Database Systems

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Evolving database systems : a persistent view

Evolving database systems : a persistent view

It is interesting to compare this style of viewing with encapsulation and information hiding in object oriented database systems. In the latter the raw data may only be viewed through one interface and the information is essentially trapped in the object once instantiated. In this technique, the data is placed in an object (view) dynamically when the data modelling requires it. The viewed value and the viewing value are both available to other views of the data and there is no sense in which the viewed value becomes unavailable, or encapsulated, in the viewing object. Of course, it is not always desirable to expose all views of data and techniques are required to limit the visibility of certain data. However this is achieved by information hiding and not encapsulation and thus overcomes any conceptual difficulties in breaking encapsulation even when the views are abstract.
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FUNDAMENTALS OF DATABASE SYSTEMS ELMASRI PDF

FUNDAMENTALS OF DATABASE SYSTEMS ELMASRI PDF

Are you also searching for Chapter 3 Entity Relationship Model Database Design Process? Get it only at our library now. © Addison Wesley Longman Inc 2000 ElmasriNavathe Fundamentals of Database Systems Third Edition Miniworld REQUIREMENTS COLLECTION AND ANALYSIS. Chapter 3 Entity Relationship Model Database Design Process eBooks is available in digital format.

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Database Systems Performance Evaluation

Database Systems Performance Evaluation

The last few decades has seen a huge transformation in the way businesses are conducted. There has been a paradigm shift from product portfolio based marketing strategies to customer focused marketing strategies. The growth and diversity of the market has greatly profited consumers through higher availability, better quality and lower prices. The same factors however has made it more difficult for businesses to maintain their competitive edge over one another and hence has forced them to think beyond their product portfolio and look at other means to gain higher visibility and customer satisfaction, maintaining all the while their core advantages on pricing and product through improved and more efficient methods of manufacturing and distribution. The advent and spread of computers and networking has been one of the single largest factors that has spurred and aided this enormous movement. More specifically, database management systems now form the core of almost all enterprise logic and business intelligence solutions. This survey tries to emphasize the importance of database systems in enterprise setups and looks at the methods and metrics that are used to evaluate the performance of these database systems.
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Relational Database Systems 1

Relational Database Systems 1

Relational Database Systems 1 – Wolf-Tilo Balke – Institut für Informationssysteme – TU Braunschweig 57 7.2 Domain Relational Calculus.. Relational Database Systems 1 – Wolf-Tilo Balke[r]

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Requirements for XML Document Database Systems

Requirements for XML Document Database Systems

In the time since the publication of the SGML standard, no widely accepted single model or technology for the management of SGML/XML document repositories as a database has evolved. The generic names for candidate management systems have varied, and the boundaries between types of systems are fuzzy. For example, under the title of “XML database products” Ronald Bourret separates the following categories: middleware, XML- enabled databases, native XML databases, XML servers, XML application servers, content management systems, and XML query engines [9]. There are also activities towards developing specialized search engines for XML documents on the Web. These systems, such as Xyleme [51] and Niagara [39], bring new information management capabilities to the Web, but they cannot be characterized as complete database systems. Below we consider two broad categories of systems: native SGML/XML systems and extensions of relational and object-oriented database systems. We characterize the categories in terms of the features we discussed in the previous sections: data model, data definition and data manipulation.
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Investigation and Comparison of Distributed NoSQL Database Systems

Investigation and Comparison of Distributed NoSQL Database Systems

There are two major categories of indexing involved in distributed NoSQL database systems: primary indexing and secondary indexing. In terms of distributed index storage, there are two ways of index partitioning: partition by original data or partition by index key. “Partition by original data” means that each node in the cluster only maintains the secondary index for the portion of the original data that is locally hosted by this node. In this case, when a query involving an indexed field is evaluated, the query must be sent to every node in the cluster. Each node will use the local portion of secondary index to do a “partial evaluation” of the query, and return a subset of result. The final result is generated by combining results from all the nodes. Figure 6 illustrates partition by original data. “Partition by index key” means that a global index is built for the whole data set on all the nodes, and then distributed among the nodes by making partitions with the key of the index. To evaluate a query about an indexed field value, only the node maintaining the index for that queried field value is contacted, and it processes all related index entries to get the query result. Figure 7 illustrates partition by index key.
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The Case for Predictive Database Systems: Opportunities and Challenges

The Case for Predictive Database Systems: Opportunities and Challenges

At present, predictive applications are not well supported by database systems, despite their growing prevalence and importance. Most prediction functionality is provided outside the database system by specialized prediction software, which uses the DBMS primarily as a backend data server. Some commercial database systems (e.g., the data mining tools for Oracle [Ora], SQL Server [SS08], and DB2 [DB2]) provide basic extensions that facilitate the execution of predictive models on database tables in a manner similar to stored procedures. As we discuss below, and also noted by others (e.g., [DB07, AM06]), this loose coupling misses significant opportunities for improved performance and usability. There has also been recent work on custom integration of specific models (e.g., [JXW08, HR07, ACU10, AU07, APC08]).
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Rethinking Cost and Performance of Database Systems

Rethinking Cost and Performance of Database Systems

The beauty of this architecture is that it serves extremely well the requirements of traditional database systems (Table 1). The main constraint of keeping data consistent is achieved in the bottom tier, inside the database server. The main optimization goal of mini- mizing response times and maximizing throughput is achieved by a series of techniques that have been developed over the last four decades in all tiers: caching, indexing and data partitioning, to name just a few. Furthermore, scalability is achieved on the two top tiers: at those tiers, the architecture of Figure 1 scales almost infinitely. Scalability, however, is limited at the bottom tier. Unfortunately, the three-tier architecture of Figure 1 is not a good fit for the new requirements of Table 1. Most importantly, this architecture is expensive. Typically, significant investments are needed for the bottom tier, thereby involving expensive hardware (rather than cheap hardware). The hardware must be provisioned for expected peak performance, which can be orders of magnitudes higher than the real average performance. As a result, a great
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manual solutions database systems elmasri

manual solutions database systems elmasri

Looking to produce a manual and wish to accomplish this? There are various names for manual solutions database systems elmasri, and several different examples. For instance you could include a handbook with your new product to indicate the buyer had to apply your item. Or perhaps an other example is really a document but chose one of the employees how you can run a piece of equipment. This document would've step-by-step instructions and operation procedures. Basically a person manual is really a document that instructs someone has to execute a task or use a product.

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Real-Time Database Systems: Concepts and Design

Real-Time Database Systems: Concepts and Design

Performance studies of concurrency control algorithms for conventional database systems have shown that under most operating circumstances, locking protocols outperform optimistic techniques [AGR 87] . However, RTDB systems have a different set of characteristics, design objectives, and constraints, necessitating various performance measures and previous assertions to be reevaluated [HAR 90a, HAR92] . A close look at 2PL reveals that transactions can not release locks as soon as they are through using the corresponding data objects; rather, they must wait until they enter their second phase. The period between a transaction finishes using a data object and releasing the lock is bounded by the lifetime of the transaction, during which other transactions might be blocked on that particular unused data object. Thus, 2PL can limit the degree of concurrency in the system, which lowers the system’s utilization level. Such delay gets even worse under conservative and strict 2PL protocols [BER87] . A Strict 2PL protocol is the most common protocol implemented in commercial database sys- tems, in which a transaction cannot release any of its locks until after it terminates (commits or aborts). Thus, a strict 2PL is too “strict” and introduces extra delays. Meanwhile, sacrificing strictness subjects the system to cascading-aborts [BER87, ELM 94] . Blocking-based protocols, i.e., 2PL, suffer long blocking delays and lack of consideration to timing information, they tend to introduce deadlocks and priority inversion, which is clearly unsuitable for any time-critical environment [HAR90, HAR92 KAO95, STA91, and ULU 95b] . Due to the negative impact of 2PL on transaction processing in a real-time environment, many attempts have been made to augment it with priority cognizance techniques or circumvent its use along with its associated drawbacks. We review such en- deavors in the rest of this chapter.
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Modelling recovery in database systems

Modelling recovery in database systems

To reduce complexity in the initial design of MaStA the effects of concurrency were omitted. This factor will be included in future developments in the model so that applications exhibiting concurrent behaviour are accommodated. Modifications required to achieve this include the development of new I/O categories and workload variables to calculate costs such as the overheads of performing transaction aborts. The cost of recovering a database after system failure was also omitted to reduce complexity. The cost of providing for recovery during normal processing and the cost of performing recovery after failure may not be easily combined into one useful value for each application and mechanism since the relative importance of these two costs depends largely on the style of application. These costs will therefore be calculated separately allowing the costs to be analysed individually when making a choice of mechanism for a particular application. Future investigation should also include incorporating the wide range of object logging schemes used in database systems into MaStA. This may involve the design and implementation of such schemes in Flask to allow the validation of MaStA for object based mechanisms.
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Smart Search Methods in Expert Database Systems

Smart Search Methods in Expert Database Systems

The motivations driving the integration of these two technologies include the need for (a) access to large amounts of shared data for knowledge processing, (b) efficient management of data as well as knowledge, and (c) intelligent processing of data. The integration between different fields of AI, database systems and logic programming leads to the data became more complicated and complex to discover knowledge from it. More researchers concentrated on this challenge to reach valuable information from it, some of these focused on optimization of query processing and others used AI methods to detect stylized pattern exist.
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Security Risks in Online Distributed Database Systems

Security Risks in Online Distributed Database Systems

In recent years, the availability of databases and computer networks has promoted the development of a new field known as distributed databases. A distributed database is an integrated database which is built over a computer network instead of a single computer. The distributed databases offer several advantages to designers and users of databases. Among the most important is the transparency in accessing and locating information. However, the design and management of distributed databases faces major challenge that includes problems not found in centralized databases. There are two forces driving the evolution of database systems. On the one hand users as part of more complex organizations have demanded a number of capabilities that have been incorporated in database systems. An example of this is the need to integrate information from various sources. Technology has made it possible for some facilities initially imagined only in dreams come true. Online transaction that allows the current banking system would not have been possible without the development of communication equipment. Distributed computing systems are clear examples where organizational pressures combined with the availability of new technologies enable the realization of such applications. In its simplest definition, distributed database systems pursue the integration of diverse and heterogeneous database systems. Its main goal is to provide the user with a global vision of the available information. This integration process does not involve the centralization of information, rather, with the help of computer networking technology available, the information is kept distributed and the systems of distributed databases allow access to it as if it were located in one place. The distribution of information
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Comparative Analysis of Cloud Based Database Systems

Comparative Analysis of Cloud Based Database Systems

A NoSQL (initially alluding to "non SQL", "non- social" or "not just SQL") database gives a component to capacity and recovery of information which is displayed in means other than the forbidden relations utilized as a part of social databases. This offers a new approach that is, simplicity of design, horizontal scaling to clusters of machines. The data structures used by NoSQL are different making it faster than other databases Classification include Column, Document, Key-value, Multi-model.

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In-memory database systems, NVDIMMs and data durability

In-memory database systems, NVDIMMs and data durability

Database management system (DBMS) software is increasingly common in electronics, spurred by growing data management demands within technology ranging from communications equipment to avionics gear and industrial controllers, and facilitated by these devices’ increasing on-board CPU, RAM and storage resources. The size of on-device databases varies, ranging from a few gigabytes of data to support a telecom billing/credit system’s rating and balance management application, to 10+ GB for an IP router’s control plane database, and more than 100 GB for a telecom call routing database.
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Report for the seminar Algorithms for Database Systems F1: A Distributed SQL Database That Scales

Report for the seminar Algorithms for Database Systems F1: A Distributed SQL Database That Scales

The architecture proposed by F1 is to build such a scalable SQL database by adding a layer of SQL processing over a distributed key-value store. The distributed key-value store should fulfill the scalability and consistency requirements for simple operations that operate on key-value pairs, while relational abstractions like tables, SQL processing and ACID transactions are implemented by an additional database middleware. The main issue that appears in such a system is the additional network latency added by the middleware server layer. Considering that distributed databases can be deployed over multiple data centers, the additional network latency could add a significant penalty to the read and write latencies. And in an age of Big Data, databases with high latencies and potentially low throughput are not acceptable. In this context, this report will present the architecture of the multilayer system proposed earlier and analyze the impact of the additional layer on the read and write latencies. Additionally, the report will present some solutions to mitigate the extra latency added by the additional layer.
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NoSQL Database Systems and their Security Challenges

NoSQL Database Systems and their Security Challenges

NoSQL Major Security Challenges • Threats Posed By Distributed Environments • Authorization and Access Control • Safeguarding Integrity • Protection of Data at Rest • User Data Privacy 4[r]

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Stress Testing of Transactional Database Systems

Stress Testing of Transactional Database Systems

to write test cases and load scenarios. However, they concentrate on presenting performance results and do not seek for load-related defects. They also miss a testing methodology and do not take into account to the various biases that may affect the test results (e.g., memory, network, CPU usage). In addition, they are based on a single test driver [ISO9646 1991] that only allows testing in a point- to-point connection. Reaching stress conditions requires multiple and distributed test drivers that communicate with the system under test (SUT) interface. The testing tool Agenda [Deng et al. 2005] provides both a methodology and a test driver. However, it focuses on database applications, rather than the DBMS itself. Agenda presents a technique for checking database properties (e.g., ACIDity), and does not focus on performance issues.
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CS411 Database Systems

CS411 Database Systems

Product(name, price, category, manufacturer) EducationalProduct( name, ageGroup, topic ). SoftwareProduct( name, platforms, requiredMemory ) No need for a relation EducationalSoftware[r]

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DATABASE SYSTEMS (INTERMEDIATE 2)

DATABASE SYSTEMS (INTERMEDIATE 2)

A database is a collection of related information about a set of persons or objects. Traditionally, databases were manual paper-based systems. For example, the Yellow Pages telephone directory is a database of companies’ names, addresses and telephone numbers, organised in business categories.

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