they are set apart by the end user. If a product is to be used for personal use, then it is a consumer product while a product used for a company or to be made into a product is a business product. Machine learning is artificial intelligence where computers can learn without being explicitly programmed. Most machine learning methods include supervised learning, where algorithms use labeled examples, and unsupervised learning, where data has no historical labels. When machine learning is applied to business products and services, it deals with the behind the scenes to help a business operate.

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Machine Learning Applied to Agriculture {0}

By Johnny C.

Technology is always changing and growing. In the modern day, advancements in technology come at exponential rates. One of the fastest ways to get ahead in an industry is by investing in cutting-edge technology. Agriculture is a huge part of the economy and is very important in sustaining countries. A more recent development in agriculture industry, is machine learning. Machine learning is a type of artificial intelligence that provides computers with the ability to learn without being explicitly programmed (Rouse, 2016). An example of this is, is an advancement made by biologist David Hughes and epidemiologist Marcel Salathe. “They fed a computer more than 50,000 images, and by learning on its own, the program can correctly identify 99.35 percent of the new images they throw at it.” This is a huge advantage for farmers, allowing them to understand what is affecting the crops, and finding faster ways to cure them. Machine learning can also help discover the best times and locations for crops, maximizing the yield. Although there are limits and challenges to machine learning, it is capable of pushing the boundaries in agriculture.

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