The Usefulness of Multilevel Hash Tables with Multiple Hash Functions in Large Databases

In this work, attempt is made to select three good hash functions which uniformly distribute hash values that permute their internal states and allow the input bits to generate different output bits. These functions are used in different levels of hash tables that are coded in Java Programming Language and a quite number of data records serve as primary data for testing the performances. The result shows that the two-level hash tables with three different hash functions give a superior performance over one-level hash table with two hash functions or zero-level hash table with one function in term of reducing the conflict keys and quick look-up for a particular element. The result assists to reduce the complexity of join operation in query language from O(n2) to O(1) by placing larger query result, if any, in multilevel hash tables with multiple hash functions and generate shorter query result

PARAMETER VARIATION FOR LINEAR EQUATION SOLVER USING GENETIC ALGORITHM

Genetic Algorithm has been successfully applied for solving systems of Linear Equations; however the effects of varying the various Genetic Algorithms parameters on the GA systems of Linear Equations solver have not been investigated. Varying the GA parameters produces new and exciting information on the behaviour of the GA Linear Equation solver. In this paper,  a general introduction on the Genetic Algorithm, its application on finding solutions to the Systems of Linear equation as well as the effects of varying the Population size and Number of Generation is presented. The genetic algorithm simultaneous linear equation solver program was run several times using different sets of simultaneous linear equation while varying the population sizes as well as the number of generations in order to observe their effects on the solution generation. It was observed that small population size does not produce perfect solutions as fast as when large population size is used and small or large number of generations did not really have much impact on the attainment of perfect solution as much as population size.

PERFORMANCE APPRAISAL OF TREAP AND HEAP SORT ALGORITHMS

The task of storing items to allow for fast access to an item given its key is an ubiquitous problem in many organizations. Treap as a method uses key and priority for searching in databases. When the keys are drawn from a large totally ordered set, the choice of storing the items is usually some sort of search tree. The simplest form of such tree is a binary search tree. In this tree, a set X of n items is stored at the nodes of a rooted binary tree in which some item y ϵ X is chosen to be stored at the root of the tree. Heap as data structure is an array object that can be viewed as a nearly complete binary tree in which each node of the tree corresponds to an element of the array that stores the value in the node. Both algorithms were subjected to sorting under the same experimental environment and conditions. This was implemented by means of threads which call each of the two methods simultaneously. The server keeps records of individual search time which was the basis of the comparison. It was discovered that treap was faster than heap sort in sorting and searching for elements using systems with homogenous properties.   &nbsp

RSA ENCRYPTION ALGORITHM AUGUMENTED WITH BIT-STUFFING TECHNIQUE FOR DATA SECURITY

Data transmission through the internet applications is growing very fast, and this continuous growth demands for new network bandwidth and data security. Encryption plays a major role in security of information systems and internet based applications. In this study, the RSA algorithm was modified with bit-stuffing technique to improve the protection and security of confidential data while in transits or in storage. Our modified algorithm, RSA Bit-stuffed, was implemented and compared with the modified Ron Divest Code4 and the modified RSA in MATLAB using time complexity and avalanche effect as performance metrics. The experimental results showed that our augmented bit-insertion technique increased the time complexity against different attacks, boost the randomness of encrypted messages, and also improve security of encryption keys with bit-length lower than that of the standard RSA. &nbsp

AN ONTOLOGY-BASED KNOWLEDGE REPRESENTATION USING ANALYTIC HIERARCHY PROCESS FOR ENHANCING SELECTION OF PRODUCT PREFERENCES

Product alternatives, which emerges from large number of websites during searching, accounts for some hesitation experienced by customers in selecting satisfying product. As a result, making useful decision with many trade-off considerations becomes a major cause of such problem. Several approaches have been employed for product selection such as, fuzzy logic, Neuro-fuzzy, and weighted least square. However, these could not solve the problem of inconsistency and irrelevant judgement that occur in decision making. In this study, Ontology-based Analytic Hierarchy Process (AHP) was used for enhancing selection of product preferences. The model involved three fundamental components: product gathering, selection and decision making. Ontology Web Language (OWL) was utilized to define ontology in expressing product information gathering in a standard and structured manner for the purpose of interoperability while AHP was employed in making optimal choices. The procedure accepts customers’ perspectives as inputs which are classified into criteria and sub-criteria. Owl was created to foster customers’ interaction and priority estimation tool for AHP in order to generate the consistency ratio of individual judgements. The model was benchmarked with Geometric Mean (GM), Eigenvector (EV), Normalized Column Sum (NCS) Weighted Least Square (WLS) and Fuzzy Preference Programming (FPP). First and second order total deviations and violation rate were the performance parameters evaluation with AHP. The results showed that the minimum and maximum units of products are 2,452and 3,574, respectively. These implied that the proposed model was consistent, relevant and reflected a non-violation of judgment in selection of product preferences. &nbsp

INVESTIGATION OF FACTORS AFFECTING CLOUD COMPUTING ADOPTION IN NIGERIA

Cloud computing is a viable alternative for meeting the technological needs of many  enterprises with the benefits of instantaneous computing resource fulfillment, technology expenditures at lower costs, common technology platforms that can facilitate standardization and decreased  need for internal technology support personnel. This paper examined the behavioral intention to adopt cloud computing services in large and small organization using an Enhanced Technology Acceptance Model (ETAM). The aim is to investigate the factors affecting cloud computing adoption in Nigeria. The model includes variables that other research has found related to adoption of new computing services and technologies. Regression Analysis was then deployed to test the research hypotheses. The result of regression analysis revealed that attitude and adopters ability to use cloud computing (self-efficacy) were better predictor of intention; perceived usefulness and perceived ease of use of cloud computing were better predictor of attitude; perceived ease of use and the relevant of cloud computing to adopters’ work (job relevance) were the predictor of perceived usefulness.

BIG DATA AND REAL ESTATE: A REVIEW OF LITERATURE

The concept of big data though relatively new has brought a lot of solutions to modern day challenges. Many authors, particularly in developed countries, have adopted the concept in tackling the numerous challenges unfolding in the real estate profession. However, most of the findings from these authors are on individual bases and as such, there is a need to reach a general consensus on the relevance of big data to the real estate profession. The review shows the impact of big data to include digitization of records, information on user preferences, sensor information on the urban environment and sensor information on movement. The paper concludes that the relevance of big data to the real estate profession cannot be over-emphasised