Emmanuel J. Yannakoudakis

The rules of spelling errors

Abstract This paper demonstrates that the vast majority of spelling errors follow specific rules which are based on phonological and sequential considerations. It introduces and describes three categories of spelling errors (consonantal, vowel and sequential) and presents the results of the analysis of 1377 spelling error forms.

An intelligent spelling error corrector

Abstract This paper describes an intelligent spelling error correction system for use in a word processing environment. The system employs a dictionary of 93,769 words and provided the intended word is in the dictionary it identifies 80 to 90% of spelling and typing errors.

The generation and use of text fragments for data compression

Abstract An analysis of 31,369 bibliographic titles was carried out to obtain statistics on frequently occurring character groups to increase the effective character set with the aim of estimating possible compression factors for text. It was found that a common set could be used to obtain compression ranging between 30 and 53% over a wide variety of original text.

An insight into the entropy and redundancy of the English dictionary

The inherent statistical characteristics, including the economy, entropy, and redundancy, of a very large set containing 93681 words from the Shorter Oxford English Dictionary is investigated. Analytical n-gram statistics are also presented for applications in natural language understanding, text processing, test compression, error detection and correction, and speech synthesis and recognition. Experimental results show how the distribution of n-grams in the dictionary varies from the ideal as n increases from 2 to 5, that is, from bigrams to pentagrams; it is shown that the corresponding redundancy increases from 0.1067 to 0.3409. The results are of interest because, (1) the dictionary provides a finite list for deterministic analyses, (2) each entry (word) appears once, compared to free-running text where words are repeated, and (3) all entries, even rarely occurring ones, have equal weight. >

A new framework for dynamically evolving database environments

This paper describes research work carried out with the aim to investigate dynamically evolving database environments and corresponding schemata, allowing storage and manipulation of variable length data, a variable number of fields per record, variable length records, manipulation of authority records and links between records and fields, and dynamically defined objects (relations in the traditional sense). The paper proposes a new framework for the definition of a unified schema that eliminates completely the need for reorganisation at both logical and internal levels. Retrieval of data is optimised through self‐contained storage chunks that also vary dynamically.

CUDL language semantics: Updating FDB data

The semantics for data manipulation of the database language CUDL - Conceptual Universal Database Language - designed to manage dynamic database environments, are presented. This language conforms to the FDB (Frame DataBase) data model, offering a simple, easy and efficient platform for the use of the FDB model. Otherwise the management and operation of FDB data is laborious and time-consuming and it requires from the user a very good acquaintance of the proposed model, the structures and organisation of it as well as the processes of the management of elements that compose it. In this paper we present in depth the semantics of the way of handling the data, in order to search and transform information, in an FDB data source. We present the analysis of simple and complex cases that led us to synthesize valid and simple semantic rules that determine the data manipulation operations. The more sophisticated and demanding constructs, used in the language, for query specification, query processing and object manipulation are discussed and evaluated.

A domain-oriented approach to improve the user-friendliness of SQL

Abstract In this paper we propose a domain-oriented predicate in SQL (Structures Query Language), which can help reduce the complexity of the formulation of some types of queries and therefore improve the readability and user-friendliness of SQL. This approach enables the programmer to express SQL logic in human-like terms while at the same time keep the source code short. Though the predicate proposed is new in syntax, it can nevertheless be transformed to standard SQL using an algorithm which is presented here. Thus, users are provided with a more friendly version of SQL without having to modify current standard SQL programs.

An efficient file structure for specialized dictionaries and other “lumpy” data

Abstract There are many cases where it is necessary to store sets of data that are variable in length, and to search these in order to satisfy requests for subsets with a common characteristic. This article presents a file structure that holds an integrated English dictionary used to locate clusters of words for presentation to an intelligent spelling error correction system. Although the emphasis has been on misspelling, the structure presented is capable of handling any other types of lumpy data provided the characteristics used in search requests can be translated into a set of integer numbers.

Deterministic graph exploration for efficient graph sampling

Graph sampling is a widely used procedure in social network analysis, has attracted great interest in the scientific community and is considered as a very powerful and useful tool in several domains of network analysis. Apart from initial research in this area, which has proposed simple processes such as the classic Random Walk algorithm, Random Node and Random Edge sampling, during the last decade, more advanced graph sampling approaches have been emerged. In this paper, we extensively study the properties of a newly proposed method, the Rank Degree method, which leads to representative graph subgraphs. The Rank Degree is a novel graph exploration method which significantly differs from other existing methods in the literature. The novelty of the Rank Degree lies on the fact that its core methodology corresponds to a deterministic graph exploration; one specific variation corresponds to a number of parallel deterministic traverses that explore the graph. We perform extensive experiments on twelve real-world datasets of a different type, using a variety of measures and comparing our method with Forest Fire, Metropolis Hastings Random Walk and Metropolis Hastings. We provide strong evidence that our approach leads to highly efficient graph sampling; the generated samples preserve several graph properties, to a large extent.

Identifying Influential Spreaders by Graph Sampling

The complex nature of real world networks is a central subject in several disciplines, from Physics to computer science. The complex network dynamics of peers communication and information exchange are specified to a large degree by the most efficient spreaders - the entities that play a central role in various ways such as the viruses propagation, the diffusion of information, the viral marketing and network vulnerability to external attacks. In this paper, we deal with the problem of identifying the influential spreaders of a complex network when either the network is very large or else we have limited computational capabilities to compute global centrality measures. Our approach is based on graph sampling and specifically on Rank Degree, a newly published graph exploration sampling method. We conduct extensive experiments in five real world networks using four centrality metrics for the nodes spreading efficiency. We present strong evidence that our method is highly effective. By sampling 30% of the network and using at least two out of four centrality measures, we can identify more than 80% of the influential spreaders, while at the same time, preserving the original ranking to a large extent.