Database Systems

In Industrial environments, huge amount of data is being generated which in turn collected indatabase anddata warehouses from all involved areas such as planning, process design, materials, assembly, production, quality, process control, scheduling, fault detection,shutdown, customer relation management, and so on. Data Mining has become auseful tool for knowledge acquisition for industrial process of Iron and steel making. Due to the rapid growth in Data Mining, various industries started using data mining technology to search the hidden patterns, which might further be used to the system with the new knowledge which might design new models to enhance the production quality, productivity optimum cost and maintenance etc. The continuous improvement of all steel production process regarding the avoidance of quality deficiencies and the related improvement of production yield is an essential task of steel producer. Therefore, zero defect strategy is popular today and to maintain it several quality assurance techniques areused. The present report explains the methods of data mining and describes its application in the industrial environment and especially, in the steel industry.

In recent years, media has become ubiquitous
and important for networking and content
sharing. Interest in the impact of media on the
“real world” is as old as media itself. At the
dawn of a new era, vast expansion in the
advancement of technology have placed
media on an important platform The
conventional mass media has been replaced
by up-to-date most complicated and most
sophisticated means. The technological
revolution has made the information process
expedient. The importance of media
technology is widely recognized as a body of
work which has focused almost exclusively
on technology as a driving force of social
change. Then we go further and examine how
the development and application of
technology have advanced the media means.
This discussion takes into account, the
technologies that form the basis of our media
and how it can be a revolution to whole of the
world. In recent years, the explosion of new
media has caused new anxieties. In this
respect a research is needed to enhance the
use of media+ technology in detection of
crimes, corruption live telecasting etc.,
making the work much more easier for the
society to face and handle everyday life in a
secured and peaceful manner.
So, finally we focus some researches
to be made in the field of media + technology
that how it can be used for crime detection,
medical and live telecast and also how it can
be made easier for the blind ,the deaf and the
illiterate to get cognizant of everyday
happenings all over the world and get
updated.

A hostel management system (HOMASY) was designed to provide a computerized process that is stress free, reliable and quick through the use of PHP computer programming language and MySQL database application to both the students and the staff in charge of the registration and hostel management processes. HTML would be at the front-end and provide the graphical user interface that relates with the user, while the MySQL database will be at the back-end to handle the data storage process. The porter and students' affairs officers will also be able to access and create student records with ease and regular update of student profile is enhanced when adopted. The usual practice of filing and procedures involved in students' registration and hostel accommodation in Lagos State University and other university was appraised for efficiency, economics and time management. The existing procedure was formed to be manually carried out. This is associated with inadequacies as more personnel are often required and a lot of times are wasted while the semester is on with lectures and other activities.

SQL dilinin yeteneklerinin sınırlı olması sebebiyle, SQL üzerine çeşitli iyileştirmeler ve eklemeler yapılmıştır. Microsoft kendi platformu için SQL üzerine yaptığı iyileştirmeleri standart haline getirmiş ve  T-SQL ismini vermiştir. Transact-SQL‘in kısaltması olan T-SQL günümüz veritabanı yönetim ihtiyaçlarının tamamını karşılayabilecek yeterliliğe sahiptir. Bu makalede ayrıca temel T-SQL komutları açıklanmış ve örneklere yer verilmiştir.

TheInternationalJournalofDatabaseManagementSystems(IJDMS)isabimonthly  open access  peer-reviewed  journal  that  publishes  articles  which  contributenew  resultsin  all  areas  of the  database  management  systems&  its  applications.The  goal  of  this  journal  is  to  bring togetherresearchers  and  practitioners  fromacademia  andindustry  tofocuson  understanding Modern developments in thisfiled and establishing newcollaborationsintheseareas

Abstract
The aim of this research to extract meaningful crime trends regarding offences against children from the data in existing police records with the help of data mining techniques. We know children are exposed to different offences but we do not know which children are exposed to what type of offence (crime category). The output from this research helps to identify which children are exposed to which crime categories. Currently the police officers try to understand the relation between any two attributes but they do not know the relation among more than two attributes and the relationship between other variables and a class variable. This is why this can be achieved by using data mining techniques in an efficient and accurate manner than those achieved by trained personnel and traditional simple statistics to analyze crime data. The researcher used the six-phased CRISP-DM for data mining process and each of the steps in this model starting from business understanding up to evaluation and deployment phases are performed step wise and iteratively when needed. Even though all the phases are equally important the data pre-processing part has got due emphasis since police records are inconsistent and frequently incomplete making task of formal analysis inaccurate and time consuming. These analytical processes would benefit from using data mining techniques in a structured approach. Both unsupervised and supervised learning are used within the structured methodology to mine the police data. This research will serve as a reference material for researchers, crime investigators, planners and NGOs that work on prevention and control of offences against children. Based on this, it can also help to implement different crime preventive programs like through awareness creation programs.
The research demonstrates that data mining techniques can be successfully used in proactive policing to prevent crimes. This is more applicable for high volume crimes such as theft, violence and sexual assaults that have been committed most commonly. These crimes can often be segmented and classified and the generated models can be used to
predict potential victims of a specified crime category through predictive models as well as to attribute the profile of victims with the help of descriptive techniques.
Some of the rules in association rule are not interesting due to few values that are unable to generate patterns. From all the crime categories in the crime records sexual assault has the highest number and best rules are generated related with sexual assault. Almost all interesting rules generated in association rule are included in the rules generated by the classification model. Generally, promising results are registered with encourage further researches in the area.

The most critical and important aspect of disaster recovery is to protect the data from application fail over, natural disasters and infrastructure failures. Taking frequent backups of the huge volumes of data and storing it is also an integral part of the disaster recovery plan. Various scenarios of database replication strategies and techniques are provided in this survey paper addressing the need for replication of data. A wide range of open source and commercial tools have evolved over a period of time to deal with the issues and challenges involved during the process of database replication. This paper describes the general characteristics of replication & disaster recovery. Later the features of open source and commercial replication tools are compared.

ABSRACT: Diseases have been increased tremendously. Eight person from ten have diabeties, so the data in the hospital is increasing day by day. A huge amount of data has to be handled and for that we are using classification and clustering techniques which classifies the data and forms the clusters as well as we developed a tool for Apriori which predicts the strong rules. The tool is developed using .NET framework. For Clustering and Classification WEKA tool is used for extracting the results.

Make a foundation library from all of the SQL Server servers of your company, either grouped or by function like Production, QA, DRP, Corporate, Branch, Development, Test, on subjects like tables, views, columns, constraints and defaults among many other indicators like the ones listed below for information schema views.
ZeusVsZeus

To support Moving Mexico into a Developed State. Mover a México.

NoSQL databases have become more popular and preferable in modern data architecture. It has become a powerful way to store data in a specialized format that yields fast performance for a large amount of data. NoSQL databases are designed for modern web-scale databases. Although, nowadays, it is used to handle big data and many real-time web applications. They can handle enormous amount of unstructured data and structured data. As a result, developers will be able to be more nimble. NoSQL database developers, for example, may deploy code updates more quickly than relational database developers. However, security remains a very difficult issue even with NoSQL databases, as the security concerns inherent in previous databases have not been resolved in NoSQL. The most common example is that their password storage mechanism is weak, meaning it can be decrypted easily. This means that NoSQL databases have weaknesses and one of them is authentication weakness. This paper evaluates how strong the authentication aspect towards different kinds of attacks on a NoSQL database.

There has been an unprecedented growth and increase in the domain unstructured/semi-structured data is increasing over the years. While relational database systems remain popular and relevant, they are incapable of handling the growth level of unstructured data in the area of web applications. Conflating the benefits of a simple NoSQL storage engine with the relational databases and the unique ability of presenting query results from the duo to users at minimal costs have been a critical challenge in the research community. So far so good there has been a tremendous works such as using SQL with an extension of NQP on transformed NoSQL data which is store as SQL virtual relation, querying Apache Cassandra with SQL after altering the data structure, querying both world of relational and NoSQL and produced two results instead of single output. The TripleFetchQL system developed enables users to query relational and NoSQL databases and presents query results as if they were querying the familiar relational database alone. Furthermore, TripleFetchQL provide applications the ability of leveraging the benefits of relational and NoSQL databases at the small cost of learning the simple syntax of the TripleFetchQL system.

DESCRIPTION DU COURS SELON L'ANNUAIRE

Connaître les bases de données et leur utilisation.

Concept de base de données. Modèle de données relationnel : algèbre relationnelle, langage de requêtes SQL, accès à une base à partir d'un programme d'application, les vues. Mécanismes d'intégrité et de sécurité. Indexage. Logiciels typiques. Applications. 


INSERTION DU COURS DANS LE PROGRAMME

Ce cours est obligatoire pour les étudiants au baccalauréat en informatique et il est au choix pour les étudiants du certificat en informatique. Il a comme pré-requis INF-112-99 Programmation II et il est lui-même préalable au cours INF-452-99 Compléments sur les bases de données.

— For business and real time applications large set of data sets are used to extract the unknown patterns which is termed as data mining approach. Clustering and classification algorithm are used to classify the unlabeled data from the large data set in a supervised and unsupervised manner. The assignment of a set of observations into subsets (called clusters) so that observations in the same cluster are similar in any way is termed as cluster analysis or clustering. Through these algorithms the inferences of the clustering process and its domain application competence is determined.the research work deals with K-means and MCL algorithms which are most delegated clustering algorithms.

Decreasing disk costs have made it practical to retain longlived snapshots, enabling new applications that analyze past states and infer about future states. Current approaches offer no satisfactory way to organize long-lived snapshots because they disrupt the database in either short or long run. Split snapshots are a recent approach that overcomes some of the limitations. An unsolved problem has been how to support efficient application code access to arbitrarily long-lived snapshots. We describe Skippy, a new approach that solves this problem. Performance evaluation of Skippy, based on theoretical analysis and experimental measurements, indicates that the new approach is effective and efficient.

As well as studying social and demographic changes. The use of business intelligence techniques have now become a requirement for journalists answering political and social science questions as well as government claims and statements. As the government releases more complex and larger data, the need for greater data understanding and data integrity is paramount. Facts quoted by politicians are often subjective, published data needs to be carefully collected and collated into forms which enable for data journalism to take place. In an article written by Tim Lewis for the Guardian, a question was raised,
“If the mayor of London, Boris Johnson, wanted to raise the profile of cycling then he has certainly succeeded the so-called "Boris bikes" are impossible to miss. But has the scheme made a substantive difference to the lives of Londoners?” (Tim Lewis, 2011).
In answering this question the study has taken three perspectives,
i. “Has there been a negative or positive result on traffic and congestion”,
ii. “Have sales decreased on other related transport services as a result of the introduction of the scheme in associated boroughs or routes”.
iii. Is it possible to answer the question being posed?
The questions asked, although simple, to provide an answer to Tim Lewis’s posed statement, may not be so easy to produce.  The major problems with such a study are that the data is held across multiple departments and the data collection methods are mixed. The data provided is not universal and often the data is incomplete. Is there enough time to collect all of the necessary data for this study?  A review of the data collection techniques will be taken and the creation of a data ware house comprised of the facts associated with the usage data, which are associated with the areas of the introduction of the cycle scheme, will be created.  In the collation of statistics from open data sources, an understanding of the freedom of information request procedure will be gained, an understanding of the tools and techniques faced by modern data journalists as well as businesses leveraging open data sources.  The data needed to answer such a question is held and collected by multiple departments and systems. The data has been provided by Transport for London, in their annual reports, and from the National Office of Statistics which data has been collated and made available to the public via their respective websites. Projects faced by businesses leveraging open data face similar issues in procurement, and interpreting the data for integration to their respective data warehouses.  The approach taken for this study is the CRISP-DM, This methodology provides an industry standard to approaching a data project. The following chapters follow this methodology.  A review of each stage at the end of each chapter is taken, providing a clear pathway for upcoming stages. The steps taken and the transformations required have been made using Microsoft Access & Microsoft Excel. The results given in this report will show an overall reduction in the rate of growth of the current road traffic sectors of the transport system. In the creation of local transport policies a similar comparison would also be taken by local council departments in assessing the viability of the introduction of the cycling schemes into other boroughs and wards. 
These results and findings enable an understanding of the type of results that would be gained in such a study and the associated issues surrounding the data such as collection and collation methods as well as software required to facilitate the digital analysis associated with the creation of a report which also enables visually recognizable trends as well as analytical statistical information.  

previous sections, are: scalability with respect to the number of nodes in the network, self-organization, self-healing, energy efficiency, a sufficient degree of connectivity among nodes, low-complexity, low cost and size of nodes. Those protocol architectures and technical solutions providing such features can be considered as a potential framework for the creation of these networks, but, unfortunately, the definition of such a protocol architecture and technical solution is not simple, and the research still needs to work on it.

High-dimensional indexes do not work because of the often-cited "curse of dimensionality." However, users are usually interested in querying data over a relatively small subset of the entire attribute set at a time. A potential solution is to use lower dimensional indexes that accurately represent the user access patterns. To address these issues, in this paper we propose a parameterizable technique to recommend indexes based on index types.

The EPNet project aims to examine the framework of the Roman economic organisation and its networks by analysing epigraphical data from amphorae. This aim is to be realised through complex network analysis, model building and computer simulation. The objective is to create an experimental laboratory for the exploration, validation and refutation of historical theories, and the formulation of new ones.

Poor performance can turn a successful data warehousing project into a failure. Consequently, several attempts have been made by various researchers to deal with the problem of scheduling the Extract-Transform-Load (ETL) process. In this paper we therefore present several approaches in the context of enhancing the data warehousing Extract, Transform and loading stages. We focus on enhancing the performance of extract and transform phases by proposing two algorithms that reduce the time needed in each phase through employing the hidden semantic information in the data. Using the semantic information, a large volume of useless data can be pruned in early design stage. We also focus on the problem of scheduling the execution of the ETL activities, with the goal of minimizing ETL execution time. We explore and invest in this area by choosing three scheduling techniques for ETL. Finally, we experimentally show their behavior in terms of execution time in the sales domain to understand the impact of implementing any of them and choosing the one leading to maximum performance enhancement.

We introduce the continuous convergence protocol for handling locally committed and possibly concurrent updates to replicated data objects in distributed real-time databases. The protocol is designed for systems where replicated database objects are used to share data between collaborating real-time applications. We define the properties of the protocol, and describe how it can be implemented using data structures such as version vectors and conflict sets. We also show how applications can exploit the protocol's properties, and how the protocol supports forward resolution of update conflicts.

Evaluating teachers and students are very critical factors in the education process. The problems that exist in this context are problems of identifying the methods of evaluation and adopting the right evaluation techniques for traditional and non-traditional students. This work was motivated by the need for educators in academic institutions to choose the right evaluation technique which is universally accepted in educational or academic institutions. However, this work examines the formative and summative assessment methodologies which exist and recommendations were made. Data will be collected from educators in both government and private owned secondary schools, analyzed using the Likert scale method of data analysis, SPSS statistical package and Microsoft excel spreadsheet package which are advanced software used by social scientists for in-depth analysis and prediction; thereafter, a universally accepted method will be recommended.

Computational complexity of multiscale methods based on the first-principles energetics is analyzed. Computational cost of such methods is dominated by expensive first-principles determination of structural energies and atomic forces, including structural relaxations. Information integration is an opportunity to eliminate recalculation of known data, and, consequently, to reduce the total cost of multiscale calculations. We propose the Structural Database as a universal tool for structural data integration, and explain its conceptual design and functionality, including data mining options. To exemplify benefits provided by the Structural Database, we consider search for new ground states, construction of a phase diagram for a bulk material, and prediction of surface patterning. We show that the Structural Database is a powerful tool for information integration and data mining, which can greatly reduce the cost of multiscale computations and materials design.

Data cube has been playing an essential role in fast OLAP (online analytical processing) in many multi-dimensional data warehouses. However, there exist data sets in applications like bioinformatics, statistics, and text processing that are characterized by high dimensionality, e.g., over 100 dimensions, and moderate size, e.g., around 10 6 tuples. No feasible data cube can be constructed with such data sets. In this paper we will address the problem of developing an efficient algorithm to perform OLAP on such data sets. Experience tells us that although data analysis tasks may involve a high dimensional space, most OLAP operations are performed only on a small number of dimensions at a time. Based on this observation, we propose a novel method that computes a thin layer of the data cube together with associated value-list indices. This layer, while being manageable in size, will be capable of supporting flexible and fast OLAP operations in the original high dimensional space. Through experiments we will show that the method has I/O costs that scale nicely with dimensionality. Furthermore, the costs are comparable to that of accessing an existing data cube when full materialization is possible.

This work is proposed the feed forward neural network with symmetric table addition method to design the neuron synapses algorithm of the sine function approximations, and according to the Taylor series expansion. Matlab code and LabVIEW are used to build and create the neural network, which has been designed and trained database set to improve its performance, and gets the best a global convergence with small value of MSE errors and 97.22% accuracy.

In constraint-based program testing, symbolic execution is a technique which allows to generate test data exercising a given execution path, selected within the program to be tested. Applied to a set of paths covering a sufficient part of the code under test, this technique permits to generate automatically adequate test sets for units of code. As databases are ubiquitous in software, generalizing such a technique for efficient testing of programs manipulating databases is an interesting approach to enhance the reliability of software. In this work, we propose a relational symbolic execution algorithm to be used for testing of simple Java methods, reading and writing with transactional SQL in a relational database, subject to integrity constraints. This algorithm considers the Java method under test as a sequence of operations over a set of constrained relational variables, modeling both the database tables and the method variables. By integrating this relational model of the method and database with the classical symbolic execution process, the algorithm can generate a set of Alloy constraints for any finite path to test in the control-flow graph of the method. Solutions of these constraints are data which constitute a test case, including valid content for the database, which exercises the selected path in the method. A tool implementing the proposed algorithm is demonstrated over a number of examples.

The traditional R-Trees has serious bottleneck, giving less efficient results in spatial querying. We can list them as i) The overlap between the brother nodes (Minimum Bounding Rectangles, MBR). ii) Multiple paths in search, meaning that for exact match query, the algorithm can not give a single path. Even though the Hilbert R-Tree scheme gives efficient results, it doesn't effectively break the bottleneck. So in order to effectively break the bottleneck, the solution proposed in this paper is based on hybrid clustering on Hilbert R-Trees which practically proved to yield faster response and higher query efficiency.

1. Location, shape, size and orientation are potentially relevant characteristics of geographic objects. Try to provide an application example in which these characteristics do make sense for (a) point objects, (b) line objects, (c) area objects. 2.To distinguish with examples, the raster-based and vector-based representations of geographic phenomena and Discuss the advantages and disadvantages of each. The following is the difference between the raster based and vector based representation of geographical phenomena: In raster data, the cell typically represents the predominant phenomena of the area covered by a cell, whereas vector data can accurately delineate or identify individual features. As a result, when representing geographic features in a raster dataset, features become collections of cells with the same attribute values but lose their unique identities. Raster data is best used when a primary concern is with the locational relationships of the phenomena represented by geographic features, not the features themselves. Examples of raster data includes thematic data (also known as discrete), representing features such as land-use or soils data, Continuous data, representing phenomena such as temperature, elevation or spectral data such as satellite images and aerial photographs and Pictures, such as scanned maps or drawings and building photographs Examples of vector data includes individual points, country boundaries, and dots which (for 2D data) are stored as pairs of (x, y) coordinates. The points may be joined in a particular order to create lines, or joined into closed rings to create polygons, but all vector data fundamentally consists of lists of coordinates that define vertices, together with rules to determine whether and how those vertices are joined. Below are set of discussed advantages and disadvantages of each. Vector Data Advantages: Data can be represented at its original resolution and form without generalization. Graphic output is usually more aesthetically pleasing (traditional cartographic representation); Since most data, e.g. hard copy maps, is in vector form no data conversion is required. Accurate geographic location of data is maintained. Allows for efficient encoding of topology, and as a result more efficient operations that require topological information, e.g. proximity, network analysis. Disadvantages: The location of each vertex needs to be stored explicitly. For effective analysis, vector data must be converted into a topological structure. This is often processing intensive and usually requires extensive data cleaning. As well, topology is static, and any updating or editing of the vector data requires rebuilding of the topology. Algorithms for manipulative and analysis functions are complex and may be processing intensive. Often, this inherently limits the functionality for large data sets, e.g. a large number of features. Continuous data, such as

Database security is a growing concern as the amount of sensitive data collected and retained in databases is fast growing and most of these data are being made accessible via the internet. Majority of the companies, organizations and teaching and learning institutions store sensitive data in databases .As most of these data are electronically accessed , It can therefore be assumed that , the integrity of these numerous and sensitive data is prone to different kind of threat such as{Unauthorized access, theft as well access denial}. Therefore, the need for securing databases has also increased The primary objectives of database security are to prevent unauthorized access to data, prevent unauthorized tampering or modification of data, and to also ensure that, these data remains available whenever needed. In this paper, we developed a database security framework by combining different security mechanism on a sensitive students information database application designed for Shehu Shagari College of Education Sokoto (SSCOE) with the aim of minimizing and preventing the data from Confidentiality, Integrity and Availability threats.

As projects progress from pilot studies with few simple variables and small samples, the research process as a whole becomes qualitatively more complex and subject to an array of contamination by errors and mistakes. Data usually undergo a series of manipulations (e.g., recording, computer entry, transmission) prior to final statistical analysis. The process, then, consists of numerous operations only ending with eventual statistical analysis and write-up. We present a means of estimating the impact of process error in the same terms as psychometric reliability and discuss the implications for reducing the impact of errors on overall data quality.

Enhancing the educational corporations is truly challenging mission due to the highly competitive nature of the business. Currently, there is emerging development within organizations to capitalize on their internal resources. This paper is taking data mining approach to strategize marketing for postgraduate studies by means of cluster analysis. The experiments were carried out using Oracle Data Miner tool, results are analyzed and discussed.

Continuous advancements and rapid developments of electronics, computing, network and web technologies have been much concerned in recent period of time. The emerging technologies including teleoperation, communication and control via web, visual servo control and so forth, have induced high motivation and demand towards the researchers who belong to conventional control and robotics. In this research, teleoperation and template matching techniques are applied to control the motion of industrial robot arms remotely via network. The system has two motion control behaviors; rough motion of the robot arm is handled by teleoperation technique and accurate motion is governed by visual template matching. Two behaviors are switched, from one to another appropriately according to the desired motion. The effectiveness of the proposed methodology is verified by the experimental results.

Current survey done on today's scenario shows, result gadget declared by Universities(eg. Pune Uni.) for engineering is in PDF file format. The PDF data contents detail such as seat no, centre, permanent registration no.(PRN), Name, Subjects, Marks, etc. Presently PDF file is extracted in excel file format, this conversion is done in order to extract various reporting formats required by department/college/university at various level. Thus, it involves somewhat manual process. However, all these operation have certain limitations such as semi-automated process, no GUI present, SMS gateway is not support, E-mail gateway is not supported, and mainly graphical analysis of data is not available. On the basis of survey done, we came across existing applications which are semi-automated or automated with some restrictions which does not allow full automation of result analysis in proper format. Thus none of the applications supported the full automation. To overcome above said drawbac...