Aim: The aim of this study is to analyze the usage of Search Engine Optimization and Knowledge Graphs and the connection between them to achieve profitable business visibility and reach. Methods: Following a qualitative method together with an inductive approach, ten marketing professionals were interviewed via an online questionnaire. To conduct this study both primary and secondary data was utilized. Scientific theory together with empirical findings were linked and discussed in the analysis chapter. Findings: This study establishes current Search Engine Optimization utilization by businesses regarding common techniques and methods. We demonstrate their effectiveness on the Google Knowledge Graph, Google My Business and resulting positive business impact for increased visibility and reach. Difficulties remain in accurate tracking procedures to analyze quantifiable results. Contribution of the thesis: This study contributes to the literature of both Search Engine Optimization and Knowledge Graphs by providing a new perspective on how these subjects have been utilized in modern marketing. In addition, this study provides an understanding of the benefits of SEO utilization on Knowledge Graphs. Suggestions for further research: We suggest more extensive investigation on the elements and utilization of Knowledge Graphs; how the structure can be affected; which techniques are most effective on a bigger scale and how effectively the benefits can be measured. Key Words: Search Engine, Search Engine Optimization, SEO, Knowledge Graphs, Google My Business, Google Search Engine, Online Marketing.

Background: Search engine result pages (SERPs) act as an information channel between businesses and consumers. There is no way of denying search engines massive success in marketing for companies. SERP allows companies to be seen, communicate, and promote their products and services in an environment sorted by relevance for the consumer. The search engine is one way of building a solid brand online; therefore, it is crucial to investigate how to catch the user's attention and understand their attitudes in the first step of the decision-making process.  Purpose: The purpose of this research is to explore the generated attitudes towards the content that is displayed in each listing in the search engine result pages (SERPs).  Methodology: This research undertook an inductive qualitative approach with an exploratory research design. A pre-study with three cases was developed and eight semi-structured interviews were conducted. The eight participants were based on non-probability judgemental sampling. The respondents were equally diverse between men and women between the ages of 24-29.  Conclusion: This study concludes that the main explored positive attitudes that were generated towards the content of the listings in the SERPs were primarily the relevance of the search term throughout the listing, as well as brand familiarity. Other positive influences of favorable attitudes were short and concise but informative listings as well as displaying pictures. Furthermore, negative attitudes were found to be generated towards poor relevance and paid search in the listings, where organic results generated only positive attitudes towards it.

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Master of Science / Department of Computing and Information Sciences / William H. Hsu / Entity search has been proposed as a search method for domain-specific Internet applications. It differs from the classical approaches used by search engines which give a "page-view result": listing the URLs of web pages containing the desired keywords. Entity search returns more structured results listing the specific information that a user seeks, such as an email address or a phone number. It not only provides the URL links to targets, but also attributes of target entities (e.g., email address, phone number, etc.). Compared to classical search methods, entity search is a more direct and user-friendly method for searching through a large volume of web documents. After the user submits a query, the extracted entities are ordered by their relevance to the query. While previous work has proposed various complex formulas for entity ranking, it has not been shown whether such complexity is needed. In this research I explore the problem of whether a simpler method can achieve reasonable results. I have designed an entity-search and ranking algorithm using a formula that simply combines a page’s PageRank and an entity's distance to the query keywords to produce a metric for ranking discovered entities. My research goal is to answer the question of whether effective entity ranking can be performed by an algorithm that computes matching scores specific to the entity search domain, and what improvements are necessary to refine the result. My approach takes into account the entity's proximity to the keywords in the query as well as the quality of the page where it is contained. I implemented a system based on the algorithm and perform experiments to show that in most cases the result is consistent with the user's desired outcome.

Master of Science / Department of Computing and Information Sciences / Daniel A. Andresen / The aim of this project is to help students find a job that suits their profile. This provides a common platform for the job seekers to search for jobs on one website instead of searching them on multiple websites which highly reduces the time of searching for a suitable job. This website also provides a platform for the recruiters to post a job and search for the resume suitable to their job requirements. This website allows the job seekers to build a resume using resume builder (using this students can design their resume online), search for a job (search is based on different selection criteria like location, salary, job type, company, category etc), check apply history (can go through the list of jobs applied), create a search agent according to their priorities through which they can be updated with all the latest jobs posted on the website. This application also allows the recruiters to post a new job available in their organization, can search for resume and can schedule the interview if the person’s profile matches with the job requirements posted by the recruiter. This website is developed using ASP.NET 2005 and MS SQL SERVER 2005. The main goal in designing this website was to get familiar with .NET technology.

This work investigates the factors that contribute to financial crises. We first study the Dow Jones index performance by grouping the daily adjusted closing value into a two-month window and finding several critical quantiles in each window. Then, we identify severe downturn in these quantiles and find that the 5th quantile is the best to identify financial crises. We then matched these quantiles with historical financial crises and gave a basic explanation about them. Next, we introduced all exogenous factors that could be related to the crises. Then, we applied a rapid Bayesian variable selection technique - Stochastic Search Variable Selection (SSVS) using a Bayesian logistic regression model. Finally, we analyzed the result of SSVS, leading to the conclusion that that the dummy variable we created for disastrous hurricane, crude oil price and gold price (GOLD) should be included in the model. / text

There is growing literature on search behavior and using search for prediction of market share or macroeconomic indicators. This research explores investors' stock search behaviors and investigates whether there are patterns in stock returns using those for return prediction. Stock search behaviors may reveal common interest among investors. In the first study, we use graph theory to find investment habitats (or search clusters) formed by users who search common set of stocks frequently. We study stock returns of stocks within the clusters and across the clusters to provide theoretical arguments that drive returns among search clusters. In the second study, we analyze return comovement and cross-predictability among economically related stocks searched frequently by investors. As search requires a considerable amount of cognitive resources of investors, they only search a few stocks and pay high attention to them. According to attention theory, the speed of information diffusion is associated with the level of attention. Quick information diffusion allows investors to receive relevant information immediately and take instantaneous trading action. This immediate action may lead to correlated return comovement. Slow information diffusion creates latency between the occurrence of an event and the action of investors. The slower response may lead to cross-predictability. Making use of the discrepancy in information diffusion, we implement a trading strategy to establish arbitrage opportunities among stocks due to difference in user attention. This research enriches the growing IS literature on information search by (1) identifying new investment habitats based on user search behaviors, (2) showing that varying degrees of co-attention and economic linkages may lead to different speed of information diffusion (3) developing a stock forecasting model based on real-time co-attention intensity of a group economically linked stocks and (4) embarking a new research area on search attention in stock market. The methods in handling complex search data may also contribute to big data research. / text

The popularity of Craigslist has enabled users worldwide to find almost any product at prices significantly less than retail prices or market-prices. Craigslist has thus enabled lot of resellers to enter the market and has created a huge market for used/pre-owned products. The key to find products at prices less than their market-prices is to find the right classified and act immediately before other users. Craigslist search agents, with ability to search classifieds, that run on mobile/handheld devices are increasing in popularity with ubiquitous internet connectivity, convenience and speed. AnveShA is an automatic search and monitoring agent for craigslist that has been developed for Android platforms. AnveShA provides easy access and a rich feature-set that is not available in the state-of-the-art craigslist search applications available on the Android market. AnveShA has been developed to provide the user a rapid and intelligent search agent that can proactively search and monitor classifieds for desired products, contact sellers and increase the chances of obtaining the desired product at the best possible price. To achieve this, AnveShA has many unique features like the ability to schedule and execute automatic searches, search classifieds based on geographical location, automatically respond to classifieds, store price history for classifieds, get comparative prices from other retail/shopping websites, store favorite classifieds/reminder lists and predict the offer price based on a number of parameters. With such unique features, AnveSha assists users or resellers to find desired products at the best possible prices on Craigslist and hence have a significant advantage over the competition. / text

In this thesis we focus on Stochastic combinatorial Optimization Problems (SCOPs), a wide class of combinatorial optimization problems under uncertainty, where part of the information about the problem data is unknown at the planning stage, but some knowledge about its probability distribution is assumed. Optimization problems under uncertainty are complex and difficult, and often classical algorithmic approaches based on mathematical and dynamic programming are able to solve only very small problem instances. For this reason, in recent years metaheuristic algorithms such as Ant Colony Optimization, Evolutionary Computation, Simulated Annealing, Tabu Search and others, are emerging as successful alternatives to classical approaches. In this thesis, metaheuristics that have been applied so far to SCOPs are introduced and the related literature is thoroughly reviewed. In particular, two properties of metaheuristics emerge from the survey: they are a valid alternative to exact classical methods for addressing real-sized SCOPs, and they are flexible, since they can be quite easily adapted to solve different SCOPs formulations, both static and dynamic. On the base of the current literature, we identify the following as the key open issues in solving SCOPs via metaheuristics: (1) the design and integration of ad hoc, fast and effective objective function approximations inside the optimization algorithm; (2) the estimation of the objective function by sampling when no closed-form expression for the objective function is available, and the study of methods to reduce the time complexity and noise inherent to this type of estimation; (3) the characterization of the efficiency of metaheuristic variants with respect to different levels of stochasticity in the problem instances. We investigate the above issues by focusing in particular on a SCOP belonging to the class of vehicle routing problems: the Probabilistic Traveling Salesman Problem (PTSP). For the PTSP, we consider the Ant Colony Optimization metaheuristic and we design efficient local search algorithms that can enhance its performance. We obtain state-of-the-art algorithms, but we show that they are effective only for instances above a certain level of stochasticity, otherwise it is more convenient to solve the problem as if it were deterministic. The algorithmic variants based on an estimation of the objective function by sampling obtain worse results, but qualitatively have the same behavior of the algorithms based on the exact objective function, with respect to the level of stochasticity. Moreover, we show that the performance of algorithmic variants based on ad hoc approximations is strongly correlated with the absolute error of the approximation, and that the effect on local search of ad hoc approximations can be very degrading. Finally, we briefly address another SCOP belonging to the class of vehicle routing problems: the Vehicle Routing Problem with Stochastic Demands (VRPSD). For this problem, we have implemented and tested several metaheuristics, and we have studied the impact of integrating in them different ad hoc approximations.

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