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- All Subjects: Computer Science
- Creators: Barrett, The Honors College
- Creators: Tong, Hanghang
- Creators: Bansal, Ajay
Description
Mobile apps have improved human lifestyle in various aspects ranging from instant messaging to tele-health. In the current app development paradigm, apps are being developed individually and agnostic of each other. The goal of this thesis is to allow a new world where multiple apps communicate with each other to achieve synergistic benefits. To enable integration between apps, manual communication between developers is needed, which can be problematic on many levels. In order to promote app integration, a systematic approach towards data sharing between multiple apps is essential. However, current approaches to app integration require large code modifications to reap the benefits of shared data such as requiring developers to provide APIs or use large, invasive middlewares. In this thesis, a data sharing framework was developed providing a non-invasive interface between mobile apps for data sharing and integration. A separate app acts as a registry to allow apps to register database tables to be shared and query this information. Two health monitoring apps were developed to evaluate the sharing framework and different methods of data integration between apps to promote synergistic feedback. The health monitoring apps have shown non-invasive solutions can provide data sharing functionality without large code modifications and manual communication between developers.
ContributorsMilazzo, Joseph (Author) / Gupta, Sandeep K.S. (Thesis advisor) / Varsamopoulos, Georgios (Committee member) / Nelson, Brian (Committee member) / Arizona State University (Publisher)
Created2014
Description
The Internet is transforming its look, in a short span of time we have come very far from black and white web forms with plain buttons to responsive, colorful and appealing user interface elements. With the sudden rise in demand of web applications, developers are making full use of the power of HTML5, JavaScript and CSS3 to cater to their users on various platforms. There was never a need of classifying the ways in which these languages can be interconnected to each other as the size of the front end code base was relatively small and did not involve critical business logic. This thesis focuses on listing and defining all dependencies between HTML5, JavaScript and CSS3 that will help developers better understand the interconnections within these languages. We also explore the present techniques available to a developer to make his code free of dependency related defects. We build a prototype tool, HJCDepend, based on our model, which aims at helping developers discover and remove defects early in the development cycle.
ContributorsVasugupta (Author) / Gary, Kevin (Thesis advisor) / Lindquist, Timothy (Committee member) / Bansal, Ajay (Committee member) / Arizona State University (Publisher)
Created2014
Description
Many web search improvements have been developed since the advent of the modern search engine, but one underrepresented area is the application of specific customizations to search results for educational web sites. In order to address this issue and improve the relevance of search results in automated learning environments, this work has integrated context-aware search principles with applications of preference based re-ranking and query modifications. This research investigates several aspects of context-aware search principles, specifically context-sensitive and preference based re-ranking of results which take user inputs as to their preferred content, and combines this with search query modifications which automatically search for a variety of modified terms based on the given search query, integrating these results into the overall re-ranking for the context. The result of this work is a novel web search algorithm which could be applied to any online learning environment attempting to collect relevant resources for learning about a given topic. The algorithm has been evaluated through user studies comparing traditional search results to the context-aware results returned through the algorithm for a given topic. These studies explore how this integration of methods could provide improved relevance in the search results returned when compared against other modern search engines.
ContributorsVan Egmond, Eric (Author) / Burleson, Winslow (Thesis advisor) / Syrotiuk, Violet (Thesis advisor) / Nelson, Brian (Committee member) / Arizona State University (Publisher)
Created2014
Description
The processing of large volumes of RDF data require an efficient storage and query processing engine that can scale well with the volume of data. The initial attempts to address this issue focused on optimizing native RDF stores as well as conventional relational databases management systems. But as the volume of RDF data grew to exponential proportions, the limitations of these systems became apparent and researchers began to focus on using big data analysis tools, most notably Hadoop, to process RDF data. Various studies and benchmarks that evaluate these tools for RDF data processing have been published. In the past two and half years, however, heavy users of big data systems, like Facebook, noted limitations with the query performance of these big data systems and began to develop new distributed query engines for big data that do not rely on map-reduce. Facebook's Presto is one such example.
This thesis deals with evaluating the performance of Presto in processing big RDF data against Apache Hive. A comparative analysis was also conducted against 4store, a native RDF store. To evaluate the performance Presto for big RDF data processing, a map-reduce program and a compiler, based on Flex and Bison, were implemented. The map-reduce program loads RDF data into HDFS while the compiler translates SPARQL queries into a subset of SQL that Presto (and Hive) can understand. The evaluation was done on four and eight node Linux clusters installed on Microsoft Windows Azure platform with RDF datasets of size 10, 20, and 30 million triples. The results of the experiment show that Presto has a much higher performance than Hive can be used to process big RDF data. The thesis also proposes an architecture based on Presto, Presto-RDF, that can be used to process big RDF data.
This thesis deals with evaluating the performance of Presto in processing big RDF data against Apache Hive. A comparative analysis was also conducted against 4store, a native RDF store. To evaluate the performance Presto for big RDF data processing, a map-reduce program and a compiler, based on Flex and Bison, were implemented. The map-reduce program loads RDF data into HDFS while the compiler translates SPARQL queries into a subset of SQL that Presto (and Hive) can understand. The evaluation was done on four and eight node Linux clusters installed on Microsoft Windows Azure platform with RDF datasets of size 10, 20, and 30 million triples. The results of the experiment show that Presto has a much higher performance than Hive can be used to process big RDF data. The thesis also proposes an architecture based on Presto, Presto-RDF, that can be used to process big RDF data.
ContributorsMammo, Mulugeta (Author) / Bansal, Srividya (Thesis advisor) / Bansal, Ajay (Committee member) / Lindquist, Timothy (Committee member) / Arizona State University (Publisher)
Created2014
Description
Gathering and managing software requirements, known as Requirement Engineering (RE), is a significant and basic step during the Software Development Life Cycle (SDLC). Any error or defect during the RE step will propagate to further steps of SDLC and resolving it will be more costly than any defect in other steps. In order to produce better quality software, the requirements have to be free of any defects. Verification and Validation (V&V;) of requirements are performed to improve their quality, by performing the V&V; process on the Software Requirement Specification (SRS) document. V&V; of the software requirements focused to a specific domain helps in improving quality. A large database of software requirements from software projects of different domains is created. Software requirements from commercial applications are focus of this project; other domains embedded, mobile, E-commerce, etc. can be the focus of future efforts. The V&V; is done to inspect the requirements and improve the quality. Inspections are done to detect defects in the requirements and three approaches for inspection of software requirements are discussed; ad-hoc techniques, checklists, and scenario-based techniques. A more systematic domain-specific technique is presented for performing V&V; of requirements.
ContributorsChughtai, Rehman (Author) / Ghazarian, Arbi (Thesis advisor) / Bansal, Ajay (Committee member) / Millard, Bruce (Committee member) / Arizona State University (Publisher)
Created2012
Description
This thesis studies recommendation systems and considers joint sampling and learning. Sampling in recommendation systems is to obtain users' ratings on specific items chosen by the recommendation platform, and learning is to infer the unknown ratings of users to items given the existing data. In this thesis, the problem is formulated as an adaptive matrix completion problem in which sampling is to reveal the unknown entries of a $U\times M$ matrix where $U$ is the number of users, $M$ is the number of items, and each entry of the $U\times M$ matrix represents the rating of a user to an item. In the literature, this matrix completion problem has been studied under a static setting, i.e., recovering the matrix based on a set of partial ratings. This thesis considers both sampling and learning, and proposes an adaptive algorithm. The algorithm adapts its sampling and learning based on the existing data. The idea is to sample items that reveal more information based on the previous sampling results and then learn based on clustering. Performance of the proposed algorithm has been evaluated using simulations.
ContributorsZhu, Lingfang (Author) / Xue, Guoliang (Thesis advisor) / He, Jingrui (Committee member) / Tong, Hanghang (Committee member) / Arizona State University (Publisher)
Created2015
Description
The amount of time series data generated is increasing due to the integration of sensor technologies with everyday applications, such as gesture recognition, energy optimization, health care, video surveillance. The use of multiple sensors simultaneously
for capturing different aspects of the real world attributes has also led to an increase in dimensionality from uni-variate to multi-variate time series. This has facilitated richer data representation but also has necessitated algorithms determining similarity between two multi-variate time series for search and analysis.
Various algorithms have been extended from uni-variate to multi-variate case, such as multi-variate versions of Euclidean distance, edit distance, dynamic time warping. However, it has not been studied how these algorithms account for asynchronous in time series. Human gestures, for example, exhibit asynchrony in their patterns as different subjects perform the same gesture with varying movements in their patterns at different speeds. In this thesis, we propose several algorithms (some of which also leverage metadata describing the relationships among the variates). In particular, we present several techniques that leverage the contextual relationships among the variates when measuring multi-variate time series similarities. Based on the way correlation is leveraged, various weighing mechanisms have been proposed that determine the importance of a dimension for discriminating between the time series as giving the same weight to each dimension can led to misclassification. We next study the robustness of the considered techniques against different temporal asynchronies, including shifts and stretching.
Exhaustive experiments were carried on datasets with multiple types and amounts of temporal asynchronies. It has been observed that accuracy of algorithms that rely on data to discover variate relationships can be low under the presence of temporal asynchrony, whereas in case of algorithms that rely on external metadata, robustness against asynchronous distortions tends to be stronger. Specifically, algorithms using external metadata have better classification accuracy and cluster separation than existing state-of-the-art work, such as EROS, PCA, and naive dynamic time warping.
for capturing different aspects of the real world attributes has also led to an increase in dimensionality from uni-variate to multi-variate time series. This has facilitated richer data representation but also has necessitated algorithms determining similarity between two multi-variate time series for search and analysis.
Various algorithms have been extended from uni-variate to multi-variate case, such as multi-variate versions of Euclidean distance, edit distance, dynamic time warping. However, it has not been studied how these algorithms account for asynchronous in time series. Human gestures, for example, exhibit asynchrony in their patterns as different subjects perform the same gesture with varying movements in their patterns at different speeds. In this thesis, we propose several algorithms (some of which also leverage metadata describing the relationships among the variates). In particular, we present several techniques that leverage the contextual relationships among the variates when measuring multi-variate time series similarities. Based on the way correlation is leveraged, various weighing mechanisms have been proposed that determine the importance of a dimension for discriminating between the time series as giving the same weight to each dimension can led to misclassification. We next study the robustness of the considered techniques against different temporal asynchronies, including shifts and stretching.
Exhaustive experiments were carried on datasets with multiple types and amounts of temporal asynchronies. It has been observed that accuracy of algorithms that rely on data to discover variate relationships can be low under the presence of temporal asynchrony, whereas in case of algorithms that rely on external metadata, robustness against asynchronous distortions tends to be stronger. Specifically, algorithms using external metadata have better classification accuracy and cluster separation than existing state-of-the-art work, such as EROS, PCA, and naive dynamic time warping.
ContributorsGarg, Yash (Author) / Candan, Kasim Selcuk (Thesis advisor) / Chowell-Punete, Gerardo (Committee member) / Tong, Hanghang (Committee member) / Davulcu, Hasan (Committee member) / Sapino, Maria Luisa (Committee member) / Arizona State University (Publisher)
Created2015
Description
Micro-blogging platforms like Twitter have become some of the most popular sites for people to share and express their views and opinions about public events like debates, sports events or other news articles. These social updates by people complement the written news articles or transcripts of events in giving the popular public opinion about these events. So it would be useful to annotate the transcript with tweets. The technical challenge is to align the tweets with the correct segment of the transcript. ET-LDA by Hu et al [9] addresses this issue by modeling the whole process with an LDA-based graphical model. The system segments the transcript into coherent and meaningful parts and also determines if a tweet is a general tweet about the event or it refers to a particular segment of the transcript. One characteristic of the Hu et al’s model is that it expects all the data to be available upfront and uses batch inference procedure. But in many cases we find that data is not available beforehand, and it is often streaming. In such cases it is infeasible to repeatedly run the batch inference algorithm. My thesis presents an online inference algorithm for the ET-LDA model, with a continuous stream of tweet data and compare their runtime and performance to existing algorithms.
ContributorsAcharya, Anirudh (Author) / Kambhampati, Subbarao (Thesis advisor) / Davulcu, Hasan (Committee member) / Tong, Hanghang (Committee member) / Arizona State University (Publisher)
Created2015
Description
Online social media is popular due to its real-time nature, extensive connectivity and a large user base. This motivates users to employ social media for seeking information by reaching out to their large number of social connections. Information seeking can manifest in the form of requests for personal and time-critical information or gathering perspectives on important issues. Social media platforms are not designed for resource seeking and experience large volumes of messages, leading to requests not being fulfilled satisfactorily. Designing frameworks to facilitate efficient information seeking in social media will help users to obtain appropriate assistance for their needs
and help platforms to increase user satisfaction.
Several challenges exist in the way of facilitating information seeking in social media. First, the characteristics affecting the user’s response time for a question are not known, making it hard to identify prompt responders. Second, the social context in which the user has asked the question has to be determined to find personalized responders. Third, users employ rhetorical requests, which are statements having the
syntax of questions, and systems assisting information seeking might be hindered from focusing on genuine questions. Fouth, social media advocates of political campaigns employ nuanced strategies to prevent users from obtaining balanced perspectives on
issues of public importance.
Sociological and linguistic studies on user behavior while making or responding to information seeking requests provides concepts drawing from which we can address these challenges. We propose methods to estimate the response time of the user for a given question to identify prompt responders. We compute the question specific social context an asker shares with his social connections to identify personalized responders. We draw from theories of political mobilization to model the behaviors arising from the strategies of people trying to skew perspectives. We identify rhetorical questions by modeling user motivations to post them.
and help platforms to increase user satisfaction.
Several challenges exist in the way of facilitating information seeking in social media. First, the characteristics affecting the user’s response time for a question are not known, making it hard to identify prompt responders. Second, the social context in which the user has asked the question has to be determined to find personalized responders. Third, users employ rhetorical requests, which are statements having the
syntax of questions, and systems assisting information seeking might be hindered from focusing on genuine questions. Fouth, social media advocates of political campaigns employ nuanced strategies to prevent users from obtaining balanced perspectives on
issues of public importance.
Sociological and linguistic studies on user behavior while making or responding to information seeking requests provides concepts drawing from which we can address these challenges. We propose methods to estimate the response time of the user for a given question to identify prompt responders. We compute the question specific social context an asker shares with his social connections to identify personalized responders. We draw from theories of political mobilization to model the behaviors arising from the strategies of people trying to skew perspectives. We identify rhetorical questions by modeling user motivations to post them.
ContributorsRanganath, Suhas (Author) / Liu, Huan (Thesis advisor) / Lai, Ying-Cheng (Thesis advisor) / Tong, Hanghang (Committee member) / Vaculin, Roman (Committee member) / Arizona State University (Publisher)
Created2017
Description
With the rise of the Big Data Era, an exponential amount of network data is being generated at an unprecedented rate across a wide-range of high impact micro and macro areas of research---from protein interaction to social networks. The critical challenge is translating this large scale network data into actionable information.
A key task in the data translation is the analysis of network connectivity via marked nodes---the primary focus of our research. We have developed a framework for analyzing network connectivity via marked nodes in large scale graphs, utilizing novel algorithms in three interrelated areas: (1) analysis of a single seed node via it’s ego-centric network (AttriPart algorithm); (2) pathway identification between two seed nodes (K-Simple Shortest Paths Multithreaded and Search Reduced (KSSPR) algorithm); and (3) tree detection, defining the interaction between three or more seed nodes (Shortest Path MST algorithm).
In an effort to address both fundamental and applied research issues, we have developed the LocalForcasting algorithm to explore how network connectivity analysis can be applied to local community evolution and recommender systems. The goal is to apply the LocalForecasting algorithm to various domains---e.g., friend suggestions in social networks or future collaboration in co-authorship networks. This algorithm utilizes link prediction in combination with the AttriPart algorithm to predict future connections in local graph partitions.
Results show that our proposed AttriPart algorithm finds up to 1.6x denser local partitions, while running approximately 43x faster than traditional local partitioning techniques (PageRank-Nibble). In addition, our LocalForecasting algorithm demonstrates a significant improvement in the number of nodes and edges correctly predicted over baseline methods. Furthermore, results for the KSSPR algorithm demonstrate a speed-up of up to 2.5x the standard k-simple shortest paths algorithm.
A key task in the data translation is the analysis of network connectivity via marked nodes---the primary focus of our research. We have developed a framework for analyzing network connectivity via marked nodes in large scale graphs, utilizing novel algorithms in three interrelated areas: (1) analysis of a single seed node via it’s ego-centric network (AttriPart algorithm); (2) pathway identification between two seed nodes (K-Simple Shortest Paths Multithreaded and Search Reduced (KSSPR) algorithm); and (3) tree detection, defining the interaction between three or more seed nodes (Shortest Path MST algorithm).
In an effort to address both fundamental and applied research issues, we have developed the LocalForcasting algorithm to explore how network connectivity analysis can be applied to local community evolution and recommender systems. The goal is to apply the LocalForecasting algorithm to various domains---e.g., friend suggestions in social networks or future collaboration in co-authorship networks. This algorithm utilizes link prediction in combination with the AttriPart algorithm to predict future connections in local graph partitions.
Results show that our proposed AttriPart algorithm finds up to 1.6x denser local partitions, while running approximately 43x faster than traditional local partitioning techniques (PageRank-Nibble). In addition, our LocalForecasting algorithm demonstrates a significant improvement in the number of nodes and edges correctly predicted over baseline methods. Furthermore, results for the KSSPR algorithm demonstrate a speed-up of up to 2.5x the standard k-simple shortest paths algorithm.
ContributorsFreitas, Scott (Author) / Tong, Hanghang (Thesis advisor) / Maciejewski, Ross (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2018