Search results for: IF Rough Relational Database

Authors: Sandeep Chandana, Rene V. Mayorga

Abstract:

The paper presents a new hybridization methodology involving Neural, Fuzzy and Rough Computing. A Rough Sets based approximation technique has been proposed based on a certain Neuro – Fuzzy architecture. A New Rough Neuron composition consisting of a combination of a Lower Bound neuron and a Boundary neuron has also been described. The conventional convergence of error in back propagation has been given away for a new framework based on 'Output Excitation Factor' and an inverse input transfer function. The paper also presents a brief comparison of performances, of the existing Rough Neural Networks and ANFIS architecture against the proposed methodology. It can be observed that the rough approximation based neuro-fuzzy architecture is superior to its counterparts.

Keywords: Boundary neuron, neuro-fuzzy, output excitation factor, RANFIS, rough approximation, rough neural computing.

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Authors: Chanchai Supaartagorn

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Structured Query Language (SQL) is the standard de facto language to access and manipulate data in a relational database. Although SQL is a language that is simple and powerful, most novice users will have trouble with SQL syntax. Thus, we are presenting SQL generator tool which is capable of translating actions and displaying SQL commands and data sets simultaneously. The tool was developed based on Model-View-Controller (MVC) pattern. The MVC pattern is a widely used software design pattern that enforces the separation between the input, processing, and output of an application. Developers take full advantage of it to reduce the complexity in architectural design and to increase flexibility and reuse of code. In addition, we use White-Box testing for the code verification in the Model module.

Keywords: MVC, relational database, SQL, White-Box testing.

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Authors: Rajeev Kaula

Abstract:

Business rules and data warehouse are concepts and technologies that impact a wide variety of organizational tasks. In general, each area has evolved independently, impacting application development and decision-making. Generating knowledge from data warehouse is a complex process. This paper outlines an approach to ease import of information and knowledge from a data warehouse star schema through an inference class of business rules. The paper utilizes the Oracle database for illustrating the working of the concepts. The star schema structure and the business rules are stored within a relational database. The approach is explained through a prototype in Oracle-s PL/SQL Server Pages.

Keywords: Business Rules, Data warehouse, PL/SQL ServerPages, Relational model, Web Application.

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Authors: Reza Ghaemi, Amin Milani Fard, Hamid Tabatabaee, Mahdi Sadeghizadeh

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Due to new distributed database applications such as huge deductive database systems, the search complexity is constantly increasing and we need better algorithms to speedup traditional relational database queries. An optimal dynamic programming method for such high dimensional queries has the big disadvantage of its exponential order and thus we are interested in semi-optimal but faster approaches. In this work we present a multi-agent based mechanism to meet this demand and also compare the result with some commonly used query optimization algorithms.

Keywords: Information retrieval systems, list fusion methods, document score, multi-agent systems.

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Authors: H. M. Al-Qassem

Abstract:

We obtain appropriate sharp estimates for rough oscillatory integrals. Our results represent significant improvements as well as natural extensions of what was known previously.

Keywords: Oscillatory singular integral, Rough kernel, Singular integral, L^{p} boundedness.

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Authors: K. M. Alsager, N. O. Alshehri

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In this paper, we proposed the notion of single valued neutrosophic hesitant fuzzy rough set, by combining single valued neutrosophic hesitant fuzzy set and rough set. The combination of single valued neutrosophic hesitant fuzzy set and rough set is a powerful tool for dealing with uncertainty, granularity and incompleteness of knowledge in information systems. We presented both definition and some basic properties of the proposed model. Finally, we gave a general approach which is applied to a decision making problem in disease diagnoses, and demonstrated the effectiveness of the approach by a numerical example.

Keywords: Single valued neutrosophic hesitant set, single valued neutrosophic hesitant relation, single valued neutrosophic hesitant fuzzy rough set, decision making method.

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Authors: Georgia Garani

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A generalised relational data model is formalised for the representation of data with nested structure of arbitrary depth. A recursive algebra for the proposed model is presented. All the operations are formally defined. The proposed model is proved to be a superset of the conventional relational model (CRM). The functionality and validity of the model is shown by a prototype implementation that has been undertaken in the functional programming language Miranda.

Keywords: nested relations, recursive algebra, recursive nested operations, relational data model.

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Authors: Chen Wu, Komal Narejo, Dandan Li

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Some extended rough set models in incomplete information system cannot distinguish the two objects that have few known attributes and more unknown attributes; some cannot make a flexible and accurate discrimination. In order to solve this problem, this paper suggests an improved limited tolerance rough set model using two thresholds to control what two objects have a relationship between them in limited tolerance relation and to classify objects. Our practical study case shows the model can get fine and reasonable decision results.

Keywords: Decision rule, incomplete information system, limited tolerance relation, rough set model.

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Authors: P. Ashok, G. M. Kadhar Nawaz

Abstract:

Rough set theory is used to handle uncertainty and incomplete information by applying two accurate sets, Lower approximation and Upper approximation. In this paper, the rough clustering algorithms are improved by adopting the Similarity, Dissimilarity–Similarity and Entropy based initial centroids selection method on three different clustering algorithms namely Entropy based Rough K-Means (ERKM), Similarity based Rough K-Means (SRKM) and Dissimilarity-Similarity based Rough K-Means (DSRKM) were developed and executed by yeast dataset. The rough clustering algorithms are validated by cluster validity indexes namely Rand and Adjusted Rand indexes. An experimental result shows that the ERKM clustering algorithm perform effectively and delivers better results than other clustering methods. Outlier detection is an important task in data mining and very much different from the rest of the objects in the clusters. Entropy based Rough Outlier Factor (EROF) method is seemly to detect outlier effectively for yeast dataset. In rough K-Means method, by tuning the epsilon (ᶓ) value from 0.8 to 1.08 can detect outliers on boundary region and the RKM algorithm delivers better results, when choosing the value of epsilon (ᶓ) in the specified range. An experimental result shows that the EROF method on clustering algorithm performed very well and suitable for detecting outlier effectively for all datasets. Further, experimental readings show that the ERKM clustering method outperformed the other methods.

Keywords: Clustering, Entropy, Outlier, Rough K-Means, validity index.

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Authors: Yasser F. Hassan

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The reduction or removal of noise in a color image is an essential part of image processing, whether the final information is used for human perception or for an automatic inspection and analysis. This paper describes the modeling system based on the rough neural network model to adaptive cellular automata for various image processing tasks and noise remover. In this paper, we consider the problem of object processing in colored image using rough neural networks to help deriving the rules which will be used in cellular automata for noise image. The proposed method is compared with some classical and recent methods. The results demonstrate that the new model is capable of being trained to perform many different tasks, and that the quality of these results is comparable or better than established specialized algorithms.

Keywords: Rough Sets, Rough Neural Networks, Cellular Automata, Image Processing.

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Authors: Iftikhar U. Sikder

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This paper illustrates an application of granular computing approach, namely rough set theory in data mining. The paper outlines the formalism of granular computing and elucidates the mathematical underpinning of rough set theory, which has been widely used by the data mining and the machine learning community. A real-world application is illustrated, and the classification performance is compared with other contending machine learning algorithms. The predictive performance of the rough set rule induction model shows comparative success with respect to other contending algorithms.

Keywords: Concept approximation, granular computing, reducts, rough set theory, rule induction.

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Authors: Chen Wu, Xiaohua Hu

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This paper proposes rough set models with three different level knowledge granules in incomplete information system under tolerance relation by similarity between objects according to their attribute values. Through introducing dominance relation on the discourse to decompose similarity classes into three subclasses: little better subclass, little worse subclass and vague subclass, it dismantles lower and upper approximations into three components. By using these components, retrieving information to find naturally hierarchical expansions to queries and constructing answers to elaborative queries can be effective. It illustrates the approach in applying rough set models in the design of information retrieval system to access different granular expanded documents. The proposed method enhances rough set model application in the flexibility of expansions and elaborative queries in information retrieval.

Keywords: Incomplete information system, Rough set model, tolerance relation, dominance relation, approximation, decomposition, elaborative query.

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Authors: Richard Elling Moe

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Computer languages are usually lumped together into broad -paradigms-, leaving us in want of a finer classification of kinds of language. Theories distinguishing between -genuine differences- in language has been called for, and we propose that such differences can be observed through a notion of expressive mode. We outline this concept, propose how it could be operationalized and indicate a possible context for the development of a corresponding theory. Finally we consider a possible application in connection with evaluation of language revision. We illustrate this with a case, investigating possible revisions of the relational algebra in order to overcome weaknesses of the division operator in connection with universal queries.

Keywords: Expressive mode, Computer language species, Evaluation of revision, Relational algebra, Universal database queries

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Authors: N. Soussi, M. Bahaj

Abstract:

The enormous amount of information stored on the web increases from one day to the next, exposing the web currently faced with the inevitable difficulties of research pertinent information that users really want. The problem today is not limited to expanding the size of the information highways, but to design a system for intelligent search. The vast majority of this information is stored in relational databases, which in turn represent a backend for managing RDF data of the semantic web. This problem has motivated us to write this paper in order to establish an effective approach to support semantic transformation algorithm for SPARQL queries to SQL queries, more precisely SPARQL SELECT queries; by adopting this method, the relational database can be questioned easily with SPARQL queries maintaining the same performance.

Keywords: RDF, Semantic Web, SPARQL, SPARQL Query Transformation, SQL.

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Authors: Mihai Rebenciuc, Simona Mihaela Bibic

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Branch of modern mathematics, graphs represent instruments for optimization and solving practical applications in various fields such as economic networks, engineering, network optimization, the geometry of social action, generally, complex systems including contemporary urban problems (path or transport efficiencies, biourbanism, & c.). In this paper is studied the interconnection of some urban network, which can lead to a simulation problem of a digraph through another digraph. The simulation is made univoc or more general multivoc. The concepts of fragment and atom are very useful in the study of connectivity in the digraph that is simulation - including an alternative evaluation of k- connectivity. Rough set approach in (bi)digraph which is proposed in premier in this paper contribute to improved significantly the evaluation of k-connectivity. This rough set approach is based on generalized rough sets - basic facts are presented in this paper.

Keywords: (Bi)digraphs, rough set theory, systems of interacting agents, complex systems.

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Authors: Mehdi Emadi, Masoud Rahgozar, Adel Ardalan, Alireza Kazerani, Mohammad Mahdi Ariyan

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The volume of XML data exchange is explosively increasing, and the need for efficient mechanisms of XML data management is vital. Many XML storage models have been proposed for storing XML DTD-independent documents in relational database systems. Benchmarking is the best way to highlight pros and cons of different approaches. In this study, we use a common benchmarking scheme, known as XMark to compare the most cited and newly proposed DTD-independent methods in terms of logical reads, physical I/O, CPU time and duration. We show the effect of Label Path, extracting values and storing in another table and type of join needed for each method's query answering.

Keywords: XML Data Management, XPath, DTD-IndependentXML Data

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Authors: Mehdi Emadi, Masoud Rahgozar, Adel Ardalan, Alireza Kazerani, Mohammad Mahdi Ariyan

Abstract:

The volume of XML data exchange is explosively increasing, and the need for efficient mechanisms of XML data management is vital. Many XML storage models have been proposed for storing XML DTD-independent documents in relational database systems. Benchmarking is the best way to highlight pros and cons of different approaches. In this study, we use a common benchmarking scheme, known as XMark to compare the most cited and newly proposed DTD-independent methods in terms of logical reads, physical I/O, CPU time and duration. We show the effect of Label Path, extracting values and storing in another table and type of join needed for each method-s query answering.

Keywords: XML Data Management, XPath, DTD-Independent XML Data.

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Authors: Kashif Bilal, Uzair Muhammad, Atif Khan, M. Nasir Khan

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Machine Translation, (hereafter in this document referred to as the "MT") faces a lot of complex problems from its origination. Extracting multiword expressions is also one of the complex problems in MT. Finding multiword expressions during translating a sentence from English into Urdu, through existing solutions, takes a lot of time and occupies system resources. We have designed a simple relational data approach, in which we simply set a bit in dictionary (database) for multiword, to find and handle multiword expression. This approach handles multiword efficiently.

Keywords: Machine Translation, Multiword Expressions, Urdulanguage processing, POS (stands for Parts of Speech) Tagging forUrdu, Expert Systems.

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Authors: Surat Srinoy, Werasak Kurutach, Witcha Chimphlee, Siriporn Chimphlee

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One main drawback of intrusion detection system is the inability of detecting new attacks which do not have known signatures. In this paper we discuss an intrusion detection method that proposes independent component analysis (ICA) based feature selection heuristics and using rough fuzzy for clustering data. ICA is to separate these independent components (ICs) from the monitored variables. Rough set has to decrease the amount of data and get rid of redundancy and Fuzzy methods allow objects to belong to several clusters simultaneously, with different degrees of membership. Our approach allows us to recognize not only known attacks but also to detect activity that may be the result of a new, unknown attack. The experimental results on Knowledge Discovery and Data Mining- (KDDCup 1999) dataset.

Keywords: Network security, intrusion detection, rough set, ICA, anomaly detection, independent component analysis, rough fuzzy .

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Authors: Properties, Approximation Distribution Reductions in Multigranulation Rough Set Model

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Some properties of approximation sets are studied in multi-granulation optimist model in rough set theory using maximal compatible classes. The relationships between or among lower and upper approximations in single and multiple granulation are compared and discussed. Through designing Boolean functions and discernibility matrices in incomplete information systems, the lower and upper approximation sets and reduction in multi-granulation environments can be found. By using examples, the correctness of computation approach is consolidated. The related conclusions obtained are suitable for further investigating in multiple granulation RSM.

Keywords: Incomplete information system, maximal compatible class, multi-granulation rough set model, reduction.

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Authors: L. Tao, T. J. Sun, Z. H. Li

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The knowledge base of welding defect recognition is essentially incomplete. This characteristic determines that the recognition results do not reflect the actual situation. It also has a further influence on the classification of welding quality. This paper is concerned with the study of a rough set based method to reduce the influence and improve the classification accuracy. At first, a rough set model of welding quality intelligent classification has been built. Both condition and decision attributes have been specified. Later on, groups of the representative multiple compound defects have been chosen from the defect library and then classified correctly to form the decision table. Finally, the redundant information of the decision table has been reducted and the optimal decision rules have been reached. By this method, we are able to reclassify the misclassified defects to the right quality level. Compared with the ordinary ones, this method has higher accuracy and better robustness.

Keywords: intelligent decision, rough set, welding defects, welding quality level

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Authors: L. Ludovic, R. Daniel, S-E Tbahriti

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Database management systems that integrate user preferences promise better solution for personalization, greater flexibility and higher quality of query responses. This paper presents a tentative work that studies and investigates approaches to express user preferences in queries. We sketch an extend capabilities of SQLf language that uses the fuzzy set theory in order to define the user preferences. For that, two essential points are considered: the first concerns the expression of user preferences in SQLf by so-called fuzzy commensurable predicates set. The second concerns the bipolar way in which these user preferences are expressed on mandatory and/or optional preferences.

Keywords: Flexible query language, relational database, userpreference.

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Authors: Xun Ge, Jinjin Li, Ying Ge

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Adopting Zakowski-s upper approximation operator C and lower approximation operator C, this paper investigates granularity-wise separations in covering approximation spaces. Some characterizations of granularity-wise separations are obtained by means of Pawlak rough sets and some relations among granularitywise separations are established, which makes it possible to research covering approximation spaces by logical methods and mathematical methods in computer science. Results of this paper give further applications of Pawlak rough set theory in pattern recognition and artificial intelligence.

Keywords: Rough set, covering approximation space, granularitywise separation.

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Authors: Benedetto Manganelli, Beniamino Murgante

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The distinction among urban, periurban and rural areas represents a classical example of uncertainty in land classification. Satellite images, geostatistical analysis and all kinds of spatial data are very useful in urban sprawl studies, but it is important to define precise rules in combining great amounts of data to build complex knowledge about territory. Rough Set theory may be a useful method to employ in this field. It represents a different mathematical approach to uncertainty by capturing the indiscernibility. Two different phenomena can be indiscernible in some contexts and classified in the same way when combining available information about them. This approach has been applied in a case of study, comparing the results achieved with both Map Algebra technique and Spatial Rough Set. The study case area, Potenza Province, is particularly suitable for the application of this theory, because it includes 100 municipalities with different number of inhabitants and morphologic features.

Keywords: Land Classification, Map Algebra, Periurban Fringe, Rough Set, Urban Planning, Urban Sprawl.

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Authors: Chen Wu, Lijuan Wang

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Relation between tolerance class and indispensable attribute and knowledge dependency in rough set model with tolerance relation is explored. After giving definitions and concepts of knowledge dependency and knowledge dependency degree for incomplete information system in tolerance rough set model by distinguishing decision attribute containing missing attribute value or not, the result of maintaining reflectivity, transitivity, augmentation, decomposition law and merge law for complete knowledge dependency is proved. Knowledge dependency degrees (not complete knowledge dependency degrees) only satisfy some laws after transitivity, augmentation and decomposition operations. An algorithm to solve attribute reduction in an incomplete decision table is designed. The correctness is checked by an example.

Keywords: Incomplete information system, rough set, tolerance relation, knowledge dependence, attribute reduction.

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Authors: Sukaesi Marianti

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This study aims to develop the Relational Mobility Scale for the Indonesian population and to investigate its psychometric properties. New items of the scale were created taking into account the Indonesian population which consists of two parallel forms (A and A’). This study uses 30 newly orchestrated items while keeping in mind the characteristics of the targeted population. The scale was administered to 433 public high school students in Malang, Indonesia. Construct validity of its factor structure was demonstrated using exploratory factor analysis and confirmatory factor analysis. The result exhibits that he model fits the data, and that the delayed alternate form method shows acceptable result. Results yielded that 21 items of the three-dimensional Relational Mobility Scale is suitable for measuring relational mobility in high school students of Indonesian population.

Keywords: Confirmatory factor analysis, exploratory factor analysis, delayed alternate form, Indonesian population, relational mobility scale.

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Authors: Siyuan Jing, Kun She

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Neighborhood Rough Sets (NRS) has been proven to be an efficient tool for heterogeneous attribute reduction. However, most of researches are focused on dealing with complete and noiseless data. Factually, most of the information systems are noisy, namely, filled with incomplete data and inconsistent data. In this paper, we introduce a generalized neighborhood rough sets model, called VPTNRS, to deal with the problem of heterogeneous attribute reduction in noisy system. We generalize classical NRS model with tolerance neighborhood relation and the probabilistic theory. Furthermore, we use the neighborhood dependency to evaluate the significance of a subset of heterogeneous attributes and construct a forward greedy algorithm for attribute reduction based on it. Experimental results show that the model is efficient to deal with noisy data.

Keywords: attribute reduction, incomplete data, inconsistent data, tolerance neighborhood relation, rough sets

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Authors: Girish Kumar Singh, Sonajharia Minz

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Granular computing deals with representation of information in the form of some aggregates and related methods for transformation and analysis for problem solving. A granulation scheme based on clustering and Rough Set Theory is presented with focus on structured conceptualization of information has been presented in this paper. Experiments for the proposed method on four labeled data exhibit good result with reference to classification problem. The proposed granulation technique is semi-supervised imbibing global as well as local information granulation. To represent the results of the attribute oriented granulation a tree structure is proposed in this paper.

Keywords: Granular computing, clustering, Rough sets, datamining.

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Authors: S. Kulsri, M. Jaroensutasinee, K. Jaroensutasinee

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We developed a method based on quasi-molecular modelling to simulate the fall of water drops on horizontally smooth and rough surfaces. Each quasi-molecule was a group of particles that interacted in a fashion entirely analogous to classical Newtonian molecular interactions. When a falling water droplet was simulated at low impact velocity on both smooth and rough surfaces, the droplets moved periodically (i.e. the droplets moved up and down for a certain period, finally they stopped moving and reached a steady state), spreading and recoiling without splash or break-up. Spreading rates of falling water droplets increased rapidly as time increased until the spreading rate reached its steady state at time t ~ 0.25 s for rough surface and t ~ 0.40 s for smooth surface. The droplet height above both surfaces decreased as time increased, remained constant after the droplet diameter attained a maximum value and reached its steady state at time t ~ 0.4 s. However, rough surface had higher spreading rates of falling water droplets and lower height on the surface than smooth one.

Keywords: Quasi-molecular modelling, particle modelling, molecular aggregate approach.

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Authors: Norjihan Abdul Ghani, Zailani Mohd Sidek

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Nowadays, organizations and business has several motivating factors to protect an individual-s privacy. Confidentiality refers to type of sharing information to third parties. This is always referring to private information, especially for personal information that usually needs to keep as a private. Because of the important of privacy concerns today, we need to design a database system that suits with privacy. Agrawal et. al. has introduced Hippocratic Database also we refer here as a privacy-aware database. This paper will explain how HD can be a future trend for web-based application to enhance their privacy level of trustworthiness among internet users.

Keywords: Hippocratic database, privacy, privacy-aware.

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