Matching Items (9)
Filtering by
- All Subjects: Clustering
- Creators: Davulcu, Hasan
- Creators: Ahn, Gail-Joon
- Status: Published
Description
Multidimensional data have various representations. Thanks to their simplicity in modeling multidimensional data and the availability of various mathematical tools (such as tensor decompositions) that support multi-aspect analysis of such data, tensors are increasingly being used in many application domains including scientific data management, sensor data management, and social network data analysis. Relational model, on the other hand, enables semantic manipulation of data using relational operators, such as projection, selection, Cartesian-product, and set operators. For many multidimensional data applications, tensor operations as well as relational operations need to be supported throughout the data life cycle. In this thesis, we introduce a tensor-based relational data model (TRM), which enables both tensor- based data analysis and relational manipulations of multidimensional data, and define tensor-relational operations on this model. Then we introduce a tensor-relational data management system, so called, TensorDB. TensorDB is based on TRM, which brings together relational algebraic operations (for data manipulation and integration) and tensor algebraic operations (for data analysis). We develop optimization strategies for tensor-relational operations in both in-memory and in-database TensorDB. The goal of the TRM and TensorDB is to serve as a single environment that supports the entire life cycle of data; that is, data can be manipulated, integrated, processed, and analyzed.
ContributorsKim, Mijung (Author) / Candan, K. Selcuk (Thesis advisor) / Davulcu, Hasan (Committee member) / Sundaram, Hari (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2014
Description
US Senate is the venue of political debates where the federal bills are formed and voted. Senators show their support/opposition along the bills with their votes. This information makes it possible to extract the polarity of the senators. Similarly, blogosphere plays an increasingly important role as a forum for public debate. Authors display sentiment toward issues, organizations or people using a natural language.
In this research, given a mixed set of senators/blogs debating on a set of political issues from opposing camps, I use signed bipartite graphs for modeling debates, and I propose an algorithm for partitioning both the opinion holders (senators or blogs) and the issues (bills or topics) comprising the debate into binary opposing camps. Simultaneously, my algorithm scales the entities on a univariate scale. Using this scale, a researcher can identify moderate and extreme senators/blogs within each camp, and polarizing versus unifying issues. Through performance evaluations I show that my proposed algorithm provides an effective solution to the problem, and performs much better than existing baseline algorithms adapted to solve this new problem. In my experiments, I used both real data from political blogosphere and US Congress records, as well as synthetic data which were obtained by varying polarization and degree distribution of the vertices of the graph to show the robustness of my algorithm.
I also applied my algorithm on all the terms of the US Senate to the date for longitudinal analysis and developed a web based interactive user interface www.PartisanScale.com to visualize the analysis.
US politics is most often polarized with respect to the left/right alignment of the entities. However, certain issues do not reflect the polarization due to political parties, but observe a split correlating to the demographics of the senators, or simply receive consensus. I propose a hierarchical clustering algorithm that identifies groups of bills that share the same polarization characteristics. I developed a web based interactive user interface www.ControversyAnalysis.com to visualize the clusters while providing a synopsis through distribution charts, word clouds, and heat maps.
In this research, given a mixed set of senators/blogs debating on a set of political issues from opposing camps, I use signed bipartite graphs for modeling debates, and I propose an algorithm for partitioning both the opinion holders (senators or blogs) and the issues (bills or topics) comprising the debate into binary opposing camps. Simultaneously, my algorithm scales the entities on a univariate scale. Using this scale, a researcher can identify moderate and extreme senators/blogs within each camp, and polarizing versus unifying issues. Through performance evaluations I show that my proposed algorithm provides an effective solution to the problem, and performs much better than existing baseline algorithms adapted to solve this new problem. In my experiments, I used both real data from political blogosphere and US Congress records, as well as synthetic data which were obtained by varying polarization and degree distribution of the vertices of the graph to show the robustness of my algorithm.
I also applied my algorithm on all the terms of the US Senate to the date for longitudinal analysis and developed a web based interactive user interface www.PartisanScale.com to visualize the analysis.
US politics is most often polarized with respect to the left/right alignment of the entities. However, certain issues do not reflect the polarization due to political parties, but observe a split correlating to the demographics of the senators, or simply receive consensus. I propose a hierarchical clustering algorithm that identifies groups of bills that share the same polarization characteristics. I developed a web based interactive user interface www.ControversyAnalysis.com to visualize the clusters while providing a synopsis through distribution charts, word clouds, and heat maps.
ContributorsGokalp, Sedat (Author) / Davulcu, Hasan (Thesis advisor) / Sen, Arunabha (Committee member) / Liu, Huan (Committee member) / Woodward, Mark (Committee member) / Arizona State University (Publisher)
Created2015
Description
Corporations invest considerable resources to create, preserve and analyze
their data; yet while organizations are interested in protecting against
unauthorized data transfer, there lacks a comprehensive metric to discriminate
what data are at risk of leaking.
This thesis motivates the need for a quantitative leakage risk metric, and
provides a risk assessment system, called Whispers, for computing it. Using
unsupervised machine learning techniques, Whispers uncovers themes in an
organization's document corpus, including previously unknown or unclassified
data. Then, by correlating the document with its authors, Whispers can
identify which data are easier to contain, and conversely which are at risk.
Using the Enron email database, Whispers constructs a social network segmented
by topic themes. This graph uncovers communication channels within the
organization. Using this social network, Whispers determines the risk of each
topic by measuring the rate at which simulated leaks are not detected. For the
Enron set, Whispers identified 18 separate topic themes between January 1999
and December 2000. The highest risk data emanated from the legal department
with a leakage risk as high as 60%.
their data; yet while organizations are interested in protecting against
unauthorized data transfer, there lacks a comprehensive metric to discriminate
what data are at risk of leaking.
This thesis motivates the need for a quantitative leakage risk metric, and
provides a risk assessment system, called Whispers, for computing it. Using
unsupervised machine learning techniques, Whispers uncovers themes in an
organization's document corpus, including previously unknown or unclassified
data. Then, by correlating the document with its authors, Whispers can
identify which data are easier to contain, and conversely which are at risk.
Using the Enron email database, Whispers constructs a social network segmented
by topic themes. This graph uncovers communication channels within the
organization. Using this social network, Whispers determines the risk of each
topic by measuring the rate at which simulated leaks are not detected. For the
Enron set, Whispers identified 18 separate topic themes between January 1999
and December 2000. The highest risk data emanated from the legal department
with a leakage risk as high as 60%.
ContributorsWright, Jeremy (Author) / Syrotiuk, Violet (Thesis advisor) / Davulcu, Hasan (Committee member) / Yau, Stephen (Committee member) / Arizona State University (Publisher)
Created2014
Description
This thesis proposed a novel approach to establish the trust model in a social network scenario based on users' emails. Email is one of the most important social connections nowadays. By analyzing email exchange activities among users, a social network trust model can be established to judge the trust rate between each two users. The whole trust checking process is divided into two steps: local checking and remote checking. Local checking directly contacts the email server to calculate the trust rate based on user's own email communication history. Remote checking is a distributed computing process to get help from user's social network friends and built the trust rate together. The email-based trust model is built upon a cloud computing framework called MobiCloud. Inside MobiCloud, each user occupies a virtual machine which can directly communicate with others. Based on this feature, the distributed trust model is implemented as a combination of local analysis and remote analysis in the cloud. Experiment results show that the trust evaluation model can give accurate trust rate even in a small scale social network which does not have lots of social connections. With this trust model, the security in both social network services and email communication could be improved.
ContributorsZhong, Yunji (Author) / Huang, Dijiang (Thesis advisor) / Dasgupta, Partha (Committee member) / Syrotiuk, Violet (Committee member) / Arizona State University (Publisher)
Created2011
Description
This research start utilizing an efficient sparse inverse covariance matrix (precision matrix) estimation technique to identify a set of highly correlated discriminative perspectives between radical and counter-radical groups. A ranking system has been developed that utilizes ranked perspectives to map Islamic organizations on a set of socio-cultural, political and behavioral scales based on their web site corpus. Simultaneously, a gold standard ranking of these organizations was created through domain experts and compute expert-to-expert agreements and present experimental results comparing the performance of the QUIC based scaling system to another baseline method for organizations. The QUIC based algorithm not only outperforms the baseline methods, but it is also the only system that consistently performs at area expert-level accuracies for all scales. Also, a multi-scale ideological model has been developed and it investigates the correlates of Islamic extremism in Indonesia, Nigeria and UK. This analysis demonstrate that violence does not correlate strongly with broad Muslim theological or sectarian orientations; it shows that religious diversity intolerance is the only consistent and statistically significant ideological correlate of Islamic extremism in these countries, alongside desire for political change in UK and Indonesia, and social change in Nigeria. Next, dynamic issues and communities tracking system based on NMF(Non-negative Matrix Factorization) co-clustering algorithm has been built to better understand the dynamics of virtual communities. The system used between Iran and Saudi Arabia to build and apply a multi-party agent-based model that can demonstrate the role of wedges and spoilers in a complex environment where coalitions are dynamic. Lastly, a visual intelligence platform for tracking the diffusion of online social movements has been developed called LookingGlass to track the geographical footprint, shifting positions and flows of individuals, topics and perspectives between groups. The algorithm utilize large amounts of text collected from a wide variety of organizations’ media outlets to discover their hotly debated topics, and their discriminative perspectives voiced by opposing camps organized into multiple scales. Discriminating perspectives is utilized to classify and map individual Tweeter’s message content to social movements based on the perspectives expressed in their tweets.
ContributorsKim, Nyunsu (Author) / Davulcu, Hasan (Thesis advisor) / Sen, Arunabha (Committee member) / Hsiao, Sharon (Committee member) / Corman, Steven (Committee member) / Arizona State University (Publisher)
Created2018
Description
Malware forensics is a time-consuming process that involves a significant amount of data collection. To ease the load on security analysts, many attempts have been made to automate the intelligence gathering process and provide a centralized search interface. Certain of these solutions map existing relations between threats and can discover new intelligence by identifying correlations in the data. However, such systems generally treat each unique malware sample as its own distinct threat. This fails to model the real malware landscape, in which so many ``new" samples are actually variants of samples that have already been discovered. Were there some way to reliably determine whether two malware samples belong to the same family, intelligence for one sample could be applied to any sample in the family, greatly reducing the complexity of intelligence synthesis. Clustering is a common big data approach for grouping data samples which have common features, and has been applied in several recent papers for identifying related malware. It therefore has the potential to be used as described to simplify the intelligence synthesis process. However, existing threat intelligence systems do not use malware clustering. In this paper, we attempt to design a highly accurate malware clustering system, with the ultimate goal of integrating it into a threat intelligence platform. Toward this end, we explore the many considerations of designing such a system: how to extract features to compare malware, and how to use these features for accurate clustering. We then create an experimental clustering system, and evaluate its effectiveness using two different clustering algorithms.
ContributorsSmith, Joshua Michael (Author) / Ahn, Gail-Joon (Thesis director) / Zhao, Ziming (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / Computer Science and Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
Traditionally, visualization is one of the most important and commonly used methods of generating insight into large scale data. Particularly for spatiotemporal data, the translation of such data into a visual form allows users to quickly see patterns, explore summaries and relate domain knowledge about underlying geographical phenomena that would not be apparent in tabular form. However, several critical challenges arise when visualizing and exploring these large spatiotemporal datasets. While, the underlying geographical component of the data lends itself well to univariate visualization in the form of traditional cartographic representations (e.g., choropleth, isopleth, dasymetric maps), as the data becomes multivariate, cartographic representations become more complex. To simplify the visual representations, analytical methods such as clustering and feature extraction are often applied as part of the classification phase. The automatic classification can then be rendered onto a map; however, one common issue in data classification is that items near a classification boundary are often mislabeled.
This thesis explores methods to augment the automated spatial classification by utilizing interactive machine learning as part of the cluster creation step. First, this thesis explores the design space for spatiotemporal analysis through the development of a comprehensive data wrangling and exploratory data analysis platform. Second, this system is augmented with a novel method for evaluating the visual impact of edge cases for multivariate geographic projections. Finally, system features and functionality are demonstrated through a series of case studies, with key features including similarity analysis, multivariate clustering, and novel visual support for cluster comparison.
This thesis explores methods to augment the automated spatial classification by utilizing interactive machine learning as part of the cluster creation step. First, this thesis explores the design space for spatiotemporal analysis through the development of a comprehensive data wrangling and exploratory data analysis platform. Second, this system is augmented with a novel method for evaluating the visual impact of edge cases for multivariate geographic projections. Finally, system features and functionality are demonstrated through a series of case studies, with key features including similarity analysis, multivariate clustering, and novel visual support for cluster comparison.
ContributorsZhang, Yifan (Author) / Maciejewski, Ross (Thesis advisor) / Mack, Elizabeth (Committee member) / Liu, Huan (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2016
Description
E-Mail header injection vulnerability is a class of vulnerability that can occur in web applications that use user input to construct e-mail messages. E-Mail injection is possible when the mailing script fails to check for the presence of e-mail headers in user input (either form fields or URL parameters). The vulnerability exists in the reference implementation of the built-in “mail” functionality in popular languages like PHP, Java, Python, and Ruby. With the proper injection string, this vulnerability can be exploited to inject additional headers and/or modify existing headers in an e-mail message, allowing an attacker to completely alter the content of the e-mail.
This thesis develops a scalable mechanism to automatically detect E-Mail Header Injection vulnerability and uses this mechanism to quantify the prevalence of E- Mail Header Injection vulnerabilities on the Internet. Using a black-box testing approach, the system crawled 21,675,680 URLs to find URLs which contained form fields. 6,794,917 such forms were found by the system, of which 1,132,157 forms contained e-mail fields. The system used this data feed to discern the forms that could be fuzzed with malicious payloads. Amongst the 934,016 forms tested, 52,724 forms were found to be injectable with more malicious payloads. The system tested 46,156 of these and was able to find 496 vulnerable URLs across 222 domains, which proves that the threat is widespread and deserves future research attention.
This thesis develops a scalable mechanism to automatically detect E-Mail Header Injection vulnerability and uses this mechanism to quantify the prevalence of E- Mail Header Injection vulnerabilities on the Internet. Using a black-box testing approach, the system crawled 21,675,680 URLs to find URLs which contained form fields. 6,794,917 such forms were found by the system, of which 1,132,157 forms contained e-mail fields. The system used this data feed to discern the forms that could be fuzzed with malicious payloads. Amongst the 934,016 forms tested, 52,724 forms were found to be injectable with more malicious payloads. The system tested 46,156 of these and was able to find 496 vulnerable URLs across 222 domains, which proves that the threat is widespread and deserves future research attention.
ContributorsChandramouli, Sai Prashanth (Author) / Doupe, Adam (Thesis advisor) / Ahn, Gail-Joon (Committee member) / Zhao, Ziming (Committee member) / Arizona State University (Publisher)
Created2016
Description
One of the most remarkable outcomes resulting from the evolution of the web into Web 2.0, has been the propelling of blogging into a widely adopted and globally accepted phenomenon. While the unprecedented growth of the Blogosphere has added diversity and enriched the media, it has also added complexity. To cope with the relentless expansion, many enthusiastic bloggers have embarked on voluntarily writing, tagging, labeling, and cataloguing their posts in hopes of reaching the widest possible audience. Unbeknown to them, this reaching-for-others process triggers the generation of a new kind of collective wisdom, a result of shared collaboration, and the exchange of ideas, purpose, and objectives, through the formation of associations, links, and relations. Mastering an understanding of the Blogosphere can greatly help facilitate the needs of the ever growing number of these users, as well as producers, service providers, and advertisers into facilitation of the categorization and navigation of this vast environment. This work explores a novel method to leverage the collective wisdom from the infused label space for blog search and discovery. The work demonstrates that the wisdom space can provide a most unique and desirable framework to which to discover the highly sought after background information that could aid in the building of classifiers. This work incorporates this insight into the construction of a better clustering of blogs which boosts the performance of classifiers for identifying more relevant labels for blogs, and offers a mechanism that can be incorporated into replacing spurious labels and mislabels in a multi-labeled space.
ContributorsGalan, Magdiel F (Author) / Liu, Huan (Thesis advisor) / Davulcu, Hasan (Committee member) / Ye, Jieping (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2015