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Text classification is still an area growing as text sizes grow rapidly with the information and internet revolution. The majority of conventional text classification approaches are hierarchical training, where the classification system explicitly differentiates between groups. However, this is different from the nature of language processing which suffers from ambiguity and lack of clarity. That is, the instance could be of more than one class. This paper handles the problem of text classification by applying a novel classification method based on sequence alignment with simple fuzzy concepts. The experiments showed expected performance compared to other conventional classifications of natural languages.

Managing temporal data gets increased demand for many significant areas such as scientific and financial applications. This paper proposes a new index (TOIX), which is designed in order to provide more efficient evaluation of temporal queries on XML documents. The index lies on mapping the twigs into temporal objects and then using these objects instead for answering the query. An improvement of the naive algorithm using a B-tree as well. Finally, a set of conducted experiments were performed and showed that our proposed algorithm outperforms the state of the art indexing algorithms in certain cases.

Different models have been proposed recently for representing temporal data, tracking historical information and retrieving temporal queries results efficiently. We consider the problem of indexing temporal XML documents. In particular, we propose an indexing scheme that uses a summary structure and a matrix that captures the structural relationships as well as time intervals inside a temporal XML document. We introduce an algorithm to efficiently process all types of temporal queries with any depth using our newly proposed index. We show that our proposed index out-performs the state of the art indices in terms of both query processing time and support for different temporal query types.

Inspired by the great success of information retrieval (IR) style keyword search on the web, keyword search on XML has emerged recently. Existing methods cannot resolve challenges addressed by using keyword search in Temporal XML documents. We propose a way to evaluate temporal keyword search queries over Temporal XML documents. Moreover, we propose a new ranking method based on the time-aware IR ranking methods to rank temporal keyword search queries results. Extensive experiments have been conducted to show the effectiveness of our approach.