BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Computer Science and Engineering Department - ECPv5.11.0//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Computer Science and Engineering Department
X-ORIGINAL-URL:https://www.cse.uconn.edu
X-WR-CALDESC:Events for Computer Science and Engineering Department
BEGIN:VTIMEZONE
TZID:America/New_York
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20180311T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20181104T060000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180412T140000
DTEND;TZID=America/New_York:20180412T150000
DTSTAMP:20211204T024919
CREATED:20200528T181506Z
LAST-MODIFIED:20200528T184208Z
UID:7649-1523541600-1523545200@www.cse.uconn.edu
SUMMARY:PhD Defense - Abdullah Al-Mamun
DESCRIPTION:Title: Novel Algorithms for Some Fundamental Big Data Problems \nMajor Advisor: Dr. Sanguthevar Rajasekaran \nAssociate Advisors: Dr. Reda Ammar\, Dr. Ion Mandoiu \nDate/Time: Thursday\, Apr 12th\, 2018 at 2:00 pm \nLocation: Moved! to HBL Heritage Room 4118 \n \nAbstract: \nIn this digital era data sets are growing rapidly. Storing\, processing\, and analyzing large volume of data require efficient techniques. These techniques deal with big data problems by providing time efficient methods\, external memory algorithms\, distributed processing and so on. This thesis studies three important areas of big data problems and presents state of the art approaches to address them. \n \nThe first part of this thesis discusses k-mer counting problem. A massive number of bioinformatics applications require counting of k-length substrings in genetically important long strings. Genome assembly\, repeat detection\, multiple sequence alignment\, error detection\, and many other related applications use a k-mer counter as a building block. Very fast and efficient algorithms are necessary to count k-mers in large data sets to be useful in such applications. We propose a novel trie-based algorithm for this k-mer counting problem. \n \nIn the second part\, we present algorithms on record linkage problems. Integrating data from multiple sources is a crucial and challenging problem. Here we have come up with efficient sequential and parallel algorithms for record linkage which can handle any number of datasets. Our methods employ single linkage as well as complete linkage hierarchical clustering to address this problem. \n \nThe last part explains three problems with algorithmic challenges. The first one is minimum spanning tree problem. Finding minimum spanning trees (MST) in various types of networks is a well-studied problem in theory and practical applications. We have devised a very efficient algorithm which combines ideas from randomized selection\, Kruskal’s algorithm and Prim’s algorithm. The second problem is closest l-mers problem. Algorithms for finding the closest l-mers have been used in solving the (l\, d)-motif search problem. We describe exact as well as very fast approximate algorithms for computing a group of three l-mers having minimum combined distance among all possible such combinations. The third problem is higher order spectra analysis of nonlinear time series. It has applications in biomedical signal processing\, communications\, geophysics\, speech processing\, etc. We address this problem by providing space and time efficient sequential and parallel algorithms.
URL:https://www.cse.uconn.edu/event/phd-defense-abdullah-al-mamun/
CATEGORIES:Colloquia
END:VEVENT
END:VCALENDAR