The Next-generation Search Engine Challenges And Key Technologies

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This is a guest post by entrepreneur Lars Hård

  1. The Next-generation Search Engine Challenges And Key Technologies List
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  6. Modern web search engines are highly intricate software systems that employ technology that has evolved over the years. There are a number of sub-categories of search engine software that are.

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Over the past 14 years, Google has set the standard for online search. The ability to access expansive amounts of information on a global scale and deliver links full of information to our fingertips was, and is, revolutionary.

On an average day, Google crawls through 20 billion web pages, and serves 100 billion searches every month. These numbers will only continue to increase, as data increases exponentially. It’s no secret that this data overload is causing a lot of problems.

One unexpected and dramatic impact of this influx of information is that it has exposed the weaknesses of the current design of search as we know it.

Today’s search is flawed

Today’s search function is mainly linking to mostly static content. It is not able to differentiate on an individual level which of the potentially relevant answers is the most accurate one for your particular search just by referencing popular keywords — it uses a popularity algorithm as a proxy to solve this. But, as we know, what’s popular isn’t always the answer to our specific question or search. Likewise, modern lifestyles have experienced the limitations of the mobile interface, making it difficult to research topics on the go.

These factors reveal a fundamental problem with search today: it’s not a dynamic and flexible process. Surely, in today’s world we need more than a search page with a list of blue URL links to sort through when we’re looking for recommendations, advice, diagnosis and other methods of finding and exploring information and products in the digital age.

The next frontier: mobile search

Consumers are beginning to demand a better, more comprehensive search experience. People are already using highly specific apps on their smartphones, rather than traditional search engines to find information. According to Roger McNamee 1 out of a 100 Google searches are conducted on mobiles devices and the rest accounts for PC web searches.

Yet, this doesn’t actually mean people aren’t searching on their mobile device, it only means they aren’t using Google to do so. In most cases today, a subject-specific app is more likely to generate the tailored content you are looking for.

For example, suppose you are shopping for an outfit for an upcoming holiday party and want to get a sense of what kinds of new styles are available in your favorite stores. Does your online search begin with Google or your favorite store’s website? Or, image you are in transit on your way to now buy the outfit from your favorite store and you want to double check your bank funds before purchasing. Do you type in your bank’s name to the Google search bar, or do you tap on your banking app to access your bank account?

Increasingly, the answers to these questions do not involve a traditional search engine. As our lives become a bundle of digital data — online and on our mobile devices — we’re approaching the next generation of access to information; a new form of search and discovery. This new phase is all about the Internet growing up and starting to provide the same kind of service we get when we’re offline. With data consumption and data creation growing by the minute, search and discovery must evolve to not only deliver specific keyword matches, but to offer a personalized experience based on the individual needs and wants of a user.

Consumers are beginning to require their search functionality to be more tailored to specific preferences and constraints, and hence, the app-centric world we live in today is beginning to take root. A recent Nielsen report shared that the average number of apps owned by a U.S. smartphone user is now at 41 — a rise of 28 percent on the 32 apps owned on average last year. To access our version of the information we’re looking for, we now tap on our shopping apps, our banking apps, our news apps, our entertainment apps, our social network apps.

The future of Artificial Intelligence and search (AI)

Artificial Intelligence will take this one important step further by providing the deep personalization and rich interactivity that the consumer is now craving by referencing a users’ usage, profile and behavior, and resulting in delivering information that’s much more relevant.

Additionally, AI can handle the complex task of optimizing recommendations and advice based on your contextual information (such as location and time) but also personal taste, needs wants and constraints. Therefore, a shopping app with AI integration could come in the form of a real estate app that’s able to alert you when your dream house comes up for sale after taking into your personal basic financial information, travel, school needs, entertainment and work preferences without you having to constantly spend hours poring over possible houses.

But how will AI help us in daily tasks versus just daily questions in tomorrow’s search? Both online and mobile users will become increasingly reliant on AI virtual assistants, or “smart” apps, in place of search engines, to procure relevant information. These assistants will have the ability to perform human-like reasoning and problem-solving, and better analyze and predict our digital content.

Many more providers of digital content will be offering their own virtual assistants and “smart” apps that will offer services that mirror how you would engage with a sales assistant that knows you very well and who would be able to recommend or advise you on products and services. It will be completely natural to outsource the search for news to a personalized magazine that knows what you want and need and bring it together for you on demand. You will have access to medical diagnosis that offer advice on your health, replacing the various searches you conduct online to find out specific or unusual aliments.

In this world the value of search moves from the central search engines to the individual companies and apps that provide the expertise or services you want, as the search of tomorrow requires more knowledge and expertise than a central search tool could ever handle.

The Next-generation Search Engine Challenges And Key Technologies List

Lars Hård has over 20 years of experience in running advanced AI development teams, both in Europe and North America. In 2006, Lars founded Expertmaker, an Artificial Intelligence platform solution based in Malmo, Sweden and San Francisco.

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The Next-generation Search Engine Challenges And Key Technologies Reviews

Lars is also a guest lecturer at Lund University on the topics of theoretical ecology and genetics and is a frequent speaker at conferences on technology innovation and mobile evolution.

The Next-generation Search Engine Challenges And Key Technologies Inc

  1. Gantz, J.F., Chute, C., Manfrediz, A., et al.: The Diverse and Exploding Digital Universe - An Updated Forecast of Worldwide Information Growth Through 2011. An IDC White Paper sponsored by EMC (2008)Google Scholar
  2. Google, http://www.google.com
  3. Box Computing http://boxcomputing.baidu.com/
  4. Yi, X., Raghavan, H., Leggetter, C.: Discovering users’ specific geo intention in web search. In: Proceedings of the 18th International Conference on World Wide Web, Madrid, Spain (2009)Google Scholar
  5. Bao, S., Xue, G., Wu, X., Yu, Y., et al.: Optimizing web search using social annotations. In: Proceedings of the 16th International Conference on World Wide Web, Banff, Alberta, Canada (2007)Google Scholar
  6. Lowe, D.G.: Distinctive Image Features from Scale-Invariant Keypoints. Int. J. Comput. Vision 60, 91–110 (2004)CrossRefGoogle Scholar
  7. Facebook, http://www.facebook.com
  8. Technology Review, Special Report: Next-Generation Search (2010)Google Scholar
  9. Gou, L., Chen, H., Kim, J., Zhang, X., Giles, C.L.: SNDocRank: a social network-based video search ranking framework. In: MIR 2010, pp. 367–376. ACM, New York (2010)Google Scholar
  10. Nister, D., Stewenius, H.: Scalable Recognition with a Vocabulary Tree. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 2161–2168 (2006)Google Scholar
  11. Ngo, C., Ma, Y., Zhang, H.: Automatic Video Summarization by Graph Modeling. In: ICCV, Washington, DC, p. 104 (2003)Google Scholar
  12. Datar, M., Immorlica, N., Indyk, P., Mirrokni, V.S.: Locality-Sensitive Hashing Scheme Based on p-Stable Distributions. In: Proceedings of the Symposium on Computational Geometry (2004)Google Scholar
  13. Chang, S.-F., Sikora, T., Puri, A.: Overview of the mpeg-7 standard. IEEE Transactions on Circuits and Systems for Video Technology 11(6), 688–695 (2001)CrossRefGoogle Scholar
  14. Dean, J., Ghemawat, S.: MapReduce: Simplified Data Processing on Large Clusters. Communications of the ACM 51(1), 107–113 (2008)CrossRefGoogle Scholar
  15. Li, W., Lang, B.: A tetrahedral data model for unstructured data management. Science China Information Sciences 53(8), 1497–1510 (2010)CrossRefGoogle Scholar