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- All Subjects: Computer Science
- Creators: Sen, Arunabha
- Member of: ASU Electronic Theses and Dissertations
- Member of: Theses and Dissertations
Description
Social media has become popular in the past decade. Facebook for example has 1.59 billion active users monthly. With such massive social networks generating lot of data, everyone is constantly looking for ways of leveraging the knowledge from social networks to make their systems more personalized to their end users. And with rapid increase in the usage of mobile phones and wearables, social media data is being tied to spatial networks. This research document proposes an efficient technique that answers socially k-Nearest Neighbors with Spatial Range Filter. The proposed approach performs a joint search on both the social and spatial domains which radically improves the performance compared to straight forward solutions. The research document proposes a novel index that combines social and spatial indexes. In other words, graph data is stored in an organized manner to filter it based on spatial (region of interest) and social constraints (top-k closest vertices) at query time. That leads to pruning necessary paths during the social graph traversal procedure, and only returns the top-K social close venues. The research document then experimentally proves how the proposed approach outperforms existing baseline approaches by at least three times and also compare how each of our algorithms perform under various conditions on a real geo-social dataset extracted from Yelp.
ContributorsPasumarthy, Nitin (Author) / Sarwat, Mohamed (Thesis advisor) / Papotti, Paolo (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2016
Description
In this thesis multiple approaches are explored to enhance sentiment analysis of tweets. A standard sentiment analysis model with customized features is first trained and tested to establish a baseline. This is compared to an existing topic based mixture model and a new proposed topic based vector model both of which use Latent Dirichlet Allocation (LDA) for topic modeling. The proposed topic based vector model has higher accuracies in terms of averaged F scores than the other two models.
ContributorsBaskaran, Swetha (Author) / Davulcu, Hasan (Thesis advisor) / Sen, Arunabha (Committee member) / Hsiao, Ihan (Committee member) / Arizona State University (Publisher)
Created2016
Description
Predicting when an individual will adopt a new behavior is an important problem in application domains such as marketing and public health. This thesis examines the performance of a wide variety of social network based measurements proposed in the literature - which have not been previously compared directly. This research studies the probability of an individual becoming influenced based on measurements derived from neighborhood (i.e. number of influencers, personal network exposure), structural diversity, locality, temporal measures, cascade measures, and metadata. It also examines the ability to predict influence based on choice of the classifier and how the ratio of positive to negative samples in both training and testing affect prediction results - further enabling practical use of these concepts for social influence applications.
ContributorsNanda Kumar, Nikhil (Author) / Shakarian, Paulo (Thesis advisor) / Sen, Arunabha (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2016
Description
Internet and social media devices created a new public space for debate on political
and social topics (Papacharissi 2002; Himelboim 2010). Hotly debated issues
span all spheres of human activity; from liberal vs. conservative politics, to radical
vs. counter-radical religious debate, to climate change debate in scientific community,
to globalization debate in economics, and to nuclear disarmament debate in
security. Many prominent ’camps’ have emerged within Internet debate rhetoric and
practice (Dahlberg, n.d.).
In this research I utilized feature extraction and model fitting techniques to process
the rhetoric found in the web sites of 23 Indonesian Islamic religious organizations,
later with 26 similar organizations from the United Kingdom to profile their
ideology and activity patterns along a hypothesized radical/counter-radical scale, and
presented an end-to-end system that is able to help researchers to visualize the data
in an interactive fashion on a time line. The subject data of this study is the articles
downloaded from the web sites of these organizations dating from 2001 to 2011,
and in 2013. I developed algorithms to rank these organizations by assigning them
to probable positions on the scale. I showed that the developed Rasch model fits
the data using Andersen’s LR-test (likelihood ratio). I created a gold standard of
the ranking of these organizations through an expertise elicitation tool. Then using
my system I computed expert-to-expert agreements, and then presented experimental
results comparing the performance of three baseline methods to show that the
Rasch model not only outperforms the baseline methods, but it was also the only
system that performs at expert-level accuracy.
I developed an end-to-end system that receives list of organizations from experts,
mines their web corpus, prepare discourse topic lists with expert support, and then
ranks them on scales with partial expert interaction, and finally presents them on an
easy to use web based analytic system.
and social topics (Papacharissi 2002; Himelboim 2010). Hotly debated issues
span all spheres of human activity; from liberal vs. conservative politics, to radical
vs. counter-radical religious debate, to climate change debate in scientific community,
to globalization debate in economics, and to nuclear disarmament debate in
security. Many prominent ’camps’ have emerged within Internet debate rhetoric and
practice (Dahlberg, n.d.).
In this research I utilized feature extraction and model fitting techniques to process
the rhetoric found in the web sites of 23 Indonesian Islamic religious organizations,
later with 26 similar organizations from the United Kingdom to profile their
ideology and activity patterns along a hypothesized radical/counter-radical scale, and
presented an end-to-end system that is able to help researchers to visualize the data
in an interactive fashion on a time line. The subject data of this study is the articles
downloaded from the web sites of these organizations dating from 2001 to 2011,
and in 2013. I developed algorithms to rank these organizations by assigning them
to probable positions on the scale. I showed that the developed Rasch model fits
the data using Andersen’s LR-test (likelihood ratio). I created a gold standard of
the ranking of these organizations through an expertise elicitation tool. Then using
my system I computed expert-to-expert agreements, and then presented experimental
results comparing the performance of three baseline methods to show that the
Rasch model not only outperforms the baseline methods, but it was also the only
system that performs at expert-level accuracy.
I developed an end-to-end system that receives list of organizations from experts,
mines their web corpus, prepare discourse topic lists with expert support, and then
ranks them on scales with partial expert interaction, and finally presents them on an
easy to use web based analytic system.
ContributorsTikves, Sukru (Author) / Davulcu, Hasan (Thesis advisor) / Sen, Arunabha (Committee member) / Liu, Huan (Committee member) / Woodward, Mark (Committee member) / Arizona State University (Publisher)
Created2016
Description
With the advent of Internet, the data being added online is increasing at enormous rate. Though search engines are using IR techniques to facilitate the search requests from users, the results are not effective towards the search query of the user. The search engine user has to go through certain webpages before getting at the webpage he/she wanted. This problem of Information Overload can be solved using Automatic Text Summarization. Summarization is a process of obtaining at abridged version of documents so that user can have a quick view to understand what exactly the document is about. Email threads from W3C are used in this system. Apart from common IR features like Term Frequency, Inverse Document Frequency, Term Rank, a variation of page rank based on graph model, which can cluster the words with respective to word ambiguity, is implemented. Term Rank also considers the possibility of co-occurrence of words with the corpus and evaluates the rank of the word accordingly. Sentences of email threads are ranked as per features and summaries are generated. System implemented the concept of pyramid evaluation in content selection. The system can be considered as a framework for Unsupervised Learning in text summarization.
ContributorsNadella, Sravan (Author) / Davulcu, Hasan (Thesis advisor) / Li, Baoxin (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2015
Description
The presence of a rich set of embedded sensors on mobile devices has been fuelling various sensing applications regarding the activities of individuals and their surrounding environment, and these ubiquitous sensing-capable mobile devices are pushing the new paradigm of Mobile Crowd Sensing (MCS) from concept to reality. MCS aims to outsource sensing data collection to mobile users and it could revolutionize the traditional ways of sensing data collection and processing. In the meantime, cloud computing provides cloud-backed infrastructures for mobile devices to provision their capabilities with network access. With enormous computational and storage resources along with sufficient bandwidth, it functions as the hub to handle the sensing service requests from sensing service consumers and coordinate sensing task assignment among eligible mobile users to reach a desired quality of sensing service. This paper studies the problem of sensing task assignment to mobile device owners with specific spatio-temporal traits to minimize the cost and maximize the utility in MCS while adhering to QoS constraints. Greedy approaches and hybrid solutions combined with bee algorithms are explored to address the problem.
Moreover, the privacy concerns arise with the widespread deployment of MCS from both the data contributors and the sensing service consumers. The uploaded sensing data, especially those tagged with spatio-temporal information, will disclose the personal information of the data contributors. In addition, the sensing service requests can reveal the personal interests of service consumers. To address the privacy issues, this paper constructs a new framework named Privacy-Preserving Mobile Crowd Sensing (PP-MCS) to leverage the sensing capabilities of ubiquitous mobile devices and cloud infrastructures. PP-MCS has a distributed architecture without relying on trusted third parties for privacy-preservation. In PP-MCS, the sensing service consumers can retrieve data without revealing the real data contributors. Besides, the individual sensing records can be compared against the aggregation result while keeping the values of sensing records unknown, and the k-nearest neighbors could be approximately identified without privacy leaks. As such, the privacy of the data contributors and the sensing service consumers can be protected to the greatest extent possible.
Moreover, the privacy concerns arise with the widespread deployment of MCS from both the data contributors and the sensing service consumers. The uploaded sensing data, especially those tagged with spatio-temporal information, will disclose the personal information of the data contributors. In addition, the sensing service requests can reveal the personal interests of service consumers. To address the privacy issues, this paper constructs a new framework named Privacy-Preserving Mobile Crowd Sensing (PP-MCS) to leverage the sensing capabilities of ubiquitous mobile devices and cloud infrastructures. PP-MCS has a distributed architecture without relying on trusted third parties for privacy-preservation. In PP-MCS, the sensing service consumers can retrieve data without revealing the real data contributors. Besides, the individual sensing records can be compared against the aggregation result while keeping the values of sensing records unknown, and the k-nearest neighbors could be approximately identified without privacy leaks. As such, the privacy of the data contributors and the sensing service consumers can be protected to the greatest extent possible.
ContributorsWang, Zhijie (Thesis advisor) / Xue, Guoliang (Committee member) / Sen, Arunabha (Committee member) / Li, Jing (Committee member) / Arizona State University (Publisher)
Created2016
Description
We live in a networked world with a multitude of networks, such as communication networks, electric power grid, transportation networks and water distribution networks, all around us. In addition to such physical (infrastructure) networks, recent years have seen tremendous proliferation of social networks, such as Facebook, Twitter, LinkedIn, Instagram, Google+ and others. These powerful social networks are not only used for harnessing revenue from the infrastructure networks, but are also increasingly being used as “non-conventional sensors” for monitoring the infrastructure networks. Accordingly, nowadays, analyses of social and infrastructure networks go hand-in-hand. This dissertation studies resource allocation problems encountered in this set of diverse, heterogeneous, and interdependent networks. Three problems studied in this dissertation are encountered in the physical network domain while the three other problems studied are encountered in the social network domain.
The first problem from the infrastructure network domain relates to distributed files storage scheme with a goal of enhancing robustness of data storage by making it tolerant against large scale geographically-correlated failures. The second problem relates to placement of relay nodes in a deployment area with multiple sensor nodes with a goal of augmenting connectivity of the resulting network, while staying within the budget specifying the maximum number of relay nodes that can be deployed. The third problem studied in this dissertation relates to complex interdependencies that exist between infrastructure networks, such as power grid and communication network. The progressive recovery problem in an interdependent network is studied whose goal is to maximize system utility over the time when recovery process of failed entities takes place in a sequential manner.
The three problems studied from the social network domain relate to influence propagation in adversarial environment and political sentiment assessment in various states in a country with a goal of creation of a “political heat map” of the country. In the first problem of the influence propagation domain, the goal of the second player is to restrict the influence of the first player, while in the second problem the goal of the second player is to have a larger market share with least amount of initial investment.
The first problem from the infrastructure network domain relates to distributed files storage scheme with a goal of enhancing robustness of data storage by making it tolerant against large scale geographically-correlated failures. The second problem relates to placement of relay nodes in a deployment area with multiple sensor nodes with a goal of augmenting connectivity of the resulting network, while staying within the budget specifying the maximum number of relay nodes that can be deployed. The third problem studied in this dissertation relates to complex interdependencies that exist between infrastructure networks, such as power grid and communication network. The progressive recovery problem in an interdependent network is studied whose goal is to maximize system utility over the time when recovery process of failed entities takes place in a sequential manner.
The three problems studied from the social network domain relate to influence propagation in adversarial environment and political sentiment assessment in various states in a country with a goal of creation of a “political heat map” of the country. In the first problem of the influence propagation domain, the goal of the second player is to restrict the influence of the first player, while in the second problem the goal of the second player is to have a larger market share with least amount of initial investment.
ContributorsMazumder, Anisha (Author) / Sen, Arunabha (Thesis advisor) / Richa, Andrea (Committee member) / Xue, Guoliang (Committee member) / Reisslein, Martin (Committee member) / Arizona State University (Publisher)
Created2016
Description
The critical infrastructures of the nation are a large and complex network of human, physical and cyber-physical systems. In recent times, it has become increasingly apparent that individual critical infrastructures, such as the power and communication networks, do not operate in isolation, but instead are part of a complex interdependent ecosystem where a failure involving a small set of network entities can trigger a cascading event resulting in the failure of a much larger set of entities through the failure propagation process.
Recognizing the need for a deeper understanding of the interdependent relationships between such critical infrastructures, several models have been proposed and analyzed in the last few years. However, most of these models are over-simplified and fail to capture the complex interdependencies that may exist between critical infrastructures. To overcome the limitations of existing models, this dissertation presents a new model -- the Implicative Interdependency Model (IIM) that is able to capture such complex interdependency relations. As the potential for a failure cascade in critical interdependent networks poses several risks that can jeopardize the nation, this dissertation explores relevant research problems in the interdependent power and communication networks using the proposed IIM and lays the foundations for further study using this model.
Apart from exploring problems in interdependent critical infrastructures, this dissertation also explores resource allocation techniques for environments enabled with cyber-physical systems. Specifically, the problem of efficient path planning for data collection using mobile cyber-physical systems is explored. Two such environments are considered: a Radio-Frequency IDentification (RFID) environment with mobile “Tags” and “Readers”, and a sensor data collection environment where both the sensors and the data mules (data collectors) are mobile.
Finally, from an applied research perspective, this dissertation presents Raptor, an advanced network planning and management tool for mitigating the impact of spatially correlated, or region based faults on infrastructure networks. Raptor consolidates a wide range of studies conducted in the last few years on region based faults, and provides an interface for network planners, designers and operators to use the results of these studies for designing robust and resilient networks in the presence of spatially correlated faults.
Recognizing the need for a deeper understanding of the interdependent relationships between such critical infrastructures, several models have been proposed and analyzed in the last few years. However, most of these models are over-simplified and fail to capture the complex interdependencies that may exist between critical infrastructures. To overcome the limitations of existing models, this dissertation presents a new model -- the Implicative Interdependency Model (IIM) that is able to capture such complex interdependency relations. As the potential for a failure cascade in critical interdependent networks poses several risks that can jeopardize the nation, this dissertation explores relevant research problems in the interdependent power and communication networks using the proposed IIM and lays the foundations for further study using this model.
Apart from exploring problems in interdependent critical infrastructures, this dissertation also explores resource allocation techniques for environments enabled with cyber-physical systems. Specifically, the problem of efficient path planning for data collection using mobile cyber-physical systems is explored. Two such environments are considered: a Radio-Frequency IDentification (RFID) environment with mobile “Tags” and “Readers”, and a sensor data collection environment where both the sensors and the data mules (data collectors) are mobile.
Finally, from an applied research perspective, this dissertation presents Raptor, an advanced network planning and management tool for mitigating the impact of spatially correlated, or region based faults on infrastructure networks. Raptor consolidates a wide range of studies conducted in the last few years on region based faults, and provides an interface for network planners, designers and operators to use the results of these studies for designing robust and resilient networks in the presence of spatially correlated faults.
ContributorsDas, Arun (Author) / Sen, Arunabha (Thesis advisor) / Xue, Guoliang (Committee member) / Fainekos, Georgios (Committee member) / Qiao, Chunming (Committee member) / Arizona State University (Publisher)
Created2016
Description
As networks are playing an increasingly prominent role in different aspects of our lives, there is a growing awareness that improving their performance is of significant importance. In order to enhance performance of networks, it is essential that scarce networking resources be allocated smartly to match the continuously changing network environment. This dissertation focuses on two different kinds of networks - communication and social, and studies resource allocation problems in these networks. The study on communication networks is further divided into different networking technologies - wired and wireless, optical and mobile, airborne and terrestrial. Since nodes in an airborne network (AN) are heterogeneous and mobile, the design of a reliable and robust AN is highly complex. The dissertation studies connectivity and fault-tolerance issues in ANs and proposes algorithms to compute the critical transmission range in fault free, faulty and delay tolerant scenarios. Just as in the case of ANs, power optimization and fault tolerance are important issues in wireless sensor networks (WSN). In a WSN, a tree structure is often used to deliver sensor data to a sink node. In a tree, failure of a node may disconnect the tree. The dissertation investigates the problem of enhancing the fault tolerance capability of data gathering trees in WSN. The advent of OFDM technology provides an opportunity for efficient resource utilization in optical networks and also introduces a set of novel problems, such as routing and spectrum allocation (RSA) problem. This dissertation proves that RSA problem is NP-complete even when the network topology is a chain, and proposes approximation algorithms. In the domain of social networks, the focus of this dissertation is study of influence propagation in presence of active adversaries. In a social network multiple vendors may attempt to influence the nodes in a competitive fashion. This dissertation investigates the scenario where the first vendor has already chosen a set of nodes and the second vendor, with the knowledge of the choice of the first, attempts to identify a smallest set of nodes so that after the influence propagation, the second vendor's market share is larger than the first.
ContributorsShirazipourazad, Shahrzad (Author) / Sen, Arunabha (Committee member) / Xue, Guoliang (Committee member) / Richa, Andrea (Committee member) / Saripalli, Srikanth (Committee member) / Arizona State University (Publisher)
Created2014
Description
There has been extensive study of the target tracking problems in the recent years. Very little work has been done in the problem of continuous monitoring of all the mobile targets using the fewest number of mobile trackers, when the trajectories of all the targets are known in advance. Almost all the existing research discretized time (and/or space), or assume infinite tracker velocity. In this thesis, I consider the problem of covering (tracking) target nodes using a network of Unmanned Airborne Vehicles (UAV's) for the entire period of observation by adding the constraint of fixed velocity on the trackers and observing the targets in continuous time and space. I also show that the problem is NP-complete and provide algorithms for handling cases when targets are static and dynamic.
ContributorsVachhani, Harsh (Author) / Sen, Arunabha (Thesis advisor) / Saripalli, Srikanth (Committee member) / Shirazipour, Shahrzad (Committee member) / Arizona State University (Publisher)
Created2014