Big Data is Transforming the Travel Industry

Big data is transforming the way businesses conduct operations. Data is gathered in many ways through online searches, analysis of consumer buying behavior and more, and companies use this data to improve their profit margin and provide an overall better experience to customers. While big data is used in many industries around the globe, the travel industry stands to gain a tremendous amount from its use. Many larger companies are already using big data creatively, but you may not understand the true value it can provide for your business. With a closer look at how big data is transforming the travel industry, you can better determine how your own business can benefit from its use.

Greater Personalization

The travel industry includes a wide range of businesses, such as rental car companies, hotels, airlines, tour operators, cruise lines and more. Each of these companies must find a way to improve the overall customer experience and to meet the unique needs of each customer, and big data assists with this process. Through the use and analysis of big data, travel industry companies can learn more about the preferences of smaller segments of their target audience or even about individuals in some cases. This gives them the ability to tailor special promotions, deals, experiences and more specifically to them. For example, a Las Vegas resort may determine that its customer base is largely comprised of younger adults, so it may host a popular national hip hop star’s concert in its auditorium to attract more visitors. Improving the customer experience through personalization may also enable travel businesses to generate repeat business through loyalty and to get more word-of-mouth referrals.

Unique Differentiation

Big data analysis also gives travel businesses the opportunity to determine more easily why their customers are choosing them over the competition and vice versa. It is necessary to stand out in a crowded marketplace, and businesses that understand why customers are choosing their business over the competition can tailor marketing and products specifically to that niche. Unique differentiation can be used to improve branding, make marketing more cost-effective and even design new products or promotions that appeal specifically to consumers based on why they are choosing to work with a specific company.

Improvement of Business Operations

Travel industry businesses may also use big data analysis to improve operations in numerous ways. Some data analysis may reveal, for example, that one specific aspect of marketing is ineffective, and the company can alter marketing efforts to generate a greater return on investment. Another company may learn from big data that the customers are choosing the competition more heavily because of special price promotions or a perception of better quality. These are only a few of the ways that big data can be revealing, and proper analysis of it can help travel businesses to improve operations for enhanced success and improved profitability going forward.

Real-Time Travel Assistance

Big data captured through mobile devices can provide travel businesses with insight about their current locations as they travel around the world. Some travel companies are harnessing this real-time data to provide travel assistance and recommendations. For example, if a travel app determines that your smartphone is located next to a popular theme park, restaurant or other attraction, it may send your special offers or deals that you can use to save money on a visit to these places. Some also use helpful travel tips or links to local services that you may find helpful.

The Ability to Meet Future Needs

Airlines and cruise lines are just a few of the travel companies that need to know where customers are interested in visiting so that they can customize future travel options available through their companies. For example, one cruise line may see that there is an increased interest in travelers wishing to stop in Costa Rica or Cuba through their use of big data analysis. An airline company may determine that they need a direct flight from Houston to Phoenix several times per week to meet customers’ needs. Perhaps there is an increased interest in people looking for the best places to live in Michigan as a vacation spot, and local businesses can benefit by appealing to these potential long-term vacationers. By analyzing big data, travel companies can better determine how to allocate resources going forward in the most cost-effective way.

Some consumers are understandably timid and even alarmed by how big data is being used to gather information about them. They may view it as an invasion of privacy, for example. However, many travel industry companies successfully use the data that they gather through legitimate means to improve the customer experience in various ways, all parties can benefit. If you work in the travel industry, analyze the big data stats that you may have access to today to determine how you can use the information in positive, productive ways.

Contributed by: Rick Delgado. He’s been blessed to have a successful career and has recently taken a step back to pursue his passion of writing. Rick loves to write about new technologies and how it can help us and our planet.



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Peering into Neural Networks

Neural networks, which learn to perform computational tasks by analyzing large sets of training data, are responsible for today’s best-performing artificial intelligence systems, from speech recognition systems, to automatic translators, to self-driving cars. But neural nets are black boxes. Once they’ve been trained, even their designers rarely have any idea what they’re doing — what data elements they’re processing and how.

Two years ago, a team of computer-vision researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) described a method for peering into the black box of a neural net trained to identify visual scenes. The method provided some interesting insights, but it required data to be sent to human reviewers recruited through Amazon’s Mechanical Turk crowdsourcing service.

At this year’s Computer Vision and Pattern Recognition conference, CSAIL researchers will present a fully automated version of the same system. Where the previous paper reported the analysis of one type of neural network trained to perform one task, the new paper reports the analysis of four types of neural networks trained to perform more than 20 tasks, including recognizing scenes and objects, colorizing grey images, and solving puzzles. Some of the new networks are so large that analyzing any one of them would have been cost-prohibitive under the old method.

The researchers also conducted several sets of experiments on their networks that not only shed light on the nature of several computer-vision and computational-photography algorithms, but could also provide some evidence about the organization of the human brain.

Neural networks are so called because they loosely resemble the human nervous system, with large numbers of fairly simple but densely connected information-processing “nodes.” Like neurons, a neural net’s nodes receive information signals from their neighbors and then either “fire” — emitting their own signals — or don’t. And as with neurons, the strength of a node’s firing response can vary.

In both the new paper and the earlier one, the MIT researchers doctored neural networks trained to perform computer vision tasks so that they disclosed the strength with which individual nodes fired in response to different input images. Then they selected the 10 input images that provoked the strongest response from each node.

In the earlier paper, the researchers sent the images to workers recruited through Mechanical Turk, who were asked to identify what the images had in common. In the new paper, they use a computer system instead.

We catalogued 1,100 visual concepts — things like the color green, or a swirly texture, or wood material, or a human face, or a bicycle wheel, or a snowy mountaintop,” says David Bau, an MIT graduate student in electrical engineering and computer science and one of the paper’s two first authors. “We drew on several data sets that other people had developed, and merged them into a broadly and densely labeled data set of visual concepts. It’s got many, many labels, and for each label we know which pixels in which image correspond to that label.”

The paper’s other authors are Bolei Zhou, co-first author and fellow graduate student; Antonio Torralba, MIT professor of electrical engineering and computer science; Aude Oliva, CSAIL principal research scientist; and Aditya Khosla, who earned his PhD as a member of Torralba’s group and is now the chief technology officer of the medical-computing company PathAI.

The researchers also knew which pixels of which images corresponded to a given network node’s strongest responses. Today’s neural nets are organized into layers. Data are fed into the lowest layer, which processes them and passes them to the next layer, and so on. With visual data, the input images are broken into small chunks, and each chunk is fed to a separate input node.

For every strong response from a high-level node in one of their networks, the researchers could trace back the firing patterns that led to it, and thus identify the specific image pixels it was responding to. Because their system could frequently identify labels that corresponded to the precise pixel clusters that provoked a strong response from a given node, it could characterize the node’s behavior with great specificity.

The researchers organized the visual concepts in their database into a hierarchy. Each level of the hierarchy incorporates concepts from the level below, beginning with colors and working upward through textures, materials, parts, objects, and scenes. Typically, lower layers of a neural network would fire in response to simpler visual properties — such as colors and textures — and higher layers would fire in response to more complex properties.

But the hierarchy also allowed the researchers to quantify the emphasis that networks trained to perform different tasks placed on different visual properties. For instance, a network trained to colorize black-and-white images devoted a large majority of its nodes to recognizing textures. Another network, when trained to track objects across several frames of video, devoted a higher percentage of its nodes to scene recognition than it did when trained to recognize scenes; in that case, many of its nodes were in fact dedicated to object detection.

One of the researchers’ experiments could conceivably shed light on a vexed question in neuroscience. Research involving human subjects with electrodes implanted in their brains to control severe neurological disorders has seemed to suggest that individual neurons in the brain fire in response to specific visual stimuli. This hypothesis, originally called the grandmother-neuron hypothesis, is more familiar to a recent generation of neuroscientists as the Jennifer-Aniston-neuron hypothesis, after the discovery that several neurological patients had neurons that appeared to respond only to depictions of particular Hollywood celebrities.

Many neuroscientists dispute this interpretation. They argue that shifting constellations of neurons, rather than individual neurons, anchor sensory discriminations in the brain. Thus, the so-called Jennifer Aniston neuron is merely one of many neurons that collectively fire in response to images of Jennifer Aniston. And it’s probably part of many other constellations that fire in response to stimuli that haven’t been tested yet.

Because their new analytic technique is fully automated, the MIT researchers were able to test whether something similar takes place in a neural network trained to recognize visual scenes. In addition to identifying individual network nodes that were tuned to particular visual concepts, they also considered randomly selected combinations of nodes. Combinations of nodes, however, picked out far fewer visual concepts than individual nodes did — roughly 80 percent fewer.

To my eye, this is suggesting that neural networks are actually trying to approximate getting a grandmother neuron,” Bau says. “They’re not trying to just smear the idea of grandmother all over the place. They’re trying to assign it to a neuron. It’s this interesting hint of this structure that most people don’t believe is that simple.”


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Case Study: More Efficient Numerical Simulation in Astrophysics

Novosibirsk State University is one of the major research and educational centers in Russia and one of the largest universities in Siberia. When researchers at the University were looking to develop and optimize a software tool for numerical simulation of magnetohydrodynamics (MHD) problems with hydrogen ionization —part of an astrophysical objects simulation (AstroPhi) project—they needed to optimize the tool’s performance on Intel® Xeon Phi™ processor-based hardware.

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