Browsing by Subject "Proximity"
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Item Large-scale network analytics(2011-08) Song, Han Hee, 1978-; Zhang, Yin, doctor of computer scienceScalable and accurate analysis of networks is essential to a wide variety of existing and emerging network systems. Specifically, network measurement and analysis helps to understand networks, improve existing services, and enable new data-mining applications. To support various services and applications in large-scale networks, network analytics must address the following challenges: (i) how to conduct scalable analysis in networks with a large number of nodes and links, (ii) how to flexibly accommodate various objectives from different administrative tasks, (iii) and how to cope with the dynamic changes in the networks. This dissertation presents novel path analysis schemes that effectively address the above challenges in analyzing pair-wise relationships among networked entities. In doing so, we make the following three major contributions to large-scale IP networks, social networks, and application service networks. For IP networks, we propose an accurate and flexible framework for path property monitoring. Analyzing the performance side of paths between pairs of nodes, our framework incorporates approaches that perform exact reconstruction of path properties as well as approximate reconstruction. Our framework is highly scalable to design measurement experiments that span thousands of routers and end hosts. It is also flexible to accommodate a variety of design requirements. For social networks, we present scalable and accurate graph embedding schemes. Aimed at analyzing the pair-wise relationships of social network users, we present three dimensionality reduction schemes leveraging matrix factorization, count-min sketch, and graph clustering paired with spectral graph embedding. As concrete applications showing the practical value of our schemes, we apply them to the important social analysis tasks of proximity estimation, missing link inference, and link prediction. The results clearly demonstrate the accuracy, scalability, and flexibility of our schemes for analyzing social networks with millions of nodes and tens of millions of links. For application service networks, we provide a proactive service quality assessment scheme. Analyzing the relationship between the satisfaction level of subscribers of an IPTV service and network performance indicators, our proposed scheme proactively (i.e., detect issues before IPTV subscribers complain) assesses user-perceived service quality using performance metrics collected from the network. From our evaluation using network data collected from a commercial IPTV service provider, we show that our scheme is able to predict 60% of the service problems that are complained by customers with only 0.1% of false positives.Item ProxStor : flexible scalable proximity data storage & analysis(2014-12) Giannoules, James Peter; Aziz, AdnanProxStor is a cloud-based human proximity storage and query informational system taking advantage of both the near ubiquity of mobile devices and the growing digital infrastructure in our everyday physical world, commonly referred to as the Internet of Things (IoT). The combination provides the opportunity for mobile devices to identify when entering and leaving the proximity of a space based upon this unique identifying infrastructure information. ProxStor provides a low-overhead interface for storing these proximity events while additionally offering search and query capabilities to enable a richer class of location aware applications. ProxStor scales up to store and manage more than one billion objects, while enabling future horizontal scaling to expand to multiple systems working together supporting even more objects. A single seamless web interface is presented to clients system.. More than 18 popular graph database systems are supported behind ProxStor. Performance benchmarks while running on Neo4j and OrientDB graph database systems are compared to determine feasibility of the design.Item Re/connect : an interdisciplinary exploration of wearable technology in devised theatre(2015-05) Weller, Kristen Ann; Glavan, James; Beckham, Andrea; Lowery, AllisonHow can theatrical costumes help develop a narrative about intimacy in a world that is increasingly detaching from physical contact? My thesis explores this question through interactive costumes and the use of Wearable technology. I created two micro-controlled costumes that employed a variety of proximity sensors and LEDs that light in reaction to the touch and closeness of another person. The costumes are a response to the statement made by MIT psychologist Sherry Turkle: "We're lonely, but afraid of intimacy." The garments were featured in both an interdisciplinary devised theatrical production I helped create, entitled RE/CONNECT, and an interactive educational exhibit, illustrating the importance of physical touch in an increasingly digital age. Only by integrating new and old technologies will theatre remain relevant and funded in a world that is losing interest in physical interaction. Beyond the benefits of study for the production team, the final thesis performance attracted audience members from a wide demographic range, including those outside of the theatrical community with positive results. By incorporating nontraditional technologies in performance, and allowing audience members to experience these technologies firsthand outside of a museum, I have challenged my colleagues in the theatre and sciences to further investigate applications of developing technologies, and put to art and technology in deeper conversation.Item Secure protocols for contactless credit cards and electronic wallets(2017-05) Jensen, Oliver Christopher; Gouda, Mohamed G., 1947-; Alvisi, Lorenzo; Qiu, Lili; Garg, Vijay KThe contactless credit card protocol in use today is insecure. The credit card industry has chosen to use the NFC channel for contactless transactions. However, reliance on NFC's short range has led to poor assumptions in the contactless credit card protocol. For example, the card assumes (sometimes incorrectly) that its ability to receive a solicitation implies the cardholder's intent to purchase. In this dissertation, we examine the protocol currently in use, and present a family of three replacement protocols to defend against its deficiencies. First, we consider "outsider" attacks (e.g. eavesdropping, skimming attacks, relay attacks, and attacks facilitated by compromised points of sale) and design our first protocol to defend against these attacks. We call this protocol the Externally Secure CC Protocol, and design it using stepwise refinement. This protocol makes use of single-use "charge tokens" verifiable by the bank, while minimizing computation that needs to occur on the card. Second, we identify two attacks which may be carried out by malicious retailers: Over-charge attacks and Transparent Bridge attacks. Both attacks are predicated on the customer's lack of participation in the protocol, and involve modifying or replacing a charge after it has been confirmed by the customer. We look to Electronic Wallet applications (such as Android Pay and Apple Wallet), which provide a channel between customer and card. We augment the Externally Secure CC Protocol using this channel to construct the Secure CC Protocol, binding charge tokens to a given price, and thus stymieing both outsider and malicious retailer attacks. The Secure CC Protocol supports a property known as linkability: while only the bank can verify charge tokens, tokens from the same card can be recognized as such by the retailer. This property is also supported by the (insecure) protocol in use today, and is commonly used by retailers to construct marketing profiles on their customers. However, linkability has serious consumer privacy consequences, so we consider the converse property of unlinkability, where a retailer cannot identify different purchases as having been made by the same card. We require that our unlinkable protocol make use of existing infrastructure, so as not to require retailer cooperation. In response, we design the Unlinkable Wallet Protocol, leveraging techniques from the Secure CC Protocol to guard against malicious outsiders and retailers, while tunneling secure and unlinkable charge tokens through the protocol in use today.Item The impact of light rail transit on residential rental market : case study of Dallas Area Rapid Transit(2016-08) Haque, Antora Mohsena; Zhang, Ming, 1963 April 22-; Wegmann, JakeThis research was undertaken to quantify the relationship between residential rent and proximity to light rail transit in Dallas, an auto-oriented city. This correlation is of importance to real estate developers and transportation planners as they seek to make the most efficient use of developable land and to decide on the allocation of funding for future transportation projects. This study shows that proximity to DART rail stations is associated with residential rent up to half mile radius area of the stations. Hedonic regression models in simple Ordinary Least Squares (OLS) and semi log form were used for the analysis. The semi log model showed that light rail stations have the strongest relationship with rent in the 0.1 mile to 0.2 mile distance buffer, where the rent/sq. ft. is 20.92% higher than for units between 0.4 and 0.5 miles distance from stations. After 0.2 miles distance from the stations, the rent starts to drop and continues to go down till 0.5 miles distance from a station. The simple OLS model showed similar results and according to this model within 0.1 to 0.2 mile buffer area the rent is 27.6 cents/sq. ft. higher than the rent/sq. ft. in the 0.4 to 0.5 mile buffer area. This result will help to manage the extent of investment in light rail in Dallas in the future.