Department of Computer Science and Electrical Engineering

ACM Distinguished Speaker

Cloud based Active Archiving Solution for Databases

Dr. Mukesh Mohania
IBM Research – India

2:30pm Friday, 30 November 2012
Room 102 (LH8), ITE Building, UMBC

In the second talk of the UMBC ACM Student Chapter's Tech Talk Series, ACM Distinguished Speaker Dr. Mukesh Mohania will visit UMBC and talk about "Cloud based Active Archiving Solution for Databases".

Cloud computing offers an exciting opportunity to bring on-demand applications to customers and is being used for delivering hosted services over the Internet and/or processing massive amount of data for business intelligence. In this talk, we will discuss the architecture of cloud computing, MapReduce, and Hadoop. We will then discuss how the cloud infrastructure can be used for data management services, how the massive amount of data can be processed over cloud for various business intelligence applications, and how the cloud can be used for 'Active' Data Archival for near real-time data access. We discuss various issues concerning the active archive system including schema modification, query federation, query optimization, access control and data provenance. Using TPC-DS benchmark data, we present evaluation results that shows the ability of our system to seamlessly query archive data along with data stored in the warehouse in order of minutes compared to hours required to move the data into the warehouse from traditional archival systems.

Mukesh Mohania received his Ph.D. in Computer Science & Engineering from Indian Institute of Technology, Bombay, India in 1995. Currently, he is a Senior Technical Staff Member and IBM Master Inventor in IBM Research – India. He has worked extensively in the areas of distributed databases, data warehousing, data integration, and autonomic computing. He has published more than 120 papers and also filed more than 50 patents in these or related areas, and more than 14 have already been granted. He received the best paper awards in CIKM 2004 and CIKM 2005. His work on Data Quality, Information Integration, and Autonomic Computing has led to the development of new products and also influenced several existing IBM products. He has received several awards within IBM, such as "Excellence in People Management", “Outstanding Innovation Award”, "Technical Accomplishment Award", “Leadership By Doing”, and many more. He also received IEEE Meritorious Service Award. He is an ACM Distinguished Scientist, and a member of IBM Academy of Technology.

Light refreshments will be served after the talk outside ITE-325

RSVP via Facebook https://facebook.com/events/378277722253548/

More information and directions: http://bit.ly/UMBCtalks

“Minimizing Intrusiveness in Home Energy Measurement”, a paper written by CSEE Assisant Professor Nilanjan Banerjee, Computer Science graduate student David Lachut, and their colleagues at the University of San Francisco, was nominated for the best paper award at ACM's BuildSys workshop.

The paper outlines the design of a system that will analyze and manage energy use in homes. “The overarching goal of our work is to automate the process of adapting energy demand to meet supply, which requires a comprehensive understanding of home energy use,” explains the abstract. Banerjee and his collaborators have collected data on energy-consumption from six both on and off-grid homes. “Our techniques reduce the energy footprint of the system as well as the amount of physical infrastructure required, making adoption of the system more attractive, particularly to those who live in homes powered by renewable energy sources.”

You can learn more about Dr. Banerjee’s work with renewable-energy driven devices and green homes at the website for his Mobile, Pervasive, and Sensor Systems Laboratory.

MS Thesis Defense

Using Mobile Data Collectors to Federate Clusters
of Disjoint Sensor Network Segments

Bhuvana Kalyanasundaram

11:00am Tuesday 2 October 2012, ITE 346

Wireless Sensor Networks (WSN) operating unattended in harsh environments have the higher probability of suffering from large scale damage, where many nodes fail simultaneously and the network gets partitioned into several disjoint segments. Restoring connectivity of structurally damaged WSN’s segments may be very urgent considering that they are employed to assist in risky missions. A similar scenario is when multiple standalone networks are to be federated to serve an emerging event such as an earthquake and conduct search-and-rescue. To deal with these scenarios, Mobile Data Mules (MDMs) are employed to establish intermittent links by moving around and carrying data from one segment to another. To limit data delivery latency and minimize the motion overhead, the travel path of the MDM should be shortened. We present a novel algorithm that groups the segments into k overlapping clusters based on the inter-segment proximity. Each cluster is assigned a distinct MDM to tour its segments. A segment that belongs to two clusters serves as a gateway that enables data transfer across clusters. Our algorithm minimizes the tour length for each MDM and sets the speed of the individual MDMs to rendezvous at the gateway nodes so that buffering space and time for inter-cluster traffic are minimized.

Committee: Drs. Mohamed Younis (chair), Charles Nicholas and Chintan Patel

CSEE Colloquium

Volume Calculation of Magnetic Resonance
Tissues via Image Classification

Shih-Yu Chen
Remote Sensing Signal & Image Processing Laboratory
UMBC Computer Science and Electrical Engineering

1:00pm Friday 5 October 2012, ITE 227

Magnetic resonance (MR) tissue volume calculation is very important in medical diagnosis. A general approach is to first perform image classification of desired tissue substances slice by slice and then calculate tissue volumes via classified data samples in each slice. Two issues are generally involved; (1) selection of training samples which are slice-dependent, i.e., each slice requires its own specific training samples and (2) classification which must be carried out slice by slice individually because training samples obtained from one slice are not necessarily applicable to another. We develop a volume sphering analysis (VSA) approach which can process all MR image slices as one single image cube to calculate tissue volumes via image classification using only one set of training samples that is obtained from a single image slice. The proposed VSA using one set of training samples not only performs comparably to that using training samples specifically selected for individual image slices, but also saves significant amounts of selecting training samples and computing time.

Shih-Yu Chen received the BS degree in Electrical Engineering from Da-Yeh University in 2005, and the MS EE degree from National Chung Hsing University in 2010. He is currently a PhD (EE) student at UMBC. Mr. Chen's research interest includes medical image, remote sensing image and vital sign signal processing.

more information and directions

The Association for Computing Machinery (ACM) is the world’s largest educational and scientific computing society. UMBC has an active ACM student chapter that is open to all UMBC undergraduate and graduate students of any major.

While you do not need to join ACM to be a part of the local chapter, the annual membership dues for students is only $19, heavily discounted from the non-student rate. See the ACM site for more information on student membership and its benefits.

This year the chapter is planning to have monthly meetings where faculty members, ACM distinguished speakers, and local tech companies will talk about various interesting topics. These meetings are tentatively planned for the third Wednesday of every month starting in October. Other activities like board game nights or our Welcome Back Picnic are also in the works. Suggestions on speakers or other events are welcome and can be sent to .

Please stop by for these events for which we will send out detail as they get confirmed. Sign up for the UMBC ACM mailing list to become a part of the local chapter and receive updates and news of its activities and events.

Ph.D. Dissertation Proposal

Virtual Circuit Provisioning in Challenged Sensor Internetworks,
with Application to the Solar System Internet

Ed Birrane

9:00am Friday, 21 September 2012, ITE 325b, UMBC

As sensing devices are applied to increasingly diverse tasks the network architectures that connect them must handle increasingly complex sets of operational constraints. One dimension in which these networks must scale is in their spatial footprint: there is a desire to distribute sensing devices over areas from miles to hundreds of miles to millions of miles. A second dimension in which these networks scale is in their media access heterogeneity: to gradually cover larger distances, existing networks (that may not otherwise communicate amongst themselves) must be stitched together. Examples of such networks include the Solar System Internet (SSI), Autonomous Underwater Surveillance (ASU), National Border Protection (NBP) and Intelligent Highway Initiatives (IHI).

I propose that the non-random sensing performed in these networks supports the establishment of virtual circuits that communicate information more efficiently than in broadcast mesh networks. Specifically, virtual circuits may be pre-negotiated using data-link-agnostic overlay techniques based on directed, weighted, time-variant graphs. The construction and maintenance of these circuits is feasible in non-random networks and may be accomplished through proposed protocols and stochastic processes. My first contribution will define an emerging, useful special case of networks. I label this architecture the "Challenged Sensor Internetwork" (CSI) and provide models relating to data motion and path selection. My second contribution will provide algorithms and associated analysis for path selection and synchronization. The network topology created by a CSI is graphically modeled as a multi-hypergraph. Since transmission in a CSI is wireless, a single transmission may be received by multiple nodes in the network, hence a hypergraph. However, as a challenged network, link opportunities amongst nodes will change as a function of time, hence a multigraph. I will show that the multi-cast problem, as formulated for CSIs, is NP-Complete, propose an approximation algorithm for the generation of paths in such a multi-hypergraph, and provide an analysis of the performance of this algorithm. My third contribution will provide heuristic algorithms and performance measurements. Each node in the CSI must store its own copy of the network graph so as to make local routing decisions. Synchronization of these network graphs across the network is often impossible. I propose two heuristic mechanisms, based on my proposed principle of path locality, to synchronize preferred path information in the network: exchanging relevant sub-graphs along paths as part of nominal messaging and altering local graphs based on predicted congestion based on observed traffic. Finally, I propose a method for inferring overlay-level contact opportunities from routing information available to local nodes via existing physical and data link layer mechanisms. My final contribution will demonstrate this work in the context of a real-world CSI deployment. I will provide a case study demonstrating how the SSI networking concept exemplifies the definition and characteristics of a CSI and showing how my proposed algorithms are mission enabling to existing, published SSI scenarios.

Several portions of the proposed dissertation work have been completed and validated through simulation and peer-reviewed publication. To complete the dissertation, I plan to finalize the problem statements, proofs, and algorithm analysis supporting achieved heuristic results. I will also apply these algorithms to scaled simulations and emulations of increasingly complex CSIs.

Committee: Drs. Dr. Mohammed Younis (Chair), Alan Sherman (Co-Advisor) Dhananjay Phatak, Vinton Cerf, Keith Scott, Hans Kruse

More information and directions

UMBC CSEE Colloquium

Human Sensor Networks for Improved Modeling
of Natural and Human-Caused Disasters

Oleg Aulov, Computer Science Ph.D. Student, UMBC

1:00pm Friday, 14 September 2012, ITE 227, UMBC

This talk will discuss the importance of different roles that social media can play in management, monitoring, modeling and mitigation of natural and human-caused disasters. We will present a novel approach that views social media data as a human sensor network. These data can serve as a low-cost augmentation to an observing system, which can be incorporated into geophysical models together with other scientific data such as satellite observations and sensor measurements. As a use case scenario, we analyze the Deepwater Horizon oil spill disaster. We gather the social media data that mention sightings of oil from Flickr, geolocate them, and use them as boundary forcings in the General NOAA Oil Modeling Environment (GNOME) software for oil spill predictions. We show how social media data can be incorporated into the GNOME model to obtain improved estimates of the model parameters such as rates of oil spill, couplings between surface winds and ocean currents, diffusion coefficient, and other model parameters. Other social media mining and citizen science projects performed by groups outside of UMBC, on air quality, earthquakes and the Fukushima disaster will also be summarized as related work.

Oleg Aulov received B.S. degree in mathematics from the University of Central Missouri, Warrensburg, MO, in 2004 and M.S. degree in Computer Science with a concentration in Computer Security and Information Assurance from George Washington University, Washington, DC, in 2006. He is currently working toward a Ph.D. degree in the Department of Computer Science and Electrical Engineering at University of Maryland, Baltimore County, Baltimore, MD. His topics of interest include social media mining, citizen science, machine learning, trust establishment and management, information assurance, and social engineering.

For more information and directions see http://bit.ly/UMBCtalks

MS Thesis Defense

Calculating Representativeness of
Geographic Sites Across the World

Ashwinkumar Ganesan

11:00am Friday, 31 August 2012, ITE 325b, UMBC

GLOBE is a global correlation engine, a project to study the effects of human activity on land change based on a set of parameters that include temperature, forest cover, human population, atmospheric parameters, and many other variables. The aim of this research is to understand how a land change study or set of studies of specific geographic areas generalizes to other areas of the world. The generic form of the question is – given a set of data points with a set of variables, how can we determine how much a selected subset of points represents the rest of the distribution. The research aims to answer a set of questions which include the definition of representativeness of a geographical site and how the representativeness can be computed. Land change researchers will dynamically select a subset of variables which they would like to study. Hence the method developed not only computes representativeness, but must do so in an efficient manner. For this purpose, we apply dimension reduction techniques to reduce the size of computation and analyze the effectiveness of using these techniques to calculate representativeness.

Committee: Drs. Tim Oates (chair), Tim Finin and Dr. Matt Schmill

MS Thesis Defense

Predicting the Activities of Mobile Phone Users
with Hidden Markov Models

Amey Sane

9:00am Tuesday, 28 August 2012, ITE 325b, UMBC

Mobile phones are ubiquitous and increasingly capable, with sophisticated sensors, network access, significant storage and processing power and access to a wide range of application data. They can improve the range and quality of their services by acquiring and using models of their context, including the activities in which their users are engaged. This thesis explores the use of supervised machine learning techniques for predicting a smartphone user's activities from available sensor data. We have specifically concentrated on applying classifiers and ensembles using hidden markov models for activity recognition. Our classifiers predict a user's current activity from among a set of conceptual activity classes such as sleeping, traveling, playing, working, and chatting/watching TV. We have experimented with and evaluated the effectiveness of different approaches on data collected on Android smartphones by university faculty and students.

Committee: Drs. Tim Finin (chair), Anupam Joshi and Yelena Yesha

MS Thesis Defense

A Specialist Approach for Classification of Column Data

Nikhil Puranik

1:30pm Friday 24 August, 2012, 325b ITE, UMBC

Much information is encoded in spreadsheets, databases, and tables on the Web and in documents. Interpreting this content and making its meaning explicit in a representation language like RDF enables many applications. This thesis addresses the problem of identifying the semantic type of the information represented in a table column containing conventionally encoded data such as phone numbers or stock ticker symbols. We describe a ‘specialist’ approach for classification in which different specialists work together to come up with a ranked list for the given input column. We use three types of specialists: those based on regular expressions, dictionaries and classifiers. We discuss a serial and parallel framework for the specialists. We evaluate our system in two ways: by testing individual specialist for accuracy and by testing the performance of the overall system in terms of generation of ranked list. We also discuss the scalability of the system in terms of addition of new specialists and performance impact for systems with hundreds of specialists.

Committee: Drs. Tim Finin (chair), Anupam Joshi, Tim Oates and Yelena Yesha