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- Genre: Doctoral Dissertation
- Creators: Liu, Huan
- Creators: Davulcu, Hasan
Description
Automating aspects of biocuration through biomedical information extraction could significantly impact biomedical research by enabling greater biocuration throughput and improving the feasibility of a wider scope. An important step in biomedical information extraction systems is named entity recognition (NER), where mentions of entities such as proteins and diseases are located within natural-language text and their semantic type is determined. This step is critical for later tasks in an information extraction pipeline, including normalization and relationship extraction. BANNER is a benchmark biomedical NER system using linear-chain conditional random fields and the rich feature set approach. A case study with BANNER locating genes and proteins in biomedical literature is described. The first corpus for disease NER adequate for use as training data is introduced, and employed in a case study of disease NER. The first corpus locating adverse drug reactions (ADRs) in user posts to a health-related social website is also described, and a system to locate and identify ADRs in social media text is created and evaluated. The rich feature set approach to creating NER feature sets is argued to be subject to diminishing returns, implying that additional improvements may require more sophisticated methods for creating the feature set. This motivates the first application of multivariate feature selection with filters and false discovery rate analysis to biomedical NER, resulting in a feature set at least 3 orders of magnitude smaller than the set created by the rich feature set approach. Finally, two novel approaches to NER by modeling the semantics of token sequences are introduced. The first method focuses on the sequence content by using language models to determine whether a sequence resembles entries in a lexicon of entity names or text from an unlabeled corpus more closely. The second method models the distributional semantics of token sequences, determining the similarity between a potential mention and the token sequences from the training data by analyzing the contexts where each sequence appears in a large unlabeled corpus. The second method is shown to improve the performance of BANNER on multiple data sets.
ContributorsLeaman, James Robert (Author) / Gonzalez, Graciela (Thesis advisor) / Baral, Chitta (Thesis advisor) / Cohen, Kevin B (Committee member) / Liu, Huan (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2013
Description
This dissertation presents the Temporal Event Query Language (TEQL), a new language for querying event streams. Event Stream Processing enables online querying of streams of events to extract relevant data in a timely manner. TEQL enables querying of interval-based event streams using temporal database operators. Temporal databases and temporal query languages have been a subject of research for more than 30 years and are a natural fit for expressing queries that involve a temporal dimension. However, operators developed in this context cannot be directly applied to event streams. The research extends a preexisting relational framework for event stream processing to support temporal queries. The language features and formal semantic extensions to extend the relational framework are identified. The extended framework supports continuous, step-wise evaluation of temporal queries. The incremental evaluation of TEQL operators is formalized to avoid re-computation of previous results. The research includes the development of a prototype that supports the integrated event and temporal query processing framework, with support for incremental evaluation and materialization of intermediate results. TEQL enables reporting temporal data in the output, direct specification of conditions over timestamps, and specification of temporal relational operators. Through the integration of temporal database operators with event languages, a new class of temporal queries is made possible for querying event streams. New features include semantic aggregation, extraction of temporal patterns using set operators, and a more accurate specification of event co-occurrence.
ContributorsShiva, Foruhar Ali (Author) / Urban, Susan D (Thesis advisor) / Chen, Yi (Thesis advisor) / Davulcu, Hasan (Committee member) / Sarjoughian, Hessam S. (Committee member) / Arizona State University (Publisher)
Created2012
Description
The pay-as-you-go economic model of cloud computing increases the visibility, traceability, and verifiability of software costs. Application developers must understand how their software uses resources when running in the cloud in order to stay within budgeted costs and/or produce expected profits. Cloud computing's unique economic model also leads naturally to an earn-as-you-go profit model for many cloud based applications. These applications can benefit from low level analyses for cost optimization and verification. Testing cloud applications to ensure they meet monetary cost objectives has not been well explored in the current literature. When considering revenues and costs for cloud applications, the resource economic model can be scaled down to the transaction level in order to associate source code with costs incurred while running in the cloud. Both static and dynamic analysis techniques can be developed and applied to understand how and where cloud applications incur costs. Such analyses can help optimize (i.e. minimize) costs and verify that they stay within expected tolerances. An adaptation of Worst Case Execution Time (WCET) analysis is presented here to statically determine worst case monetary costs of cloud applications. This analysis is used to produce an algorithm for determining control flow paths within an application that can exceed a given cost threshold. The corresponding results are used to identify path sections that contribute most to cost excess. A hybrid approach for determining cost excesses is also presented that is comprised mostly of dynamic measurements but that also incorporates calculations that are based on the static analysis approach. This approach uses operational profiles to increase the precision and usefulness of the calculations.
ContributorsBuell, Kevin, Ph.D (Author) / Collofello, James (Thesis advisor) / Davulcu, Hasan (Committee member) / Lindquist, Timothy (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2012
Description
In most social networking websites, users are allowed to perform interactive activities. One of the fundamental features that these sites provide is to connecting with users of their kind. On one hand, this activity makes online connections visible and tangible; on the other hand, it enables the exploration of our connections and the expansion of our social networks easier. The aggregation of people who share common interests forms social groups, which are fundamental parts of our social lives. Social behavioral analysis at a group level is an active research area and attracts many interests from the industry. Challenges of my work mainly arise from the scale and complexity of user generated behavioral data. The multiple types of interactions, highly dynamic nature of social networking and the volatile user behavior suggest that these data are complex and big in general. Effective and efficient approaches are required to analyze and interpret such data. My work provide effective channels to help connect the like-minded and, furthermore, understand user behavior at a group level. The contributions of this dissertation are in threefold: (1) proposing novel representation of collective tagging knowledge via tag networks; (2) proposing the new information spreader identification problem in egocentric soical networks; (3) defining group profiling as a systematic approach to understanding social groups. In sum, the research proposes novel concepts and approaches for connecting the like-minded, enables the understanding of user groups, and exposes interesting research opportunities.
ContributorsWang, Xufei (Author) / Liu, Huan (Thesis advisor) / Kambhampati, Subbarao (Committee member) / Sundaram, Hari (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2013
Description
Biological organisms are made up of cells containing numerous interconnected biochemical processes. Diseases occur when normal functionality of these processes is disrupted, manifesting as disease symptoms. Thus, understanding these biochemical processes and their interrelationships is a primary task in biomedical research and a prerequisite for activities including diagnosing diseases and drug development. Scientists studying these interconnected processes have identified various pathways involved in drug metabolism, diseases, and signal transduction, etc. High-throughput technologies, new algorithms and speed improvements over the last decade have resulted in deeper knowledge about biological systems, leading to more refined pathways. Such pathways tend to be large and complex, making it difficult for an individual to remember all aspects. Thus, computer models are needed to represent and analyze them. The refinement activity itself requires reasoning with a pathway model by posing queries against it and comparing the results against the real biological system. Many existing models focus on structural and/or factoid questions, relying on surface-level information. These are generally not the kind of questions that a biologist may ask someone to test their understanding of biological processes. Examples of questions requiring understanding of biological processes are available in introductory college level biology text books. Such questions serve as a model for the question answering system developed in this thesis. Thus, the main goal of this thesis is to develop a system that allows the encoding of knowledge about biological pathways to answer questions demonstrating understanding of the pathways. To that end, a language is developed to specify a pathway and pose questions against it. Some existing tools are modified and used to accomplish this goal. The utility of the framework developed in this thesis is illustrated with applications in the biological domain. Finally, the question answering system is used in real world applications by extracting pathway knowledge from text and answering questions related to drug development.
ContributorsAnwar, Saadat (Author) / Baral, Chitta (Thesis advisor) / Inoue, Katsumi (Committee member) / Chen, Yi (Committee member) / Davulcu, Hasan (Committee member) / Lee, Joohyung (Committee member) / Arizona State University (Publisher)
Created2014
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
Crises or large-scale emergencies such as earthquakes and hurricanes cause massive damage to lives and property. Crisis response is an essential task to mitigate the impact of a crisis. An effective response to a crisis necessitates information gathering and analysis. Traditionally, this process has been restricted to the information collected by first responders on the ground in the affected region or by official agencies such as local governments involved in the response. However, the ubiquity of mobile devices has empowered people to publish information during a crisis through social media, such as the damage reports from a hurricane. Social media has thus emerged as an important channel of information which can be leveraged to improve crisis response. Twitter is a popular medium which has been employed in recent crises. However, it presents new challenges: the data is noisy and uncurated, and it has high volume and high velocity. In this work, I study four key problems in the use of social media for crisis response: effective monitoring and analysis of high volume crisis tweets, detecting crisis events automatically in streaming data, identifying users who can be followed to effectively monitor crisis, and finally understanding user behavior during crisis to detect tweets inside crisis regions. To address these problems I propose two systems which assist disaster responders or analysts to collaboratively collect tweets related to crisis and analyze it using visual analytics to identify interesting regions, topics, and users involved in disaster response. I present a novel approach to detecting crisis events automatically in noisy, high volume Twitter streams. I also investigate and introduce novel methods to tackle information overload through the identification of information leaders in information diffusion who can be followed for efficient crisis monitoring and identification of messages originating from crisis regions using user behavior analysis.
ContributorsKumar, Shamanth (Author) / Liu, Huan (Thesis advisor) / Davulcu, Hasan (Committee member) / Maciejewski, Ross (Committee member) / Agarwal, Nitin (Committee member) / Arizona State University (Publisher)
Created2015
Description
Social networking services have emerged as an important platform for large-scale information sharing and communication. With the growing popularity of social media, spamming has become rampant in the platforms. Complex network interactions and evolving content present great challenges for social spammer detection. Different from some existing well-studied platforms, distinct characteristics of newly emerged social media data present new challenges for social spammer detection. First, texts in social media are short and potentially linked with each other via user connections. Second, it is observed that abundant contextual information may play an important role in distinguishing social spammers and normal users. Third, not only the content information but also the social connections in social media evolve very fast. Fourth, it is easy to amass vast quantities of unlabeled data in social media, but would be costly to obtain labels, which are essential for many supervised algorithms. To tackle those challenges raise in social media data, I focused on developing effective and efficient machine learning algorithms for social spammer detection.
I provide a novel and systematic study of social spammer detection in the dissertation. By analyzing the properties of social network and content information, I propose a unified framework for social spammer detection by collectively using the two types of information in social media. Motivated by psychological findings in physical world, I investigate whether sentiment analysis can help spammer detection in online social media. In particular, I conduct an exploratory study to analyze the sentiment differences between spammers and normal users; and present a novel method to incorporate sentiment information into social spammer detection framework. Given the rapidly evolving nature, I propose a novel framework to efficiently reflect the effect of newly emerging social spammers. To tackle the problem of lack of labeling data in social media, I study how to incorporate network information into text content modeling, and design strategies to select the most representative and informative instances from social media for labeling. Motivated by publicly available label information from other media platforms, I propose to make use of knowledge learned from cross-media to help spammer detection on social media.
I provide a novel and systematic study of social spammer detection in the dissertation. By analyzing the properties of social network and content information, I propose a unified framework for social spammer detection by collectively using the two types of information in social media. Motivated by psychological findings in physical world, I investigate whether sentiment analysis can help spammer detection in online social media. In particular, I conduct an exploratory study to analyze the sentiment differences between spammers and normal users; and present a novel method to incorporate sentiment information into social spammer detection framework. Given the rapidly evolving nature, I propose a novel framework to efficiently reflect the effect of newly emerging social spammers. To tackle the problem of lack of labeling data in social media, I study how to incorporate network information into text content modeling, and design strategies to select the most representative and informative instances from social media for labeling. Motivated by publicly available label information from other media platforms, I propose to make use of knowledge learned from cross-media to help spammer detection on social media.
ContributorsHu, Xia, Ph.D (Author) / Liu, Huan (Thesis advisor) / Kambhampati, Subbarao (Committee member) / Ye, Jieping (Committee member) / Faloutsos, Christos (Committee member) / Arizona State University (Publisher)
Created2015
Description
A myriad of social media services are emerging in recent years that allow people to communicate and express themselves conveniently and easily. The pervasive use of social media generates massive data at an unprecedented rate. It becomes increasingly difficult for online users to find relevant information or, in other words, exacerbates the information overload problem. Meanwhile, users in social media can be both passive content consumers and active content producers, causing the quality of user-generated content can vary dramatically from excellence to abuse or spam, which results in a problem of information credibility. Trust, providing evidence about with whom users can trust to share information and from whom users can accept information without additional verification, plays a crucial role in helping online users collect relevant and reliable information. It has been proven to be an effective way to mitigate information overload and credibility problems and has attracted increasing attention.
As the conceptual counterpart of trust, distrust could be as important as trust and its value has been widely recognized by social sciences in the physical world. However, little attention is paid on distrust in social media. Social media differs from the physical world - (1) its data is passively observed, large-scale, incomplete, noisy and embedded with rich heterogeneous sources; and (2) distrust is generally unavailable in social media. These unique properties of social media present novel challenges for computing distrust in social media: (1) passively observed social media data does not provide necessary information social scientists use to understand distrust, how can I understand distrust in social media? (2) distrust is usually invisible in social media, how can I make invisible distrust visible by leveraging unique properties of social media data? and (3) little is known about distrust and its role in social media applications, how can distrust help make difference in social media applications?
The chief objective of this dissertation is to figure out solutions to these challenges via innovative research and novel methods. In particular, computational tasks are designed to {\it understand distrust}, a innovative task, i.e., {\it predicting distrust} is proposed with novel frameworks to make invisible distrust visible, and principled approaches are develop to {\it apply distrust} in social media applications. Since distrust is a special type of negative links, I demonstrate the generalization of properties and algorithms of distrust to negative links, i.e., {\it generalizing findings of distrust}, which greatly expands the boundaries of research of distrust and largely broadens its applications in social media.
As the conceptual counterpart of trust, distrust could be as important as trust and its value has been widely recognized by social sciences in the physical world. However, little attention is paid on distrust in social media. Social media differs from the physical world - (1) its data is passively observed, large-scale, incomplete, noisy and embedded with rich heterogeneous sources; and (2) distrust is generally unavailable in social media. These unique properties of social media present novel challenges for computing distrust in social media: (1) passively observed social media data does not provide necessary information social scientists use to understand distrust, how can I understand distrust in social media? (2) distrust is usually invisible in social media, how can I make invisible distrust visible by leveraging unique properties of social media data? and (3) little is known about distrust and its role in social media applications, how can distrust help make difference in social media applications?
The chief objective of this dissertation is to figure out solutions to these challenges via innovative research and novel methods. In particular, computational tasks are designed to {\it understand distrust}, a innovative task, i.e., {\it predicting distrust} is proposed with novel frameworks to make invisible distrust visible, and principled approaches are develop to {\it apply distrust} in social media applications. Since distrust is a special type of negative links, I demonstrate the generalization of properties and algorithms of distrust to negative links, i.e., {\it generalizing findings of distrust}, which greatly expands the boundaries of research of distrust and largely broadens its applications in social media.
ContributorsTang, Jiliang (Author) / Liu, Huan (Thesis advisor) / Xue, Guoliang (Committee member) / Ye, Jieping (Committee member) / Aggarwal, Charu (Committee member) / Arizona State University (Publisher)
Created2015
Description
While discrete emotions like joy, anger, disgust etc. are quite popular, continuous
emotion dimensions like arousal and valence are gaining popularity within the research
community due to an increase in the availability of datasets annotated with these
emotions. Unlike the discrete emotions, continuous emotions allow modeling of subtle
and complex affect dimensions but are difficult to predict.
Dimension reduction techniques form the core of emotion recognition systems and
help create a new feature space that is more helpful in predicting emotions. But these
techniques do not necessarily guarantee a better predictive capability as most of them
are unsupervised, especially in regression learning. In emotion recognition literature,
supervised dimension reduction techniques have not been explored much and in this
work a solution is provided through probabilistic topic models. Topic models provide
a strong probabilistic framework to embed new learning paradigms and modalities.
In this thesis, the graphical structure of Latent Dirichlet Allocation has been explored
and new models tuned to emotion recognition and change detection have been built.
In this work, it has been shown that the double mixture structure of topic models
helps 1) to visualize feature patterns, and 2) to project features onto a topic simplex
that is more predictive of human emotions, when compared to popular techniques
like PCA and KernelPCA. Traditionally, topic models have been used on quantized
features but in this work, a continuous topic model called the Dirichlet Gaussian
Mixture model has been proposed. Evaluation of DGMM has shown that while modeling
videos, performance of LDA models can be replicated even without quantizing
the features. Until now, topic models have not been explored in a supervised context
of video analysis and thus a Regularized supervised topic model (RSLDA) that
models video and audio features is introduced. RSLDA learning algorithm performs
both dimension reduction and regularized linear regression simultaneously, and has outperformed supervised dimension reduction techniques like SPCA and Correlation
based feature selection algorithms. In a first of its kind, two new topic models, Adaptive
temporal topic model (ATTM) and SLDA for change detection (SLDACD) have
been developed for predicting concept drift in time series data. These models do not
assume independence of consecutive frames and outperform traditional topic models
in detecting local and global changes respectively.
emotion dimensions like arousal and valence are gaining popularity within the research
community due to an increase in the availability of datasets annotated with these
emotions. Unlike the discrete emotions, continuous emotions allow modeling of subtle
and complex affect dimensions but are difficult to predict.
Dimension reduction techniques form the core of emotion recognition systems and
help create a new feature space that is more helpful in predicting emotions. But these
techniques do not necessarily guarantee a better predictive capability as most of them
are unsupervised, especially in regression learning. In emotion recognition literature,
supervised dimension reduction techniques have not been explored much and in this
work a solution is provided through probabilistic topic models. Topic models provide
a strong probabilistic framework to embed new learning paradigms and modalities.
In this thesis, the graphical structure of Latent Dirichlet Allocation has been explored
and new models tuned to emotion recognition and change detection have been built.
In this work, it has been shown that the double mixture structure of topic models
helps 1) to visualize feature patterns, and 2) to project features onto a topic simplex
that is more predictive of human emotions, when compared to popular techniques
like PCA and KernelPCA. Traditionally, topic models have been used on quantized
features but in this work, a continuous topic model called the Dirichlet Gaussian
Mixture model has been proposed. Evaluation of DGMM has shown that while modeling
videos, performance of LDA models can be replicated even without quantizing
the features. Until now, topic models have not been explored in a supervised context
of video analysis and thus a Regularized supervised topic model (RSLDA) that
models video and audio features is introduced. RSLDA learning algorithm performs
both dimension reduction and regularized linear regression simultaneously, and has outperformed supervised dimension reduction techniques like SPCA and Correlation
based feature selection algorithms. In a first of its kind, two new topic models, Adaptive
temporal topic model (ATTM) and SLDA for change detection (SLDACD) have
been developed for predicting concept drift in time series data. These models do not
assume independence of consecutive frames and outperform traditional topic models
in detecting local and global changes respectively.
ContributorsLade, Prasanth (Author) / Panchanathan, Sethuraman (Thesis advisor) / Davulcu, Hasan (Committee member) / Li, Baoxin (Committee member) / Balasubramanian, Vineeth N (Committee member) / Arizona State University (Publisher)
Created2015