User:Lambsharkxo/sandbox

Google Search (Analyzed as a KM Technology), the advanced web search engine.


 * What are knowledge management (KM) tools/technologies?

KM tools and technologies are not only additional tools to help the stregic objective of the KM process, but some are the actual media through which steps must be taken, as the fundamental function of these tools is to “structure and organize knowledge content for each retrieval and maintenance” (Dalkir, 2017). The process and step of applying knowledge systematically examines the KM cycle to see what can be used for orgazational performance.


 * What is a search engine?

A search engine is defined as “a system that accepts a text query as input and returns a list of results, ranked by their purported relevance to the query” (Mitra, Craswell, 2017). An advanced search engine simply contains more intelligence, options, and function above and beneath the surface. These search engines may be part of a document retrieval or content management system, local to an organization, or may operate independently on their own as most web search engines do.

Some useful functions:

1. * One function is putting double quotes around the text being searched, making the search engine only look for that exact phrase. Though the engine highlights the text that was searched in whatever results that contain it, the quotations will generate more specific results the phrase being searched, eliminating lesser relevant articles from cluttering up generated results. 2. * Another, more powerful tool the search engine has acquired, relatively recently, is the ability to limit the search to one website of choosing, by use of the function “site:”. I find this to be extremely powerful in part because the websites do not need to grant any sort of permission for this pairing in compatibility, the search engine is simply powerful and fast enough to show such limited, specific results accordingly. 3. * Finally, there is even a function to exclude certain words, or rather categories, from search results. An example of this would be if you wanted to search “Lotus”, but not Lotus cars, you would enter “Lotus -cars”. These few basic functions would save a lot of time in information retrieval over time by themselves, but in pair with everything else the search engine has to offer, Google displays why an advanced search engine such as itself is required for retrieving knowledge from big data libraries.


 * How does a web search engine work?

1. Web engines use “spiders” to “web crawl” from site to site, of Google’s do continuously, sending information back to the index on a variety of factors. 2. Indexing, alternatively called “web indexing” in the context of search engines designed to find web pages, collects and captures the data that will be displayed in the results. This is either done automatically, through a series of processes by machines intelligent enough to do so, or manually by a user. Web indexing, at least when done by any online, independent search engine, is done automatically by intelligent technology. Indexing done manually by a user is more appropriate for local databases and libraries, digital or physical, confound to only within an organization or company. Web indexing can be performed quickly because much of the data is already collected and stored in indexes. After all, the purpose of storing an index is to “optimize speed and performance in finding relevant documents for a search query” (Spencer, Dodd, Friedman,& Xu, 2011). Without an index, the search engine would have to scan every document found, which would require a considerable amount of time and computing power. For example, while an index of five thousand documents can be queried within milliseconds, a detailed scan of every word in five thousand large documents could take hours.

3. After indexing comes the step of query formulation. A query is simply a request for information from a database, with the index allowing it to do so. Query it is an extremely broad concept, consisting of multiple languages, such as the popular Structured Query Language (SQL), which generate different types of data for different types of databases. However, in terms of this paper, the fundamental function of query formulation, or query transformation, is for the description of the query to be transformed into a representation that is more comprehensible to the search engine. It is in this window of increased transparency that the search engine deems what is useful and most relevant in whatever document relative to the query.

4. The final step of the information retrieval process is selection, where the results that are returned from the Google’s repository, or “retrieval corpus,” are displayed to the searcher in a form of ranking, by relevance being the default and most popular. Though Google does rank search results by relevance, or rather proximity, it also considers anchor text and something called Page Rank.


 * Page Rank is an intelligent, trademarked algorithm used by Google Search that works by counting the number and quality of links to a page to determine a rough estimate of how important the website is (Haveliwala, 2002). This is in part operating on the assumption that the more links to a page there are, the more important that page is. However, there are other factors that contribute to a web page’s PageRank, the first of which being the frequency and location of keywords within the web page. The lower amount of times a key word appears in the document, the lower the score that word will receive. The other contributor to PageRank is how long the web page has been online. Though we likely do not notice most of the time, web pages are created and removed daily. So, in turn, the amount of time a web page has existed on the internet is a heavily relevant factor, just as much as the first factor.


 * What makes an advanced search engine so advanced?

Advanced search engines, though used by the public as well, are nonetheless a knowledge management (KM) tool used strategically to retrieve bits of information from huge pools of data when it comes to organizational use. Advanced search engines such as Google Search also have more complex functions such as query expansion and query understanding, in addition to its trademarked PageRank algorithm.

1. Query Understanding According to the Association of Computing Machinery (ACM), query understanding is the process of inferring the intent of a search engine user by extracting semantic meaning from the searcher’s keywords. This type of technology is at the heart of services like Google Assistant, Amazon Alexa, and Microsoft Cortana. Among other elements within query understanding such as spelling correction, exist the process of tokenization. Tokenization can be thought of the as the “dissection” of a query by the advanced search engine, breaking up a text string into words or other meaningful elements called tokens (Trim, 2013). Though still relevant to web search engines, this function is more noticeable in the voice assistant and voice-search services, when the query being presented is done so verbally rather than typed text.

2. Query Expansion Query expansion is something we are all familiar with, well, at least familiar with one of its more prominent functions, autocomplete, not to be confused with auto-fill. While both are extremely convenient and utilized, autocomplete deals with adding to or completing a query while it is being typed, and this is not just limited to web search engines but also the search engine built into a computer’s operating software (OS). Auto-fill on the other hand can automatically fill at web forms, sometimes confidential, with stored information the user instructed it to use. Nonetheless, both concepts fall under query expansion, being “the process of evaluating a user's input (what words were typed into the search query area, and sometimes other types of data) and expanding the search query to match additional documents” (Vechtomova & Wang, 2006).


 * Autocomplete, a convenience that is at times, inconvenient.

Due to the extreme number of searches, approximately sixty-three thousand per second, it is inevitable for certain things to slip through the cracks of coded security. And due to the fact, that autocomplete utilizes the PageRank algorithm of generating results influenced by popular searches, inappropriate or even prejudice results will sometimes creep to the surface, as a study done by Paul Baker and Amanda Potts in 2012 would indicate. This study, titled “Why do white people have small lips,” reveals what happens when the searcher combines “why do” with the name of whatever random social group. For example, when “Why do gay” was typed into the search box, the five suggestions were: “men have high voices, men get aids, men lisp, [‘s] exist, people talk funny” (Baker & Pots, 2013). Now as you can see, none of these suggestions are likely what the searcher intended, at least hopefully not.

The autocomplete algorithm was updated with some moderation-related fixes, ensuring that results such as these, among many results deemed inappropriate or offensive, do not appear in the predictive text. This highlights one of the challenges Google Search faces, that being the process of continuously and constantly updating not only their intelligent algorithms, but also the moderation and censoring rules that apply to them as well. It should be noted that exploits still do exist in the algorithm, but most of the quirks were removed, specifically concerning racial-related queries.


 * Google Search in the six Dimensions of KM

Organizational processes

We can consider how Google responded to the issue with autocomplete; to act on an exploit so quickly and efficiently requires information implementation with the correct processes done in the correct environment, correct in this sense meaning the appropriate and professional standard for a company as powerful as this one. These organizational processes, whether it be updates to algorithms or patching exploits, are done daily and a lot of the time automatically, requiring an extreme amount of efficiency.

Management and Leadership

Google has a reputation for being a good employer to their employees, ranging from internship programs and other opportunities for everyone, to elements within the organization such as having people who’s sole job is to keep employees happy and maintain productivity. Though that may seem slightly extreme, there must exist a lot of innovation, among other things, to a company that gets over 2.5 million applicants a year. Management, including the quality of their managers, has always been a huge priority to Google, hence their Project Oxygen in 2008. In their research, the organization identified eight common behaviors among their highest performing managers, and then incorporated them into their management development programs. The behaviors were; is a good coach, empowers team and does not micromanage, creates an inclusive team environment showing concern for success and well-being, is productive and results-oriented, is a good communicator — listens and shares information, supports career development and discusses performance, has a clear vision/strategy for the team, has key technical skills to help advise the team, collaborates across Google, and is a strong decision maker (Garvin,Wagonfeld, & Kind (2013). As a result, statistics showed significant improvement in manager quality in 75% of the company’s worst-performing managers

Organizational culture

Google puts a lot of effort toward employee happiness, with perks ranging from “free food to free classes, lectures from global thought leaders to free gymnasiums” (Krapivin, 2018). However, the company puts just as much effort into the actual work place setting as it does in the settings surrounding it. It only makes sense the happier employees are the better they will perform in the workplace, as many studies have proven. In 2013, a study done by a team from Warwick University showed the correlation, or rather causation, of happy employees and better productivity. Though the company is well known for it, it was in this study a Google spokesperson stated the company’s belief that investing in employee happiness is convenient in every aspect within the organizations culture, financial convenience included

Technology In addition to query expansion and and query understanding, google search also automatically stores private and or tracking information. As a result, you see advertisements for things you may have recently searched online, or in the case that your cell phone as its microphone permissions enabled by default, have recently said out loud. Though this is convenient in many cases, it can also be too much for some individuals, or even overwhelming. Of course, you can always disable trackers in the Google Search settings but ridding of trackers entirely can prove to be easier said than done, with options in so many different avenues to attempt to do so such as your browser settings browser addons, OS settings, as well as settings in third-party security services like McAfee. However, intelligent algorithms like PageRank and webcrawers are vital in Google Search and although this technology must regularly be adjusted, that is part of the cost of being a web search engine as popular and powerful as google search.

Politics Politics in the realm of KM, collectively, is all of the long-term investments and decision making that involves “virtually all organizational functions, which may be costly to implement (both from the perspective of time and money), and which often do not have a directly visible return on investment.” (Hajric). Aside from things like Project Oxygen and the strategic steps it takes to keep its reputation healthy and consistent, Google also invests in start-up companies, specifically technological ones that range from the internet, software and hardware of life science, artificial intelligence, and cyber security. It should be noted that Google does partake in investing in technological startups, though most of that investing is done by its parent company, Alphabet Inc. with a firm called “GV,” formerly known as Google Ventures. The firm’s main job is to provide seed, venture and growth stage funding to technology companies.

Strategy

It could be said the four dimensions of KM listed thus far all fall under the dimension of strategy, naturally, as the efficiency and effort that goes into an organization’s KM results in outcomes seen across the organization. This is because KM strategy must be dependent on corporate strategy, though the corporation’s success depends on the success of its KM sector. So, when Google invests heavily towards employee satisfaction through various media like free food and lecture as well as free facility usage, it is a part of the company’s strategy as greater employee happiness yields greater productivity.


 * Conclusion

The company is clearly on a correct path of a sort, at the very least from an organizational perspective. However, with cyber security needing to be made stronger daily, as recent hacks have showed, cyber security and moderation are both obvious sectors not get the slightest of careless in. It should be noted the company has yet to be hacked into, but with the amount of personal and tracking information the company has in its storage, it is not the most reassuring thing to know that you can, to this day, abuse an exploit in the search engine to get racist and unintended autocomplete results to show up just by starting a query with the words, “Are Jews…” (Baker & Pots, 2013). It a process though obviously, one that the average human let alone college student cannot fathom all of what it takes to actively keep such a popular web search engine up and working.

References


 * Baker, P., & Potts, A. (2013). ‘Why do white people have thin lips?’Google and the 	perpetuation of stereotypes via auto-complete search forms. Critical Discourse Studies, 	10(2), 187-204.
 * Garvin, D. A., Wagonfeld, A. B., & Kind, L. (2013). Google's Project Oxygen: Do Managers 	Matter?. Harvard Business School publishing corporation.
 * Hajric, E. (n.d.). Knowledge Management Tools. Retrieved May 6, 2019, from https://www.knowledge-management-tools.net/
 * Haveliwala, T. H. (2002, May). Topic-sensitive pagerank. In Proceedings of the 11th international conference on World Wide Web (pp. 517-526). ACM.
 * Krapivin, P. (2018, September 17). How Google's Strategy For Happy Employees Boosts Its Bottom Line. Retrieved May 6, 2019, from https://www.forbes.com/sites/pavelkrapivin/2018/09/17/how-googles-strategy-for-happy-employees-boosts-its-bottom-line/#739b71b522fc
 * Mitra, B., & Craswell, N. (2017). Neural models for information retrieval. arXiv preprint arXiv:1705.01509.


 * Oswald, A., Proto, E., & Sgroi, D. (2015). Happiness and Productivity. Journal of Labor Economics, 33(4), 789-822. doi:10.1086/681096
 * Spencer, B., Dodd, L. B., Friedman, W. C., & Xu, Q. (2011). The Web Beyond Google: Innovative Search Tools and Their Implications for Reference Services. Internet Reference Services Quarterly, 16(1-2), 11-34. doi:10.1080/10875301.2011.570110


 * Trim / IBM, C. (2013, January 23). The Art of Tokenization. Retrieved May 6, 2019, from https://www.ibm.com/developerworks/community/blogs/nlp/entry/tokenization?lang=en


 * Vechtomova, O., & Wang, Y. (2006). A study of the effect of term proximity on query expansion. Journal of Information Science, 32(4), 324–333. https://doi.org/10.1177/0165551506065787