Entries Tagged as 'Data'

Rollback (data management)

In database technologies, a rollback is an operation which returns the database to some previous state. Rollbacks are important for database integrity, because they mean that the database can be restored to a clean copy even after erroneous operations are performed. They are crucial for recovering from database server crashes; by rolling back any transaction which was active at the time of the crash, the database is restored to a consistent state.

In SQL, ROLLBACK is a command that causes all data changes since the last BEGIN WORK, or START TRANSACTION to be discarded by the relational database management system (RDBMS), so that the state of the data is “rolled back” to the way it was before those changes were made.

A ROLLBACK statement will also release any existing savepoints that may be in use.

In most SQL dialects, ROLLBACKs are connection specific. This means that if two connections are made to the same database, a ROLLBACK made in one connection will not affect any other connections. This is vital for proper concurrency.

The rollback feature is usually implemented with a transaction log, but can also be implemented via multiversion concurrency control.

A cascading rollback occurs in database systems when a transaction (T1) causes a failure and a rollback must be performed. Other transactions dependent on T1’s actions must also be rolled back due to T1’s failure, thus causing a cascading effect.


References


See also

  • Savepoint
  • Commit

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Piggybacking (data transmission)

Piggybacking is a bi-directional data transmission technique in network layer (OSI model). It makes the most of the sent data frames from receiver to emitter, adding to them the confirmation that the data frame sent from emitter has arrived successfully (ACK acknowledge). This practically means, that instead of sending ACK in an individual frame it is piggy-backed on the the data frame.


Working Principle

Piggybacking data transfer is a bit different from Sliding Window Protocol used in the OSI model. In the data frame itself, we incorporate one additional field for acknowledgment (called ACK).

Whenever party A wants to send data to party B, it will send the data along with this ACK field. Considering the sliding window here of size 8 bits, if A has received frames up to 5 correctly (from B), and wants to send frames starting from frame 3, it will send ACK5 with the data.

Three rules govern the piggybacking data transfer.

  • If station A wants to send both data and acknowledgment, it keeps both the field there.
  • If a station wants to send just the acknowledgment, then a separate ACK frame is sent.
  • If a station wants to send just the data, then the last acknowledgment field is sent along with the data. The other station B upon receiving this duplicate ACK frame, simply ignores it.


Advantages and Disadvantages

Advantages: Improves the efficiency

Disadvantages: Receiver can jam the service if he/she has nothing to send. It could be solved if receiver adds a counter (Receiver timeout) which is activated when a data frame is received. If count ends with no data frames being sent, receiver will send an ACK control frame. Emitter also adds a counter (Emitter timeout), if the counter ends without receiving confirmation, emitter sends the frame again.

  • Dan's Data - PC hardware and gadget reviews! PC hardware reviews, tutorials, and reprints of Daniel Rutter's Step By Step column. Based in Sydney, Australia.
  • Data - ScottGu's Blog The ListView control supports the data editing, insertion, deleting, paging and sorting semantics of higher-level controls like the GridView.
  • EROS Data Center at the US Geological Survey The EROS Data Center is home to the US National Satellite Land Remote Sensing Data Archive. The EROS Data Center is managed by the United States Geological
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  • Education Data Partnership Home Page Fiscal, demographic, and performance data about California's K-12 public schools.
  • First Data The world's largest provider of merchant processing services.
  • International Data Corporation Research of information technology companies and markets. Free newsletters. Custom consulting services.
  • Data Information on different ways to find and utilize ICPSR data.
  • ERS/USDA Data Sets released November 2, 2007 Fruit and tree nuts yearbook data tables released November 2, 2007 released October 25, 2007 See all data products by date
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  • ADDS - Aviation Digital Data Service Aviation Digital Data Service (ADDS) provides comprehensive user-friendly aviation weather graphics including icing, turbulence, and convection.
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  • Advertising Age - Data Center Also in this Marketers/Advertisers section: Global Marketers, Top 200 Brands, annual data from Universal McCann's Robert J. Coen and more.

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Key Sequenced Data Set

A Key Sequenced Data Set (KSDS) is a type of data set used by the IBM VSAM computer data storage system. Each record in a KSDS data file is embedded with a unique key. A KSDS consists of two parts, the data component and a separate index file known as the index component which allows the system to physically locate the record in the data file by its key value. Together, the data and index components are called a cluster.

Records can be accessed randomly or in sequence and can be variable-length.

As a VSAM data set, the KSDS data and index components consist of control intervals which are further organized in control areas. As records are added at random to a KSDS, control intervals fill and need to be split into two new control intervals, each new control interval receiving roughly half of the records. Similarly, as the control intervals in a control area are used up, a control area will be split into two new control areas, each new control area receiving roughly half the control intervals.

While a basic KSDS only has one key (the primary key), alternate indices may be defined to permit the use of additional fields as secondary keys. An alternate index is itself a KSDS.


See also

  • Entry Sequenced Data Set
  • Relative Record Data Set


References

  • http://www.jaymoseley.com/hercules/vstutor/vstutor.htm
  • http://www.mvsforums.com/techfaqsvsam.html
  • http://www.lascon.co.uk/d002303.htm

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Commit (data management)

In the context of computer science and data management, commit refers to the idea of making a set of tentative changes permanent. A popular usage is at the end of a transaction. A commit is the act of committing.

A COMMIT statement in SQL ends a transaction within a relational database management system (RDBMS) and makes all changes visible to other users. The general format is to issue a BEGIN WORK statement, one or more SQL statements, and then the COMMIT statement. Alternatively, a ROLLBACK statement can be issued, which undoes all the work performed since BEGIN WORK was issued. A COMMIT statement will also release any existing savepoints that may be in use.

In terms of transactions, the opposite of commit to discard the tentative changes of a transaction, a rollback.


See also

  • Two-phase commit
  • Atomic commit


References

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Commit (data management)

In the context of computer science and data management, commit refers to the idea of making a set of tentative changes permanent. A popular usage is at the end of a transaction. A commit is the act of committing.

A COMMIT statement in SQL ends a transaction within a relational database management system (RDBMS) and makes all changes visible to other users. The general format is to issue a BEGIN WORK statement, one or more SQL statements, and then the COMMIT statement. Alternatively, a ROLLBACK statement can be issued, which undoes all the work performed since BEGIN WORK was issued. A COMMIT statement will also release any existing savepoints that may be in use.

In terms of transactions, the opposite of commit to discard the tentative changes of a transaction, a rollback.


See also

  • Two-phase commit
  • Atomic commit


References

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Data compaction

In telecommunication, data compaction is the reduction of the number of data elements, bandwidth, cost, and time for the generation, transmission, and storage of data without loss of information by eliminating unnecessary redundancy, removing irrelevancy, or using special coding.

Examples of data compaction methods are the use of fixed-tolerance bands, variable-tolerance bands, slope-keypoints, sample changes, curve patterns, curve fitting, variable-precision coding, frequency analysis, and probability analysis.

Simply squeezing noncompacted data into a smaller space, for example by increasing packing density or by transferring data on punched cards onto magnetic tape, is not data compaction.

Whereas data compaction reduces the amount of data used to represent a given amount of information, data compression does not.

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