Download Learning MySQL by Hugh E. Williams, Seyed M. M. Tahaghoghi PDF

By Hugh E. Williams, Seyed M. M. Tahaghoghi

No matter if you're operating a company, maintaining a tally of contributors and conferences for a membership, or simply attempting to set up a wide and numerous selection of info, you'll locate the MySQL database engine helpful for answering questions such as:

* that are my best ten fastest-selling products?
* How usually does this individual come to our facility?
* What used to be the top, lowest, and typical rating of the crew final season?

MySQL, the preferred open-source database, bargains the ability of a relational database in a package deal that's effortless to establish and administer, and studying MySQL presents all of the instruments you must start. This densely packed educational contains special directions that can assist you arrange and layout an efficient database, create strong queries utilizing SQL, configure MySQL for more desirable safety, and squeeze details from your data.

After protecting the fundamentals, the publication travels a ways into MySQL's subtleties, together with advanced queries and joins, find out how to engage with the database over the internet utilizing personal home page or Perl, and critical house-keeping corresponding to backups and security.

Topic include:

* install on Linux, home windows, and Mac OS X
* simple and complicated querying utilizing SQL
* person administration and security
* Backups and recovery
* Tuning for more advantageous efficiency
* constructing command-line and internet database functions utilizing the personal home page and Perl programming languages

The authors, Saied Tahaghoghi and Hugh E. Williams, have careers in academia and company, and proportion a prepared curiosity in examine into seek technologies.

Whether you've by no means touched a database or have already accomplished a few MySQL initiatives, you'll locate insights in studying MySQL that may final a occupation.

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Extra resources for Learning MySQL

Example text

That is only to collect a subset of the network [3]. It has selectively collected on the network associated with the predetermined topic pages. Topic, that is * Corresponding author. -S. Huang et al. ): ICIC 2012, LNAI 7390, pp. 35–42, 2012. -H. Zhang and F. Zhang the user defined type to be collected can be specified by the user to determine the series of web pages, you can also describe the types of features by a series of words to determine the characteristics. The main technologies involved in its information mining, text classification, text extraction, network collection, etc.

Rough fuzzy sets and fuzzy rough sets. International Journal of General Systems 17, 191–209 (1990) 3. : Uncertainty measures of rough set prediction. Artificial Intelligence 106, 109–137 (1998) 4. : Rough approximation quality revisited. Artificial Intelligence 132, 219–234 (2001) 5. : Fuzzy-rough sets assisted attribute selection. IEEE Transactions on Fuzzy Systems 15(1), 73–89 (2007) 6. : A new method for measuring uncertainty and fuzziness in rough set theory. International Journal of General Systems 31(4), 331–342 (2002) 7.

Let S = (U, AT ∪ {d}, V, f, g) be an interval ordered information system and X, Y ⊆ U , A ⊆ AT , then its lower and upper approximations satisfy the following properties. ≥ ≥ (X) ⊆ X ⊆ RA (X). (1) RA ≥ ≥ ≥ ≥ ≥ ≥ (2) RA (X ∩ Y ) = RA (X) ∩ RA (Y ); RA (X ∪ Y ) = RA (X) ∪ RA (Y ). ≥ ≥ ≥ ≥ ≥ ≥ (X ∪ Y ) ⊇ RA (X) ∪ RA (Y ); RA (X ∩ Y ) ⊆ RA (X) ∩ RA (Y ). (3) RA ≥ ≥ ≥ ≥ (4) RA (X) =∼ RA (∼ X); RA (X) =∼ RA (∼ X). ≥ ≥ ≥ ≥ (5) RA (∅) = RA (∅) = ∅; RA (U ) = RA (U ) = U . ≥ ≥ ≥ ≥ ≥ ≥ (6) RA (RA (X)) = RA (X); RA (RA (X)) = RA (X).

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