data mining Archive

How Government Services Are Using Data Technologies

Nelly L.

We think of citizens as legally recognized subjects of a particular state or community. As citizens, they are bound to certain regulations and rules that are maintained and controlled by governmental agencies, who oversee every aspect of their lives for the well-being of the inhabitants as a whole. These government agencies control the economy, social freedoms, and political systems of the citizens. Unsurprisingly, they also take advantage of the most sophisticated business techniques today in order to do so. If that is so, how is business related to the government? And how does the government use data obtained from these business technologies to oversee citizen community engagement? read more...

Data Mining in Health

By Amanda L.

Data mining has proven itself to be a highly useful analytic process and instrumental in effective decision making in various industries. By analyzing large sets of data to discover patterns and correlations, an e-commerce organization can understand consumer shopping habits and predict future behavior, or manufacturer can assess quality control and schedule maintenance accordingly. Nevertheless, data mining in health provides nearly as many benefits, with its use becoming increasingly more extensive and popular. read more...

Data-mining in Environmental Services

By Jeremy K.

Data-mining for environmental services refers to any research or examination of large-scale data in relation to the environment. Some examples of environmental services that use databases are forecasting, ecosystems, and recycling. The benefits of data-mining in an environmental standpoint are to collect data and analyze the areas that directly affect the environment. In doing so, projects can be started in hopes of resolving the issues that negatively affect our economy. Analysis of such environmental factors can also allow us to improve the efficiency of certain aspects of the environment such as how to make traffic flow smoother. Benefits of datamining in environmental services also include efficiently analyzing issues that arise through cluster diagrams and determining the best option through regression models and various software technologies. Data-mining in environmental services can also help influence the health industry through new discoveries of habits that cause certain diseases such as cancer. read more...

Data mining in Entertainment

By Malcolm I.

The entertainment industry is always evolving to keep up with fans, ratings, and sending information to viewers. With today’s technology it makes entertainment easier to access from viewers without any technical difficulties. Big data is there to help make things easier for the industry. Data mining in the entertainment industry is sometimes used to give insights on what the audience really wants. The information they receive can help expand a show already on the air or help develop new ones (Rijamen, M. V.). Big data also helps the entertainment industry understand whether a movie or series will be a hit. read more...

Data Mining in the Engineering Industry

By Lillian H.

Data mining is the act of collecting data, transforming the data into information, and eventually using the information to improve, create, and fulfill goals. Considering the engineering industry and the massive amounts of data generated by each of its sectors (software, electrical, mechanical, etc.), data mining is extremely valuable in this field. This paper will go further in depth on the benefits, tools, and challenges data mining provides specifically in the engineering fields of software, and electrical. read more...

Machine Learning: Consumer Products and Services

By Alexander E.
With the evolution of machine learning, computers have the ability to learn without being programmed by using algorithms to iteratively learn from data. Machine learning is similar to data mining in that they both look for patterns in data. It then uses that data to perform tasks without being explicitly programmed. Machine learning has been developed in consumer products and services, which are items that are purchased by individuals or households for daily consumption. The impact of machine learning has increased the ability to anticipate consumer needs and help consumers make decisions without using their input. When dealing with consumer products and services, machine learning produces many benefits, utilizes tools and software for data to be processed, and faces challenges in the process.
Machine learning can be classified into two different types including supervised learning and unsupervised learning. In supervised learning, there are input variables and output variables in which the input data can predict the output for that data. This algorithm compares the actual and correct output to find errors so that it can modify it. Unsupervised learning is when the system has input variables without output variables and has to figure out what the data is showing. In this algorithm, there are no correct answers.
For machine learning to perform, it requires tools and software that provide functionality. According to the InfoWorld.com article, one of the open source tools used is called Shogun, which is a machine learning library written in C++ used in: Java, Python, C#, Ruby, R, Lua, Octave, and Matlab. (Yegulalp, 2014). Another tool is called Accord, which is a machine learning and signal processing framework for .Net. Signal processing is “a range of machine learning algorithms for images and audio that stitches together images or performing face detection.” (Yegulalp). H20 is used for business processes, including fraud or trend predictions.
Consumers can benefit from machine learning in many ways. For example, machine learning makes it possible for consumers to personalize their online experiences without having to input any data. In the Wired.com article, it mentions that “from a consumer perspective, everything that is performed online, every sales process, product interaction… is being tracked by various sources.” (“Use Data to Tell the Future: Understanding Machine Learning”, 2014). Machine learning can be effective when perceptions need to be found from Big Data that’s always changing and diverse. It can outperform traditional methods on accuracy, scale, and speed. These methods are highly superior when analyzing potential consumers across data from many sources including transactional and social media sources. (“Use Data to Tell the Future: Understanding Machine Learning”). There are services like Netflix and Facebook that predict future outcomes for the users. When watching favorite shows on Netflix, it can generate suggestions based on user preferences. Facebook and other social media have the ability to collect data from users and present suggestions of what friends to add or what pages to like.
An application that is using machine learning is The North Face, which is a U.S. outdoor apparel and retail store. The ComputerWorld.com article discusses how North Face created the Expert Personal Shopper that is an online shopping assistant. (Gaudin, 2016). This system asks questions based on what the consumer may be using his/her desired product for. It then offers the best choices based on the criteria selection. Another consumer service that is using machine learning is Amazon. According to TheNextWeb.com article, Amazon is using machine learning to make an improvement in product reviews. (Ghoshal, 2015). The company wants to upgrade the system so that reviews can be more helpful to consumers on products. Amazon also wants to help consumers to learn about the manufacturer changes of existing products that are yet to be announced or mentioned.
Machine learning deals with challenges in the system. It requires a huge amount of data from humans to understand different concepts. In TheVerge.com article by James Vincent, he talks about how big firms like Facebook or Google have a copious amount of data to improve machine learning systems. (Vincent, 2016). On the other hand, small tech firms don’t have the luxury in the same amount of data. Another challenge is having artificial intelligence be able to do multiple tasks at once. The current systems are able to be efficient in whatever task they are designed for, but are not able to fully multitask. The third major challenge is trying to understand how artificial intelligence comes to its conclusions. Machines aren’t fully advanced in thinking exactly like humans which limits its capabilities.
In conclusion, machine learning can benefit consumer needs in the future with the expansion of artificial intelligence. It has the potential to analyze what consumers desire and make recommendations or suggestions based on personalization. Consumers will be able to produce less and less input data for their personal needs with the growth of machine learning. read more...

Machine Learning: Business Products and Services

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. read more...

Applications of Machine Learning in Advertising and Marketing

By Germaine A.

For years now, machine learning has been an integral part of the advertising and marketing industry. With so much data pouring in from users, effective leveraging that data requires the help of machine learning. It is no wonder that advertising agencies are using machine learning to more effectively cater to potential customers through personalized advertising and returning customers by offering more effective customer service and support (Biewald 2016).
In the domain of web-based advertising, machine learning has made its mark known. Since ads are sold on a per-click basis it is useful to know how effective they are before committing time and resources to their deployment. In the past, advertising firms relied on simple human intuition. However, to gain a competitive advantage they need more: enter ad click-through rate prediction. Google’s AdSense is a juggernaut in this arena. Utilizing machine learning for predicting ad click-through rates, Google AdSense can maximize advertising effectiveness by honing in on a user’s particular wants and needs to provide a tailored experience using data pulled from search history, page dwell time, website content, and click behavior (McMahan 2013). A number of advertising applications such as sponsored search advertising, contextual advertising, display advertising, and real-time bidding auctions have benefitted from the increased efficiency from ad click-through prediction.
Moving from cyberspace to human space, machine learning-augmented advertising continues to flourish. Data intelligence firms are seeking ways to tie in a customers’ digital footprint with their footprints in the real world. That is, matching a user’s browsing data on an online store to their buying habits at a physical store location. Firms like Qualia are using an opt-in location system to collect information by pulling from GPS and wi-fi signals and integrating them into machine learning algorithms so that advertisers can offer specialized services and recommend commodities (Qorbani 2017).
In the customer relations theatre of marketing, machine learning is also making large strides. Gone are the days of having the user fill out forms with varying degrees of accuracy, instead machine learning can produce help tickets by gleaning relevant information from the substance of the user’s complaint directly (Biewald 2016). The result is efficient ticket tagging and routing, saving time and money for any firm’s customer service department.
The benefits of machine learning aren’t limited to fully automating processes: it can also augment a marketers’ skills and bolster their toolset. Salespeople face the problem of personalizing messages to every potential customer while also reaching out to a large number of people. The email analytics platform offered by Nova provides a solution. Integrating directly into the salesperson’s gmail inbox, Nova’s software tracks the rates of potential customers’ clicks, reads, replies, and bounces to the salesperson’s correspondence and correlating it with public data (Matney 2016).
As with any new technology, machine learning is not without its limitations and concerns especially in the field of advertising and marketing. Even if information is publicly available it does not necessarily mean that accessing it and correlating it with other sources is not a breach of privacy. The main advantage of machine learning is in “filling the gaps” and it is precisely this feature that makes the potential breach of privacy so severe, especially if location services are involved. It allows, potentially, for a corporation to track a person’s movements and location at any time. Even assuming that corporation is entirely benign, a data breach can put many customers at risk. For customer service, machine learning cannot replace a true one-to-one client interaction no matter how much it can streamline.
Machine learning-augmented advertising and marketing is a powerful tool to reach and service customers. As this technology grows and matures, it will no doubt form a greater part of the advertising industry.  read more...

Database and Data Mining for Consumer Products and Services

By Jonathan C.

A successful business needs to have quality products and services. Today, to achieve both at the same time with efficiency, business must have a way to achieve the data collecting, storing, analyzing and sharing the information. The introduction of computer enabled business a new way to deal with increase data instead of pen and papers. Some company have their database as early as 1970s. Per Jianfeng Wang, Wal-Mart’s database of programs and control systems were activated in 1972 for store and management levels use. At the early stage, the database is mostly used for product distribution and communication between the suppliers and retailer. Moreover, per Constance L. Hays from The New York Times, database and mining for consumer did not start until early 2000. Now many major business services such as retail, banking, insurance, health, and many more are using database and mining to benefit their relationship with their customers. The world of marketing has change from product-orientation to customer-orientation. read more...

Machine Learning Applied to Agriculture

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. read more...