RDBMS

ABSTRACT The World Wide Web is currently considered as the most appropriate software platform for the deployment of applications in wide area networks (telematics) as well as corporate intranets. Such applications are, in the majority of cases, tightly coupled with legacy databases hosted by relational management systems. However, the nature of the database enabled WWW systems is quite different from that of classical database applications developed with tools like 4GLs, Forms, Menus, etc. The basic difference lies in the stateless character of the WWW. The `user session' concept, encountered in conventional database applications, does not apply in the WWW environment. Instead, interaction with the information server is accomplished through a series of hits (request–response interactions) which are treated independently. This paper presents an architecture for the deployment of stateful database gateways for WWW servers. Although the server still treats each individual hit independently, state information maintained in the WWW browser as well as in specialized agents that operate behind the WWW server renders the WWW appropriate for a `session-aware' database application. The effort required to port an existing `session-aware' database application to the WWW environment is minimal.

Existing commercial database management systems offer little or no functionality to handle the complexity of geoscience data-and other environmental science data-particularly in respect of missing and partially missing (incomplete or imprecise) data items. The emphasis of both the relational theorists (Codd, Date, and others) and the developers of database systems is on commercial applications where only rudimentary treatment of missing data is required, in the form of NULLs, and even these are not handled properly by the SQL language.

In the time of enormous information, information examination, business knowledge database administration assumes an imperative part from specialized business administration and exploration perspective. Over numerous decades, database administration has been a subject of dynamic examination. There are distinctive kind of database administration framework have been proposed over a time frame yet Relational Database Management System (RDBMS) is the one which has been most prevalently utilized as a part of scholastic examination and technical setup. As of late, Graphs databases recovered enthusiasm among the analysts for certain conspicuous reasons. A standout amongst the most critical explanations behind such an enthusiasm for a graph database is a result of the inalienable property of charts as a Graphs structure. Charts are available all around in the information structure, which speaks to the solid availability inside the information. The vast majority of the Graph database models are characterized in which information structure for graph and occasions are displayed as graph or speculation of a graph. In such graph database models, information controls are communicated by chart arranged operations and sort constructors. Presently days, the vast majority of this present reality applications can be demonstrated as a graph and one of the best genuine illustrations is social or organic system. This paper gives an outline of the diverse sort of graph databases, applications, and correlation between their models in view of a few properties.

Nowadays, RFID applications have attracted a great deal of interest due to their increasing adoptions in supply chain management, logistics and security. They have posed many new challenges to existing underlying database technologies, such as the requirements of supporting big volume data, preserving data transition path and handling new types of queries. In this paper, we propose an efficient method to manage RFID data. We explore and take advantage of the containment relationships in the relational tables in order to support special queries in the RFID applications. The experimental evaluation conducted on an existing RDBMS demonstrates the efficiency of our method.

Normalization is a significant aspect of relational database theory. It organizes the data to make the database easy to manage. Normalizing a database enables the user to create relationship strands between the pieces of information and eliminates redundancies. In this paper, an algorithm is presented, which if used in Entity Relationship Diagram (ERD) leads to database that is always be in a normalized form. In this algorithm, some constraints are imposed so that the Entity Relationship (ER) model when mapped to relational database leads to a normalized database. This paper enhances the process of normalization by proposing an algorithm up to the third normal form.

With the substantial growth in volume and complexity of data used in the digital world large organizations need to handle a variety of unstructured data. Traditionally relational database systems have been used to store, process and retrieve data although other types of databases such as hierarchical, network, graph have existed before 1970, yet their commercial goals were not achieved until recent years. These databases (called NoSQL or Non-relational databases) are complements to relational databases and are used by world's largest organizations such as Google, Amazon and Facebook. This research compares the performance of relational and non-relational databases namely Oracle, and MongoDB by executing complex queries on a large set of data that is available in document-based mode and is converted to Oracle tables. The results show that data retrieval is significantly faster in MongoDB however some mathematical queries such as aggregation functions(sum, count, AVG) are better suited to Oracle RDBMS.

Versatility of NewSQL databases is to achieve low latency constrains as well as to reduce cost commodity nodes. Out work emphasize on how big data is addressed through top NewSQL databases considering their features. This NewSQL databases paper conveys some of the top NewSQL databases [54] features collection considering high demand and usage. First part, around 11 NewSQL databases have been investigated for eliciting, comparing and examining their features so that they might assist to observe high hierarchy of NewSQL databases and to reveal their similarities and their differences. Our taxonomy involves four types categories in terms of how NewSQL databases handle, and process big data considering technologies are offered or supported. Advantages and disadvantages are conveyed in this survey for each of NewSQL databases. At second part, we register our findings based on several categories and aspects: first, by our first taxonomy which sees features characteristics are either functional or non-functional. A second taxonomy moved into another aspect regarding data integrity and data manipulation; we found data features classified based on supervised, semi-supervised, or unsupervised. Third taxonomy was about how diverse each single NewSQL database can deal with different types of databases. Surprisingly, Not only do NewSQL databases process regular (raw) data, but also they are stringent enough to afford diverse type of data such as historical and vertical distributed system, real-time, streaming, and timestamp databases. Thereby we release NewSQL databases are significant enough to survive and associate with other technologies to support other database types such as NoSQL, traditional, distributed system, and semi-relationship to be as our fourth taxonomy-based. We strive to visualize our results for the former categories and the latter using chart graph. Eventually, NewSQL databases motivate us to analyze its big data throughput and we could classify them into good data or bad data. We conclude this paper with couple suggestions in how to manage big data using Predictable Analytics and other techniques.

In the time of enormous information, information examination, business knowledge database administration assumes an imperative part from specialized business administration and exploration perspective. Over numerous decades, database administration has been a subject of dynamic examination. There are distinctive kind of database administration framework have been proposed over a time frame yet Relational Database Management System (RDBMS) is the one which has been most prevalently utilized as a part of scholastic examination and technical setup. As of late, Graphs databases recovered enthusiasm among the analysts for certain conspicuous reasons. A standout amongst the most critical explanations behind such an enthusiasm for a graph database is a result of the inalienable property of charts as a Graphs structure. Charts are available all around in the information structure, which speaks to the solid availability inside the information. The vast majority of the Graph database models a...

With the substantial growth in volume and complexity of data used in the digital world large organizations need to handle a variety of unstructured data. Traditionally relational database systems have been used to store, process and retrieve data although other types of databases such as hierarchical, network, graph have existed before 1970, yet their commercial goals were not achieved until recent years. These databases (called NoSQL or Non-relational databases) are complements to relational databases and are used by world's largest organizations such as Google, Amazon and Facebook. This research compares the performance of relational and non-relational databases namely Oracle, and MongoDB by executing complex queries on a large set of data that is available in document-based mode and is converted to Oracle tables. The results show that data retrieval is significantly faster in MongoDB however some mathematical queries such as aggregation functions(sum, count, AVG) are better suited to Oracle RDBMS.

Today, IT professionals are challenged with the task of ongoing improvements to achieve goals of businesses. Unfortunately, some factor/Resources, skill environment does not dynamically grow as fast as business needs. That sequence of events creates major obstacles for DB infrastructure, deployment, administration and maintenance. This paper discusses the performance issues, different bottlenecks such as CPU bottlenecks, Memory structures, Input output capacity issue, Database Design issues and Indexing issues. Also this paper address Tuning stages and how SQL queries can be optimized for better performance. In this paper we are focusing on query tuning tips & tricks which can be applied to gain immediate performance gain by creating Query Execution Flow Chart. We demonstrate the application of this technique in an Employee Biometric Attendance Management System.

در هزاره سوم بكارگيري تكنولوژي براي توسعه اقتصاد، دستيابي به علوم و فنون جديد، آموزش، توسعه اجتماعي، امري اجتناب ناپذير است. به واسطه
دستيابي به تكنولوژي نوين داشتن اطلاعات و برقراري ارتباطات نيز ضروري و نفي در آن انكار ناپذير است.
وري اطلاعات در توسعه اقتصادي و اشتغال كه خود بحث مفصل و خارج از موضوع تعيين Ĥ در اين مقاله نگارنده سعي بر آن دارد صرفنظر از كاربرد فن
شده است در نگاهي تخصصي به چگونگي بكارگيري از اين علم در ارائه خدمات مشاورهاي، تسهيل در راهنماي انتخاب شغل متناسب با علايق افراد
بپردازد.
وري اطلاعات Ĥ تاسيس مراكز مشاوره شغلي و ارائه خدمات مشاورهاي از جمله موضوعات روز در كشورهاي توسعه يافته است. با روند رو به رشد فن
ميدان فعاليت متخصصان و صاحبان كسب و كار محدود به حوزه و حرفه خاصي نبوده و بر خلاف نظر برخي از نظريه پردازان مبني بر جايگزيني تكنولوژي
به جاي نيروي انسان، جذب نيروهاي كارآمد ومتخصص، مطابق با نيازهاي بازار كار از مهمترين اهداف صاحبان صنعت و سرمايهگذاران صنعت ميباشد.
وري اطلاعات و ارتباطات، سعي در بكارگيري اين علم از دو منظر Ĥ از اينرو در كشورهاي توسعه يافته ضمن جامعيت و عموميت بخشيدن به تكنولوژي فن
نمودهاند.

With the substantial growth in volume and complexity of data used in the digital world large organizations need to handle a variety of unstructured data. Traditionally relational database systems have been used to store, process and retrieve data although other types of databases such as hierarchical, network, graph have existed before 1970, yet their commercial goals were not achieved until recent years. These databases (called NoSQL or Non-relational databases) are complements to relational databases and are used by world's largest organizations such as Google, Amazon and Facebook. This research compares the performance of relational and non-relational databases namely Oracle, and MongoDB by executing complex queries on a large set of data that is available in document-based mode and is converted to Oracle tables. The results show that data retrieval is significantly faster in MongoDB however some mathematical queries such as aggregation functions(sum, count, AVG) are better s...

A data warehouse is a repository of subjectively selected and adapted operational data which can successfully answer any ad hoc, statistical, complex or analytical queries. Data warehousing technology is becoming essential for effective business intelligence, business strategy formulation and implementation in a globally competitive environment where in larger and larger amounts of data are required to be processed faster and faster for comprehension of its real meaning and impact. A warehouse is a subjectoriented, integrated, time-variant and non-volatile collection of data in support of management's decision making process”. Data that gives information about a particular subject instead of about a company's ongoing operations. It is integrated as data that is gathered into the data warehouse from a variety of sources and merged into a coherent whole. Data warehouse system is time variant as all data in the data warehouse is identified with a particular time period This paper discusses in detail two existing database models used for data warehousing, the relational database and object oriented database and does an analysis of the benefits of Object Oriented databases over the conventional relational database in Data warehousing.

Every technology has strengths and weaknesses, and relational database technology is no different. At this point in time, however, relational database management systems are the predominant storage technology; they are used to store information which is then available for easy retrieval based on various call procedures. While a database management system (DMS) controls storage, retrieval, security, and control of data within a database, a relational database management system (RDBMS) stores the data in a specific way. Other technologies are in the process of developing, but in their infancy not enough information is available for the average company to decide whether or not their use would be efficacious. RDBMS will be around for many years, but alternatives are gradually becoming available as technologies develop and improve. Companies need to remain on top of new developments, but remain very aware of limitations and how they can affect the company before jumping to new database systems. This paper critically evaluates the strengths and weaknesses of relational database technology.

In this era of technologies, where due to the advancement in several web technologies and frequent growth of portable devices, and sensors linked over the web are resulting to the huge amount of data. Due to this rapid increase of well-structured, semi-structured and other types of unstructured data called Big Data, traditional database systems are facing several difficulties.Since forty years, traditional databases are the leading model for several data manipulation tasks such as data storing, data retrieving and managing the data. However, because of growing requirements for better scalability and higher performance, other alternative database technologies, namely NOSQL andNewSQL technology have emerged. In order to overcome the challenges faced by traditional database system, there are numerous NOSQL and NEWSQL databases in the industry. It becomes challenging to select appropriate database solution for Big Data Management. This research work will present the comparative analysis and performance evaluation of most popular NOSQL and NEWSQL databases on the basis of several criteria. In this survey paper, there are mainly five sections: first section comprises of basic introduction and background of research in this field. In further sections, there is a brief introduction about adopting nosql and newsql technologies. Finally, there is a literature survey and survey report of this field.

Tidak ada satu pun sesuatu yang tidak memiliki arti di dunia ini, karena segala sesuatu dalam hidup pasti memiliki suatu keterkaitan satu sama lain. Sama halnya dengan basis data yang memiliki banyak keterkaitan dari berbagai aspek kehidupan, maka sangat beruntunglah orang-orang yang mempelajari basis data. Cara agar kita dapat memanfaatkan basis data dengan sebaik mungkin yaitu terlebih dahulu kita mengetahui dan mengerti konsep basis data. mengetahui mengenai komponen-komponen sistem basis data, dan mengetahui mengenai abstraksi data dan Database Language. 1.1 Pengertian Basis Data, Data dan Informasi Data merupakan respresantasi untuk mewakili nilai fakta dunia nyata. data dapat merepresentasikan nilai fakta dunia nyata dengan berbagai cara, bisa dengan numerik, alphanumerik, gambar, suara, video, dan lain-lain. Fakta dunia nyata dapat berupa barang, kejadian, aktivitas, perasaan dan semua tentang dunia nya. sedangkan basis dapat diartikan sebagai kumpulan, dimana kumpulan tersebut menjadi sistem yang memiliki input-proses-output untuk menjadi satu. Dapat disimpulkan, maka Basis data adalah adalah sekumpulan data yang disimpan secara sistematis di dalam komputer dan dapat diolah atau dimanipulasi menggunakan perangkat lunak untuk menghasilkan informasi. Informasi adalah data yang telah dikelola dalam bentuk tertentu untuk memberikan makna atau arti bagi penerimanya.

Following best practices, while writing SQL code is important. In most of the cases, the coding has not met the standard and database script computer agendas are deserted. Those who are beginners in SQL server and the experts, following the best practices would be highly benefited. Coding is always a fun but challenging job. It is not only about producing the desired result as required by the business requirement but also a mandatory requirement to maintain the correct coding standards by using the optimum requirement of variable size, indexes and other key parameters to maintain the best coding practices. This article gives an overview of best practices in SQL server and guidelines. Many of us already have been following the best practices but the document will narrate the salient points to be considered while developing any code to get the optimum results to keep the Microsoft defined standards intact.

NoSQL is a free and open-source, scattered, extensive column store database management system intended to handle large amounts of data across many product servers, providing high obtainability and accessibility with no single point of failure. It is the easiest truly big-data database that can scale and replicate data globally in a master-less configuration. A NoSQL database delivers a mechanism for storage and recovery of data that is demonstrated in means other than the tabular relations used in relational databases. NoSQL databases usually understood by engineers as 'not only SQL databases' neither 'no SQL', it is an alternative to the most widely used relational databases. As the given name proposed, it is a substitute for SQL that uses in such a way that the SQL is co-existed. A relational database management system (RDBMS) is a database management system based on the relational model of data and it is a completely structured way of storing data. But NoSQL is an unstructured way of storing data. Is NoSQL an RDBMS? This is an article we will discuss various features of NoSQL, various advantages over RDBMS and the future of NoSQL. This article also reviews the basics of NoSQL and its usages.

Social platforms such as Facebook and Twitter have been growing exponentially in the last few years. As a result of this growth, the amount of social data increased enormously. The need for storing and analyzing social data became crucial. New storage solutions -also called NoSQL -were therefore created to fulfill this need. This thesis will analyze the structure of social data and give an overview of currently used storage systems and their respective advantages and disadvantages for differently structured social data. Thus, the main goal of this thesis is to find out the structure of social data and to identify which types of storage systems are suitable for storing and processing social data. Based on concrete implementations of the different storage systems it is analyzed which solutions fit which type of data and how the data can be processed and analyzed in the respective system. A focus lies on simple analyzing methods such as the degree centrality and simplified PageRank calculations.

The Hadoop framework transparently provides both reliability and data motion to applications. Hadoop implements a computational paradigm named MapReduce, where the application is divided into many small fragments of work, each of which may be executed or re-executed on any node in the cluster. In addition, it provides a distributed file system that stores data on the compute nodes, providing very high aggregate bandwidth across the cluster. Both map/reduce and the distributed file system are designed so that node failures are automatically handled by the framework. It enables applications to work with thousands of computation-independent computers and petabytes of data. The entire Apache Hadoop “platform” is now commonly considered to consist of the Hadoop kernel, MapReduce and Hadoop Distributed File System (HDFS), as well as a number of related projects – including Apache Hive. This paper briefs on HDFS Architecture, MapReduce Technique and it also provides a glimpse on Pig  and Hive

Security has become one of the key features of data transmission on large database. RDBMS are used for storage purposes but with applications generating enormous amount of data, RDBMS is no longer efficient because RDBMS doesn't support quick data access and computations as it do not support processing of data in distributed manner. NoSQL databases are nowadays popular in handling the unstructured data that are available as open source databases such as MongoDB, Cassandra, etc. This paper make a detailed study on the encryption techniques of NoSQL databases especially MongoDB which becomes popular in data management. Since encryption features are not applied on handling the data in MongoDB, In this paper, security for users data is provided by using additive homomorphic asymmetric cryptosystem which encrypts the users data in MongoDB(CryptMDB) and achieve strong user's data privacy protection. This also supports the database operations over the encrypted data.

Dashboards have become a popular way to display key success indicators at a glance to multiple users across an organization. This paper presents two possible methods for storing, retrieving, displaying and sharing data in a dashboard: 1) a MySQL database hosted on Amazon Web Services (AWS) for storing the data and Amazon QuickSight for creating and sharing the dashboard, and 2) local MySQL for storing the data, Microsoft Excel for creating the dashboard, and a Dropbox for sharing it. The two approaches are compared for cost, ease of creation and use, update speed, and shareability

Database Management System is very dynamic and popular subject in Computer Science and Information Technology. Modern society is based on 'e' concept means every task is achieved by interactive applications like e-commerce applications and the production of data is more and more. The present digital world becomes more complex with Big Data means the volume, velocity and variety of data. The Big Data is a global system and is considered a data collection that increases so large and can't be managed efficiently by traditional databases management systems. The aim of this paper is to identify the new data management strategies and how to tackle Big Data through a new breed of databases commonly known as NOSQL databases.

With the substantial growth in volume and complexity of data used in the digital world large organizations need to handle a variety of unstructured data. Traditionally relational database systems have been used to store, process and retrieve data although other types of databases such as hierarchical, network, graph have existed before 1970, yet their commercial goals were not achieved until recent years. These databases (called NoSQL or Non-relational databases) are complements to relational databases and are used by world's largest organizations such as Google, Amazon and Facebook. This research compares the performance of relational and non-relational databases namely Oracle, and MongoDB by executing complex queries on a large set of data that is available in document-based mode and is converted to Oracle tables. The results show that data retrieval is significantly faster in MongoDB however some mathematical queries such as aggregation functions(sum, count, AVG) are better s...

Today, IT professionals are challenged with the task of ongoing improvements to achieve goals of businesses. Unfortunately, some factor/Resources, skill environment does not dynamically grow as fast as business needs. That sequence of events creates major obstacles for DB infrastructure, deployment, administration and maintenance. This paper discusses the performance issues, different bottlenecks such as CPU bottlenecks, Memory structures, Input output capacity issue, Database Design issues and Indexing issues. Also this paper address Tuning stages and how SQL queries can be optimized for better performance. In this paper we are focusing on query tuning tips & tricks which can be applied to gain immediate performance gain by creating Query Execution Flow Chart. We demonstrate the application of this technique in an Employee Biometric Attendance Management System.

Literature has established the effectiveness of formal specifications for modeling specifications for computer system properties as well as for verifying program correctness. However, tools that can enable the typical commercial software developer to utilize formal methods are not in widespread use. A possible reason for this is that commercial tools can more easily be produced if industrystandard storage mechanisms such as relational database management systems (RDBMS) can be leveraged as repositories. This paper explores how to model and store formal specifications, specifically those based on the Zed method, in a relational database. We explore a technique for fully decomposing the predicate calculus to a relational database to provide a framework for manipulation and reverse generation of formal specifications and automating the implementation of relational states.

We have extended the canonical relational data model to enable the management of multimedia objects. In an attempt to provide a smooth paradigm shift to multimedia information system development, we have enhanced the relational data model framework with techniques for modeling, storing and manipulating multimedia data. In particular, we have provided a graphical conceptual model for structuring a multimedia document and mapping rules for translating it into an extended relational data schema. Extensions have regarded the management of foreign keys, active components, mechanisms for the management of spatial and temporal relations, and finally functions for handling multimedia presentations. As a consequence, we have also provided extensions to the SQL language to handle these new mechanisms.

In the past, selling needs brick and mortar store and it is limited to a local customer base. But today, ecommerce websites make it easy for the customers around the world to shop by virtual store. Performance of ecommerce websites depend not only the quality and price of the product but also the customer centric suggestions about the product and services and retrieval speed of websites. There is more possibility to buy a product brand suggested by friends or relatives than by viewing commends from unknown people. We can implement such a customer centric approach using Neo4j database, which provides easy and fast retrieval of information based on multiple customer attributes and relations than relational databases. This system is also compared with other NoSQL database such as MongoDB. MongoDB is well suited for application like E-Commerce because it support semi structured data and high availability. Integrating E-commerce solution with social network MongoDB is slower and not provides an optimal solution. The MongoDB imposes certain limitations for fast and efficient retrieval. This paper presents the advantages of using Neo4j over MongoDB for E-Commerce applications. Experiment shows the enhancement of performance in terms of time and complexity.

Coronary artery disease (CAD) is a leading cause of death worldwide. Heart rate variability (HRV) has been proven to be a non-invasive marker of the autonomic modulation of the heart. Nonlinear analyses of HRV signals have shown that the HRV is reduced signi¯cantly in patients with CAD. Therefore, in this work, we extracted nonlinear features from the HRV signals using the following techniques: recurrence plots (RP), Poincare plots, and detrended°uctuation analysis (DFA). We also extracted three types of entropy, namely, Shannon entropy (ShanEn), approximation entropy (ApEn), and sample entropy (SampEn). These features were subjected to principal component analysis (PCA). The signi¯cant principal components were evaluated using eight classi¯cation techniques, and the performances of these techniques were evaluated to determine which presented the highest accuracy in classifying normal and CAD classes. We observed that the multilayer perceptron (MLP) method resulted in the highest classi¯cation accuracy (89.5%) using our proposed technique.

Fourth chapter excerpt from the first volume of MAPPA. Methodology Applied to Archaeological Potential Predictivity, which illustrates the structure of the archaeological RDBMS and of the archaeological GIS, and the system of categorization of archaeological data used to manage their heterogeneity.

The vast majority of the world's structured data are now stored and managed by relational database management systems (RDBMSs). These systems provide powerful data management capabilities. However, as the power and functionality of these systems has grown, so has the complexity of their administration. In this paper, we show how the IBM DB2 Universal Database for Linux, UNIX, and Windows (DB2 UDB) product has exploited autonomic computing to reduce this administration complexity and become more self-managing. We survey the major autonomic computing features in the DB2 UDB product and describe the benefits, with experimental data in some cases. in 2002. He is a member of the DB2 Engine Development team at the IBM Toronto Laboratory, Markham, ON, Canada. In the last three years he has mainly worked on several memoryrelated enhancements for DB2 UDB. These projects have included enhancements to the configuration advisor, sort memory management, and exploring the affects of online memory the Design Advisor. Currently, he is focused on the development of the selftuning memory manager (STMM) for DB2 UDB. Sam S. Lightstone received the B.Sc. Eng. degree in electrical engineering from Queen'

Server-sided applications often use a persistence system for storing and managing their data in a database. Such a system has become complex and includes but is not limited to a database system, drivers, connection pools, query languages, the mapping between application logic and a database data model and it involves the optimization of resources. One important resource is the connection from applications to the database system since acquiring a connection is a very expensive operation. This paper introduces monitoring facilities for the use or misuse of connections in a persistence system. In particular, it explains how the monitoring can take place in order to configure connection pooling for optimal performance. The implemented solution uses conventional programming methods as well as an aspect-oriented approach. The described facilities are integrated into the development of an enterprise-scale system implementing a communications middleware.

Relational Database Management Systems (RDBMS) maintain a collection of huge data files to provide fast and efficient methods in order to access and modify data which is necessary (Martin et al., 1992). Therefore, RDBMS have supported indexing techniques in order to access the data efficiently in static and dynamic manners. The majority of commercial (RDBMS) Relational database management systems performance is relied on I/O operations rather than other computing resources. This is because the performance cost of I/O is expensive and there are other costs such as memory allocations and CPU consumption. The most important factor to consider is whether the I/O subsystem of a given (RDBMS) will support a reliable performance as time passes. This article describes a methodology for evaluating indexing techniques for relational databases. The methodology is based on a number of experiments to test a set of indexing techniques on two different platforms (Oracle and MS-SQL Server) with different data sizes (small, medium, and very large) over the same technical environment (Multiple processors, memory, and I/O devices). This factor is necessary to satisfy the real results on different platforms: Oracle and MS-SQL Server. To run the experiments, we have taken the following indexing techniques in Oracle: B-tree, Bitmap, Reversed, and organization index. In the meanwhile, we have taken the following indexing techniques in MS-SQL Server: B-tree, Clustered index, and unique non-clustered index and Primary Key Clustered index. The methodology includes the technical environment, platforms, table schema, table sizes, and a number of indexing techniques. We have also established a number of test scenarios to achieve the real results. The methodology procedure and flowchart include an ordered set of steps that have been taken to run the experiments in this article. The aim of the methodology contribution is to measure overall performance and behavior of indexing techniques that are performed against the same set of data: As a non-clustered index on a specified set of columns, and as a clustered index on the same set of columns. Note that, we have measured the performance of SELECT operation over Oracle 10g and MS SQL Server on data sizes (5000K).

In Databases one of the active research fields is mapping relational databases (RDB) into Resource Description Framework (RDF). An enormous data is kept in the form of relational databases and accessing of data is done in the semantic web. The data stored in RDB is to be efficiently mapped to the semantic web or RDF for data availability to the users. There is a definite need for improvement in technologies for efficient mapping languages from RDB to RDF in semantic web. This paper presents an up-to-date survey of different RDB to RDF mapping languages proposed in recent times. It outlines the main features or characteristics to be considered for efficient mapping in different scenarios. The main objective of this content, pictures identification of limitations existing in the mapping languages. It also enhances the comparisons between each language and helps researchers to propose further better proposals in their future scope of work to improve better mapping techniques.

Quarto capitolo estratto dal primo volume di MAPPA. Metodologie Applicate alla Predittività del Potenziale Archeologico, che illustra la struttura dell'archivio dati (RDBMS) archeologico, la restituzione georeferenziata dei dati stessi (GIS) e il sistema di categorizzazione dei dati utilizzato per gestire la loro eterogeneità.

ABSTRACT In this paper we will examine the key feature of asynchronous replication in three of the most used relational database management systems. First we will test CRUD operations on single instances of these RDBMS and then we will compare the synchronization time of CRUD operations for asynchronous replication.

ABSTRACT The World Wide Web is currently considered as the most appropriate software platform for the deployment of applications in wide area networks (telematics) as well as corporate intranets. Such applications are, in the majority of cases, tightly coupled with legacy databases hosted by relational management systems. However, the nature of the database enabled WWW systems is quite different from that of classical database applications developed with tools like 4GLs, Forms, Menus, etc. The basic difference lies in the stateless character of the WWW. The `user session' concept, encountered in conventional database applications, does not apply in the WWW environment. Instead, interaction with the information server is accomplished through a series of hits (request–response interactions) which are treated independently. This paper presents an architecture for the deployment of stateful database gateways for WWW servers. Although the server still treats each individual hit independently, state information maintained in the WWW browser as well as in specialized agents that operate behind the WWW server renders the WWW appropriate for a `session-aware' database application. The effort required to port an existing `session-aware' database application to the WWW environment is minimal.

Several information systems use relational database systems for effective dealing, storage, and accessing large measures of data. On the contrary, object-oriented programming has earned wide acceptance in the programming community as a epitome for developing typical applications that are easy to expand and uphold. This paper deal with development of an unified environment which relates a relational schema to an object-oriented schema without the requirement to manipulate the existing relational schema and providing a scenario for transferring data from relational database to object-oriented database.

Con l'incremento esplosivo della quantità di informazioni nel mondo, la capacità di analizzarle in maniera relativamente semplice e veloce sta diventando un elemento fondamentale. Informazioni in enormi quantità, delle più svariate tipologie e formati: sono queste le caratteristiche dei cosiddetti Big Data, un binomio che è recentemente entrato nell'uso comune, sebbene le sue prime introduzioni risalgano addirittura all'inizio del nuovo millennio. Quando i computer hanno iniziato a diffondersi come strumenti di business, oltre che di ricerca e di analisi, la prima sfida è stata quella di riuscire ad archiviare e gestire le informazioni aziendali e legate ai vari ambiti di ricerca. Le limitazioni tecnologiche del tempo hanno portato alla creazione dei database così come li conosciamo, e il passo successivo è stato quello di iniziare a utilizzare concretamente questi dati. Poiché i database sono composti da righe e colonne, i computer erano assolutamente in grado di gestirli senza particolari requisiti prestazionali. Le informazioni costituiscono una vera e propria ricchezza per le aziende, e la possibilità di organizzarle all'interno dei database ha permesso di sfruttarne le potenzialità lasciando ai computer il compito di eseguire attività essenziali di analisi e automazione. Oggi la nuova sfida riguarda invece i volumi di dati - la pura quantità (e velocità di crescita) delle informazioni che vengono prodotte quotidianamente e che risultano problematiche da gestire anche per il database più potente al mondo. Dal momento che la produzione dell'informazione avviene tramite mezzi che sono i più variegati (dagli smartphone, ai social network, fino a tutto ciò che può rientrare nell'Internet delle cose), altrettanto variegato è il tipo di informazione che viene prodotta. Siamo lontani dall'informazione fatta solo di parole e numeri, in quest'epoca dove anche immagini e video sono sempre più importanti, nella nostra vita. Tutto è informazione, tutto è correlabile e sempre meno è trascurabile, anche in archeologia. Quindi, la gestione dei Big Data, in passato, è stata un semplice problema tecnologico. Oggi, non è più cosi: esistono framework scalabili come Hadoop che nascono proprio per la gestione di grosse moli di dati e, addirittura, database “ad hoc” (che rientrano sotto il cappello di NoSQL e di NewSQL) che nascono con l'obiettivo di superare i limiti strutturali e intrinseci dei database tradizionali (i database relazionali o RDBMS). Settori quali il data warehousing e la business intelligence stanno convergendo sempre più verso nuove metodologie di analisi dei dati (in quantità sempre crescenti) che prendono il nome di Big Data analytics. Esattamente come per il data warehousing e la business intelligence, questa tecnica permette di gestire grandi quantità di dati e di estrarre informazioni preziose per il business o per la ricerca quali, ad esempio, le tendenze tra i consumatori o correlazini di dati che potevano sembrare slegati. Ovviamente, le organizzazioni più grandi (sicuramente quelle tecnologiche quali Google, Yahoo e Microsoft ma anche altri grossi nomi nel settore automotive, farmaceutico e statistico) sono le principali che hanno dimostrato interesse in questo settore ma anche aziende più piccole e enti di ricerca, che si trovino a gestire qualche terabyte di dati potranno trarre grossi vantaggi da tecniche di analisi evolute, adatte ad ambienti eterogenei. Secondo alcune stime, i dati contenuti nei grandi data warehouse cresceranno tra il 10 e il 20 per cento nel prossimo anno, complicando e rendendo sempre meno efficiente e rapida l'analisi dei dati tramite questi tool. La necessità di tecniche di analisi di Big Data attraversa tutti i segmenti industriali: classiche analisi e segmentazione della clientela, gestione delle reti di trasporto e di distribuzione dell’energia, iniziative antiterrorismo e molto altro ancora. Ovviamente, però, nel caso in cui i dati rientrino nella categoria dei Big Data, non è immediato fare il passaggio dalle metodologie tradizionali di analisi alla Big Data analytics: si parla, infatti, di un cambio radicale di paradigma legato alle modalità di analisi e, di conseguenza, le skill richieste al personale IT dedicato a questa funzionalità sono totalmente diverse da quelle che sono spesso possedute da chi si occupa di analisi in ambito tradizionale I tool stessi, sia per la memorizzazione dei dati, sia per l'analisi, sono diversi: raramente si parla di database relazionali (principalmente per via dei loro limiti di scalabilità) ma, sempre più frequentemente, si parla di structured storage, file system distribuiti e DBMS di tipo NoSQL e NewSQL. Questi fattori, però, non devono essere visti come problematiche o barriere all'ingresso ma come opportunità: riqualificando il proprio personale IT (creando anche nuove figure professionali) e adottando queste tecniche e questi strumenti, ci si rende immediatamente conto dei notevoli vantaggi ottenibili con queste tecniche di analisi avanzata. I risultati così ottenuti permettono di avere dei vantaggi competitivi notevoli su chi adotta le tradizionali tecniche di analisi, più lente e macchinose e, potenzialmente, meno accurate, oltre a permettere l'ottenimento di risultati migliori in meno tempo. I database relazionali (RDBMS), come abbiamo già detto, non rendono semplice e immediata la gestione di grosse moli di dati, specie se devono essere distribuiti su cluster composti da diversi nodi di calcolo. Basti pensare ai principali social network quali Facebook, Digg o Twitter e agli enormi sforzi che, in passato, hanno dovuto compiere per gestire tutti gli iscritti e i dati da loro prodotti. Questi sforzi crescono in base alla quantità di informazioni da memorizzare e sono incentrati sulla gestione di basi dati replicate e distribuite, alle quali vengono richieste caratteristiche di sicurezza, affidabilità, tolleranza ai guasti e performance. Scalare orizzontalmente (cioè incrementare il numero di macchine per incrementare la potenza a disposizione, sia essa di calcolo, sia di memorizzazione all’interno della base dati), in queste situazioni, è sempre stato un problema per i database relazionali in quanto richiedono l’utilizzo di tecniche ad-hoc (sharding, replicazione…) spesso complicate e/o costose, sia in termini prestazionali, sia in termini economici. Proprio per risolvere questo problema, Facebook ha deciso di sviluppare internamente un proprio database che permettesse di scalare senza problemi, mantenendo alcune delle caratteristiche tipiche dei RDBMS. Questo software, dopo poco tempo, è stato reso open source ed è tutt'ora conosciuto con il nome di Cassandra. In seguito a questo episodio, molti altri social network hanno deciso di migrare verso Cassandra per la gestione dei loro dati, sebbene esso non sia il solo database “specializzato”, nato per uno scopo ben preciso. Infatti, sono stati sviluppati molti prodotti, ognuno con caratteristiche differenti: alcuni di questi database sono ottimizzati per escludere i single point of failure tramite svariate tecniche di replica dei dati (fra rack differenti, fra data center differenti…), altri puntano ad ottenere prestazioni elevate nonostante la struttura distribuita (magari, basandosi su strutture dati allocate interamente in memoria), altri ancora puntano alla semplicità di utilizzo e di gestione. Oltre ai social network citati in precedenza, moltissime altre aziende hanno avuto necessità analoghe e hanno fatto ricorso a prodotti specializzati, spesso abbandonando gli RDBMS general purpose che adottavano in ambienti di produzione da anni; alcuni esempi possono essere Sourceforge che ha scelto MongoDB, Yahoo che ha sviluppato PNUTS o LinkedIn con l’adozione di Voldemort. Fattore che accomuna questa moltitudine di database, evidenziato anche dall’appartenenza ad una famiglia chiamata NOSQL (o, come più correttamente si dovrebbe definirla, “Structured storage”), è proprio il fatto che non sfruttano il classico linguaggio di interrogazione SQL e che utilizzano una rappresentazione dei dati spesso slegata dal consueto concetto di tabella. Il movimento NOSQL, al contrario di quello che il nome sembrerebbe indicare, non nasce in opposizione alla “tradizione” dettata dal linguaggio SQL ma come valida alternativa per la risoluzione di problematiche differenti, proprio legate alla continua crescita dei dati e alla loro eterogeneità, tipica dei Big Data. Infatti, NOSQL è acronimo di Not Only SQL ed è un movimento che sostiene l’utilizzo dei database di questo tipo laddove i tradizionali RDBMS risultino carenti o troppo complessi/costosi, seguendo la filosofia “the right tool for the right job”. I campi di applicazione ideali per le basi dati NOSQL sono, come già detto in precedenza, quelli dove è necessario gestire ambienti distribuiti e replicati, specie se si è disposti a “sacrificare” alcune delle caratteristiche tipiche dei RDBMS (ACID - Atomicity, Consistency, Isolation, Durability) verso un insieme di caratteristiche differenti (BASE - Basically Available, Soft state, Eventually consistent) permettendo, a discapito di una diversa gestione della consistenza dei dati e della loro disponibilità, un notevole incremento della scalabilità (come esplicitato anche dal teorema CAP, per il quale non si può avere Consistency, Availability e Partition tolerance al tempo stesso). Inoltre, uno degli obiettivi dei sistemi informativi distribuiti odierni è di poter garantire una disponibilità elevata, persino quando alcuni nodi non sono raggiungibili a causa di guasti improvvisi; è importante, quindi, che il servizio possa continuare a funzionare anche in presenza di una failure che determini un partizionamento della rete e che ogni richiesta ricevuta sia comunque esaudita, anche se il sistema presenta al suo interno dei nodi non operativi. Le basi di dati NOSQL, fra le altre cose, cercano di risolvere (almeno in parte) la suddetta problematica. Inoltre, il fatto che molte di esse siano open source, permette di avere un’elevata adattabilità alle problematiche specifiche che si vogliono risolvere, oltre ad un costo nettamente inferiore rispetto all’utilizzo di soluzioni ad-hoc abbinate ai tradizionali RDBMS (sia per prodotti proprietari, sia per prodotti free), alla possibilità di conservare nel tempo i propri dati e di utilizzare “commodity hardware” come nodi della propria infrastruttura di memorizzazione. A tutto questo si aggiunge una fervente community di sviluppatori che offrono costantemente nuove caratteristiche e contribuiscono al supporto dei vari prodotti. Questi database sembrano sposarsi perfettamente con la crescente metodologia di computazione cloud-based, che richiede l’utilizzo di ambienti fortemente distribuiti, scalabili e dinamici, all’interno dei quali il numero di macchine (virtuali o meno) è in continua variazione. Inoltre, molti di questi prodotti sono in grado di gestire più o meno facilmente i Big Data e permettono l'integrazione con tool di Data Analytics. Oltre alla categoria dei database NOSQL esiste anche la famiglia di DBMS nota con il nome di NewSQL. Questi database cercano di prendere i pregi del mondo del NOSQL e di unirli con i pregi del mondo dei RDBMS. Normalmente, quello che fanno è nascere per funzionare in ambienti distribuiti e supportare la gestione di grosse moli di dati, pur mantenendo gli standard dominanti nel mondo dei RDBMS, cioè l'ACIDità delle transazioni e la possibilità di utilizzare il linguaggio SQL. Il cuore dell'infrastruttura da noi assemblata è dato da Hadoop. Quest'ultimo è un framework che supporta applicazioni distribuite con elevato accesso ai dati, fino a permettere alle applicazioni di lavorare con migliaia di nodi e petabyte di dati. Hadoop, ispirandosi alla tecnologia Map/Reduce di Google e al Google File System, è un progetto top-level di Apache, costruito e usato da una comunità globale di contributori, che usano il linguaggio di programmazione Java. Hadoop è usato anche da alcune grosse società come AOL, Ebay, Facebook, IBM, Imageshack, Joost, Linkedin, The New York Times, Twitter e Yahoo; quest'ultimo è il più grande contribuente al progetto, oltre a farne un uso massiccio. Intorno a questo cuore ruotano tutta una serie di altri strumenti come Fuse, Apache web server (con il modulo WebDav), Hbase, HAproxy, Keepalived e R. Fuse, acronimo di File system in USErspace, è un progetto open source, rilasciato sotto la licenza GPL e LGPL, volto alla realizzazione di un modulo per il kernel Linux che permetta agli utenti non privilegiati di un sistema di creare un proprio file system senza la necessità di scrivere codice a livello kernel. Fuse è divenuto ufficialmente parte del codice del kernel Linux a partire dalla release 2.6.14 ed è particolarmente utile per scrivere file system virtuali. A differenza dei file system tradizionali, che si preoccupano principalmente di organizzare e memorizzare i dati su disco, i file system virtuali non memorizzano realmente i dati per conto proprio; agiscono infatti come un tramite fra l'utente ed il filesystem reale sottostante. Fuse è un sistema molto potente: virtualmente ogni risorsa disponibile ad essere implementata sfruttando Fuse può divenire un file system virtuale. Apache HTTP Server, o più comunemente Apache, è il nome dato alla piattaforma server web modulare più diffusa (ma anche al gruppo di lavoro open source che ha creato, sviluppato e aggiornato il software server), in grado di operare da sistemi operativi UNIX/Linux e Microsoft. Apache è un software open source che realizza le funzioni di trasporto delle informazioni, di internetwork e di collegamento ed ha il vantaggio di offrire anche funzioni di controllo per la sicurezza come quelli che compie il proxy. Questo applicativo, grazie all'installazione di un piccolo modulo, supporta anche il protocollo WebDav (Web-based Distributed Authoring and Versioning). WebDav si riferisce a un set di istruzioni del protocollo HTTP, che permettono all'utente di gestire in modo collaborativo dei file in un server remoto. Questo protocollo ha lo scopo di rendere il World Wide Web un mezzo di lettura e scrittura, seguendo le linee guida dell'idea originale di Tim Berners-Lee. Offre delle funzionalità che permettono la creazione, la modifica lo spostamento di documenti situati in un server remoto. Molti sistemi operativi moderni hanno un supporto integrato per il protocollo WebDav ma, con il giusto client ed una rete veloce, la gestione dei file su server WebDAV è facile quasi quanto una gestione dei file in locale, anche da smartphone e PDA. HBase è una base di dati NOSQL distribuita open source modellata sull’idea di BigTable di Google e scritta in Java. Per usare Hbase è necessario adottare un cluster Hadoop sul quale sia configurato HDFS (Hadoop Distributed File System) come sistema di storage. HAProxy è una soluzione gratuita e open source per ottenere l'alta affidabilità e il load balancing di un servizio basato su protocollo TCP (come accade per il protocollo HTTP) e replicato su più macchine. E' particolarmente adatto per i server web che devono gestire migliaia di connessioni e, come tale, calza a pennello con la nostra architettura. Keepalived è un altro software open source che ci permette di costruire un'architettura active-passive dove al fallimento di una macchina, un'altra ne prende immediatamente le veci, in modo da non interrompere mai il servizio e offrire continuità operativa. R è un ambiente di sviluppo specifico per l'analisi statistica dei dati; è un software libero, distribuito con la licenza GNU GPL, ed è disponibile per diversi sistemi operativi. Questo linguaggio offre molte funzionalità per l'analisi dei dati, sia in campo statistico che in moltissimi altri campi (anche grazie ad una notevole estendibilità basata su pacchetti aggiuntivi) quali da manipolazione dei dati e la loro rappresentazione grafica. Mettendo insieme tutti questi applicativi open source, si può ottenere un web-based file storage collaborativo che può essere raggiungibile da qualsiasi punto e con ogni dispositivo (PC, smartphone, tablet...); questo sistema integra già alcune metodologie adatte ad effettuare operazioni di Big Data Analytics ma si presta in maniera molto semplice ad interagire con sistemi di analisi avanzata già esistenti (es.: Solr) o creati ad hoc (grazie alla semplice integrazione di Hadoop con molti linguaggi di programmazione, Java in primis). Un dimostrativo dell'architettura è stato ricreato su di un server HP ProLiant DL360 G5 con due processori Intel Xeon E5430 da 2.66Ghz, 18 Gb RAM e due hard disk SAS da 146 Gb in configurazione RAID 1 (mirroring). Su questa macchina è stato installato Ubuntu server 10.4.3 LTS abilitando la virtualizzazione con KVM e i vari software descritti in precedenza sono stati installati e configurati sopra a queste virtual machine, al fine di offrirci un'idea di massima delle caratteristiche della nostra architettura. La maggior attenzione è stata prestata alle caratteristiche di fault tolerance e di scalabilità a caldo, oltre alla capacità di memorizzare ed analizzare dati eterogenei provenienti da diversi device, permettendo di ottenere un sistema resistente e affidabile al tempo stesso. Per la limitatezza dell'hardware e la gestione dell'intera infrastruttura in virtuale, non ci è stato possibile addentrarci in analisi particolarmente interessanti (es.: moli di dati veramente enormi) ma i primi test ci hanno offerto risultati piuttosto interessanti. E' inoltre da segnalare che l'architettura in questione funziona perfettamente su commodity hardware e non ha dei requisiti tecnici particolari.

In this paper, we present a web service model to support snapshots replication in multiple database engines. This model is open and is composed by a set of web services, agents and a manage console that allows to perform administrative tasks independently from the application and the database engines. This model uses a replication master catalog (CMR) to store the replication data. We show the architecture used and the behavior of our replication model to provide a better understanding about the data replication, as well as, discussion of several papers related to the replication services independent from the database engines.

The amount of social data produced by a wide variety of social platforms grows every day. Storing and querying this huge amount of data in almost real time presents a challenge to storage systems in order to scale up to hundreds or thousands of nodes. Also the graph structure and the ...

In the past, selling needs brick and mortar store and it is limited to a local customer base. But today, ecommerce websites make it easy for the customers around the world to shop by virtual store. Performance of ecommerce websites depend not only the quality and price of the product but also the customer centric suggestions about the product and services and retrieval speed of websites. There is more possibility to buy a product brand suggested by friends or relatives than by viewing commends from unknown people. We can implement such a customer centric approach using Neo4j database, which provides easy and fast retrieval of information based on multiple customer attributes and relations than relational databases. This system is also compared with other NoSQL database such as MongoDB. MongoDB is well suited for application like E-Commerce because it support semi structured data and high availability. Integrating E-commerce solution with social network MongoDB is slower and not provides an optimal solution. The MongoDB imposes certain limitations for fast and efficient retrieval. This paper presents the advantages of using Neo4j over MongoDB for E-Commerce applications. Experiment shows the enhancement of performance in terms of time and complexity.