Timothy Clinton Bell
|University of Canterbury
|Outstanding Contribution to Computer Science Education (2018)
|Computer science education, computer music and text compression
|A unifying theory and improvements for existing approaches to text compression (1986)
Timothy Clinton Bellis a New Zealand computer scientist, with interests in computer science education, computer music and text compression. In 2017, it was announced by SIGCSE that Bell would receive the 2018 award for 'Outstanding Contribution to Computer Science Education'.
Bell was educated at Nelson College from 1975 to 1979.He completed his PhD at the University of Canterbury, with a thesis titled A unifying theory and improvements for existing approaches to text compression.
Bell joined the staff and rose to professor and head of department. In parallel with his academic work he has developed Computer Science Unplugged, a system of activities for teaching computer science without computers.The system was actively promoted by Google in 2007.
In the 2024 New Year Honours, Bell was appointed a Companion of the New Zealand Order of Merit, for services to computer science education.
In information technology, lossy compression or irreversible compression is the class of data compression methods that uses inexact approximations and partial data discarding to represent the content. These techniques are used to reduce data size for storing, handling, and transmitting content. The different versions of the photo of the cat on this page show how higher degrees of approximation create coarser images as more details are removed. This is opposed to lossless data compression which does not degrade the data. The amount of data reduction possible using lossy compression is much higher than using lossless techniques.
Golomb coding is a lossless data compression method using a family of data compression codes invented by Solomon W. Golomb in the 1960s. Alphabets following a geometric distribution will have a Golomb code as an optimal prefix code, making Golomb coding highly suitable for situations in which the occurrence of small values in the input stream is significantly more likely than large values.
Prediction by partial matching (PPM) is an adaptive statistical data compression technique based on context modeling and prediction. PPM models use a set of previous symbols in the uncompressed symbol stream to predict the next symbol in the stream. PPM algorithms can also be used to cluster data into predicted groupings in cluster analysis.
Incremental encoding, also known as front compression, back compression, or front coding, is a type of delta encoding compression algorithm whereby common prefixes or suffixes and their lengths are recorded so that they need not be duplicated. This algorithm is particularly well-suited for compressing sorted data, e.g., a list of words from a dictionary.
A dictionary coder, also sometimes known as a substitution coder, is a class of lossless data compression algorithms which operate by searching for matches between the text to be compressed and a set of strings contained in a data structure maintained by the encoder. When the encoder finds such a match, it substitutes a reference to the string's position in the data structure.
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In computer science, an inverted index is a database index storing a mapping from content, such as words or numbers, to its locations in a table, or in a document or a set of documents. The purpose of an inverted index is to allow fast full-text searches, at a cost of increased processing when a document is added to the database. The inverted file may be the database file itself, rather than its index. It is the most popular data structure used in document retrieval systems, used on a large scale for example in search engines. Additionally, several significant general-purpose mainframe-based database management systems have used inverted list architectures, including ADABAS, DATACOM/DB, and Model 204.
Grammar-based codes or Grammar-based compression are compression algorithms based on the idea of constructing a context-free grammar (CFG) for the string to be compressed. Examples include universal lossless data compression algorithms. To compress a data sequence , a grammar-based code transforms into a context-free grammar . The problem of finding a smallest grammar for an input sequence is known to be NP-hard, so many grammar-transform algorithms are proposed from theoretical and practical viewpoints. Generally, the produced grammar is further compressed by statistical encoders like arithmetic coding.
Jeffrey Scott Vitter is a U.S. computer scientist and academic administrator. Born in 1955 in New Orleans, Vitter has served in several senior higher education administration posts. He is a former chancellor of the University of Mississippi. He assumed the chancellor position on January 1, 2016. His formal investiture to the chancellorship took place on November 10, 2016, at the University of Mississippi's Oxford Campus.
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The package-merge algorithm is an O(nL)-time algorithm for finding an optimal length-limited Huffman code for a given distribution on a given alphabet of size n, where no code word is longer than L. It is a greedy algorithm, and a generalization of Huffman's original algorithm. Package-merge works by reducing the code construction problem to the binary coin collector's problem.
The Calgary corpus is a collection of text and binary data files, commonly used for comparing data compression algorithms. It was created by Ian Witten, Tim Bell and John Cleary from the University of Calgary in 1987 and was commonly used in the 1990s. In 1997 it was replaced by the Canterbury corpus, based on concerns about how representative the Calgary corpus was, but the Calgary corpus still exists for comparison and is still useful for its originally intended purpose.
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Ian Hugh Witten was a computer scientist at the University of Waikato, New Zealand. He was a Chartered Engineer with the Institute of Electrical Engineers.
The Outstanding Contribution to Computer Science Education award is a prize granted by the Association for Computing Machinery (ACM) Special Interest Group (SIG) on Computer science education (SIGCSE). Outstanding contributions can include curriculum design, innovative teaching methods, authorship of textbooks, and the development of novel teaching tools. The award has been granted annually since 1981. The SIGCSE website contains more information about the awardees.