The use of web analytics on a small data set in an online media company : shifter´s case study

Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceThe primary struggle in data analysis is the lack of talent in performing relevant and fit-to-business analyzes that retrieve knowledge and provides concise and clear action plans to today’s startups and small enterprises that exist online. Tracking, knowing and understanding the navigational patterns of user behavior for a 3 month period collection and using an Excel spreadsheet tool obtained a context for each piece of content produced and published by Shifter, an online media company. Investigations made after acquiring Shifter’s data resulted in recommendations for rethink and redesign the editorial content of the business to answer different community’s needs

Identity and User Preference in the Presentation and Content of Digital Archives: A Study of the Plumas County Museum’s Haun Collection

This thesis explores user preference in the presentation and content of online archives in small, local institutions. To obtain data for this study, a collection from the Plumas County Museum in northern California was digitized, and three versions were presented on a custom-built test website: 1) a straightforward reproduction of documents in the collection; 2) a pairing of reproductions and typed transcripts; and 3) a selective, interpretive reproduction with supporting secondary material. Users with a variety of research backgrounds viewed the website and provided feedback through an anonymous, online survey. Google Analytics was also used to measure site traffic. During the five-week testing period, 25 complete surveys, five partial surveys, and traffic information from 183 unique users were gathered. Survey findings indicate that 46 percent of users found version 3—the highly processed, highly contextualized presentation—most useful. When controlling for research experience, scholarly and professional users preferred the straightforward reproduction (version 1), while students and teachers preferred an enhanced presentation (versions 2 and 3). Avocational researchers did not show a clear preference. Site traffic showed a heavy concentration of users (68%) from California, as well as users from 15 other states. These findings suggest that while local archives may be most relevant within their geographical range, digitization of collections can extend an archives’ usership more broadl

SenMinCom: Pervasive Distributed Dynamic Sensor Data Mining for Effective Commerce

In last few years, the use of wireless sensor networks and cell phones has become ubiquitous; fusing these technologies in the field of business will open up new possibilities. To fill this lacuna, I propose a novel idea where the combination of these will facilitate companies to receive feedback on their products and services. System\u27s unobtrusive sensors will not only collect shopping, mobile usage data from consumers but will also make effective use of this information to increase revenue, cut costs, etc.; the use of mobile agent based data mining allows analyzing the data from different dimensions, categorizing it on factors such as product positioning, promotion of goods, etc. as in the case of a shopping store. Additionally, because of the dynamic mining system the companies get on-the-scene recommendation of products rather than off-the-scene. In this thesis, a novel distributed pervasive mining system is proposed to get dynamic shopping information and mobile device usage of the customers

Mobile Crowd Sensing in Edge Computing Environment

abstract: The mobile crowdsensing (MCS) applications leverage the user data to derive useful information by data-driven evaluation of innovative user contexts and gathering of information at a high data rate. Such access to context-rich data can potentially enable computationally intensive crowd-sourcing applications such as tracking a missing person or capturing a highlight video of an event. Using snippets and pictures captured from multiple mobile phone cameras with specific contexts can improve the data acquired in such applications. These MCS applications require efficient processing and analysis to generate results in real time. A human user, mobile device and their interactions cause a change in context on the mobile device affecting the quality contextual data that is gathered. Usage of MCS data in real-time mobile applications is challenging due to the complex inter-relationship between: a) availability of context, context is available with the mobile phones and not with the cloud, b) cost of data transfer to remote cloud servers, both in terms of communication time and energy, and c) availability of local computational resources on the mobile phone, computation may lead to rapid battery drain or increased response time. The resource-constrained mobile devices need to offload some of their computation. This thesis proposes ContextAiDe an end-end architecture for data-driven distributed applications aware of human mobile interactions using Edge computing. Edge processing supports real-time applications by reducing communication costs. The goal is to optimize the quality and the cost of acquiring the data using a) modeling and prediction of mobile user contexts, b) efficient strategies of scheduling application tasks on heterogeneous devices including multi-core devices such as GPU c) power-aware scheduling of virtual machine (VM) applications in cloud infrastructure e.g. elastic VMs. ContextAiDe middleware is integrated into the mobile application via Android API. The evaluation consists of overheads and costs analysis in the scenario of ``perpetrator tracking" application on the cloud, fog servers, and mobile devices. LifeMap data sets containing actual sensor data traces from mobile devices are used to simulate the application run for large scale evaluation.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

DesignSense: A Visual Analytics Interface for Navigating Generated Design Spaces

Generative Design (GD) produces many design alternatives and promises novel and performant solutions to architectural design problems. The success of GD rests on the ability to navigate the generated alternatives in a way that is unhindered by their number and in a manner that reflects design judgment, with its quantitative and qualitative dimensions. I address this challenge by critically analyzing the literature on design space navigation (DSN) tools through a set of iteratively developed lenses. The lenses are informed by domain experts\u27 feedback and behavioural studies on design navigation under choice-overload conditions. The lessons from the analysis shaped DesignSense, which is a DSN tool that relies on visual analytics techniques for selecting, inspecting, clustering and grouping alternatives. Furthermore, I present case studies of navigating realistic GD datasets from architecture and game design. Finally, I conduct a formative focus group evaluation with design professionals that shows the tool\u27s potential and highlights future directions

How do ICP variants perform when used for scan matching terrain point clouds?

Many variants of the Iterative Closest Point (ICP) algorithm have been proposed for registering point clouds. This paper explores the performance of 20,736 ICP variants applied to the registration of point clouds for the purpose of terrain mapping, using data obtained from a mobile platform. The methodology of the study has involved taking sequences of 100 consecutive scans at three distinct scenes along the route of a mining haul truck operating in a typical surface mining environment. The scan sequences were obtained at 20 Hz from a Velodyne HDL-64E mounted on the truck. The aim is to understand how well the ICP variants perform in consolidating these scans into sub-maps. Variants are compared against three metrics: accuracy, precision, and relative computational cost. The main finding of the paper is that none of the variants is simultaneously accurate, precise, and fast to compute, across all three scenes. The best performing variants employed strategies that filtered the data sets, used local surface geometry in the form normals, and used the distance between points in one point cloud to a corresponding surface from a reference point cloud as a measure of the fit between two point clouds. The significance of this work is that it: (i) provides guidance in the construction of ICP variants for terrain mapping; and (ii) identifies the significant limitations of existing ICP variants for this application

State Water Planning to Protect Public Needs

44 pages

Modeling usage of an online research community

Although online communities have been thought of as a new way for collaboration across geographic boundaries in the scientific world, they have a problem attracting people to keep visiting. The main purpose of this study is to understand how people behave in such communities, and to build and evaluate tools to stimulate engagement in a research community. These tools were designed based on a research framework of factors that influence online participation and relationship development. There are two main objectives for people to join an online community, information sharing and interpersonal relationship development, such as friends or colleagues. The tools designed in this study are to serve both information sharing and interpersonal relationship development needs. The awareness tool is designed to increase the sense of a community and increase the degree of social presence of members in the community. The recommender system is designed to help provide higher quality and personalized information to community members. It also helps to match community members into subgroups based on their interests. The designed tools were implemented in a field site - the Asynchronous Learning Networks (ALN) Research community. A longitudinal field study was used to evaluate the effectiveness of the designed tools. This research explored people\u27s behavior inside a research community by analyzing web server logs. The results show that although there are not many interactions in the community space, the WebCenter has been visited extensively by its members. There are over 2,000 hits per day on average and over 5,000 article accesses during the observation period. This research also provided a framework to identify factors that affect people\u27s engagement in an online community. The research framework was tested using the PLS modeling method with online survey responses. The results show that perceived usefulness performs a very significant role in members\u27 intention to continue using the system and their perceived preliminary networking. The results also show that the quality of the content of the system is a strong indicator for both perceived usefulness of the community space and perceived ease of use of the community system. Perceived ease of use did not show a strong correlation with intention to continue use which was consistent with other studies of Technology Acceptance Model (TAM). For the ALN research community, this online community helps its members to broaden their contacts, improve the quality and quantity of their research, and increase the dissemination of knowledge among community members