Computer & Information Science Department Theses and Dissertations

Permanent URI for this collection

https://hdl.handle.net/1805/2053

For more information about the Computer & Information Science graduate programs visit: http://www.science.iupui.edu.

Browse

Now showing 1 - 10 of 129

Key Views for Visualizing Large Spaces
(Elsevier, 2009-08) Cai, Hongyuan; Zheng, Jiang Yu; Fang, Shiaofen; Tuceryan, Mihran
Image is a dominant medium among video, 3D model, and other media for visualizing environment and creating virtual access on the Internet. The location of image capture is, however, subjective and has relied on the esthetic sense of photographers up until this point. In this paper, we will not only visualize areas with images, but also propose a general framework to determine where the most distinct viewpoints should be located. Starting from elevation data, we present spatial and content information in ground-based images such that (1) a given number of images can have maximum coverage on informative scenes; (2) a set of key views can be selected with certain continuity for representing the most distinct views. According to the scene visibility, continuity, and data redundancy, we evaluate viewpoints numerically with an object-emitting illumination model. Our key view exploration may eventually reduce the visual data to transmit, facilitate image acquisition, indexing and interaction, and enhance perception of spaces. Real sample images are captured based on planned positions to form a visual network to index the area.
Design and Implementation of Web-based Data and Network Management System for Heterogeneous Wireless Sensor Networks
(2011-03-09) Yu, Qun; Liang, Yao; Zou, Xukai; Xia, Yuni
Today, Wireless Sensor Networks (WSNs) are forming an exciting new area to have dramatic impacts on science and engineering innovations. New WSN-based technologies, such as body sensor networks in medical and health care and environmental monitoring sensor networks, are emerging. Sensor networks are quickly becoming a flexible, inexpensive, and reliable platform to provide solutions for a wide variety of applications in real-world settings. The increase in the proliferation of sensor networks has paralleled the use of more heterogeneous systems in deployment. In this thesis, our work attempts to develop a new network management and data collection framework for heterogeneous wireless sensor networks called as Heterogeneous Wireless Sensor Networks Management System (H-WSNMS), which enables to manage and operate various sensor network systems with unified control and management services and interface. The H-WSNMS framework aims to provide a scheme to manage, query, and interact with sensor network systems. By introducing the concept of Virtual Command Set (VCS), a series of unified application interfaces and Metadata (XML files) across multiple WSNs are designed and implement the scalability and flexibility of the management functions for heterogeneous wireless sensor networks, which is demonstrated though through a series of web-based WSN management Applications such as Monitoring, Configuration, Reprogram, Data Collection and so on. The tests and application trials confirm the feasibility of our approach but also still reveal a number of challenges to be taken into account when deploying wireless sensor and actuator networks at industrial sites, which will be considered by our future research work.
SCALABLE AND QoS NETWORKING SOLUTIONS FOR TELEMEDICINE
(2011-03-09) Payli, Birhan; Durresi, Arjan; Tuceryan, Mihran; Xia, Yuni
Retrieving data from a patient in real-time is a challenging operation, especially when requiring information from the network to support the patient’s health. A real-time healthcare system process is conducted with a continual input, processing, and output of data. It needs to have the ability to provide different priorities to different applications, users, or data flows, or to guarantee a certain level of performance to a data flow. The current Internet does not allow applications to request any special treatment. Every packet, including delay-sensitive audio and video packets, is treated equally at the routers. This simplest type service of network is often referred to as best effort, a network service in which the network does not provide any guarantees that data is delivered or that a user is given a guaranteed QoS level or a certain priority. Providing guaranteed services requires routers to manage per-flow states and perform per-flow operations. Such network architecture requires each router to maintain and manage perflow state on the control path, and to perform per-flow classification, scheduling, and buffer management on the data path. This complicated and expensive network architecture is less scalable and robust than today’s modern stateless network architectures such as Random Early Dropping (RED) for congestion control, DiffServ for QoS, and the original IP network. This thesis introduces a new DiffServ-based scheme of IP bandwidth allocation during congestion, called Proportional Allocation of Bandwidth (PAB) which can be used in all networks. In PAB scheme, the bandwidth is allocated in proportion to Subscripted Information Rate (SIR) of the competing flows. PAB implementation uses multiple token buckets to label the packets at the edge of the network and multilevel threshold queue at the IP routers to discard packets during congestion.
Bridging Text Mining and Bayesian Networks
(2011-03-09) Raghuram, Sandeep Mudabail; Xia, Yuni; Palakal, Mathew; Zou, Xukai, 1963-
After the initial network is constructed using expert’s knowledge of the domain, Bayesian networks need to be updated as and when new data is observed. Literature mining is a very important source of this new data. In this work, we explore what kind of data needs to be extracted with the view to update Bayesian Networks, existing technologies which can be useful in achieving some of the goals and what research is required to accomplish the remaining requirements. This thesis specifically deals with utilizing causal associations and experimental results which can be obtained from literature mining. However, these associations and numerical results cannot be directly integrated with the Bayesian network. The source of the literature and the perceived quality of research needs to be factored into the process of integration, just like a human, reading the literature, would. This thesis presents a general methodology for updating a Bayesian Network with the mined data. This methodology consists of solutions to some of the issues surrounding the task of integrating the causal associations with the Bayesian Network and demonstrates the idea with a semiautomated software system.
Biomedical Literature Mining with Transitive Closure and Maximum Network Flow
(http://doi.acm.org/10.1145/1851476.1851552, 2011-05-15) Hoblitzell, Andrew P.; Mukhopadhyay, Snehasis; Xia, Yuni; Fang, Shiafoen
The biological literature is a huge and constantly increasing source of information which the biologist may consult for information about their field, but the vast amount of data can sometimes become overwhelming. Medline, which makes a great amount of biological journal data available online, makes the development of automated text mining systems and hence “data-driven discovery” possible. This thesis examines current work in the field of text mining and biological literature, and then aims to mine documents pertaining to bone biology. The documents are retrieved from PubMed, and then direct associations between the terms are computers. Potentially novel transitive associations among biological objects are then discovered using the transitive closure algorithm and the maximum flow algorithm. The thesis discusses in detail the extraction of biological objects from the collected documents and the co-occurrence based text mining algorithm, the transitive closure algorithm, and the maximum network flow which were then run to extract the potentially novel biological associations. Generated hypotheses (novel associations) were assigned with significance scores for further validation by a bone biologist expert. Extension of the work in to hypergraphs for enhanced meaning and accuracy is also examined in the thesis.
GRAPH BASED MINING ON WEIGHTED DIRECTED GRAPHS FOR SUBNETWORKS AND PATH DISCOVERY
(2011-08-16) Abdulkarim, Sijin Cherupilly; Palakal, Mathew J.; Fang, Shiaofen; Xia, Yuni
Subnetwork or path mining is an emerging data mining problem in many areas including scientific and commercial applications. Graph modeling is one of the effective ways in representing real world networks. Many natural and man-made systems are structured in the form of networks. Traditional machine learning and data mining approaches assume data as a collection of homogenous objects that are independent of each other whereas network data are potentially heterogeneous and interlinked. In this paper we propose a novel algorithm to find subnetworks and Maximal paths from a weighted, directed network represented as a graph. The main objective of this study is to find meaningful Maximal paths from a given network based on three key parameters: node weight, edge weight, and direction. This algorithm is an effective way to extract Maximal paths from a network modeled based on a user’s interest. Also, the proposed algorithm allows the user to incorporate weights to the nodes and edges of a biological network. The performance of the proposed technique was tested using a Colorectal Cancer biological network. The subnetworks and paths obtained through our network mining algorithm from the biological network were scored based on their biological significance. The subnetworks and Maximal paths derived were verified using MetacoreTM as well as literature. The algorithm is developed into a tool where the user can input the node list and the edge list. The tool can also find out the upstream and downstream of a given entity (genes/proteins etc.) from the derived Maximal paths. The complexity of finding the algorithm is found to be O(nlogn) in the best case and O(n^2 logn) in the worst case.
TEXT MINER FOR HYPERGRAPHS USING OUTPUT SPACE SAMPLING
(2011-08-16) Tirupattur, Naveen; Mukhopadhyay, Snehasis; Fang, Shiaofen; Xia, Yuni
Text Mining is process of extracting high-quality knowledge from analysis of textual data. Rapidly growing interest and focus on research in many fields is resulting in an overwhelming amount of research literature. This literature is a vast source of knowledge. But due to huge volume of literature, it is practically impossible for researchers to manually extract the knowledge. Hence, there is a need for automated approach to extract knowledge from unstructured data. Text mining is right approach for automated extraction of knowledge from textual data. The objective of this thesis is to mine documents pertaining to research literature, to find novel associations among entities appearing in that literature using Incremental Mining. Traditional text mining approaches provide binary associations. But it is important to understand context in which these associations occur. For example entity A has association with entity B in context of entity C. These contexts can be visualized as multi-way associations among the entities which are represented by a Hypergraph. This thesis work talks about extracting such multi-way associations among the entities using Frequent Itemset Mining and application of a new concept called Output space sampling to extract such multi-way associations in space and time efficient manner. We incorporated concept of personalization in Output space sampling so that user can specify his/her interests as the frequent hyper-associations are extracted from the text.
MDE-URDS-A Mobile Device Enabled Service Discovery System
(2011-08-16) Pradhan, Ketaki A.; Raje, Rajeev; Tuceryan, Mihran; Hill, James H.
Component-Based Software Development (CSBD) has gained widespread importance in recent times, due to its wide-scale applicability in software development. System developers can now pick and choose from the pre-existing components to suit their requirements in order to build their system. For the purpose of developing a quality-aware system, finding the suitable components offering services is an essential and critical step. Hence, Service Discovery is an important step in the development of systems composed from already existing quality-aware software services. Currently, there is a plethora of new-age devices, such as PDAs, and cell phones that automate daily activities and provide a pervasive connectivity to users. The special characteristics of these devices (e.g., mobility, heterogeneity) make them as attractive choices to host services. Hence, they need to be considered and integrated in the service discovery process. However, due to their limitations of battery life, intermittent connectivity and processing capabilities this task is not a simple one. This research addresses this challenge of including resource constrained devices by enhancing the UniFrame Resource Discovery System (URDS) architecture. This enhanced architecture is called Mobile Device Enabled Service Discovery System (MDE-URDS). The experimental validation of the MDE-URDS suggests that it is a scalable and quality-aware system, handling the limitations of mobile devices using existing and well established algorithms and protocols such as Mobile IP.
e-DTS 2.0: A Next-Generation of a Distributed Tracking System
(2012-03-20) Rybarczyk, Ryan Thomas; Raje, Rajeev; Tuceryan, Mihran; Linos, Panos
A key component in tracking is identifying relevant data and combining the data in an effort to provide an accurate estimate of both the location and the orientation of an object marker as it moves through an environment. This thesis proposes an enhancement to an existing tracking system, the enhanced distributed tracking system (e-DTS), in the form of the e-DTS 2.0 and provides an empirical analysis of these enhancements. The thesis also provides suggestions on future enhancements and improvements. When a Camera identifies an object within its frame of view, it communicates with a JINI-based service in an effort to expose this information to any client who wishes to consume it. This aforementioned communication utilizes the JINI Multicast Lookup Protocol to provide the means for a dynamic discovery of any sensors as they are added or removed from the environment during the tracking process. The client can then retrieve this information from the service and perform a fusion technique in an effort to provide an estimation of the marker's current location with respect to a given coordinate system. The coordinate system handoff and transformation is a key component of the e-DTS 2.0 tracking process as it improves the agility of the system.
Iterative Visual Analytics and its Applications in Bioinformatics
(2012-03-20) You, Qian; Fang, Shiaofen; Si, Luo; Tuceryan, Mihran; Sacks, Elisha
You, Qian. Ph.D., Purdue University, December, 2010. Iterative Visual Analytics and its Applications in Bioinformatics. Major Professors: Shiaofen Fang and Luo Si. Visual Analytics is a new and developing field that addresses the challenges of knowledge discoveries from the massive amount of available data. It facilitates humans‘ reasoning capabilities with interactive visual interfaces for exploratory data analysis tasks, where automatic data mining methods fall short due to the lack of the pre-defined objective functions. Analyzing the large volume of data sets for biological discoveries raises similar challenges. The domain knowledge of biologists and bioinformaticians is critical in the hypothesis-driven discovery tasks. Yet developing visual analytics frameworks for bioinformatic applications is still in its infancy. In this dissertation, we propose a general visual analytics framework – Iterative Visual Analytics (IVA) – to address some of the challenges in the current research. The framework consists of three progressive steps to explore data sets with the increased complexity: Terrain Surface Multi-dimensional Data Visualization, a new multi-dimensional technique that highlights the global patterns from the profile of a large scale network. It can lead users‘ attention to characteristic regions for discovering otherwise hidden knowledge; Correlative Multi-level Terrain Surface Visualization, a new visual platform that provides the overview and boosts the major signals of the numeric correlations among nodes in interconnected networks of different contexts. It enables users to gain critical insights and perform data analytical tasks in the context of multiple correlated networks; and the Iterative Visual Refinement Model, an innovative process that treats users‘ perceptions as the objective functions, and guides the users to form the optimal hypothesis by improving the desired visual patterns. It is a formalized model for interactive explorations to converge to optimal solutions. We also showcase our approach with bio-molecular data sets and demonstrate its effectiveness in several biomarker discovery applications.

Browse

Browsing Computer & Information Science Department Theses and Dissertations by Issue Date

Results Per Page

Sort Options