Ahmed M. Gadallah

Flexible Querying of Relational Databases: Fuzzy Set Based Approach

This paper presents a flexible fuzzy-based approach for querying relational databases. Although, many fuzzy query approaches have been proposed, there is a need for a more flexible, simple and human-like query approach. Most of previously proposed fuzzy query approaches have a disadvantage that they interpret any fuzzy query statement into a crisp query statement then evaluate the resulted tuples to compute their matching degrees to the fuzzy query. The main objective of the proposed fuzzy query approach of this paper is to overcome the above disadvantage by evaluating each tuple directly through the use of stored database objects namely packages, procedures and functions. Consequently, the response time of executing a fuzzy query statement will be reduced. This proposed approach makes it easy to use fuzzy linguistic values in all clauses of a select statement. The added value of this proposed approach is to accelerate the execution of fuzzy query statements.

Fuzzy query approach for crops planting dates adaptation with climate changes

Commonly, climate changes in both of temperature and humidity have significant impacts on agriculture. Obviously, the ongoing changes in such climate variables affect directly the plantation dates of most crops. In other words, many crops become not suitable to plant in its traditional places at the traditional plantation dates. Instead, such crops become more suitable to plant at other new or adjusted periods that were not suitable before. This paper provides a fuzzy query approach for adapting crops plantation dates with climate changes in a given place. The proposed approach consists of three phases, one for fuzzy clustering of the year days, the second one for defining the crop suitability membership functions and the third for fuzzy-based selection of suitable plantation periods. The proposed approach based mainly on the spatial agro-climatic database. It proved that more of traditional plantation dates become not suitable and it provides new and/or adjusted more suitable periods with a suitability degree for each one. The proposed approach has been applied on the squash crop respecting climate data changes of temperature and humidity in Alexandria governorate in Egypt. In consequent, the final results are presented to a set of agriculture researcher in the Agriculture research center (ARC) and they were agreed with the results.

Semantic Information Retrieval Model: Fuzzy Ontology Approach

The paper proposes a multi-view information retrieval model. The model has the ability to deal with the multi-field topics problem using a predefined multi-field or multi-view fuzzy ontology. Respecting the natural relationship between concepts and terms, the model enhances the recall measure compared with previously proposed fuzzy ontology-based information retrieval models. It also proposes a ranking algorithm that ranks a set of relevant documents according to some criteria such as their relevance degree, confidence degree, and updating degree. General Terms Algorithms

Fuzzy based approach for discovering crops plantation knowledge from huge agro-climatic data respecting climate changes

Climate change has noticeable significant impacts on development of most countries because of its direct negative effect on the production and revenue of most crops plantation process. In reality, the ongoing changes in climate variables affect the suitability of planting some crops in their traditional places at their traditional dates. Furthermore, the availability of huge volumes of agro-climatic data that almost incorporates uncertainty increases the complexity of managing and discovering the crops suitable plantation patterns from such data. Accordingly, a need appeared to an efficient approach to handle such uncertainty and to exploit such huge data volume to manage the crops plantation process accurately. This paper presents a fuzzy approach based on Hadoop for discovering crops plantation knowledge from the agro-climatic historical database of the years from 1983 to 2016 of Egypt. Commonly, the proposed approach provides a set of scenarios for plantation dates of each crop with a suitability degree for each scenario. Also, it helps managing crops plantation process from some other aspects such as harvesting dates, candidate diseases and follow up for crops water requirements respecting the data streaming of the prevailing weather data. The proposed approach has been tested on a set of crops with cooperation of researchers from Cairo University and Agricultural Research Center. The results show the added value of the proposed approach against other works respecting the more suitable crops plantation dates, harvesting dates, expected diseases and follow up for crops water requirements. Furthermore, the proposed approach benefits from Hadoop framework capabilities of handling huge amounts of data streamed from weather stations.

An Enhanced Ant Colony-based Approach for Query Optimization

One of the mandatory processes for all those types of applications is the inquiry process of the stored huge amounts of data. Such process is either a predefined or an ad-hoc query. From the logical point of view, the query process depends mainly on many algebraic operations, including selection, projection and joining operations. The most important one of them is the join operation, which represents the key factor of the inquiry process to retrieve the related information from different data tables. Many approaches have been proposed aiming to reduce the cost of join operations. Yet, there is still a need for more query optimizing processes in order to reduce the query response time. This paper proposes an enhanced optimal query processing approach for inner and outer join, where the proposed model exploits an adopted Ant Colony Optimization.

Ant Colony-Based Approach for Query Optimization

Many approaches have been proposed aiming to reduce the cost of join operations. Such join operations represent the key factor of the inquiry process to retrieve related information from different data tables in large relational databases. Yet, there is still a need for more intelligent query optimizing approaches to reduce the response time of query execution. This paper proposes an approach for reaching optimal query access plans for complex relational database queries including a set of join operations. The proposed approach is based on ant colony optimization technique to benefit from its ability of parallel search over several constructive computational threads which aims to reach an optimal query access plan. A comparative study shows the added value of the proposed approach.

Enhanced Algorithms for Fuzzy Formal Concepts Analysis

Fuzzy formal concept analysis (FFCA) is a generalized form of traditional formal concept analysis (FCA) that exploits fuzzy set theory to process uncertain data efficiently. Generally, most real world applications incorporate uncertain data at least for some extent. Consequently, they need reliable approaches to discover potentially useful non-trivial knowledge. Commonly, FFCA aims mainly to reach such knowledge in form of fuzzy formal concepts. It is used widely in data analysis tasks, association rule discovery and extraction of essential ontology components. This paper proposes two enhanced algorithms for extracting fuzzy formal concepts based on fuzzy sets of objects and crisp sets of attributes. Such kind of FFCA best suits Ontology construction and association rule mining tasks. Commonly, extracting fuzzy concepts is considered the most time consuming process in FCA and FFCA. So, the proposed enhanced algorithms aim mainly to reduce the complexity and extraction time of fuzzy formal concepts’ extraction process. The first enhanced algorithm best fits in case of the existence of symmetric correlated attributes. On the other hand, the second enhanced algorithm generally reduces the complexity as a result of reducing total number of generated fuzzy concepts. It works extremely better when the number of distinct intents of objects is relatively smaller. The results of testing the proposed enhanced algorithms show their added value.

Term Weighting: A Multi-View Fuzzy Ontology Based Approach

The paper proposes a term weighting algorithm for research papers. It weighs a research papers annotated keywords according to a certain view. It uses a predefined multi-view fuzzy ontology and a stemmer NLP tool. The proposed algorithm is tested and results are compared with another ontology-based term weighting algorithm. The tests show that it enhances the resulted weights accuracy and decreases the execution time.

An Enhanced Fuzzy Information Retrieval Model Based on Linguistics

The paper proposes a linguistic based fuzzy ontology information retrieval model. The model deals with linguistic based queries in multi domains. Such linguistics are user defined, reflecting his subjective view. The model also proposes a ranking algorithm that ranks the set of relevant documents according to some criteria such as their relevance degree, confidence degree, and updating degree.

Fuzzy Query Approach for Crops Planting Dates Optimization Based on Climate Data

Climate change is considered one of the most environmental phenomena of interest in the world nowadays. It affects many aspects of our life. One of the most affected aspects by climate change is agriculture. It is obvious that the ongoing changes in climate variables like temperature affects the suitability of crops plantation. That is, it make some crops became not suitable to plant in its traditional places at its traditional dates while it became more suitable to plant in other new places and/or dates. Based on the available historical spatial agro-climatic database, this paper presents a fuzzy query approach for discovering the new more suitable planting dates of crops in a given governorate of Egypt. The proposed approach consists of three phases, one phase for fuzzy clustering of the year days according to climate data, the second phase for defining crop suitability fuzzy membership functions and the last phase for fuzzy selection and optimization of suitable periods to plant a given crop like squash in a given governorate like Alexandria. The proposed approach proved that most of traditional plantation dates of squash in Alexandria become not suitable compared with the new discovered more suitable periods with a suitability measure for each period.