基本信息
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职业迁徙
个人简介
Research Interests
· Approximate Processing Techniques
· Sensor Networks
· Data Warehousing, OLAP
· Data Streams
· XML
Education
2001-May 2005
University of Maryland, Dept. of Computer Science
Ph.D. Computer Science, GPA: 4.00
Thesis: Accurate Data Approximation in Constrained Environments
Advisor: Prof. Nick Roussopoulos
1999-2001
University of Maryland, Dept. of Computer Science
M.Sc. Computer Science, GPA: 4.00
Advisor: Prof. Nick Roussopoulos
1994-1999
National Technical University of Athens
Dept. of Electrical and Computer Engineering
Diploma of Electrical and Computer Engineering,
GPA: 3.832, Rank in Class: 3rd
Thesis: SISYPHUS: A Chunk-Based Storage Manager for OLAP Data Cubes
Advisor: Prof. Timos Sellis
Honors and Awards
1999-2001
Recipient of one out of two UMIACS fellowships for first year graduate students
University of Maryland
1996-1999
Recipient of Annual National Fellowship Foundation (IKY)
Awards for academic excellence, Athens, Greece (4 times)
Research Experience
June-August, 2003
Research Intern, AT&T Labs - Research
Mentor: Dr. Yannis Kotidis
Worked on designing protocols for bandwidth-efficient data aggregation of continuous queries in sensor networks. Designed and implemented a versioning file system on top of a relational engine, optimized for lightweight cgi-bin processes. My libraries are at the core of the Virtual Integration Prototype (VIP), a search engine over AT&T's Legacy Applications (ordering, billing, provisioning, inventory). In 2003 VIP served around 25 million user queries.
September 2002
Visitor, AT&T Labs and Center for Discrete Mathematics & Theoritical Computer Science (DIMACS)
Worked on designing algorithms for the efficient approximation of multiple signals in sensor network and network management applications, by exploiting piece-wise correlations between parts of the collected signals.
2001-2005
Graduate Research Assistant
University of Maryland, Dept. of Computer Science
· Approximate Processing Techniques
My work on approximate processing techniques mainly involves the approximation of data sets containing multiple measures (multiple numeric entries for each table cell). Such data sets arise in many application domains, from network management and time-series analysis/correlation systems to On-Line Analytical Processing (OLAP) environments. I introduced the extended wavelet coefficients as a flexible storage technique for wavelet coefficients in such multi-measure data sets and proposed both optimal and provably approximate algorithms on selecting which extended wavelet coefficients to retain under a given storage constraint. These algorithms can be applied to minimize various error metrics, such as the weighted sum squared, relative and absolute error of the obtained approximation. While my techniques are developed for multi-measure data sets, the algorithms that I proposed for constructing probabilistic wavelet data synopses are significantly faster than previously proposed techniques even for the single-measure case.
· Sensor Networks
The first part of my work involved the design of protocols for bandwidth-efficient evaluation of aggregate continuous queries over sensor networks for applications that are willing to tolerate a specified maximum error on the obtained answer. I then studied the dual application, where the application specifies the desired average bandwidth consumption for the posed query, and the goal is to maximize the accuracy of the reported results. I also considered the case of periodic transmission of historical information in such networks. To exploit the correlations that are typically expected between different observed quantities, I proposed the Self-Based Regression (SBR) algorithm for efficiently compressing the transmitted measurements. The SBR algorithm constructs a base signal that contains prominent features of the data, and uses this base signal as a dictionary to encode the data using linear regression.
· Data Warehousing, OLAP
Calculating and storing data cubes for high-dimensional hierarchical data sets has long been a difficult task due to the dimensionality curse; the number of views in a data cube is exponential in the number of dimensions. I helped design Dwarf, a highly-compressed data structure for computing, storing and indexing data cubes. Dwarf identifies prefix and (most importantly) suffix structural redundancies and factors them out by coalescing their store. What makes Dwarf practical is the automatic discovery, in a single pass over the fact table, of the prefix and suffix redundancies without user involvement or knowledge of the value distributions, and their elimination before the redundant areas of the cube are computed.
1999-2001
University of Maryland Institute for Advanced Computer Studies (UMIACS) Fellow
1998-1999
Research and Teaching Assistant
National Technical University of Athens, Dept. of Electrical and Computer Engineering
Designed and helped implement the storage manager of the Eratosthenes OLAP system for hierarchical data sets, developed in the National Technical University of Athens. Worked as a research assistant to help develop a new tool for software testing.
Teaching Experience
1998-1999
Courses: "Introduction to Compilers" and "Introduction to Software Engineering", National Technical University of Athens
Gave lectures, designed projects and wrote 2 chapters in a book involving the use of the flex and bison tools.
2001-2004
DBChat organizer, University of Maryland
DBChat is a weekly seminar-type database group meeting. My duties included compiling the list of papers to be discussed, arranging class schedules, and putting the class material on the Web. DBChat was offered as a course in the Fall 2001 semester.
Publications
Talks and Conference Presentations
[12]
Query Processing for the Semantic Sensor Web [** Invited Talk **]
1st International Workshop on the Semantic Sensor Web (SemSensWeb 2009)
June 2009
[11]
Another Outlier Bites the Dust: Computing Meaningful Aggregates in Sensor Networks
25th International Conference on Data Engineering (IEEE ICDE)
Shanghai, China, April 2009
[10]
Efficient Query Processing in Sensor Networks
Technical University of Crete, May 2006
[9]
A Fast Approximation Scheme for Probabilistic Wavelet Synopses
4th Hellenic Data Management Symposium,
Athens, Greece, August 2005
[8]
Efficient Query Processing in Sensor Networks
4th Hellenic Data Management Symposium
Athens, Greece, August 2005
[7]
A Fast Approximation Scheme for Probabilistic Wavelet Synopses
17th International Conference on Scientific and Statistical Database Management (SSDBM)
Santa Barbara, June 2005
[6]
Sensor Networks: Applications and Ongoing Research
National Technical University of Athens, January 2005
[5]
Data Approximation in Multi-Measure Data Sets
National Technical University of Athens, July 2004
研究兴趣
论文共 96 篇作者统计合作学者相似作者
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CoRR (2024)
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Inf. Syst. (2023): 102221-102221
PROCEEDINGS OF THE 32ND ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, CIKM 2023pp.5204-5207, (2023)
2021 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA)pp.2052-2057, (2021)
Proceedings of the 30th ACM International Conference on Information & Knowledge Management (2021)
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作者统计
#Papers: 96
#Citation: 2651
H-Index: 26
G-Index: 50
Sociability: 5
Diversity: 2
Activity: 12
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