Data integrity is a serious issue for databases. Setting Field Size for Text data and Number data is a common thing to do. Often organizations will limit the data they collect for fields such as last name and first name (for example, 30 characters). If the data type is Number then values selected for Field Size are values such as Byte, Integer, Long Integer, etc. These kinds of values are associated with increasing number of memory locations used per value. A selection of Byte restricts storage to 1 byte of memory (8 bits), and since the largest positive integer that can be stored in a byte is 255, the values stored in the field are forced to be in the range from 0 to 255. Further information is readily available if you use the F1 function key on Field Size for a Number data type.
ODBC is the older of the two connection methods and requires that you set up a data source using your Windows operating system (see the next exercise). OLE DB is more efficient, stable, and often several times faster than an ODBC connection. It does not require any initial setup. Additionally, OLE DB allows random access and relational linking to ex- ternal database structures that lack a primary key. TNTmips supports import and linking to MicrosoftAccess and Excel files using either ODBC or OLE DB. Import and linking to Oracle and Import of Oracle Spatial layers is supported only through ODBC. Import and linking to SQL Server databases is supported only through OLE DB. The TNT products also directly support linking to dBASE III/ IV, FoxPro, PostgreSQL and LiDAR point data- bases. As a result, these formats can also be imported or linked to on the Mac. Linking and import of MySQL files is also supported directly but is avail- able only for Windows.
SQL provides some support for content-based access con- trol, via the use of views, but it does not provide support for context-based access control, where access to data (or to views over them) may depend on properties of the user (or its session) such as time, the machine from which the user connected, and so on. Our trust management solution can be exploited to provide such a functionality. Also, coupled with the view mechanism it can provide a means to spec- ify accesses where each user has a particular view over the data, depending on its certified properties. This technique is simple, yet effective, and powerful. The specification of the certificate attributes follows an approach similar to the one used for the definition of trust policy conditions, thus referencing certificate attributes using a dot notation. A small difference is that the trust tables are assumed to be directly available in the definition of the trust policy con- dition, whereas they have to be explicitly cited in the from clause of the query defining the view.
When a database is in 2NF, all of the fields in each table will depend directly on the primary key. If we look at the Invoices table in its current form, we can see that this is not the case. The invoice date and invoice total are dependant on the invoice number, but the rest of the fields are not. Since there may be more than on invoice for the same customer, the fields that provide information about the customer will most likely appear on many invoices. At the moment, they will be repeated for each invoice. This problem can be eliminated by creating a separate table to store the customer details. The same problem is evident in the Transactions table. The quantity and cost is unique to each transaction and therefore dependent on the transaction number. The price and description, however, are not directly dependant on the primary key. Every time there is a transaction for a certain item, the price and description for that item are being repeated. This can be fixed by creating a separate table for the Items that can be sold. The following diagram illustrates the new relational design.
Amazon SimpleDB requires no schema, automatically indexes your data and provides a simple API for storage and access. This eliminates the administrative burden of data modeling, index maintenance, and performance tuning. Developers gain access to this functionality within Amazon’s proven computing environment, are able to scale instantly, and pay only for what they use. Traditionally, this type of functionality has been accomplished with a clustered relational database that requires a sizable upfront investment, brings more complexity than is typically needed, and often requires a DBA to maintain and administer. In contrast, Amazon SimpleDB is easy to use and provides the core functionality of a database, real-time lookup and simple querying of structured data without the operational complexity.
normalisation as an archiving method? This question is best answered via case study with current generation normalisation tools. Beginning with MS Access 2003, Microsoft has supported XML normalisation as a migration pathway for Accessdatabases. As a case study and precursor to a broader investigation, the authors undertook an XML normalisation of the MS Access 2003 database referred to in Figure 2 and evaluated the results. The criteria used were those originally developed by the Digital Preservation Testbed Project in 2003. These were:
A few years ago (the distant past, in computer time), creating a database structure involved first analyzing your needs and then laying out the database design on paper. You would decide what information you needed to track and how to store it in the database. Creating the database structure could be a lot of work, and after you created it and entered data, making changes could be difficult. Templates have changed this process, and committing yourself to a particular database structure is no longer the big decision it once was. A template is a pattern that you use to create a specific type of database. Access 2010 comes with templates for several databases typically used in business and education, and when you are connected to the Internet, many more are available from the Microsoft Office Online Web site at office.microsoft.com. By using pre-packaged templates, you can create a database application in far less time than it used to take to sketch the design on paper, because someone has already done the design work for you. Using an Access template might not produce exactly the database application you want, but it can quickly create something that you can customize to fit your needs. However, you can customize a database only if you know how to manipulate its basic building blocks: tables, forms, queries, and reports. Due to the complexity of these templates, you probably shouldn’t try to modify them until you’re comfortable working with database objects in Design view and Layout view. By the time you finish this book, you will know enough to be able to confidently work with the sophisticated pre-packaged application templates that come with Access.
Microsoft Operations Manager (MOM), uses the SQL Server for saved the data all different computer, performance and alert related knowledgeable data. We narrowed the problem down to something needed a script that can find all different MOM tables for a particular string. We had no such script, so we ended up finding data manually. That's when I really felt the requirement that script and came up with that stored procedure "SearchAllTables". It obtain a string as input query parameter goes and find all varchar, char, nvarchar, nvchar columns of all different tables, owned by all different users in the current relational database.
To determine intelligible attributes, ODDMER currently relies on a binary classifier that takes as input the values of each attribute found in a partic- ular instantiation of a relational database. To train the classifier, we labeled a set of 84 attributes be- longing to tables taken from the Microsoft Adven- tureWorks Cycle Company database, a benchmark database packaged with Microsoft SQL Server. An attribute was labeled as intelligible if its values were likely to be known to a user. Four annotators worked independently to label the attributes. Pair- wise agreement was 69%, and Krippendorff’s al- pha (Krippendorff, 1980) was 0.42. The low agreement can be attributed in part to the many ways to interpret the question annotators were to answer. The instructions indicated that the goal was to identify attributes corresponding to com- mon-sense knowledge, but for a given table, anno- tators were shown all the attributes and asked whether they would know a value. For an employ- ee table, annotators disagreed on attributes such as birthdate, hire date, and organization level. If they had instead been asked whether anyone without access to the table might know a value, there may have been more agreement.
Now-a-days, data is being expanded rapidly in the industry. The nature of data is varied and diversified such as unstructured, semi-structured and structured data. The issue is not only how to store and access such big amount of data but also need to extract meaningful knowledge from such data rapidly. The relational model has dominated the computer industry since the 1980s mainly for storing and retrieving data. Traditional databases require declarative language such as SQL to manipulate structured data.Way back people used database just for storing tabular data like purchase reports and finance records. Relationaldatabases are based on data consistency and can process the data at certain limit. To manage large datasets using relationaldatabases, organizations are required to increase their system capacity such as RAM, Disk, optimized methods of accessing data etc. many organizations are rely on unstructured data such as emails, blogs, audios, videos, images and such data is generated at very high speed. To cover the requirements of current application domains, has lead the development of new technologies called NOSQL databases. one of them is graph database. The increase of massive and complex graph-like data makes a graph database a crucial requirement. NoSQL databases are horizontal scalable databases while relationaldatabases are vertical scalable databases.
MicrosoftAccess 2013 takes a lot of the difficult and mundane work out of creating and customizing a database by providing database templates. Access also provides templates for common elements that you might want to plug into a database. These application parts consist of sets of objects—a table and related forms, queries, or reports—that together provide a complete, functioning part of a database, ready for you to customize. If none of the templates meet your needs, you can create databases manually. However, an empty database is no more useful than an empty document or worksheet. It is only when you fill a database with data (referred to as populating a database), that it starts to serve a purpose. In this chapter, you’ll examine web app templates and create a desktop database from a template. You’ll also create a table manually. Next, you’ll adjust the display of a table to meet your needs. Finally, you’ll define relationships between tables. By the end of this chapter, you’ll have a desktop database that contains a few tables and you’ll understand a bit about how the database tables you will use for the exercises in the remaining chap- ters of the book were created.
Every organization keeps a set of databases to store their information and there may be several situations to share those information with others. As we are living in the information age there are large sources of data around us. To improve the services the organizations collect and analyze the data. The Confidentiality, Integrity and Availability are termed as the [CIA-triad] designed to enable the information security within the organization. They are considered to be the essential components of the security. To ensure that only the authorized information are available only to the authorized users and access control mechanism is implemented in the databases. However there may happen the misuse of sensitive information by the authorized users to compromise the privacy of the customers. For the enhancement of the protection against the identity disclosure and enforcing the privacy policies, the concept of privacy preservation of sensitive data introduced by satisfying some privacy requirements .
based approach or heuristic approach to select an optimal execution plan. To avoid self-joins in multiple query blocks, Oracle uses windows function for efficient execution and optimization. In addition, Oracle uses PARTITION BY key or ORDER BY key for sorting data to compute window functions. In sub-query coalescing technique, two sub-queries are coalesced into one single sub-query and is used to reduce multiple table access and multi joins operations into single table access and single join operation. Sub-query coalescing works like a filter on the tables of the outer query. In Oracle, coalescing sub-queries appear in conjunction or disjunction. When two sub-queries are of the same type, e.g., both use either EXIST or NOT EXIST then sub-query coalescing result in the removal of one query. Sub- query removal using windows function technique replaces the sub-queries with windows functions to reduce the number of table access and joins to improve query efficiency. A regular Anti-join is exactly opposite to inner join. Since in SQL, any relational comparison with null always results in a null value, so there should be some strategy to deal this situation. NULL-AWARE ANTI-JOIN concept is used to handle null values in anti-join operations.
As described in the Overview section 1, Chronos does not require archiving of databases in a central facility. On the contrary, any local organization or branch office can deploy its own Chronos instance locally, and yet provide global Web access to its archives with the same principles, data formats, data quality, technology, and uniform Web GUI while using its own user access rights management. If necessary, multiple local Chronos archives can be easily merged into one central Chronos archive facility by just copying and transferring pure text files.
However, companies risk serious damage to their reputa- tions if they lose control over this data. Fine-grained per- missions provide better control over who can access what resources, and as a result, platforms such as Google An- droid and Facebook Apps have dozens of distinct permis- sions, each regulating access to a different resource or type of user data. Similarly, commercial solutions such as IBM LBAC  and Oracle VPD  give database administrators precise control over which principals can see which parts of a database. Such control is largely motivated by the Principle of Least Privilege , which states that principals should be granted the least permissions needed to do their jobs.
Let’s consider an example to understand the working of our model. Consider a policy account wherein we have two actors. First, a policyholder, and second, the admin. The policyholder wants to update his account this means the policyholder wants access to the database of the bank where he has his account , for getting the access the policyholder needs to know the secret key for his name in the database provided by the admin . After he has entered his secret only he is able to access the database of his name only.
queries (a set of keywords) to access unstructured data that users are most likely interested in, using ranking techniques. It is widely realized that the integration of information retrieval (IR) and database (DB) techniques will provide users with a wide range of high quality services. The recent studies on supporting IR style queries in RDBMS include DBXPlore , IR-Style , DISCOVER , ObjectRank , BANKS-I , and BANKS-II . All consider a RDBMS as a graph where nodes represent tuples/relations and edges represent foreign key references among tuples cross relations. And with a keyword query, users can find the connections among the tuples stored in relations without the needs of knowing the relational schema imposed by RDBMS. We show a motivation
Without using Framework, it was necessary to generate all the database schema manually in each DBMS and JDBC was employed to make the connection and to access each database. It is not possible, therefore, to compare the performance for database schema generation between these two approaches (with and without the use of Framework). Conversely, the performance considering these two approaches for the insert, update and select operations were really closed, without significant differences. Concerning performance, i.e., response time in data access, few tests were performed with a simple example and with a small number of data. Then, specific work must be done for a real performance evaluation. In direct access (JDBC), the developer needs detailed knowledge about the ORDB, DBMS used and available data types, besides the access language.
This is particularly important if more complex reporting is required. Outlook does not provide any reporting beyond what can be achieved using custom views. It is therefore necessary to use third party tools to be able to get the information out of Outlook and into another programme such as Excel or Access where there are more powerful reporting facilities available.
Calls to the exit function were especially problematic. In the stand-alone version of MonetDB the database server shuts down when a fatal error was detected (such as running with insufficient permissions or attempting to open a corrupt database). This happens mostly during start-up. This is expected behavior in a stand-alone database server, but becomes problematic when running embedded inside a different program. Attempting to access a corrupt database using the embedded database would result in the entire program crashing, rather than a simple error being thrown. Even worse, since the database would simply exit in these scenarios, no alternative path exists to only report the error. To avoid a large code rewrite, we used longjmp whenever the exit function was called, which would jump out of the exit and move to a piece of code where the error could be reported.