Can Internet search query data be a relevant predictor of financial measures of brand value? Can Internet search query data enrich existing financial measures of brand valuation tools and provide more timely insights to brand managers? Along with the financial based motivation to estimate the value of a brand for accounting purposes, marketers desire to show “accountability” of marketing activity and respond to the customer’s perception of the brand quickly to maintain their competitive advantage and value. The usefulness of the “consumer information processing” framework for brand, consumer and firm forecasting is examined. To develop our hypotheses, we draw from the growing body of work relating web searches to real world outcomes, to determine if a search query for a brand is causal to, and potentially predictive of brand, consumer and firm value. The contribution to current literature is that search queries can predict perception, whereas previous research in this nascent area predicted behavior and events. In this direction, we propose arguments underpinning this research as follows: the theoretical background relative to brand valuation and the theoretical frame based on an in-depth review of how scholars have used search query data as a predictive measure across several disciplines including economics and the health sciences. From a practitioner perspective, unlike traditional valuation methods search query data for brands is more timely, actionable, and inclusive.

Can Internet search query data be a relevant predictor of financial measures of brand value? Can Internet search query data enrich existing financial measures of brand valuation tools and provide more timely insights to brand managers? Along with the financial based motivation to estimate the value of a brand for accounting purposes, marketers desire to show “accountability” of marketing activity and respond to the customer’s perception of the brand quickly to maintain their competitive advantage and value. The usefulness of the “consumer information processing” framework for brand, consumer and firm forecasting is examined. To develop our hypotheses, we draw from the growing body of work relating web searches to real world outcomes, to determine if a search query for a brand is causal to, and potentially predictive of brand, consumer and firm value. The contribution to current literature is that search queries can predict perception, whereas previous research in this nascent area predicted behavior and events. In this direction, we propose arguments underpinning this research as follows: the theoretical background relative to brand valuation and the theoretical frame based on an in-depth review of how scholars have used search query data as a predictive measure across several disciplines including economics and the health sciences. From a practitioner perspective, unlike traditional valuation methods search query data for brands is more timely, actionable, and inclusive.

Internet technology has affected humans' lives in many disciplines. The search engine is one of the most important Internet tools in that it allows people to search for what they want. Search queries entered in a web search engine can be used to predict dengue incidence. This vector borne disease causes severe illness and kills a large number of people every year. This dissertation utilizes the capabilities of search queries related to dengue and climate to forecast the number of dengue cases. Several machine learning techniques are applied for data analysis, including Multiple Linear Regression, Artificial Neural Networks, and the Seasonal Autoregressive Integrated Moving Average. Predictive models produced from these machine learning methods are measured for their performance to find which technique generates the best model for dengue prediction. The results of experiments presented in this dissertation indicate that search query data related to dengue and climate can be used to forecast the number of dengue cases. The performance measurement of predictive models shows that Artificial Neural Networks outperform the others. These results will help public health officials in planning to deal with the outbreaks.

<p>In service-oriented architecture, services can communicate and share data among themselves. This thesis presents a solution that allows detecting several types of data leakages made by authorized insiders to unauthorized services. My solution provides role-based and attribute-based access control for data so that each service can access only those data subsets for which the service is authorized, considering a context and service’s attributes such as security level of the web browser and trust level of service. My approach provides data protection in transit and at rest for both centralized and peer-to-peer service architectures. The methodology ensures confidentiality and integrity of data, including data stored in untrusted cloud. In addition to protecting data against malicious or curious cloud or database administrators, the capability of running a search through encrypted data, using SQL queries, and building analytics over encrypted data is supported. My solution is implemented in the “WAXEDPRUNE” (Web-based Access to Encrypted Data Processing in Untrusted Environments) project, funded by Northrop Grumman Cybersecurity Research Consortium. WAXEDPRUNE methodology is illustrated in this thesis for two use cases, including a Hospital Information System with secure storage and exchange of Electronic Health Records and a Vehicle-to-Everything communication system with secure exchange of vehicle’s and drivers’ data, as well as data on road events and road hazards. </p><p>To help with investigating data leakage incidents in service-oriented architecture, integrity of provenance data needs to be guaranteed. For that purpose, I integrate WAXEDPRUNE with IBM Hyperledger Fabric blockchain network, so that every data access, transfer or update is recorded in a public blockchain ledger, is non-repudiatable and can be verified at any time in the future. The work on this project, called “Blockhub,” is in progress.</p>