Engineering Commons^™

Graphmp: An Efficient Semi-External-Memory Big Graph Processing System On A Single Machine, Peng Sun, Yonggang Wen, Nguyen Binh Duong Ta, Xiaokui Xiao

Research Collection School Of Computing and Information Systems

Recent studies showed that single-machine graph processing systems can be as highly competitive as clusterbased approaches on large-scale problems. While several outof-core graph processing systems and computation models have been proposed, the high disk I/O overhead could significantly reduce performance in many practical cases. In this paper, we propose GraphMP to tackle big graph analytics on a single machine. GraphMP achieves low disk I/O overhead with three techniques. First, we design a vertex-centric sliding window (VSW) computation model to avoid reading and writing vertices on disk. Second, we propose a selective scheduling method to skip loading and processing unnecessary edge …

A Study Of Application-Awareness In Software-Defined Data Center Networks, Chui-Hui Chiu

LSU Doctoral Dissertations

A data center (DC) has been a fundamental infrastructure for academia and industry for many years. Applications in DC have diverse requirements on communication. There are huge demands on data center network (DCN) control frameworks (CFs) for coordinating communication traffic. Simultaneously satisfying all demands is difficult and inefficient using existing traditional network devices and protocols. Recently, the agile software-defined Networking (SDN) is introduced to DCN for speeding up the development of the DCNCF. Application-awareness preserves the application semantics including the collective goals of communications. Previous works have illustrated that application-aware DCNCFs can much more efficiently allocate network resources by explicitly …

Solar Irradiance Forecasting Using Deep Neural Networks, Ahmad Alzahrani, Pourya Shamsi, Cihan H. Dagli, Mehdi Ferdowsi

Electrical and Computer Engineering Faculty Research & Creative Works

Predicting solar irradiance has been an important topic in renewable energy generation. Prediction improves the planning and operation of photovoltaic systems and yields many economic advantages for electric utilities. The irradiance can be predicted using statistical methods such as artificial neural networks (ANN), support vector machines (SVM), or autoregressive moving average (ARMA). However, they either lack accuracy because they cannot capture long-term dependency or cannot be used with big data because of the scalability. This paper presents a method to predict the solar irradiance using deep neural networks. Deep recurrent neural networks (DRNNs) add complexity to the model without specifying …

Purdue Airsense: An Open-Source Air Quality Monitoring System, Ruihang Du, Stephane Junior Nuoafo Wanko, Shadi Tariq Azouz, Brandon Emil Boor, Greg Michalski

The Summer Undergraduate Research Fellowship (SURF) Symposium

Ambient air pollutants have received increasing attention in recent years since studies have demonstrated their adverse health effects. To address the sparsity of concentration data for major ambient air pollutants, researchers have introduced several new low-cost measurement methods. Despite these efforts, only a few gas concentration data and aerosol size distribution data are publicly accessible through online platforms. In this study, we used Alphasense sensors to build an innovative low-cost portable sensor system that measures the concentration of ozone, CO, NOx, and coarse and fine particulate matter (PM). Alongside the portable sensor system, we assembled lab-grade analytical instruments in a …

Wide-Area Measurement-Driven Approaches For Power System Modeling And Analytics, Hesen Liu

Doctoral Dissertations

This dissertation presents wide-area measurement-driven approaches for power system modeling and analytics. Accurate power system dynamic models are the very basis of power system analysis, control, and operation. Meanwhile, phasor measurement data provide first-hand knowledge of power system dynamic behaviors. The idea of building out innovative applications with synchrophasor data is promising.

Taking advantage of the real-time wide-area measurements, one of phasor measurements’ novel applications is to develop a synchrophasor-based auto-regressive with exogenous inputs (ARX) model that can be updated online to estimate or predict system dynamic responses.

Furthermore, since auto-regressive models are in a big family, the ARX model …

Wide-Area Synchrophasor Data Server System And Data Analytics Platform, Dao Zhou

Doctoral Dissertations

As synchrophasor data start to play a significant role in power system operation and dynamic study, data processing and data analysis capability are critical to Wide-area measurement systems (WAMS). The Frequency Monitoring Network (FNET/GridEye) is a WAMS network that collects data from hundreds of Frequency Disturbance Recorders (FDRs) at the distribution level. The previous FNET/GridEye data center is limited by its data storage capability and computation power. Targeting scalability, extensibility, concurrency and robustness, a distributed data analytics platform is proposed to process large volume, high velocity dataset. A variety of real-time and non-real-time synchrophasor data analytics applications are hosted by …

Data Masking, Encryption, And Their Effect On Classification Performance: Trade-Offs Between Data Security And Utility, Juan C. Asenjo

CCE Theses and Dissertations

As data mining increasingly shapes organizational decision-making, the quality of its results must be questioned to ensure trust in the technology. Inaccuracies can mislead decision-makers and cause costly mistakes. With more data collected for analytical purposes, privacy is also a major concern. Data security policies and regulations are increasingly put in place to manage risks, but these policies and regulations often employ technologies that substitute and/or suppress sensitive details contained in the data sets being mined. Data masking and substitution and/or data encryption and suppression of sensitive attributes from data sets can limit access to important details. It is believed …

Conditional Correlation Analysis, Sanjeev Bhatta

Browse all Theses and Dissertations

Correlation analysis is a frequently used statistical measure to examine the relationship among variables in different practical applications. However, the traditional correlation analysis uses an overly simplistic method to do so. It measures how two variables are related in an application by examining only their relationship in the entire underlying data space. As a result, traditional correlation analysis may miss a strong correlation between those variables especially when that relationship exists in the small subpopulation of the larger data space. This is no longer acceptable and may lose a fair share of information in this era of Big Data which …

A New Evolutionary Algorithm For Mining Noisy, Epistatic, Geospatial Survey Data Associated With Chagas Disease, John P. Hanley

Graduate College Dissertations and Theses

The scientific community is just beginning to understand some of the profound affects that feature interactions and heterogeneity have on natural systems. Despite the belief that these nonlinear and heterogeneous interactions exist across numerous real-world systems (e.g., from the development of personalized drug therapies to market predictions of consumer behaviors), the tools for analysis have not kept pace. This research was motivated by the desire to mine data from large socioeconomic surveys aimed at identifying the drivers of household infestation by a Triatomine insect that transmits the life-threatening Chagas disease. To decrease the risk of transmission, our colleagues at the …

Accelerating Big Data Applications Using Lightweight Virtualization Framework On Enterprise Cloud, Janki Bhimani, Zhengyu Yang, Miriam Leeser, Ningfang Mi

Zhengyu Yang