Ensuring the correctness of answers to substring queries has not been a concern for consumers working within the traditional confines of their own organisational infrastructure. This is due to the fact that organisati...Ensuring the correctness of answers to substring queries has not been a concern for consumers working within the traditional confines of their own organisational infrastructure. This is due to the fact that organisations generally trust their handling of their own data hosted on their own servers and networks. With cloud computing however, where both data and processing are delegated to unknown servers, guarantees of the correctness of queries need to be available. The verification of the results of substring searches has not been given much focus to date within the wider scope of data and query, verification. We present a verification scheme for existential substring searc, hes on text files, which is the first of its kind to satisfy the desired properties of authenticity, completeness, and freshness. The scheme is based on suffix arrays, Merkle hash trees and cryptographic hashes to provide strong guarantees of correctness for the consumer, even in fully untrusted environments. We provide a description of our scheme, along with the results of experiments conducted on a fully-working prototype.展开更多
This paper proposes a non-segmented document clustering method using self-organizing map (SOM) and frequent max substring technique to improve the efficiency of information retrieval. SOM has been widely used for docu...This paper proposes a non-segmented document clustering method using self-organizing map (SOM) and frequent max substring technique to improve the efficiency of information retrieval. SOM has been widely used for document clustering and is successful in many applications. However, when applying to non-segmented document, the challenge is to identify any interesting pattern efficiently. There are two main phases in the propose method: preprocessing phase and clustering phase. In the preprocessing phase, the frequent max substring technique is first applied to discover the patterns of interest called Frequent Max substrings that are long and frequent substrings, rather than individual words from the non-segmented texts. These discovered patterns are then used as indexing terms. The indexing terms together with their number of occurrences form a document vector. In the clustering phase, SOM is used to generate the document cluster map by using the feature vector of Frequent Max substrings. To demonstrate the proposed technique, experimental studies and comparison results on clustering the Thai text documents, which consist of non-segmented texts, are presented in this paper. The results show that the proposed technique can be used for Thai texts. The document cluster map generated with the method can be used to find the relevant documents more efficiently.展开更多
Modern applications require large databases to be searched for regions that are similar to a given pattern. The DNA sequence analysis, speech and text recognition, artificial intelligence, Internet of Things, and many...Modern applications require large databases to be searched for regions that are similar to a given pattern. The DNA sequence analysis, speech and text recognition, artificial intelligence, Internet of Things, and many other applications highly depend on pattern matching or similarity searches. In this paper, we discuss some of the string matching solutions developed in the past. Then, we present a novel mathematical model to search for a given pattern and it’s near approximates in the text.展开更多
文摘Ensuring the correctness of answers to substring queries has not been a concern for consumers working within the traditional confines of their own organisational infrastructure. This is due to the fact that organisations generally trust their handling of their own data hosted on their own servers and networks. With cloud computing however, where both data and processing are delegated to unknown servers, guarantees of the correctness of queries need to be available. The verification of the results of substring searches has not been given much focus to date within the wider scope of data and query, verification. We present a verification scheme for existential substring searc, hes on text files, which is the first of its kind to satisfy the desired properties of authenticity, completeness, and freshness. The scheme is based on suffix arrays, Merkle hash trees and cryptographic hashes to provide strong guarantees of correctness for the consumer, even in fully untrusted environments. We provide a description of our scheme, along with the results of experiments conducted on a fully-working prototype.
文摘This paper proposes a non-segmented document clustering method using self-organizing map (SOM) and frequent max substring technique to improve the efficiency of information retrieval. SOM has been widely used for document clustering and is successful in many applications. However, when applying to non-segmented document, the challenge is to identify any interesting pattern efficiently. There are two main phases in the propose method: preprocessing phase and clustering phase. In the preprocessing phase, the frequent max substring technique is first applied to discover the patterns of interest called Frequent Max substrings that are long and frequent substrings, rather than individual words from the non-segmented texts. These discovered patterns are then used as indexing terms. The indexing terms together with their number of occurrences form a document vector. In the clustering phase, SOM is used to generate the document cluster map by using the feature vector of Frequent Max substrings. To demonstrate the proposed technique, experimental studies and comparison results on clustering the Thai text documents, which consist of non-segmented texts, are presented in this paper. The results show that the proposed technique can be used for Thai texts. The document cluster map generated with the method can be used to find the relevant documents more efficiently.
文摘Modern applications require large databases to be searched for regions that are similar to a given pattern. The DNA sequence analysis, speech and text recognition, artificial intelligence, Internet of Things, and many other applications highly depend on pattern matching or similarity searches. In this paper, we discuss some of the string matching solutions developed in the past. Then, we present a novel mathematical model to search for a given pattern and it’s near approximates in the text.
