Cryptocurrencies and blockchains are increasingly used, implemented and adapted for numerous purposes; people and businesses are integrating these technologies into their practices and strategies, creating new political economies and spaces in and of everyday life. This thesis seeks to develop a foundation of geographic theory for the study of spatial production within and surrounding blockchain technologies focusing on acute studies of Bitcoin as cryptocurrency, Ethereum as digital marketplace, and their conditions of possibility as decentralized autonomous organizations. Utilizing concepts from Henri Lefebvre's Production of Space, this thesis situates blockchain technologies within the wider discussion about the political economy of modes of spatial production, dialectical material methods, code/space, and network society through an examination of human and machine relations within their unique and emergent spaces. Combining phenomenological and dialectical material methods with the methodological practice of discourse analysis and systems theory, this thesis explores an understanding of how systemic mechanisms and actant actions driving blockchain technologies are indications of new evolutions in our conceptions of space and place in everyday life of later informational capitalism.

Many systems and applications are continuously producing events. These events are used to record the status of the system and trace the behaviors of the systems. By examining these events, system administrators can check the potential problems of these systems. If the temporal dynamics of the systems are further investigated, the underlying patterns can be discovered. The uncovered knowledge can be leveraged to predict the future system behaviors or to mitigate the potential risks of the systems. Moreover, the system administrators can utilize the temporal patterns to set up event management rules to make the system more intelligent. With the popularity of data mining techniques in recent years, these events grad- ually become more and more useful. Despite the recent advances of the data mining techniques, the application to system event mining is still in a rudimentary stage. Most of works are still focusing on episodes mining or frequent pattern discovering. These methods are unable to provide a brief yet comprehensible summary to reveal the valuable information from the high level perspective. Moreover, these methods provide little actionable knowledge to help the system administrators to better man- age the systems. To better make use of the recorded events, more practical techniques are required. From the perspective of data mining, three correlated directions are considered to be helpful for system management: (1) Provide concise yet comprehensive summaries about the running status of the systems; (2) Make the systems more intelligence and autonomous; (3) Effectively detect the abnormal behaviors of the systems. Due to the richness of the event logs, all these directions can be solved in the data-driven manner. And in this way, the robustness of the systems can be enhanced and the goal of autonomous management can be approached. This dissertation mainly focuses on the foregoing directions that leverage tem- poral mining techniques to facilitate system management. More specifically, three concrete topics will be discussed, including event, resource demand prediction, and streaming anomaly detection. Besides the theoretic contributions, the experimental evaluation will also be presented to demonstrate the effectiveness and efficacy of the corresponding solutions.

A query and display interface has been developed between the GRASS geographic information system and the SQL-based ORACLE relational database management system (DBMS) . This interface enables multiple non-spatial attributes of GRASS map features to be maintained with the DBMS. GRASS alone is capable of storing only one attribute per feature. The interface allows the user to provide both spatial (GRASS) and non-spatial (SQL) selection criteria for any query. Spatial selection methods include picking items from the GRASS map with a mouse, and specifying areas of interest with user-drawn (via a mouse) polygons and transects. The results of the combined query are displayed both graphically (the selected GRASS map features are highlighted in a graphics window) and textually (the DBMS attribute data are shown in a text display window). Options include creating reclassified maps based on the DBMS output, and updating the attributes retrieved by a query.

In the last decade, large numbers of social media services have emerged and been widely used in people's daily life as important information sharing and acquisition tools. With a substantial amount of user-contributed text data on social media, it becomes a necessity to develop methods and tools for text analysis for this emerging data, in order to better utilize it to deliver meaningful information to users. Previous work on text analytics in last several decades is mainly focused on traditional types of text like emails, news and academic literatures, and several critical issues to text data on social media have not been well explored: 1) how to detect sentiment from text on social media; 2) how to make use of social media's real-time nature; 3) how to address information overload for flexible information needs. In this dissertation, we focus on these three problems. First, to detect sentiment of text on social media, we propose a non-negative matrix tri-factorization (tri-NMF) based dual active supervision method to minimize human labeling efforts for the new type of data. Second, to make use of social media's real-time nature, we propose approaches to detect events from text streams on social media. Third, to address information overload for flexible information needs, we propose two summarization framework, dominating set based summarization framework and learning-to-rank based summarization framework. The dominating set based summarization framework can be applied for different types of summarization problems, while the learning-to-rank based summarization framework helps utilize the existing training data to guild the new summarization tasks. In addition, we integrate these techneques in an application study of event summarization for sports games as an example of how to better utilize social media data.

As a result of the rapid evolution of smart mobile devices and the wide application of satellite-based positioning devices, the moving object database (MOD) has become a hot research topic in recent years. The moving objects generate a large amount of geo-referenced data in different types, such as videos, audios, images and sensor logs. In order to better analyze and utilize the data, it is useful and necessary to visualize the data on a map. With the rise of web mapping, visualizing the moving object and geo-referenced data has never been so easy. While displaying the trajectory of a moving object is a mature technology, there is little research on visualizing both the location and data of the moving objects in a synchronized manner. This dissertation proposes a general moving object visualization model to address the above problem. This model divides the spatial data visualization systems into four categories. Another contribution of this dissertation is to provide a framework, which deals with all these visualization tasks with synchronization control in mind. This platform relies on the TerraFly web mapping system. To evaluate the universality and effectiveness of the proposed framework, this dissertation presents four visualization systems to deal with a variety of situations and different data types.

Workflow systems orchestrate various business tasks to attain an objective. Web services can be leveraged to handle individual tasks. Before anyone intends to leverage service components, it is imperative and essential to evaluate the trustworthiness of these services. Therefore, choosing a trustworthy service has become an important decision while designing a workflow system. Trustworthiness can be defined as the likelihood of a service functioning as it is intended. Selection of a service that satisfies business goals involves collecting relevant information such as security mechanisms, reliability, performance and availability. It is important to arrive at total trustworthiness, which incorporates all of the above mentioned multi-facet values relevant to a service. These values can be gathered and analyzed to derive the total trustworthiness of a service. Measuring trustworthiness of a service involves arriving at a suitable value that would help an end-user make a decision for the given business settings. The primary focus of this thesis is to gather relevant details and measure trustworthiness based on inputs provided by the user. A conceptual model was developed after extensive literature review to identify factors that influence trustworthiness of a service. A mechanism was created to gather concept values for a given service and utilize those values to calculate trustworthiness index value. A proof-of-concept prototype was also developed. The prototype is a web-based application that implements the mechanism to measure the trustworthiness of the service. The prototype was evaluated using a scenario-based analysis method to demonstrate the utility of the trustworthiness mechanism using three different scenarios. Results of the evaluation shows that trustworthiness is a multidimensional concept, the relevant conceptual values can be collected, a trustworthiness index value can be calculated based on the gathered concepts, and a trustworthiness index can be interpreted to select the most relevant service for a given requirement.

With hundreds of millions of users reporting locations and embracing mobile technologies, Location Based Services (LBSs) are raising new challenges. In this dissertation, we address three emerging problems in location services, where geolocation data plays a central role. First, to handle the unprecedented growth of generated geolocation data, existing location services rely on geospatial database systems. However, their inability to leverage combined geographical and textual information in analytical queries (e.g. spatial similarity joins) remains an open problem. To address this, we introduce SpsJoin, a framework for computing spatial set-similarity joins. SpsJoin handles combined similarity queries that involve textual and spatial constraints simultaneously. LBSs use this system to tackle different types of problems, such as deduplication, geolocation enhancement and record linkage. We define the spatial set-similarity join problem in a general case and propose an algorithm for its efficient computation. Our solution utilizes parallel computing with MapReduce to handle scalability issues in large geospatial databases. Second, applications that use geolocation data are seldom concerned with ensuring the privacy of participating users. To motivate participation and address privacy concerns, we propose iSafe, a privacy preserving algorithm for computing safety snapshots of co-located mobile devices as well as geosocial network users. iSafe combines geolocation data extracted from crime datasets and geosocial networks such as Yelp. In order to enhance iSafe's ability to compute safety recommendations, even when crime information is incomplete or sparse, we need to identify relationships between Yelp venues and crime indices at their locations. To achieve this, we use SpsJoin on two datasets (Yelp venues and geolocated businesses) to find venues that have not been reviewed and to further compute the crime indices of their locations. Our results show a statistically significant dependence between location crime indices and Yelp features. Third, review centered LBSs (e.g., Yelp) are increasingly becoming targets of malicious campaigns that aim to bias the public image of represented businesses. Although Yelp actively attempts to detect and filter fraudulent reviews, our experiments showed that Yelp is still vulnerable. Fraudulent LBS information also impacts the ability of iSafe to provide correct safety values. We take steps toward addressing this problem by proposing SpiDeR, an algorithm that takes advantage of the richness of information available in Yelp to detect abnormal review patterns. We propose a fake venue detection solution that applies SpsJoin on Yelp and U.S. housing datasets. We validate the proposed solutions using ground truth data extracted by our experiments and reviews filtered by Yelp.

With the use of the Internet and the emerging of e-commerce, new and improved technologies and modeling techniques have been used to design and implement web-based database management systems.

Ubiquitous computing aims to make computing widely available, easy to use, and completely transparent to the user. Umbilical Cord is intended to be the first step in researching ubiquitous computing at CSUSB. It implements a model scalable network with a client-server architecture that features consistent user interaction and global access to user data. It is based on the Linux operating system which can be leveraged for future research due to the open nature of its source code. It also features a scalable network swap.

In the United States, women remain underrepresented in senior level positions in the information technology (IT) field. Despite this challenge, a few women have successfully ascended into senior leadership in IT. Using the social cognitive theory as the conceptual framework, the purpose of this qualitative transcendental phenomenological study was to understand the lived experiences of senior women leaders in the IT field. The research question explored the lived personal and professional experiences of senior women leaders in IT to gain an understanding of their career advancement into senior leadership positions. Through the use of the modified Van Kaam method of data analysis, 6 major themes emerged from interviews with the 15 participants. The results indicated that self-efficacy, hard work, and mentorship may help women ascend into senior leadership in IT. This study may contribute to positive social change by promoting the understanding of the experiences and perspective strategies for increasing the career advancement of aspiring women leaders in a male dominated industry such as IT. An increased understanding of women senior leaders' experiences in IT could attract more women, leveling the playing field of men and women.