Databases and Information Systems Commons^™

Big Data Insights Using Analytics, Naga Krishna Reddy Muppidi, Sai Kiran Merugu, Khambhampati Pramod

All Capstone Projects

The main objective of this project is to find the data insights from the huge amount of data that is evolving around us day by day. In order to analyze the data we need an architecture that is suitable for all kinds of data that we see in 21st century. We are using SPLUNK architecture for analyzing the data and getting the insights that we need for taking better decisions. SPLUNK is google for datacenters. By using SPLUNK we can generate all kinds of DASHBOARDS, ALERTS, SCHEDULING, PIVOTS and a lot more important things that is very usable for managers …

Online Dormitory Reservation System, Adithya Mothe, Koushik Kumar Suragoni, Ramya Vakity

All Capstone Projects

This project is Online Dorms Systems which allows users to book their room in the dorm from anywhere; this is an automated system where the user can search the availability of rooms in the dorm.

The search can be done based on the dates. The rooms that available are come with the status available, it will display all the rooms available as of that particular search date. Once the room has been booked the user can cancel the reservation within 48 hours. And there is concept of user login. As the user creates his own account with his email id, …

E-Classroom For An Underserved Institution, Bhanuprakash Madupati, Kaleem Danish Mohammed, Dilipkumar Pampana

All Capstone Projects

The E-Class Room system is a web based project. An educational institution in India is understaffed and has limited interaction among faculty, student and industry experts. The project is to provide an online platform for the students and faculty of the institution to enhance their educational needs and to share their learning with their fellow students, faculty or industrial experts. It aims to provide a platform for mutual cooperation between different kinds of learning. The new system will provide directional way for online learning between faculty, student and industrial experts.

Data Framework Management System, Firasat Ali Mohammed, Ahmad Munir Rizwi Syed

All Capstone Projects

The goal of this project is to design a system for managing multiple data source through networks. The end user is concerned about the computations that depend on information from several data sources. The access will be from a portal. The system should use grid computing standards to fetch information from the different information sources, consolidates them, presented them as required.

Structural Constraints For Multipartite Entity Resolution With Markov Logic Network, Tengyuan Ye, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Multipartite entity resolution seeks to match entity mentions across several collections. An entity mention is presumed unique within a collection, and thus could match at most one entity mention in each of the other collections. In addition to domain-specific features considered in entity resolution, there are a number of domain-invariant structural contraints that apply in this scenario, including one-to-one assignment as well as cross-collection transitivity. We propose a principled solution to the multipartite entity resolution problem, building on the foundation of Markov Logic Network (MLN) that combines probabilistic graphical model and first-order logic. We describe how the domain-invariant structural constraints …

On Robust Image Spam Filtering Via Comprehensive Visual Modeling, Jialie Shen, Deng, Robert H., Zhiyong Cheng, Liqiang Nie, Shuicheng Yan

Research Collection School Of Computing and Information Systems

The Internet has brought about fundamental changes in the way peoples generate and exchange media information. Over the last decade, unsolicited message images (image spams) have become one of the most serious problems for Internet service providers (ISPs), business firms and general end users. In this paper, we report a novel system called RoBoTs (Robust BoosTrap based spam detector) to support accurate and robust image spam filtering. The system is developed based on multiple visual properties extracted from different levels of granularity, aiming to capture more discriminative contents for effective spam image identification. In addition, a resampling based learning framework …

Detect Rumors Using Time Series Of Social Context Information On Microblogging Websites, Jing Ma, Wei Gao, Zhongyu Wei, Yueming Lu, Kam-Fai Wong

Research Collection School Of Computing and Information Systems

Automatically identifying rumors from online social media especially microblogging websites is an important research issue. Most of existing work for rumor detection focuses on modeling features related to microblog contents, users and propagation patterns, but ignore the importance of the variation of these social context features during the message propagation over time. In this study, we propose a novel approach to capture the temporal characteristics of these features based on the time series of rumor's lifecycle, for which time series modeling technique is applied to incorporate various social context information. Our experiments using the events in two microblog datasets confirm …

Social Tag Relevance Estimation Via Ranking-Oriented Neighbour Voting, Chaoran Cui, Jialie Shen, Jun Ma, Tao Lian

Research Collection School Of Computing and Information Systems

User-generated tags associated with social images are frequently imprecise and incomplete. Therefore, a fundamental challenge in tag-based applications is the problem of tag relevance estimation, which concerns how to interpret and quantify the relevance of a tag with respect to the contents of an image. In this paper, we address the key problem from a new perspective of learning to rank, and develop a novel approach to facilitate tag relevance estimation to directly optimize the ranking performance of tag-based image search. A supervision step is introduced into the neighbour voting scheme, in which tag relevance is estimated by accumulating votes …

Scheduled Approximation For Personalized Pagerank With Utility-Based Hub Selection, Fanwei Zhu, Yuan Fang, Kevin Chen-Chuan Chang, Jing Ying

Research Collection School Of Computing and Information Systems

As Personalized PageRank has been widely leveraged for ranking on a graph, the efficient computation of Personalized PageRank Vector (PPV) becomes a prominent issue. In this paper, we propose FastPPV, an approximate PPV computation algorithm that is incremental and accuracy-aware. Our approach hinges on a novel paradigm of scheduled approximation: the computation is partitioned and scheduled for processing in an “organized” way, such that we can gradually improve our PPV estimation in an incremental manner and quantify the accuracy of our approximation at query time. Guided by this principle, we develop an efficient hub-based realization, where we adopt the metric …

Choosing Your Weapons: On Sentiment Analysis Tools For Software Engineering Research, Robbert Jongeling, Subhajit Datta, Alexander Serebrenik

Research Collection School Of Computing and Information Systems

Recent years have seen an increasing attention to social aspects of software engineering, including studies of emotions and sentiments experienced and expressed by the software developers. Most of these studies reuse existing sentiment analysis tools such as SentiStrength and NLTK. However, these tools have been trained on product reviews and movie reviews and, therefore, their results might not be applicable in the software engineering domain. In this paper we study whether the sentiment analysis tools agree with the sentiment recognized by human evaluators (as reported in an earlier study) as well as with each other. Furthermore, we evaluate the impact …

The Importance Of Being Isolated: An Empirical Study On Chromium Reviews, Subhajit Datta, Devarshi Bhatt, Manish Jain, Proshanta Sarkar, Santonu Sarkar

Research Collection School Of Computing and Information Systems

As large scale software development has become more collaborative, and software teams more globally distributed, several studies have explored how developer interaction influences software development outcomes. The emphasis so far has been largely on outcomes like defect count, the time to close modification requests etc. In the paper, we examine data from the Chromium project to understand how different aspects of developer discussion relate to the closure time of reviews. On the basis of analyzing reviews discussed by 2000+ developers, our results indicate that quicker closure of reviews owned by a developer relates to higher reception of information and insights …

Leveraging Synergy Between Database And Programming Language Courses, Brian T. Howard

Computer Science Faculty publications

Undergraduate courses in database systems and programming languages are frequently taught without much overlap. This paper argues that there is a substantial benefit to emphasizing some areas of commonality, both old and new, between the two subjects. Examples of cross-fertilization that may be used to enhance one of both of the courses include query language design and implementation, object-relational mapping, transactional memory, and various aspects of the recent "NoSQL" movement.

Telecom Data Analysis, Sai Roopak Sarva, Anudeep Masetty, Vinay Reddy Kondam

All Capstone Projects

The telecommunications industry regularly uses data analytics in fields such as customer analysis and network optimization. For financial analysis such as identifying risks, which could negatively impact an entity’s financial performance, communications service providers have traditionally used statistical sampling techniques that cover only short time periods and a limited subset of data.

Given the massive number of transactions processed by telecommunications companies; and the costs and complexity involved in their operations, data analytics offers a valuable opportunity for enhancing the frameworks and procedures they adopt to drive profitability and minimize unnecessary downside risk.

Smart Inventory Management System, Ajay Akarapu, Chandrakanth Reddy Dasari, Nagaraju Deshini, Sushmita Mamidi

All Capstone Projects

Smart Inventory Management System is an online software application which fulfills the requirement of a typical Stock Analysis in various godowns. It provides the interface to users in a graphical way to manage the daily transactions as well as historical data. Also provides the management reports like monthly inwards, monthly deliveries and monthly returns. This application maintains the centralized database so that any changes done at a location reflects immediately. This is an online tool so more than one user can login into system and use the tool simultaneously. The aim of this application is to reduce the manual effort …

Ezdi's Semantics-Enhanced Linguistic, Nlp, And Ml Approach For Health Informatics, Raxit Goswami, Neil Shah, Amit P. Sheth

Kno.e.sis Publications

ezDI uses large and extensive knowledge graph to enhance linguistics, NLP and ML techniques to improve structured data extraction from millions of EMR records. It then normalizes it, and maps it with various computer-processable nomenclature such as SNOMED-CT, RxNorm, ICD-9, ICD-10, CPT, and LOINC. Furthermore, it applies advanced reasoning that exploited domain-specific and hierarchical relationships among entities in the knowledge graph to make the data actionable. These capabilities are part of its highly scalable AWS deployed heath intelligence platform that support healthcare informatics applications, including Computer Assisted Coding (CAC), Computerized Document Improvement (CDI), compliance and audit, and core measures and …

Face Recognition On Large-Scale Video In The Wild With Hybrid Euclidean-And-Riemannian Metric Learning, Zhiwu Huang, R. Wang, S. Shan, X Chen

Research Collection School Of Computing and Information Systems

Face recognition on large-scale video in the wild is becoming increasingly important due to the ubiquity of video data captured by surveillance cameras, handheld devices, Internet uploads, and other sources. By treating each video as one image set, set-based methods recently have made great success in the field of video-based face recognition. In the wild world, videos often contain extremely complex data variations and thus pose a big challenge of set modeling for set-based methods. In this paper, we propose a novel Hybrid Euclidean-and-Riemannian Metric Learning (HERML) method to fuse multiple statistics of image set. Specifically, we represent each image …

Two Formulas For Success In Social Media: Learning And Network Effects, Liangfei Qiu, Qian Tang, Andrew B. Whinston

Research Collection School Of Computing and Information Systems

Recent years have witnessed an unprecedented explosion in information technology that enables dynamic diffusion of user-generated content in social networks. Online videos, in particular, have changed the landscape of marketing and entertainment, competing with premium content and spurring business innovations. In the present study, we examine how learning and network effects drive the diffusion of online videos. While learning happens through informational externalities, network effects are direct payoff externalities. Using a unique data set from YouTube, we empirically identify learning and network effects separately, and find that both mechanisms have statistically and economically significant effects on video views; furthermore, the …

Learning Relative Similarity From Data Streams: Active Online Learning Approaches, Shuji Hao, Peilin Zhao, Steven C. H. Hoi, Chunyan Miao

Research Collection School Of Computing and Information Systems

Relative similarity learning, as an important learning scheme for information retrieval, aims to learn a bi-linear similarity function from a collection of labeled instance-pairs, and the learned function would assign a high similarity value for a similar instance-pair and a low value for a dissimilar pair. Existing algorithms usually assume the labels of all the pairs in data streams are always made available for learning. However, this is not always realistic in practice since the number of possible pairs is quadratic to the number of instances in the database, and manually labeling the pairs could be very costly and time …

Enhancing Manufacturing Planning And Control Systems Through Artificial Intelligence Techniques, Ronald S. Dattero, John J. Kanet, Edna M. White

John J. Kanet

Manufacturing planning and control systems are currently dominated by systems based upon Material Requirements Planning (MRP). MRP systems have a number of fundamental flaws. A potential alternative to MRP systems is suggested after research into the economic batch scheduling problem. Based on the ideas of economic batch scheduling, and enhanced through artificial intelligence techniques, an alternative approach to manufacturing planning and control is developed. A framework for future research on this alternative to MRP is presented.

Production Planning And Control Systems-State Of The Art And New Directions, V. Sridharan, John Kanet

John J. Kanet

This chapter begins with a description of the role of production planning and control (PPC) within the manufacturing function. After discussing the impact of the operating environment on the choice a system for PPC, we describe some recent empirical evidence regarding the use and performance results of various PPC systems. This is followed by a brief overview of the two most widely used systems for production planning and control. We then describe a recent development in the area of short-term detailed scheduling exploiting the latest developments in computing technology. The chapter concludes with a discussion of an emerging paradigm for …