Instantaneous Database Access

The biggest threat to any business is a lack of timely and accurate information. Without all the facts, businesses are pressured to make critical decisions and assess risks and opportunities based largely on guesswork, sometimes resulting in financial losses and missed opportunities. The meteoric rise of Databases (DB) appears to confirm the adage that “information is power”, but the stark reality is that information is useless if one has no way to find what one needs to know. It is more accurate perhaps to state that, “the ability to find information is power”. In this paper we show how Instantaneous Database Access System (IDAS) can make a crucial difference by pulling data together and allowing users to summarise information quickly from all areas of a business organisation.

Automated Response to Query System

SMS (Short Message Service) is now a hugely popular and a very powerful business communication technology for mobile phones. In order to respond correctly to a free form factual question given a large collection of texts, one needs to understand the question at a level that allows determining some of constraints the question imposes on a possible answer. These constraints may include a semantic classification of the sought after answer and may even suggest using different strategies when looking for and verifying a candidate answer. In this paper we focus on various attempts to overcome the major contradiction: the technical limitations of the SMS standard, and the huge number of found information for a possible answer.

Applying Genetic Algorithm In Query Improvement Problem

This paper presents an adaptive method using genetic algorithm to modify user’s queries, based on relevance judgments. This algorithm was adapted for the three well-known documents collections (CISI, NLP and CACM). The method is shown to be applicable to large text collections, where more relevant documents are presented to users in the genetic modification. The algorithm shows the effects of applying GA to improve the effectiveness of queries in IR systems. Further studies are planned to adjust the system parameters to improve its effectiveness. The goal is to retrieve most relevant documents with less number of non-relevant documents with respect to user's query in information retrieval system using genetic algorithm.

Using a Query Expansion Technique to Improve Document Retrieval

Query expansion (QE) is a potentially useful technique to help searchers formulate improved query statements, and ultimately retrieve better search results. The objective of our query expansion technique is to find a suitable additional term. Two query expansion methods are applied in sequence to reformulate the query. Experiments on test collections show that the retrieval effectiveness is considerably higher when the query expansion technique is applied.

On the Equivalence of the Riemann-Liouville and the Caputo Fractional Order Derivatives in Modeling of Linear Viscoelastic Materials

Mathematics Subject Classification: 26A33

Query Enrichment for Image Collections by Reuse of Classification Rules

User queries over image collections, based on semantic similarity, can be processed in several ways. In this paper, we propose to reuse the rules produced by rule-based classifiers in their recognition models as query pattern definitions for searching image collections.

Representing Equivalence Problems for Combinatorial Objects

Methods for representing equivalence problems of various combinatorial objects as graphs or binary matrices are considered. Such representations can be used for isomorphism testing in classification or generation algorithms. Often it is easier to consider a graph or a binary matrix isomorphism problem than to implement heavy algorithms depending especially on particular combinatorial objects. Moreover, there already exist well tested algorithms for the graph isomorphism problem (nauty) and the binary matrix isomorphism problem as well (Q-Extension). ACM Computing Classification System (1998): F.2.1, G.4.

Equivalence Between K-functionals Based on Continuous Linear Transforms

2000 Mathematics Subject Classification: 46B70, 41A10, 41A25, 41A27, 41A35, 41A36, 42A10.