Performance Analysis of Opportunistic Content Distribution Via Data-Driven Mobility Modeling
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Improved Density-Based Spatio–Textual Clustering on Social Media
SUBMITTED TO IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 1 Improved Density-Based Spatio–Textual Clustering on Social Media Minh D. Nguyen and Won-Yong Shin, Senior Member, IEEE Abstract—DBSCAN may not be sufficient when the input data type is heterogeneous in terms of textual description. When we aim to discover clusters of geo-tagged records relevant to a particular point-of-interest (POI) on social media, examining only one type of input data (e.g., the tweets relevant to a POI) may draw an incomplete picture of clusters due to noisy regions. To overcome this problem, we introduce DBSTexC, a newly defined density-based clustering algorithm using spatio–textual information. We first characterize POI-relevant and POI-irrelevant tweets as the texts that include and do not include a POI name or its semantically coherent variations, respectively. By leveraging the proportion of POI-relevant and POI-irrelevant tweets, the proposed algorithm demonstrates much higher clustering performance than the DBSCAN case in terms of F1 score and its variants. While DBSTexC performs exactly as DBSCAN with the textually homogeneous inputs, it far outperforms DBSCAN with the textually heterogeneous inputs. Furthermore, to further improve the clustering quality by fully capturing the geographic distribution of tweets, we present fuzzy DBSTexC (F-DBSTexC), an extension of DBSTexC, which incorporates the notion of fuzzy clustering into the DBSTexC. We then demonstrate the robustness of F-DBSTexC via intensive experiments. The computational complexity of our algorithms is also analytically and numerically shown. Index Terms—Density-based clustering, fuzzy clustering, geo-tagged tweet, point-of-interest (POI), spatio–textual information. -
Thesis Proposes a New Type of Mobile/Cloud Operating System Designed to Meet the Evolving Needs of Modern Applications
©Copyright 2017 Irene Y. Zhang Towards a Flexible, High-Performance Operating System for Mobile/Cloud Applications Irene Y. Zhang A dissertation submitted in partial fulllment of the requirements for the degree of Doctor of Philosophy University of Washington 2017 Reading Committee: Henry M. Levy, Chair Arvind Krishnamurthy Luis Ceze Program Authorized to Oer Degree: Computer Science & Engineering University of Washington Abstract Towards a Flexible, High-Performance Operating System for Mobile/Cloud Applications Irene Y. Zhang Chair of the Supervisory Committee: Director Henry M. Levy Computer Science & Engineering ¿e convergence of ubiquitous mobile devices, large-scale cloud platforms and pervasive network con- nectivity have changed the face of modern user applications. Unlike a traditional desktop application, which runs on a single machine and supports a single user, the typical user-facing application today spans numerous mobile devices and cloud servers while supporting large numbers of users. ¿is shi signicantly increased the diculty of building new user applications. Programmers must now confront challenges introduced by distributed deployment (e.g., partial failures), new mobile/cloud application features (e.g., reactivity), and new mobile/cloud requirements (e.g., scalability). ¿is thesis proposes a new type of mobile/cloud operating system designed to meet the evolving needs of modern applications. Mobile/cloud applications are the standard applications of the future; thus, they deserve a rst-class operating system that simplies their development and run-time management. Our key contribution is the design, implementation and evaluation of three systems that together form the basis for a new mobile/cloud operating system: (1) Sapphire, a new distributed run-time and process management system, (2) Diamond, a new distributed memory management system, and (3) TAPIR, a new distributed storage system. -
Szekeres Washington 0250E 2
DESIGNING STORAGE AND PRIVACY-PRESERVING SYSTEMSFOR LARGE-SCALE CLOUD APPLICATIONS by Adriana SZEKERES A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy University of Washington 2020 Reading committe: Henry M. Levy, Co-Chair Arvind Krishnamurthy, Co-Chair Dan R. K. Ports Program Authorized to Offer Degree: Computer Science & Engineering ©Copyright 2020 Adriana SZEKERES University of Washington Abstract Designing Storage and Privacy-Preserving Systems for Large-Scale Cloud Applications Adriana Szekeres Chair of Supervisory Committee: Henry M. Levy, Arvind Krishnamurthy Computer Science & Engineering Today’s mobile devices sense, collect, and store huge amounts of personal information, which users share with family and friends through a wide range of cloud applications. Many of these applications are large-scale – they must support millions of users from all over the world. These applications must manage a massive amount of user-generated content, they must ensure that this content is always available, and they must protect users’ privacy. To deal with the complexity of managing a large amount of data and ensure its availability, large-scale cloud applications rely on transactional, fault-tolerant, distributed storage systems. Latest hardware advancements and new design trends, such as in-memory storage, kernel-bypass message processing, and geo-distribution, have significantly improved the transaction processing times. However, they have also exposed overheads in the protocols used to implement these systems, which hinder their performance; specifically, existing protocols require unnecessary cross-datacenter and cross-processor coordination. This thesis introduces Meerkat, a new replicated storage system that avoids all cross-core and cross-replica coordination for transactions that do not conflict.