I'm interested in designing new distributed and parallel algorithms, the distributed processing of big data, achieving fault-tolerance in networks, and secure distributed computing in dynamic environments such as peer-to-peer networks and mobile ad-hoc networks.
- General Chair of ACM PODC 2019
- Program committee member of BGP 2017, SPAA 2016 and of SIROCCO 2016
- Giving a talk at a workshop on Dynamic Graphs in Distributed Computing (co-located with DISC 2016)
- Co-chairing the program committee of ICDCN 2016
- Giving a talk at ADGA 2015, (4th Workshop on Advances in Distributed Graph Algorithms, co-located with DISC 2015 )
Keywords (Show all)«Asynchrony» «Big Data» «Byzantine Failures» «Churn» «Communication Complexity» «Distributed Agreement» «Distributed Storage» «Dynamic Network» «Fault-Tolerance» «Gossip Communication» «Graph Algorithm» «Haskell» «Leader Election» «Machine Learning» «Mobile Ad-Hoc Network» «Natural Language Processing» «P2P» «Secure Computation» «Self-Healing» «Symmetry Breaking»
Log File Processing by Machine Learning and Information Extraction
Peter Robinson. Master Thesis. TU Vienna, Institute of Computer Languages, 2006. Nominated for Distinguished Young Alumnus Award.
Abstract...In today's computer network systems lots of events are constantly written to log files. Unfortunately there is no common standard defining the structure of these event messages which are partly in human readable natural language form. Obviously, this lack of structure makes automatic processing a lot more difficult. This master thesis describes the architecture and implementation of the LoP-System, a system that attempts to create machine readable event structures from ordinary log file events by natural language processing. The thesis explains implementational details as well as the theoretical concepts used. The core of the system consists of a series of cascaded but independent components, partly enhanced with machine learning techniques. The raw input is first processed by a simple recursive descent parser which recognizes syntactical features (e.g. IP addresses) and is then passed on to a part-of-speech tagger based on a hidden Markov model. Applying regular expression patterns to the tagged words is used to combine them to basic word groups (e.g. noun groups), which are subsequently semantically analyzed. The final step is the construction of the output events by a rule based event constructor. All components are implemented in Haskell, a purely functional programming language. Some of the components developed during this thesis, especially the part-of-speech tagger, are general natural language processing tools and can be applied to other domains.
I'm interested in parallel and distributed programming and related technologies such as software transactional memory. Below is a (non-comprehensive) list of software that I have written.
- I extended Haskell's Cabal, for using a "world" file to keep track of installed packages. (Now part of the main distribution.)
- data dispersal: an implementation of an (m,n)-threshold information dispersal scheme that is space-optimal.
- secret sharing: an implementation of a secret sharing scheme that provides information-theoretic security.
- dice-entropy: a library that provides cryptographically secure dice rolls implemented by bit-efficient rejection sampling.
- TSkipList: a data structure with range-query support for software transactional memory.
- stm-io-hooks: An extension of Haskell's Software Transactional Memory (STM) monad with commit and retry IO hooks.
- Mathgenealogy: Visualize your (academic) genealogy! A program for extracting data from the Mathematics Genealogy project.
- In my master thesis I developed a system for automatically constructing events out of log files produced by various system programs. One of the core components of my work was a part-of-speech (POS) tagger, which assigns word classes (e.g. noun, verb) to the previously parsed tokens of the log file. To cope with noisy input data, I modeled the POS tagger as a hidden Markov model. I developed (and proved the correctness of) a variant of the maximum likelihood estimation algorithm for training the Markov model and smoothing the state transition distributions.