Databases and Information Systems Commons^™

Intensity And Resolution Enhancement Of Local Regions For Object Detection And Tracking In Wide Area Surveillance, Evan Krieger, Vijayan K. Asari, Saibabu Arigela, Theus H. Aspiras

Electrical and Computer Engineering Faculty Publications

Object tracking in wide area motion imagery is a complex problem that consists of object detection and target tracking over time. This challenge can be solved by human analysts who naturally have the ability to keep track of an object in a scene. A computer vision solution for object tracking has the potential to be a much faster and efficient solution. However, a computer vision solution faces certain challenges that do not affect a human analyst. To overcome these challenges, a tracking process is proposed that is inspired by the known advantages of a human analyst.

First, the focus of …

Affect And Online Privacy Concerns, David Charles Castano

CCAC Theses and Dissertations

The purpose of this study was to investigate the influence of affect on privacy concerns and privacy behaviors. A considerable amount of research in the information systems field argues that privacy concerns, usually conceptualized as an evaluation of privacy risks, influence privacy behaviors. However, recent theoretical work shows that affect, a pre-cognitive evaluation, has a significant effect on preferences and choices in risky situations. Affect is contrasted with cognitive issues in privacy decision making and the role of affective versus cognitive-consequentialist factors is reviewed in privacy context.

A causal model was developed to address how affect influences privacy concerns and …

Social Services Management Solution, Kurt A. Karner, James L. Potulny

All Capstone Projects

The modern privately run social services agency exists within a complex regulatory framework that requires detailed recordkeeping. In such an environment, paperwork tracking is best managed by a centralized enterprise data management system. While commercial applications that perform this function exist, they are frequently bundled with software for other business functions. Such additional software can be helpful, but it frequently duplicates the functionality of other enterprise software for these businesses, which is financially inefficient. This project developed a similar system using the ASP.NET 4.5 framework that is designed to provide a standalone solution to recordkeeping requirements. It followed a typical …

Hadoop "The Emerging Tool In The Present Scenario For Accessing The Large Sets Of Data", Chandra Kiran Movva, Tejaswi Sura, Ranjith Reddy Thipparthi

All Capstone Projects

Hadoop is one of the tools designed to handle big data. Hadoop and other software products work to interpret or parse the results of big data searches through specific proprietary algorithms and methods. Hadoop is an open-source program under the Apache license that is maintained by a global community of users. It includes various main components, including a MapReduce set of functions and a Hadoop distributed file system (HDFS). The idea behind MapReduce is that Hadoop can first map a large data set, and then perform a reduction on that content for specific results. A reduce function can be thought …

Context-Driven Automatic Subgraph Creation For Literature-Based Discovery, Delroy H. Cameron, Ramakanth Kavuluru, Thomas Rindflesch, Amit P. Sheth, Krishnaprasad Thirunarayan, Olivier Bodenreider

Kno.e.sis Publications

Background: Literature-based discovery (LBD) is characterized by uncovering hidden associations in non-interacting scientific literature. Prior approaches to LBD include use of: 1) domain expertise and structured background knowledge to manually filter and explore the literature, 2) distributional statistics and graph-theoretic measures to rank interesting connections and 3) heuristics to help eliminate spurious connections. However, manual approaches to LBD are not scalable and purely distributional approaches may not be sufficient to obtain insights into the meaning of poorly understood associations. While several graph-based approaches have the potential to elucidate associations, their effectiveness has not been fully demonstrated. A considerable degree of …

Big Data Analytics By Using Hadoop, Chaitanya Arava, Sudharshan Bandaru, Saradhi Bhargava Reddy Tiyyagura

All Capstone Projects

Data is large and vast, with more data coming into the system every day. Summarization analytics are all about grouping similar data together and then performing an operation such as calculating a statistic, building an index, or just simply counting.

Filtering is more about understanding a smaller piece of your data, such as all records generated from a particular user, or the top ten most used verbs in a corpus of text. In short, filtering allows you to apply a microscope to your data. It can also be considered a form of search.

Hadoop allows us to modify the way …

Sensorem – An Efficient Mobile Platform For Wireless Sensor Network Visualization, Jin Ming Koh, Marcus Sak, Hwee Xian Tan, Huiguang Liang, Fachmin Folianto, Tony Quek

Research Collection School Of Computing and Information Systems

No abstract provided.

Best Upgrade Plans For Single And Multiple Source-Destination Pairs, Yimin Lin, Kyriakos Mouratidis

Research Collection School Of Computing and Information Systems

In this paper, we study Resource Constrained Best Upgrade Plan (BUP) computation in road network databases. Consider a transportation network (weighted graph) G where a subset of the edges are upgradable, i.e., for each such edge there is a cost, which if spent, the weight of the edge can be reduced to a specific new value. In the single-pair version of BUP, the input includes a source and a destination in G, and a budget B (resource constraint). The goal is to identify which upgradable edges should be upgraded so that the shortest path distance between source and …

Mining Business Competitiveness From User Visitation Data, Doan Thanh Nam, Freddy Chong Tat Chua, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Ranking businesses by competitiveness is useful in many applications including business (e.g., restaurant) recommendation, and estimation of intrinsic value of businesses for mergers and acquisitions. Our literature reveals that previous methods of business ranking have ignored the competing relationship among businesses within their geographical areas. To account for competition, we propose the use of PageRank model and its variant to derive the Competitive Rankof businesses. We use the check-ins of users from Foursquare, a location-based social network, to model the winners of competitions among stores. The results of our experiments show that Competitive Rank works well when evaluated against ground …

Measuring User Influence, Susceptibility And Cynicalness In Sentiment Diffusion, Roy Ka-Wei Lee, Ee Peng Lim

Research Collection School Of Computing and Information Systems

Diffusion in social networks is an important research topic lately due to massive amount of information shared on social media and Web. As information diffuses, users express sentiments which can affect the sentiments of others. In this paper, we analyze how users reinforce or modify sentiment of one another based on a set of inter-dependent latent user factors as they are engaged in diffusion of event information. We introduce these sentiment-based latent user factors, namely influence, susceptibility and cynicalness. We also propose the ISC model to relate the three factors together and develop an iterative computation approach to …

A Study Of Access Control For Electronic Health Records, Sergio Vincent Senese

All Student Theses and Dissertations

The expansion between Information Technology and Healthcare has created many new options for both disciplines, as well as challenges. One of these topics is the Electronic Health Record (EHR) and the push for a universal record. A challenge for this topic is access control: how to keep patient’s personal health information secure, but at the same time accessible to all fields of healthcare and accomplish this within the federal privacy laws made by our government. This study focuses on the idea of a single EHR containing all the different medical information for all the areas of healthcare for a patient. …

Review Selection Using Micro-Reviews, Thanh-Son Nguyen, Hady W. Lauw, Panayiotis Tsaparas

Research Collection School Of Computing and Information Systems

Given the proliferation of review content, and the fact that reviews are highly diverse and often unnecessarily verbose, users frequently face the problem of selecting the appropriate reviews to consume. Micro-reviews are emerging as a new type of online review content in the social media. Micro-reviews are posted by users of check-in services such as Foursquare. They are concise (up to 200 characters long) and highly focused, in contrast to the comprehensive and verbose reviews. In this paper, we propose a novel mining problem, which brings together these two disparate sources of review content. Specifically, we use coverage of micro-reviews …

Multi-Roles Affiliation Model For General User Profiling, Lizi Liao, Heyan Huang, Yashen Wang

Research Collection School Of Computing and Information Systems

Online social networks release user attributes, which is important for many applications. Due to the sparsity of such user attributes online, many works focus on profiling user attributes automatically. However, in order to profile a specific user attribute, an unique model is built and such model usually does not fit other profiling tasks. In our work, we design a novel, flexible general user profiling model which naturally models users’ friendships with user attributes. Experiments show that our method simultaneously profile multiple attributes with better performance.

Exploring Discriminative Features For Anomaly Detection In Public Spaces, Shriguru Nayak, Archan Misra, Kasthuri Jeyarajah, Philips Kokoh Prasetyo, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Context data, collected either from mobile devices or from user-generated social media content, can help identify abnormal behavioural patterns in public spaces (e.g., shopping malls, college campuses or downtown city areas). Spatiotemporal analysis of such data streams provides a compelling new approach towards automatically creating real-time urban situational awareness, especially about events that are unanticipated or that evolve very rapidly. In this work, we use real-life datasets collected via SMU's LiveLabs testbed or via SMU's Palanteer software, to explore various discriminative features (both spatial and temporal - e.g., occupancy volumes, rate of change in topic{specific tweets or probabilistic distribution of …

Memory Dynamics In Attractor Networks, Guoqi Li, Kiruthika Ramanathan, Ning Ning, Luping Shi, Changyun Wen

Research Collection School Of Computing and Information Systems

As can be represented by neurons and their synaptic connections, attractor networks are widely believed to underlie biological memory systems and have been used extensively in recent years to model the storage and retrieval process of memory. In this paper, we propose a new energy function, which is nonnegative and attains zero values only at the desired memory patterns. An attractor network is designed based on the proposed energy function. It is shown that the desired memory patterns are stored as the stable equilibrium points of the attractor network. To retrieve a memory pattern, an initial stimulus input is presented …

Context-Sensitive Entity Resolution, William C. Decker

Theses and Dissertations

This dissertation proposes an entity resolution approach that is context-sensitive, meaning it relies less on high-risk information, such as social security numbers, to discern whether records from a data set belong to the same real-world individual or to different real-world individuals. The research follows an iterative process of assessing the quality, or fitness of use, of identity data housed in an educational institution and then processing the data with the proposed context-sensitive entity resolution (ER) rule set. The efficacy of this process is demonstrated through calculation of the four-year adjusted cohort graduation rate, a prevalent longitudinal data analysis challenge for …

Unknown Threat Detection With Honeypot Ensemble Analsyis Using Big Datasecurity Architecture, Michael Eugene Sanders

Theses and Dissertations

The amount of data that is being generated continues to rapidly grow in size and complexity. Frameworks such as Apache Hadoop and Apache Spark are evolving at a rapid rate as organizations are building data driven applications to gain competitive advantages. Data analytics frameworks decomposes our problems to build applications that are more than just inference and can help make predictions as well as prescriptions to problems in real time instead of batch processes.

Information Security is becoming more important to organizations as the Internet and cloud technologies become more integrated with their internal processes. The number of attacks and …

Using Software Defined Networking To Solve Missed Firewall Architecture In Legacy Networks, Jared Dean Vogel

Theses and Dissertations

This study is concerned with migrating traditional networks and their inherent firewall architecture to Software Defined Networking (SDN) architecture to provide an initial attempt at preventing application downtime due to hidden firewall domain rules. In legacy organization environments the networking engineers, firewall teams, and application analysts are often silo groups, but Software Defined Networking (SDN) can blur the lines between these group silos.

This thesis first outlines the interworking of SDN, traditional firewall architecture and how it interacts with SDN, an experiment of implementation, and the resulting conclusions.

Testing with SDN shows we are approaching new environments where the edges …

Sensitivity Analysis For The Winning Algorithm In Knowledge Discovery And Data Mining ( Kdd ) Cup Competition 2014, Fakhri Ghassan Abbas

Theses and Dissertations

This thesis applies multi-way sensitivity analysis for the winning algorithm in the Knowledge Discovery in Data Mining (KDD) cup competition 2014 -`Predicting Excitement at Donors.org'. Because of the highly advanced nature of this competition, analyzing the winning solution under a variety of different conditions provides insight about each of the models the winning team has used in the competition. The study follows Cross Industry Standard Process (CRISP) for data mining to study the steps taken to prepare, model and evaluate the model. The thesis focuses on a gradient boosting model. After careful examination of the models created by the researchers …

Assessing The Emphasis On Information Security In The Systems Analysis And Design Course, William David Salisbury, Thomas W. Ferratt, Donald E. Wynn

MIS/OM/DS Faculty Publications

Due to several recent highly publicized information breaches, information security has gained a higher profile. Hence, it is reasonable to expect that information security would receive an equally significant emphasis in the education of future systems professionals. A variety of security standards that various entities (e.g., NIST, COSO, ISACA-COBIT, ISO) have put forth emphasize the importance of information security from the very beginning of the system development lifecycle (SDLC) to avoid significant redesign in later phases. To determine the emphasis on security in typical systems analysis and design (SA&D) courses, we examine (1) to what extent security is emphasized in …

Leading Undergraduate Students To Big Data Generation, Jianjun Yang, Ju Shen

Computer Science Faculty Publications

People are facing a flood of data today. Data are being collected at unprecedented scale in many areas, such as networking, image processing, virtualization, scientific computation, and algorithms. The huge data nowadays are called Big Data. Big data is an all encompassing term for any collection of data sets so large and complex that it becomes difficult to process them using traditional data processing applications. In this article, the authors present a unique way which uses network simulator and tools of image processing to train students abilities to learn, analyze, manipulate, and apply Big Data. Thus they develop students hands-on …

Kinesic Patterning In Deceptive And Truthful Interactions, Judee K. Burgoon, Ryan M. Schuetzler, David W. Wilson

Information Systems and Quantitative Analysis Faculty Publications

A persistent question in the deception literature has been the extent to which nonverbal behaviors can reliably distinguish between truth and deception. It has been argued that deception instigates cognitive load and arousal that are betrayed through visible nonverbal indicators. Yet, empirical evidence has often failed to find statistically significant or strong relationships. Given that interpersonal message production is characterized by a high degree of simultaneous and serial patterning among multiple behaviors, it may be that patterns of behaviors are more diagnostic of veracity. Or it may be that the theorized linkage between internal states of arousal, cognitive taxation, and …

Reconstruction Privacy: Enabling Statistical Learning, Ke Wang, Chao Han, Ada Waichee Fu, Raymond C. Wong, Philip S. Yu

Research Collection School Of Computing and Information Systems

Non-independent reasoning (NIR) allows the information about one record in the data to be learnt from the information of other records in the data. Most posterior/prior based privacy criteria consider NIR as a privacy violation and require to smooth the distribution of published data to avoid sensitive NIR. The drawback of this approach is that it limits the utility of learning statistical relationships. The differential privacy criterion considers NIR as a non-privacy violation, therefore, enables learning statistical relationships, but at the cost of potential disclosures through NIR. A question is whether it is possible to (1) allow learning statistical relationships, …

On Efficient K-Optimal-Location-Selection Query Processing In Metric Spaces, Yunjun Gao, Shuyao Qi, Lu Chen, Baihua Zheng, Xinhan Li

Research Collection School Of Computing and Information Systems

This paper studies the problem of k-optimal-location-selection (kOLS) retrieval in metric spaces. Given a set D_A of customers, a set D_B of locations, a constrained region R , and a critical distance d_c, a metric kOLS (MkOLS) query retrieves k locations in D_B that are outside R but have the maximal optimality scores. Here, the optimality score of a location l∈D_B located outside R is defined as the number of the customers in D_A that are inside R and meanwhile have their distances to l bounded by …

Prediction Of Venues In Foursquare Using Flipped Topic Models, Wen Haw Chong, Bing Tian Dai, Ee Peng Lim

Research Collection School Of Computing and Information Systems

Foursquare is a highly popular location-based social platform, where users indicate their presence at venues via check-ins and/or provide venue-related tips. On Foursquare, we explore Latent Dirichlet Allocation (LDA) topic models for venue prediction: predict venues that a user is likely to visit, given his history of other visited venues. However we depart from prior works which regard the users as documents and their visited venues as terms. Instead we ‘flip’ LDA models such that we regard venues as documents that attract users, which are now the terms. Flipping is simple and requires no changes to the LDA mechanism. Yet …

Nirmal: Automatic Identification Of Software Relevant Tweets Leveraging Language Model, Abishek Sharma, Yuan Tian, David Lo

Research Collection School Of Computing and Information Systems

Twitter is one of the most widely used social media platforms today. It enables users to share and view short 140-character messages called 'tweets'. About 284 million active users generate close to 500 million tweets per day. Such rapid generation of user generated content in large magnitudes results in the problem of information overload. Users who are interested in information related to a particular domain have limited means to filter out irrelevant tweets and tend to get lost in the huge amount of data they encounter. A recent study by Singer et al. found that software developers use Twitter to …

Joint Search By Social And Spatial Proximity, Kyriakos Mouratidis, Jing Li, Yu Tang, Nikos Mamoulis

Research Collection School Of Computing and Information Systems

The diffusion of social networks introduces new challenges and opportunities for advanced services, especially so with their ongoing addition of location-based features. We show how applications like company and friend recommendation could significantly benefit from incorporating social and spatial proximity, and study a query type that captures these two-fold semantics. We develop highly scalable algorithms for its processing, and enhance them with elaborate optimizations. Finally, we use real social network data to empirically verify the efficiency and efficacy of our solutions.

Beyond Support And Confidence: Exploring Interestingness Measures For Rule-Based Specification Mining, Bui Tien Duy Le, David Lo

Research Collection School Of Computing and Information Systems

Numerous rule-based specification mining approaches have been proposed in the literature. Many of these approaches analyze a set of execution traces to discover interesting usage rules, e.g., whenever lock() is invoked, eventually unlock() is invoked. These techniques often generate and enumerate a set of candidate rules and compute some interestingness scores. Rules whose interestingness scores are above a certain threshold would then be output. In past studies, two measures, namely support and confidence, which are well-known measures, are often used to compute these scores. However, aside from these two, many other interestingness measures have been proposed. It is thus unclear …

A Web-Based Temperature Monitoring System For The College Of Arts And Letters, Rigoberto Solorio

Electronic Theses, Projects, and Dissertations

In general, server rooms have restricted access requiring that staff possess access codes, keys, etc. Normally, only administrators are provided access to protect the physical hardware and the data stored in the servers. Servers also have firewalls to restrict outsiders from accessing them via the Internet. Servers also cost a lot of money. For this reason, server rooms also need to be protected against overheating. This will prolong the lifecycle of the units and can prevent data loss from hardware failure.

The California State University San Bernardino (CSUSB), Specifically the College of Arts and Letters server room has faced power …

Smart Data - How You And I Will Exploit Big Data For Personalized Digital Health And Many Other Activities, Amit P. Sheth

Kno.e.sis Publications

No abstract provided.