Computer Science Dept.357 EPS Building
Montana State University
Bozeman, MT 59717
Tel: (406) 994-4780
Department Head:John Paxton
Presented by: Prof. Bela Mutschler from University of Applied Sciences Ravensburg-Weingarten (Germany)
Abstract: A continuously increasing amount of data makes it difficult for knowledge-workers to identify the information they need to perform their tasks in the best possible way. Particularly challenging in this context is the alignment of process-related information (e.g., working instructions, best practices) with business processes. In fact, process-related information (process information for short) and business processes are usually handled separately. On one hand, shared drives, databases, and information systems are used to manage process information, on the other hand, process management technology provides the basis for managing business processes. In practice, enterprises often establish (Intranet) portals to connect both perspectives. However, such conventional portals are not always sufficient. Reason is that process information is delivered without considering the process participants’ work context and business processes are usually presented in a rather static manner. Therefore, enterprises crave for new ways of making process information available in a user-adequate manner. This talk picks up this challenge and presents the niPRO framework, which is based on semantic technology, enabling the intelligent delivery and user-adequate visualization of process information.
Speaker bio: Bela Mutschler holds a Diploma and a PhD in Computer Science. Since November 2008 he has been appointed as full professor at the University of Applied Sciences Ravensburg-Weingarten in Weingarten, Germany (Business Informatics Group, Faculty of Computer Science and Electrical Engineering). His main research interests include the design and introduction of process-oriented information systems, advanced process management technologies, and process-oriented knowledge management. Bela serves on the program committees of various international academic conferences and workshops.
Presented by: Dr. Stacy Patterson
Abstract: Distributed estimation algorithms generate an estimate of a global network state using only local interactions. In large networks with a vast wealth of data, it is important that these algorithms capture relevant information in a compact form, and that they are robust to network dynamics.
In the first part of this talk, I will focus on the effects of network dynamics on a well-known distributed estimation problem, the distributed average consensus problem. Distributed average consensus algorithms have a wide variety of applications, including sensor fusion, distributed optimization, and autonomous vehicle formation control. I will present our recent work on the stability and robustness of consensus algorithms in dynamic networks and show the analytical relationship between algorithm performance and the network size, topology, and dynamic characteristics. A notable result is that network topology imposes fundamental limitations on the scalability of distributed consensus algorithms that cannot be overcome without global information.
In the second part of the talk, I will address a more sophisticated approach to distributed estimation based on compressed sensing. Recent works have demonstrated that compressed sensing is applicable to a variety of problems in sensor networks, including urban environment monitoring and traffic estimation. I will show how we leveraged work in the database literature to develop a distributed compressed sensing algorithm that outperforms previous approaches by several orders of magnitude in both time and message complexity, making it an efficient solution for resource-challenged networks. I will then connect this algorithm with distributed average consensus by showing how we combine the two algorithms to obtain an efficient distributed compressed sensing algorithm suitable for dynamic networks.
Bio: Stacy Patterson is a Technion Postdoctoral Fellow and a Viterbi Postdoctoral Fellow in the Department of Electrical Engineering at Technion – Israel Institute of Technology. She received her M.S. and Ph.D. in Computer Science from the University of California, Santa Barbara in 2003 and 2009, respectively. From 2009 to 2011, she was a postdoctoral scholar in the Center for Control, Dynamical Systems, and Computation at the University of California, Santa Barbara. Her research interests are in distributed algorithms and applications for aggregation, estimation, and control in dynamic networks.
Presented by: Eben Howard
Abstract: People who play online games want those games to be fun. Current lag mitigation techniques focus on the impact of lag only for the lagged player. We show that unlagged players are also impacted by being in a group with a lagged player. Without taking this cascading impact into account, network and application designers are not able to make fully informed systematic decisions
This talk is a part of Eben's comprehensive examination.
Presented by Isaac Griffith
Abstract: Technical debt is a metaphor describing the economic consequesnces associated with taking short cuts to satisfy the short term goals of a project (i.e., meeting a release deadline) at the expense of long term goals (i.e., refactored software that meets quality criteria). Technical debt management is essentially the identification, measurement, and decision analysis surrounding the remediation and acceptance of technical debt in a project. In this presentation I describe previous research conducted in the areas of automated refactoring and code smell detection in the context of managing technical debt. Further, I describe current research specifically dealing with the management decisions involving release planning in the context of technical debt managment. In conclusion, I will show how this previous research has chartered an initial research path towards my dissertation in design pattern evolution and design pattern grime.
Presented by: Rajesh Kommineni
Abstract: In this seminar, Rajesh will provide a high level overview of how Search and Machine Learning is currently being used at Oracle RightNow. The talk will include description of problems encountered by Artificial Intelligence applications in a typical industrial usage.
Presenter's Bio: Rajesh Kommineni works as a Development Manager at Oracle RightNow. Rajesh has been working in the area of Knowledge Systems, Search and Machine Learning for about 13 years at various companies.
Presented by: Ethan Katz-Bassett
Abstract: The Internet is now central to many aspects of modern society, yet it remains remarkably fragile. Partial outages are common, and performance problems are widespread. Operators would like to address these issues, but poor diagnostic tools hamstring their efforts. I will argue that a more robust Internet -- one with the predictable performance and high availability needed to provide critical services -- requires the development of a new generation of better tools. We must move towards a self-healing Internet that fixes problems rapidly, without requiring the hours or days that operators often currently take. With collaborators, I have developed practical distributed systems to understand Internet problems and to provide crucial steps towards automated remediation. Our systems are deployable today, without requiring modifications to the network. In the first half of the talk, I will present Reverse Traceroute, a system to measure the routing and performance behavior of reverse paths back to the local host from other networks. While tools have long existed to measure the forward direction, the reverse path has been largely opaque, hindering troubleshooting efforts. I will show how Google and other content providers can use reverse traceroute to troubleshoot their clients' performance problems. In the second half of the talk, I will present LIFEGUARD, our system that uses Reverse Traceroute and related techniques to diagnose and automatically repair availability problems, even without the participation of the network containing the failure.
Bio: Ethan Katz-Bassett is an Assistant Professor at the University of Southern California. Previously, he worked for Google investigating mobile performance and Internet routing. He earned his PhD in Computer Science at the University of Washington in 2012, where his dissertation won the department's William Chan Memorial Dissertation Award. His research also won Best Paper awards at NSDI in 2008 and 2010. Ethan's research goal is to design deployable techniques to dramatically improve Internet reliability and performance, based on the needs of operators and providers and grounded in rigorous network measurements.
Presented by Timothy Wylie
Abstract: Modern computational geometry plays a critical role across a vast number of diverse research fields where theoretical results for provably efficient algorithms are necessary. Many of these problems are based on matching geometric objects or finding paths through given points with polygonal curves. This work focuses on the study and application of polygonal curves with respect to the discrete Frechet distance. Specifically, we cover the alignment, comparison, and simplification of protein backbone chains. We address the issue of visualization via the chain pair simplification problem on the protein chains. Further, we address several applications with map routing given noisy or missing data. The presentation will overview the finished work and outline the direction of future research on the discrete Frechet distance for the completion of the doctoral research.
Presented by Dr. Rafal Angryk
In this talk Dr. Angryk will summarize recent research performed in the MSU’s Data Mining Laboratory. The focus of the entire seminar is on Big Data Analytics of Solar Dynamics Observatory (SDO), which is a flagship of NASA’s current “Living with a Star” program. The audience will first learn about the importance of solar data analysis, then about complexity of data maintained on the servers in our department. After that, three large research projects will be introduced with the hope of creating research interest among our graduate students. Finally, we will briefly talk about the future of the massive Solar Data Mining effort at MSU, and the potential for new research funding.
Presenter by Andrew Le
Abstract: Your CS degree makes for a great foundation, but if you're like most students today, you'll graduate with no job and poor prospects. This despite employers constantly complaining about how hard it is to find "good engineers". The secret is that "good" encompasses much more than just Computer Science. In this session, I'll share my experience as an independent software consultant, a business owner, an engineering manager, a quitter of jobs, and all around troublemaker. I'll show you things you didn't even know you needed to know in order to get your dream job, skip rungs on the ladder, or build your own ladder.
About the speaker: Andrew Le is a product design and development consultant based in sunny Santa Barbara, California. He is a partner at SideProject, a web products consulting company, and cofounder of Cleverific, where he helps create exceptional tools for professional photographers.
Presented by Ramya Raghavendra
Abstract: In a wide array of disciplines, data can be modeled as an interconnected network of entities, where various attributes are associated with both the entities and the relations among them, enabling us to apply the wealth of graph algorithms to query and manage the data. In this talk, we first look at a few compelling use cases for modeling physical and information networks as graphs and using graph analysis techniques to perform tasks such as network management, anomaly detection and analytics. Next, we look at the current landscape of graph storage and processing software available. Graph databases are emerging as an important technology that can support faster graph query support compared to relational databases and scale more naturally to large data sets . Graph processing techniques include classical graph algorithms such as shortest path and reachability, which depend only on the structure of the network, as well as emerging queries that require both the topology and content information of the network data. Finally, we take a look some of the current approaches to visualize large graph data.
Bio: Ramya Raghavendra is a Research Staff Member in the Wireless Networking group at IBM TJ Watson Research Center since 2010. Prior to that, she received her MS and PhD in Computer Science from University of California Santa Barbara. Her research is focused on network measurement and management, with publications including ACM Sigcomm, IMC and IEEE INFOCOM. She is the recipient of Google Anita Borg scholarship, UCSB Oustanding Graduate Student award and ACM SRC award. Complete list of publications can be found at http://researcher.watson.ibm.com/view.php?person=us-rraghav